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University admission in Russia: Do the wealthier benefit from standardized exams?
International Journal of Educational Development xxx (xxxx) xxx–xxx
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International Journal of Educational Development journal homepage: www.elsevier.com/locate/ijedudev
University admission in Russia: Do the wealthier benefit from standardized exams? ⁎
Ilya Prakhov , Maria Yudkevich Center for Institutional Studies, National Research University Higher School of Economics, 20 Myasnitskaya Street, Moscow, 101000, Russia
A R T I C L E I N F O
A B S T R A C T
Classification Codes: I21 I24 I28
This paper examines the impact of family income on the results of the newly introduced Unified State Examination (USE) in Russia. We argue that entrants from wealthy households have an advantage in terms of access to higher education, since income positively affects USE scores through a higher level of investment in pre-entry coaching. We have found positive and significant relationships between the level of income and USE results for high school graduates, given equal achievement before coaching. We demonstrate that in general, investment in pre-entry coaching has positive returns, but the most significant type of investment is pre-entry courses. However, such strategy improves USE results only for students from the most affluent households.
Keywords: Student achievement Standardized entry exams Equity in higher education University choice
1. Introduction Issues in the accessibility and equality of higher education are crucial in the education policy agendas of both developed and developing countries. One of the most significant elements concerning access to higher education is the system of admission to universities. There are various admission practices in different countries, but most of them are intended to smooth gaps in opportunities for admission between applicants from different backgrounds. The case of Russia is of particular interest because of the recent institutional reform of admission policy. While for many decades in the Soviet Union, and then in post-Soviet Russia, university-based entry exams were at the core of the university admission system, they have been recently replaced by an admission system based on the results of standardized national exams (the Unified State Examination, USE) which are obligatory for all high school graduates in Russia. The Soviet and post-Soviet system of education was characterized by the existence of unequal access to education, even though equality of chances was declared a part of the Soviet ideology. However, ‘children from privileged groups traditionally received the education and professional training which were most advantageous for their subsequent lives and careers’ (Konstantinovskiy, 2012). One of the aims of the unification of admission process was to increase the accessibility and equality of higher education. It was assumed by policymakers that under the new institutional settings, students from different backgrounds would have equal opportunities of applying to universities because of the reduction in expenditures related to the
⁎
preparatory and application processes. However, inequality still exists: evidence shows that more affluent students study in more selective universities (Prakhov, 2016). In other words, students from wealthier families are more represented in more selective universities characterized by a high level of competition among applicants (high levels of selectivity), while first year students from disadvantaged backgrounds are underrepresented in such institutions. Given that these universities provide higher quality and consequently give higher educational returns, this creates unequal conditions for graduates in the labour market. We argue that among other channels through which inequality is still supported the preparatory process is an important one. High school graduates from wealthier households still have some significant advantages, since they have more resources to invest in the general preparation process during their final year at high school. That improves their final USE scores and hence chances for better university placement. This paper analyses how family income influences students’ USE scores through investment in preparation under the new admission system, and how this creates unequal opportunities for applicants from different income groups. We offer an explanation of the unequal conditions for access to higher education in Russia for students with various income backgrounds through the channel of investment in preentry coaching and the existing variation in the returns from private tutoring expressed in USE scores. This study is an important contribution to the literature concerning
Corresponding author. E-mail address: [email protected] (I. Prakhov).
http://dx.doi.org/10.1016/j.ijedudev.2017.08.007 Received 19 October 2016; Received in revised form 7 June 2017; Accepted 11 August 2017 0738-0593/ © 2017 Elsevier Ltd. All rights reserved.
Please cite this article as: Prakhov, I., International Journal of Educational Development (2017), http://dx.doi.org/10.1016/j.ijedudev.2017.08.007
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applications and rank applicants on the basis of the total score of the required subjects. The ranking of students and the names of those recommended for admission are published online by universities; rankings are freely accessible on university web pages in mid-summer. Those students who are on this list can present their certificates and be admitted to university during this ‘first wave’ of admission. Those who do not have enough points can either apply for a paid place or take a chance on the next ‘wave’ of admissions. As students can send their USE results to up to 5 universities, and in many cases they receive offers from several institutions. As a result, by the end of the ‘first wave’ there are still vacant places in many universities. They add up their rankings again and recommend additional students for admission. This situation can affect student choice, particularly the decisions of high school graduates from low income families. Wealthy applicants, if not successful in competition for a state-subsidized place during the ‘first wave’, can afford to pay tuition fees at the same university without any decrease in quality of their higher education. Low income students, however, can take a risk and wait for the ‘second wave’, or, more likely, can choose a less selective university and the resultant lower quality of education provided. In this case, educational opportunities are not equal, and our goal is to examine how students from different backgrounds manage their USE results. The university admission reform in Russia had at least 3 consequences: first, under a new system high school students sit USE in the city or regional centre in which they live, so there is no need to pay for travel to sit examinations in their potential place of learning. Second, there is no need to prepare for any particular requirements set by the universities or to make university-specific investments. Finally, while under the former systems applicants had to choose one university to apply to well before the exams, they now can make their final choice with a range of offers. There are two features of the Russian higher education system that are important for our analysis. The first one is the existence of the dualtrack tuition system. There are both state-subsidized (tuition free) and paid places for students, and the students who pay tuition and those who do not are enrolled in the same programs and study together. Each public university is assigned a limited number of state-subsidized places each year and prospective students compete for them on the basis of their USE results. If the applicant is not successful in this competition (his/her USE final result is not enough for admission to a tuition free position), they have the opportunity to be admitted either to a private university (and pay the full tuition fee), or to a public one (and cover tuition costs themselves). With a few exceptions, there is no competition for commercial places at public universities. There are no grants or subsidies from the government or charity organizations for those who take paid places, and student loans are expensive and hence not popular among students. Second, students are admitted for tuition-free places on the assessment of applicant quality reflected in their USE scores, regardless of their financial needs. Such a system affects the incentives of low income students to study for free, given the limited nature of financial aid, and these students lack the resources to cover all the costs of higher education. Income may affect the university admission results of an individual in different ways. In particular, via USE scores (as these scores are the sole basis for university admission decisions): additional investments in pre-entry coaching and a wider choice of preparation programs, such as pre-entry courses within a university or educational centre and classes with tutors can improve USE results. Although the preparation for exams now can be done in schools, and Supplementary materials are available in bookstores and online, various forms of pre-entry coaching which existed before USE are still popular (even in the absence of specific requirements). Before the introduction of USE private tutoring was an important element of pre-entry coaching strategies because of the difference in university requirements and the need for specific knowledge which could not be obtained in high schools. When the
private tutoring and university choice. First, we show that income matters even under the unified system of admission to universities. Second, we argue that students from the most affluent household benefit from pre-entry coaching and the system of standardized admission procedure not only because they have more opportunities for investment in private tutoring, but also because their investments are the most efficient in terms of the increasing USE scores. Finally, it is shown that in general the returns from pre-entry coaching are quite low, but in the case of admission to selective universities each USE point counts since competition is high, so applicants from high income households have a better chance for success because pre-entry courses can bring additional points compared to their counterparts from less affluent families. The paper is organized as follows. Section I discusses the history of the admission systems in Russia. Section II contains findings and empirical evidence on the effects of income on academic achievement and university choice. Section III describes the methodology and hypotheses. Section IV analyses the relationship between household income and student achievement, measured by USE scores of Mathematics and Russian. We show how USE results differ for students from low, medium, and high income groups, given the same level of achievement at the end of 9th grade and that more affluent students show a higher level of achievement by the end of high school, choosing a more efficient direction of investment in pre-entry coaching and thus earning higher USE scores. Section V contains our concluding remarks and discussion.
2. The institutional history of admission systems in Russia Prior to the introduction of USE, the system of university-specific exams in Russia was organized as follows: high school graduates sit final exams within their schools, and those exams were checked by their school teachers. This examination procedure was required for getting a high school graduation certificate. Graduates then had the right to apply to different universities. Each university had the right to set up its own entrance exams (which took place at the university). Although the examination program formally corresponded to official requirements, in the most cases the format of exam was university-specific and required additional specific preparation for the target university. Under such conditions, university choice was limited, as students were forced to choose a certain university in advance and prepare according to that university’s requirements. Moreover, the system created a situation of inequality of access to higher education, because high school graduates who lived in the city where university was located and had enough money to attend preparatory courses within university and/or private classes with tutors (who usually worked as professors in the target university and could ‘help’ their students at entry exams) had a far greater chance of being admitted compared to students from other regions or from disadvantaged backgrounds. Moreover, such a system of admission created possibilities for corruption and unfair access, which worked against students from poor backgrounds who might not know how to deal with the system (Osipian, 2009). USE, which was introduced nationwide in 2009, represents a new system of university entrance and in most cases the results of USE are the only criteria for admission to state-subsidized places irrespective of income and other socio-demographic characteristics.1 Students take USE in high schools (it is also the final school exam), and USE results are comparable across schools and students. Then hopefuls can apply to up to 5 different universities by sending an application form with their USE results. Universities (with some few exceptions) do not have a right to set up their own admissions procedures. They collect student 1 Aside from the USE, there are still a few opportunities of admission. One of them is taking part in Olympiads for pupils. However, not more than 4% of students are admitted on this basis.
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Lochner (2005), using data from the National Longitudinal Survey of Youth, showed that an increase in total family income by US$1000 (after tax) improves the Mathematics results by 2.1%, and in literacy by 3.6% of the standard deviation. The positive effects of income have been shown in information gathered during a series of experiments (Morris et al., 2004). The nature of this relationship between income and achievement is discussed in several studies. Leibowitz (1977) gives the following explanation: families with a higher level of income can afford to buy more goods than less advantaged households. If the consumption of these goods positively affects a student’s performance, then there is a direct link between income and results. Davis-Kean (2005) in her conceptual model concluded that family income (and parental education) significantly and positively influences children’s school achievement. She tested the link between income, parental expectations and beliefs, parental behaviour, and child’s achievement and showed an indirectly positive relationship between income and achievement. As stated above, most studies do not focus on pre-entry coaching in explaining university choice but there is evidence that more affluent students attend preparatory courses and hence, do better during exams. Preparatory programs exist in many countries with different levels of economic and educational development. The existence of such services has two main justifications: the insufficient quality of school education (Davies, 2004), or the need for preparation to the university (Lee, 2007; ESP, 2006). In developing countries the second reason is especially important because of the barriers for entry into higher education. Such programs are widespread in Hong Kong (Bray and Kwok, 2003), Vietnam (Dang, 2007), Bangladesh (Nath, 2008), Turkey (Tansel and Bircan, 2006), Brazil (Guimarães and Sampaio, 2013), and in Eastern Europe (ESP, 2006). Russia is no exception. The results of empirical studies show that the decision to attend such courses or tutors is closely linked to family background: parental education and income (Bray, 2006; Tansel and Bircan, 2006). Income status can serve as a main factor for investment in pre-entry coaching, as in most cases there is a fee for additional preparation. Hence, most affluent students have more opportunities for such coaching than students from disadvantaged backgrounds. This investment in pre-entry coaching can increase the academic performance, e.g. test or examination results (Becker, 1990; Powers, 1993; Powers and Rock, 1999). This violates the idea of equality of opportunities of being enrolled to the university, as wealthier students being coached increase their relevant exam scores and have a better chance to be admitted (Bray, 2006; Guimarães and Sampaio, 2013). Russian evidence shows that the variation in the pre-entry coaching programs can lead to unequal opportunities in university access (Loyalka and Zakharov, 2016). In the absence of mechanisms of student financial support in many developing countries, poorer students can be excluded from good quality higher education and thus decrease their lifetime earnings and opportunities. This problem is very important for Russia where getting into highly selective universities requires high scores, so even with USE students from high income families can have an advantage in terms of higher USE results through additional investment in pre-entry coaching (Prakhov, 2016, 2017). In this paper we provide a link between income, preparation, and USE results, and we examine efficiency of ‘old’ methods of preparation under new institutional settings for student admission.
admission procedure became unified and standardized, pre-entry programs became less oriented towards a certain higher education institutions, but provide general training for USE (Prakhov, 2017). However, due to institutional inertia pre-entry courses and classes with tutors are still popular mostly because parents believe that preparation within classes in high schools is not enough to successfully pass USE (Androushchak et al., 2010). Hence, parents believe in the efficiency of pre-entry coaching. While many researchers (Coleman et al., 1966; Baird, 1984; Dahl and Lochner, 2005; Datcher, 1982) have analysed the relationship between social status and educational outcomes in stable systems of admission (when parental and student expectations were formed) and did not focus on pre-entry coaching, we offer explanations of the effects of income in Russia shortly after introduction of the new admissions system, but when students use strategies for preparation which were appropriate under the previous system. We use a combined model of university choice as a framework for our analysis (Vossensteyn, 2005; Hossler et al., 1999) and analyse students’ multistep decisions of (Chapman, 1981; Litten, 1982). 3. The impact of income on educational outcomes: the results of empirical studies The problems with the accessibility of higher education and issues concerning the equal opportunities for students from different SES have attracted the attention of many researchers in different countries, and there is a significant amount of research devoted to the effect of income on educational opportunities. This influence was examined using models of university choice, which represent the multistage process of choices before going to university (Vossensteyn, 2005). There are different conceptual approaches, which set up frameworks for the analysis of university choice. According to Hossler et al. (1999), the models of university choice can be classified into sociological models focusing on student motivation, expectations and background, economic models focusing on the analysis of costs and benefits from education, and combined models gathering and analysing information concerning universities and other features of higher education and labour market. It is assumed that student background matters, and family characteristics, such as income define the decisions made at later stages. Chapman (1981) proposed a multistep model of university choice. Based on the results of previous research, he argued that the family and its characteristics significantly affect the choice of educational institution. Income, says Chapman, has a direct influence on educational decisions, as it determines the ability to cover tuition fees at university. Litten (1982) extended this model. He showed that SES (in particular, income) are significant determinants in this process: university aspirations, the search process, the decision on where to apply, the application and enrolment. Those models were based on US evidence and corresponding educational statistics. The stage of pre-entry coaching was often neglected. In Russia USE scores serve as the basis for university choice. Empirical studies find a significant positive correlation between income and exam scores, and between income and the level of university selectivity. As a starting point we may consider Coleman report (Coleman et al., 1966), which stated that student educational achievement (expressed in scores) is to a large extent defined by family SES. In subsequent years, researchers tested various hypotheses on the influence of family SES several times, and in most cases they concluded that the level of income is an important determinant of a student’s academic success, in that income status positively affects achievement (e.g., White, 1982). In subsequent decades, research continued, Hill and O'Neill (1994) found a positive relationship between income and student academic performance. Orr (2003) showed that income is a significant predictor of the differences in test results of black and white Americans (and ethnicity is often correlated with SES, including income). Dahl and
4. Data and methodology of the study We use the results of the Russian Longitudinal Panel Study of Educational and Occupational Trajectories, run by the Higher School of Economics. This is a representative sample, which includes data provided by high school students. We use only those cases where students were applying to universities in 2014 (several years after the official introduction of the USE). The data includes information on household 3
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Table 1 Descriptive statistics. Variable
N
Minimum
Maximum
Mean
Std. Deviation
Family income (rubles per month) Mother’s education (=1 if higher education) Father’s education (=1 if higher education) Incomplete family Books at home Gender (=1 if male) Class specialization (=1 if yes) Investment in extra classes in educational centre (rubles) Investment in pre-entry courses at university (rubles) Investment in classes with tutors (rubles) Investment in extra classes at school (rubles) Total investment in pre-entry coaching SFE result in Russian SFE result in Mathematics USE result in Mathematics USE result in Russian
874 874 874 874 874 874 874 874 874 874 874 874 855 817 874 874
10000.00 0.00 0.00 0.00 5.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2 2 12.00 12.00
95000.00 1.00 1.00 1.00 650.00 1.00 1.00 12000.00 19000.00 40000.00 8000.00 40000.00 5 5 91.00 100.00
29519.21 0.47 0.41 0.14 160.80 0.39 0.70 94.89 257.27 2463.19 89.45 2904.80 4.34 4.25 50.41 68.57
22826.99 0.50 0.49 0.35 179.12 0.49 0.46 718.94 1285.20 4268.46 452.96 4521.46 0.67 0.79 15.72 13.15
Table 2 Mean USE results depending on level of income, with fixed achievement in the 9th grade. Achievement in the 9th grade Level of income
Low achievers
Subject Russian Mathematics
Low 55.3 41.5
Medium achievers Medium 56.6 41.2
High 60.5 41.5
Low 62.6 46.8
Medium 64.8 45.1
High achievers High 66.4 46.5
Low 71.5 55.3
Medium 77.9 59.2
High 75.3 60.9
The differences are significant at 1%-interval, based on chi-square test.
According to the proposed framework, we test the following hypotheses:
SES (income per person per month, parental education), student achievement (USE scores in Russian and Mathematics which are the obligatory subjects, average USE score, and average high school grade before pre-entry coaching) and their application strategies which covers time and financial investments, sets of universities and disciplines under consideration and the availability of information. Households were divided into three groups according to their level of income: First group: a monthly income of less than 20,000 rubles (under US $572), the proportion of this type of households totalling 26.4% (231 observations); Second group: a monthly income from 20,000–29,000 rubles (US $572–US$830); 30.7% (268 observations); Third group: the monthly income is higher than 30,000 rubles (over US$830); 42.9% (375 observations).2 Descriptive statistics are presented in Table 1. We used a combined multistep model of university choice. We argue that the level of income can positively influence USE results via investment in pre-entry coaching: higher levels of investment lead to higher USE scores. Higher USE scores give more opportunities for successful enrolment in university, however in this paper we concentrate only on USE scores as indicators of student performance. High income students get better results and may apply to more selective universities. Finally, the percentage of students with higher levels of income will be higher in more selective universities. Hence, we predict that income affects scores and university choice even under the unified examination system:
Hypothesis 1. High income students have higher USE scores than less affluent students. Hypothesis 2. High income students choose relatively more efficient ways of pre-entry coaching in terms of increasing their USE scores. To test these hypotheses, we examine the correlations between income and USE scores, and run several regressions which reflect the relationship between investments in pre-entry coaching and USE results. 5. The effects of income on student achievement Income is correlated with USE scores for Russian and Mathematics: with an increase in the level of income, these USE results improve. This is true for the means as well: when the level of income rises, the mean USE score in Russian increases from 65 to 69 points, and in Mathematics from 48 to 52 points. The Pearson correlation between monthly income (in rubles) and USE scores is high and positive. The coefficient of the correlation for USE results in Russian is 0.062 (significant at 10% level); for Mathematics 0.068 (significant at 5% level). In order to understand why wealthier students achieve better results, one need to address two questions: (1) how USE results are different under equal achievement before the beginning of the preparation process; and (2) how investment in the coaching process varies for students from low, medium and high income groups. Let us consider how average USE results differ in Russian and Mathematics for students from different families, but with the same achievement at the end of 9th grade. For this purpose, we split the sample into several categories according to the marks on State Final Examination (SFE, an analogue of USE which is taken two years prior to high school graduation) in Russian and Mathematics at the end of 9th grade.
Income → Pre-entry coaching → USE scores.
2 The logic of grouping households by income is as follows: monthly household income per capita is 23221 rubles, and we have identified a group whose income is significantly lower than the average per capita income (low-income group), middle-income group and the group with significantly higher income (high-income group), maintaining a balance of observations in the three groups.
4
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0.334 360 0.277 253 0.260 368 0.368 260 0.295 227 0.255 817 0.305 855 R2 Observations
−5.496*** (0.803) 1.976** (0.842) 1.983** (0.830) −1.025 (1.091) 0.003 (0.002) 2.028** (0.832)
Tis = αs + βs ln(1 + Totalinvestment i) + γs ln(Incomei) + Xi′ δs + εis, where Tis is USE result of student i for subject s (we consider results in Russian and Mathematics); ln(1 + Total investmenti) is the natural logarithm of total investment in pre-entry coaching plus one; ln(Incomei) is the natural logarithm of monthly family income; Xi is a vector of other independent variables: they include family factors (mother’s and father’s education, family structure, number of books at home), personal characteristics (student’s gender, SFE results in Russian or Mathematics according to the dependent variable) and class specialization in high school. The results of the regression analysis are presented in Table 3. First two columns contain estimations of regression coefficients for USE results in Russian (1) and Mathematics (2). Investment in pre-entry coaching is positively related to the result in Russian, but is not significant for USE score in Mathematics. One possible explanation for this is that USE scores in Mathematics are not required by all universities during the process of admission. Hence, even though this exam is obligatory for USE, not all the applicants need the best scores in Mathematics, as their desired universities do not require it. In this case USE scores in Russian are more important, as this subject is required by all universities. Other significant factors are parental education (both mother’s and father’s): students whose parents have higher education gain more USE points; gender (girls are more successful in Russian, while boys have higher USE results in Mathematics), SFE results in Russian and
Standard errors in parentheses. Significance level: * −10%, ** −5%, *** −1%.
7.134*** (1.276) 0.084 (2.180) 1.276 (2.403) 2.013 (2.425) 1.564 (2.684) −0.007 (0.006) −0.374 (2.197) −3.209*** (1.115) 2.068* (1.204) 2.667** (1.164) −1.774 (1.723) 0.000 (0.003) 0.580 (1.247) −7.525*** (1.621) 2.253 (1.546) 1.227 (1.536) −0.996 (2.134) 0.005 (0.004) 2.422 (1.628) −6.265*** (1.616) 0.851 (1.798) 2.255 (1.794) −0.324 (1.934) 0.004 (0.005) 3.337* (1.605)
7.450*** (0.819) 9.549*** (1.176) 6.921*** (1.159)
8.969*** (0.611) 2.025** (0.987) 2.390** (1.052) 2.650*** (1.029) 0.019 (1.373) 0.003 (0.003) −0.191 (1.054)
0.538 (0.677) 1.119** (0.542) 8.007*** (0.582)
25.999*** (5.628) 0.199 (0.200) 32.705*** (5.282) 0.427** (0.206) 4.432 (7.154) −0.043 (0.125) 20.048*** (6.031) 0.269*** (0.100)
(Constant) Ln (1+Total investment in pre-entry coaching) Ln (Income) SFE result in Russian SFE result in Mathematics Gender Mother’s education Father’s education Incomplete family Books at home Class specialization
According to 9th grade SFE results, 10.6% of students achieved fair marks (3 out of 5) in Russian, more than 44% of students have received good marks (4 out of 5), and 45.1% achieved excellent marks (5 out of 5) For Mathematics, 20.8% achieved fair marks, 32.3% got good marks, and 46.6% had excellent marks. For further analysis we marked students with fair marks as ‘low achievers’, students with good marks are ‘medium achievers’. Those students who have received only excellent marks are ‘high achievers’. The relationship between the level of income and USE scores is still positive and significant even for groups with the same achievement at the end of 9th grade, i.e. for each group of the low achievers, medium achievers and high achievers (see Table 2). However, the differences between the closest groups tend to be smoothed out (the difference between their achievement reflected in USE scores is minimal), but high income students have highest marks in 3 cases out of 6, and medium and high income students outperform low income students on the basis of the USE results in 5 cases out of 6. We argue that, with their prior achievements being equal, students from richer households pass USE exams more successfully than students from lower income families. How can these discrepancies in USE results be explained, if by the end of the 9th grade the overall achievement is the same? A possible explanation for this fact is that wealthier families can invest in preentry coaching more efficiently.3 Although the admission system under USE does not require any university-specific investment in the pre-entry process, the majority of students (more than 60%) still believe that high school education is insufficient for success in USE. Such a lack of confidence in the quality of high school education forces students (even before the introduction of the USE) to attend additional pre-entry classes, which is what they actively do. To evaluate the impact of income and investment in pre-entry coaching on USE results, we propose several regression models. First of all, we estimate the following model:
0.153 204
9.958*** (0.860) 4.931*** (1.366) 1.732 (1.494) 2.916** (1.428) −0.304 (2.090) 0.005 (0.004) 1.002 (1.544) 9.184*** (1.169) −0.605 (1.836) 4.031** (1.856) 2.448 (1.833) −1.948 (2.570) 0.007 (0.005) −0.472 (2.015)
3.948 (3.951) −0.071 (0.170) 8.347 (5.124) −0.039 (0.239) 19.386*** (5.442) −0.082 (0.279) 35.147*** (3.695) 0.155 (0.137)
(8) (7) (5) (4) (3) (2) (1)
High income Sample Sample
Medium income
(6)
High income Low income Low income
Medium income
Dependent variable: USE result in Mathematics Dependent variable: USE result in Russian Dependent variable: USE result in Mathematics Dependent variable: USE result in Russian Independent variables
Table 3 Results of regression analysis: impact of income and investment in pre-entry coaching on the USE results (OLS method).
I. Prakhov, M. Yudkevich
3 Certainly, more affluent families can invest more in pre-entry coaching, and may have higher returns from such an investment in terms of the USE scores. However, we cannot say that low income families do not invest in pre-entry coaching at all. For example, the average investment in pre-entry courses is 2233.08 rubles for low income families, and 3398.55 rubles for high income families. The average investment in classes with tutors is 3237.52 rubles for low income families, and 5198.46 for high income families.
5
6
0.304 855
R2 Observations 0.265 817
Standard errors in parentheses. Significance level: * − 10%, ** − 5%, *** − 1%.
−5.686*** (0.802) 1.922** (0.847) 2.017** (0.833) −0.989 (1.094) 0.003 (0.002) 2.263** (0.836)
0.000** (0.000) 0.001 (0.001) 0.559 (0.679)
0.000 (0.000) 0.000 (0.001) 1.017* (0.548) 8.086*** (0.548) 8.969*** (0.607) 1.964** (0.978) 2.595** (1.051) 2.171** (1.025) 0.100 (1.367) 0.002 (0.003) −0.039 (1.052)
0.001* (0.000)
0.001* (0.000)
(10)
(9) 4.202 (7.166) 0.000 (0.001)
Sample
Sample
21.609*** (6.088) 0.000 (0.001)
Dependent variable: USE result in Mathematics
Dependent variable: USE result in Russian
(Constant) Investment in extra classes in educational centre Investment in pre-entry courses at university Investment in classes with tutors Investment in extra classes at school Ln (Income) SFE result in Russian SFE result in Mathematics Gender Mother’s education Father’s education Incomplete family Books at home Class specialization
Independent variables
0.284 227
−6.911*** (1.611) 0.842 (1.814) 2.183 (1.810) 0.201 (1.933) 0.005 (0.005) 3.869** (1.610)
7.333*** (1.159)
0.000 (0.001)
32.709*** (5.334)
(11)
Low income
0.366 260
−7.688*** (1.613) 2.121 (1.552) 1.326 (1.536) −0.877 (2.134) 0.005 (0.004) 2.554 (1.636)
9.616*** (1.175)
0.000 (0.001)
26.731*** (5.512)
(12)
Medium income
Dependent variable: USE result in Russian
Table 4 Effects of investment in different types of pre-entry coaching on USE results for students with different levels of income.
0.277 368
−3.558*** (1.103) 2.486** (1.195) 2.588** (1.150) −2.053 (1.700) −0.001 (0.003) 1.278 (1.246)
7.441*** (0.806)
0.001*** (0.000)
35.103*** (3.637)
(13)
High income
0.153 204
7.049*** (1.268) 0.161 (2.158) 1.277 (2.404) 2.004 (2.427) 1.459 (2.661) −0.007 (0.006) −0.408 (2.196)
0.000 (0.001)
19.393*** (5.475)
(14)
Low income
0.280 253
9.129*** (1.164) −0.557 (1.821) 3.849** (1.855) 2.363 (1.822) −1.926 (2.556) 0.007 (0.005) −2.234 (2.022)
0.001 (0.001)
8.629* (5.041)
(15)
Medium income
0.343 360
9.692*** (0.857) 4.768*** (1.355) 2.052 (1.490) 2.843** (1.419) −0.465 (2.077) 0.004 (0.004) 1.556 (1.556)
0.001** (0.001)
3.922 (3.890)
(16)
High income
Dependent variable: USE result in Mathematics
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bigger cities (with best ones concentrated in Moscow and St.Petersburg) and are associated with higher costs of living and housing for students from other cities. Low-income households normally cannot afford that: according to the data of the Federal State Statistics Service,4 at the beginning of 2014, average monthly expenditures per person among households in Moscow were 2.06 times higher than in all Russia, and in St. Petersburg 1.30 times higher. In the absence of additional financial support not all students can move to these cities and study there. All these contribute to the existing inequality in the admission process and should be addressed separately. Programs of additional preentry coaching still exist and attract new customers. Does it mean that high school education is not enough for successful matriculation? The significant role of pre-entry coaching for some social groups might prompt discussion on how to improve the quality of secondary education. In other words, one direction of smoothing the inequality of access to higher education is the use of the resources of high schools, as secondary education mechanisms are not heavily influenced by cultural capital and income status. If coaching mechanisms are effective for high income students, those students would have an ‘unfair disadvantage over others’ (Powers, 1993). This fact would contradict the idea of equality of access to higher education, which prompted the introduction of USE. If access to higher education is still unequal, other options of making higher education more accessible, especially for students from disadvantaged backgrounds need to be found. For example, educational loans could be a mechanism that stimulates the demand for education and provides more opportunities for students from less affluent families. However, existing programs of student lending in Russia have high interest rates (7.5%–28.9% per year) making this mechanism of student support unpopular, especially among poor families.
Mathematics. Class specialization (extra school classes in certain subjects) is significant for USE results in Russia. In general, these results are in line with the findings of previous studies. Income is positively correlated with investment in pre-entry coaching (the coefficient of the correlation is 0.154 and it is significant at 1% level). In other words, wealthier households tend to spend more on pre-entry coaching. Consequently, there can be a multicollinearity problem when estimating the model described above. To correct for possible increased variation of the regression coefficient, we run a set of regressions for each level of income. We estimate the model:
Tis = αs + βs ln(1 + Totalinvestment i) + Xi′ δs + εis for low income, medium and high income households and look at the coefficients concerning investment in pre-entry coaching. Table 3 contains the results of regression analysis for the USE score in Russian (models (3)-(5)), and for the USE score in Mathematics (models (6)(8)). The results are ambiguous and show that investment in pre-entry coaching is significant only for low income families and only for the USE result in Russian, while such investment is not significant for other model specifications. What does this mean? One possible explanation for this result is that our variable of interest reflects total investment in pre-entry coaching, i.e. the sum of investments in various preparatory programs: classes with tutors, pre-entry courses within university, classes in educational centres and extra classes in high school. That is why we separate different types of investment in pre-entry coaching in further econometric specifications. Table 4 represents the results of the regression analysis for different types of pre-entry coaching. Models (9) and (10) include both income and different investments in extra coaching. The results show that only investment in pre-entry courses at university can improve USE results both in Russian and Mathematics (however, expenditure on private tutors is also significant for Mathematics, but its impact is lower than the effect of pre-entry courses). That is why in the next specifications we concentrate on investment in pre-entry courses at university. Next, we run regression models for every level of income. Models (11)-(13) have the USE result in Russian as the dependent variable, and models (14)-(16) are for the USE result in Mathematics (Table 4). The results of the regression analysis show that investment in pre-entry courses is significant and positively influences USE results in Russian and Mathematics only for students from the most affluent families. Even under the standardized system of examinations the most affluent families benefit by investing in more efficient types of pre-entry coaching and, hence, improve USE results.
Acknowledgements The article was prepared within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) and supported within the framework of a subsidy granted to the HSE by the Government of the Russian Federation for the implementation of the Global Competitiveness Program. The financial support from the Government of the Russian Federation within the framework of the implementation of the 5–100 Programme Roadmap of the National Research University Higher School of Economics is acknowledged. References
6. Conclusions Androushchak, G., Prakhov, I., Yudkevich, M., 2010. The Strategies of Pre-Entry Training and University Choice Under the Unified State Examination: Information Bulletin. OOO ‘Center-Print’, Moscow −Yoshkar-Ola (in Russian). Baird, L.L., 1967. Family Income and Characteristics of College-Bound Students. (ACT Research Report, No. 17). Baird, L.L., 1984. Relationships between ability, college attendance and family income. Res. High. Educ. 21 (4), 373–395. http://dx.doi.org/10.1007/BF00992632. Becker, B.J., 1990. Coaching for the scholastic aptitude test: further synthesis and appraisal. Rev. Educ. Res. 60 (3), 373–417. http://dx.doi.org/10.3102/ 00346543060003373. Bray, M., Kwok, P., 2003. Demand for private supplementary tutoring: conceptual considerations, and socio-economic patterns in Hong Kong. Econ. Educ. Rev. 22 (6), 611–620. http://dx.doi.org/10.1016/S0272-7757(03)00032-3. Bray, M., 2006. Private supplementary tutoring: comparative perspectives on patterns and implications. Compare 36 (4), 515–530. http://dx.doi.org/10.1080/ 03057920601024974. Cabrera, A.F., La Nasa, S.M., 2001. On the path to college: three critical tasks facing
We have shown that access to higher education in Russia under the system of standardized exams can be limited for lower income students, as richer families are the only income group which can benefit by investing in pre-entry courses at university which improves USE results. Pre-entry coaching is not beneficial for low income students, but preparatory effort is not the only mechanism that supports inequality; we identify two more. First, even with equal USE scores, students from different income groups may make different decisions about where to apply: in particular, richer households feel more secure about the result of applying to a university, and if unsuccessful in getting a state-subsidized place richer students have sufficient financial resources to cover the tuition fees, whereas poorer students do not and are forced to choose a lower quality institution, but with a higher probability of enrolment given the lower level of competition between applicants. In general, richer households can use USE scores more effectively which is consistent with an existing literature (see Baird, 1967; Datcher, 1982; Hearn, 1991; Cabrera and La Nasa, 2001). Second, better and more selective universities are usually situated in
4 Data from the Federal State Statistics Service: http://www.gks.ru/wps/wcm/ connect/rosstat_main/rosstat/en/main/. In the 1st quarter of 2014 average expenditures per person in households in Russia were 13895.0 rubles per month (US$398 per person per month), while the same indicator in Moscow was 28677.3 rubles (US$821 per person per month), and in St. Petersburg − 18045.6 rubles (US$516 per person per month).
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00221546.1982.11780470. Loyalka, P., Zakharov, A., 2016. Does shadow education help students prepare for college? Evidence from Russia. Int. J. Educ. Dev. 49, 22–30. http://dx.doi.org/10.1016/ j.ijedudev.2016.01.008. Morris, P., Duncan, G.J., Rodrigues, C., 2004. Does Money Really Matter? Estimating Impacts of Family Income on Children’s Achievement with Data from Random-assignment Experiments. (Unpublished manuscript). Nath, S.R., 2008. Private supplementary tutoring among primary students in Bangladesh. Educ. Stud. 34 (1), 55–72. http://dx.doi.org/10.1080/03055690701785285. Orr, A.J., 2003. Black-white differences in achievement: the importance of wealth. Sociol. Educ. 76 (4), 281–304. http://dx.doi.org/10.2307/1519867. Osipian, A.L., 2009. Vouchers, tests, loans, privatization: will they help tackle corruption in Russian higher education? Prospects 39 (1), 47. http://dx.doi.org/10.1007/ s11125-009-9117-y. Powers, D.E., Rock, D.A., 1999. Effects of coaching on SAT I: Reasoning test scores. J. Educ. Meas. 36 (2), 93–118. http://dx.doi.org/10.1111/j.1745-3984.1999. tb00549.x. Powers, D.E., 1993. Coaching for the SAT: A summary of the summaries and an update. Educ. Meas.: Issues Pract. 12 (2), 24–30. http://dx.doi.org/10.1111/j.1745-3992. 1993.tb00530.x. Prakhov, I., 2016. The barriers of access to selective universities in Russia. High. Educ. Q. 70 (2), 170–199. http://dx.doi.org/10.1111/hequ.12087. Prakhov, I., 2017. The prevalence and efficiency of investment in pre-entry coaching in Russia. Tert. Educ. Manage. 23 (2), 170–185. http://dx.doi.org/10.1080/13583883. 2017.1290820. Tansel, A., Bircan, F., 2006. Demand for education in Turkey: a tobit analysis of private tutoring expenditures. Econ. Educ. Rev. 25 (3), 303–313. http://dx.doi.org/10.1016/ j.econedurev.2005.02.003. Vossensteyn, H., 2005. Perceptions of Student Price-responsiveness. CHEPS/UT. White, K.R., 1982. The relation between socioeconomic status and academic achievement. Psychol. Bull. 91 (3), 461. http://dx.doi.org/10.1037/0033-2909.91.3.461.
America's disadvantaged. Res. High. Educ. 42 (2), 119–149. http://dx.doi.org/10. 1023/A:1026520002362. Chapman, D.W., 1981. A model of student college choice. J. High. Educ. 52 (5), 490–505. http://dx.doi.org/10.1080/00221546.1981.11778120. Coleman, J., Campbell, E., Hobson, C., McPartland, J., Mood, A., Weinfeld, F., York, R., 1966. Equality of Educational Opportunity. U.S. Government Printing Office, Washington D.C. Dahl, G.B., Lochner, L., 2005. The Impact of Family Income on Child Achievement (No. w11279). National Bureau of Economic Researchhttp://dx.doi.org/10.3386/ w11279. Dang, H.A., 2007. The determinants and impact of private tutoring classes in Vietnam. Econ. Educ. Rev. 26 (6), 683–698. http://dx.doi.org/10.1016/j.econedurev.2007.10. 003. Datcher, L., 1982. Effects of community and family background on achievement. Rev. Econ. Stat. 64 (1), 32–41. http://dx.doi.org/10.2307/1937940. Davies, S., 2004. School choice by default? Understanding the demand for private tutoring in Canada. Am. J. Educ. 110 (3), 233–255. http://dx.doi.org/10.1086/ 383073. Davis-Kean, P.E., 2005. The influence of parent education and family income on child achievement: the indirect role of parental expectations and the home environment. J. Fam. Psychol. 19 (2), 294–304. http://dx.doi.org/10.1037/0893-3200.19.2.294. ESP, 2006. Education in a Hidden Marketplace: Monitoring of Private Tutoring. Education Support Program (ESP) of the Open Society Institute, Budapest. Guimarães, J., Sampaio, B., 2013. Family background and students’ achievement on a university entrance exam in Brazil. Educ. Econ. 21 (1), 38–59. http://dx.doi.org/10. 1080/09645292.2010.545528. Hearn, J.C., 1991. Academic and nonacademic influences on the college destinations of 1980 high school graduates. Sociol. Educ. 64 (3), 158–171. http://dx.doi.org/10. 2307/2112849. Hill, M.A., O'Neill, J., 1994. Family endowments and the achievement of young children with special reference to the underclass. J. Hum. Resour. 29 (4), 1064–1100. http:// dx.doi.org/10.2307/146134. Hossler, D., Schmit, J., Vesper, N., 1999. Going to College: How Social, Economic, and Educational Factors Influence the Decisions Students Make. JHU Press. Konstantinovskiy, D.L., 2012. Social inequality and access to higher education in Russia. Eur. J. Educ. 47 (1), 9–24. http://dx.doi.org/10.1111/j.1465-3435.2011.01504.x. Lee, J., 2007. Two worlds of private tutoring: the prevalence and causes of after-school mathematics tutoring in Korea and the United States. Teach. Coll. Rec. 109 (5), 1207–1234. Leibowitz, A., 1977. Parental inputs and children's achievement. J. Hum. Res. 12 (2), 242–251. http://dx.doi.org/10.2307/145387. Litten, L.H., 1982. Different strokes in the applicant pool: some refinements in a model of student college choice. J. High. Educ. 53 (4), 383–402. http://dx.doi.org/10.1080/
Ilya Prakhov Born in 1986 in Moscow. Bachelor of Economics (2007; Higher School of Economics), Master of Economics (2009; Higher School of Economics), Candidate of Sciences (PhD, 2013; Higher School of Economics). Research interests: institutional economics, economics of education, agency theory, economic analysis of student loans. Maria Yudkevich Born in 1974 in Moscow. Diploma in Mathematics (1996; Moscow State University), Master of Economics (1999; Higher School of Economics), Candidate of Sciences (PhD, 2003; Higher School of Economics). Research interests: institutional economics, economics of education, contract theory, game theory.
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Contents lists available at ScienceDirect
International Journal of Educational Development journal homepage: www.elsevier.com/locate/ijedudev
University admission in Russia: Do the wealthier benefit from standardized exams? ⁎
Ilya Prakhov , Maria Yudkevich Center for Institutional Studies, National Research University Higher School of Economics, 20 Myasnitskaya Street, Moscow, 101000, Russia
A R T I C L E I N F O
A B S T R A C T
Classification Codes: I21 I24 I28
This paper examines the impact of family income on the results of the newly introduced Unified State Examination (USE) in Russia. We argue that entrants from wealthy households have an advantage in terms of access to higher education, since income positively affects USE scores through a higher level of investment in pre-entry coaching. We have found positive and significant relationships between the level of income and USE results for high school graduates, given equal achievement before coaching. We demonstrate that in general, investment in pre-entry coaching has positive returns, but the most significant type of investment is pre-entry courses. However, such strategy improves USE results only for students from the most affluent households.
Keywords: Student achievement Standardized entry exams Equity in higher education University choice
1. Introduction Issues in the accessibility and equality of higher education are crucial in the education policy agendas of both developed and developing countries. One of the most significant elements concerning access to higher education is the system of admission to universities. There are various admission practices in different countries, but most of them are intended to smooth gaps in opportunities for admission between applicants from different backgrounds. The case of Russia is of particular interest because of the recent institutional reform of admission policy. While for many decades in the Soviet Union, and then in post-Soviet Russia, university-based entry exams were at the core of the university admission system, they have been recently replaced by an admission system based on the results of standardized national exams (the Unified State Examination, USE) which are obligatory for all high school graduates in Russia. The Soviet and post-Soviet system of education was characterized by the existence of unequal access to education, even though equality of chances was declared a part of the Soviet ideology. However, ‘children from privileged groups traditionally received the education and professional training which were most advantageous for their subsequent lives and careers’ (Konstantinovskiy, 2012). One of the aims of the unification of admission process was to increase the accessibility and equality of higher education. It was assumed by policymakers that under the new institutional settings, students from different backgrounds would have equal opportunities of applying to universities because of the reduction in expenditures related to the
⁎
preparatory and application processes. However, inequality still exists: evidence shows that more affluent students study in more selective universities (Prakhov, 2016). In other words, students from wealthier families are more represented in more selective universities characterized by a high level of competition among applicants (high levels of selectivity), while first year students from disadvantaged backgrounds are underrepresented in such institutions. Given that these universities provide higher quality and consequently give higher educational returns, this creates unequal conditions for graduates in the labour market. We argue that among other channels through which inequality is still supported the preparatory process is an important one. High school graduates from wealthier households still have some significant advantages, since they have more resources to invest in the general preparation process during their final year at high school. That improves their final USE scores and hence chances for better university placement. This paper analyses how family income influences students’ USE scores through investment in preparation under the new admission system, and how this creates unequal opportunities for applicants from different income groups. We offer an explanation of the unequal conditions for access to higher education in Russia for students with various income backgrounds through the channel of investment in preentry coaching and the existing variation in the returns from private tutoring expressed in USE scores. This study is an important contribution to the literature concerning
Corresponding author. E-mail address: [email protected] (I. Prakhov).
http://dx.doi.org/10.1016/j.ijedudev.2017.08.007 Received 19 October 2016; Received in revised form 7 June 2017; Accepted 11 August 2017 0738-0593/ © 2017 Elsevier Ltd. All rights reserved.
Please cite this article as: Prakhov, I., International Journal of Educational Development (2017), http://dx.doi.org/10.1016/j.ijedudev.2017.08.007
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applications and rank applicants on the basis of the total score of the required subjects. The ranking of students and the names of those recommended for admission are published online by universities; rankings are freely accessible on university web pages in mid-summer. Those students who are on this list can present their certificates and be admitted to university during this ‘first wave’ of admission. Those who do not have enough points can either apply for a paid place or take a chance on the next ‘wave’ of admissions. As students can send their USE results to up to 5 universities, and in many cases they receive offers from several institutions. As a result, by the end of the ‘first wave’ there are still vacant places in many universities. They add up their rankings again and recommend additional students for admission. This situation can affect student choice, particularly the decisions of high school graduates from low income families. Wealthy applicants, if not successful in competition for a state-subsidized place during the ‘first wave’, can afford to pay tuition fees at the same university without any decrease in quality of their higher education. Low income students, however, can take a risk and wait for the ‘second wave’, or, more likely, can choose a less selective university and the resultant lower quality of education provided. In this case, educational opportunities are not equal, and our goal is to examine how students from different backgrounds manage their USE results. The university admission reform in Russia had at least 3 consequences: first, under a new system high school students sit USE in the city or regional centre in which they live, so there is no need to pay for travel to sit examinations in their potential place of learning. Second, there is no need to prepare for any particular requirements set by the universities or to make university-specific investments. Finally, while under the former systems applicants had to choose one university to apply to well before the exams, they now can make their final choice with a range of offers. There are two features of the Russian higher education system that are important for our analysis. The first one is the existence of the dualtrack tuition system. There are both state-subsidized (tuition free) and paid places for students, and the students who pay tuition and those who do not are enrolled in the same programs and study together. Each public university is assigned a limited number of state-subsidized places each year and prospective students compete for them on the basis of their USE results. If the applicant is not successful in this competition (his/her USE final result is not enough for admission to a tuition free position), they have the opportunity to be admitted either to a private university (and pay the full tuition fee), or to a public one (and cover tuition costs themselves). With a few exceptions, there is no competition for commercial places at public universities. There are no grants or subsidies from the government or charity organizations for those who take paid places, and student loans are expensive and hence not popular among students. Second, students are admitted for tuition-free places on the assessment of applicant quality reflected in their USE scores, regardless of their financial needs. Such a system affects the incentives of low income students to study for free, given the limited nature of financial aid, and these students lack the resources to cover all the costs of higher education. Income may affect the university admission results of an individual in different ways. In particular, via USE scores (as these scores are the sole basis for university admission decisions): additional investments in pre-entry coaching and a wider choice of preparation programs, such as pre-entry courses within a university or educational centre and classes with tutors can improve USE results. Although the preparation for exams now can be done in schools, and Supplementary materials are available in bookstores and online, various forms of pre-entry coaching which existed before USE are still popular (even in the absence of specific requirements). Before the introduction of USE private tutoring was an important element of pre-entry coaching strategies because of the difference in university requirements and the need for specific knowledge which could not be obtained in high schools. When the
private tutoring and university choice. First, we show that income matters even under the unified system of admission to universities. Second, we argue that students from the most affluent household benefit from pre-entry coaching and the system of standardized admission procedure not only because they have more opportunities for investment in private tutoring, but also because their investments are the most efficient in terms of the increasing USE scores. Finally, it is shown that in general the returns from pre-entry coaching are quite low, but in the case of admission to selective universities each USE point counts since competition is high, so applicants from high income households have a better chance for success because pre-entry courses can bring additional points compared to their counterparts from less affluent families. The paper is organized as follows. Section I discusses the history of the admission systems in Russia. Section II contains findings and empirical evidence on the effects of income on academic achievement and university choice. Section III describes the methodology and hypotheses. Section IV analyses the relationship between household income and student achievement, measured by USE scores of Mathematics and Russian. We show how USE results differ for students from low, medium, and high income groups, given the same level of achievement at the end of 9th grade and that more affluent students show a higher level of achievement by the end of high school, choosing a more efficient direction of investment in pre-entry coaching and thus earning higher USE scores. Section V contains our concluding remarks and discussion.
2. The institutional history of admission systems in Russia Prior to the introduction of USE, the system of university-specific exams in Russia was organized as follows: high school graduates sit final exams within their schools, and those exams were checked by their school teachers. This examination procedure was required for getting a high school graduation certificate. Graduates then had the right to apply to different universities. Each university had the right to set up its own entrance exams (which took place at the university). Although the examination program formally corresponded to official requirements, in the most cases the format of exam was university-specific and required additional specific preparation for the target university. Under such conditions, university choice was limited, as students were forced to choose a certain university in advance and prepare according to that university’s requirements. Moreover, the system created a situation of inequality of access to higher education, because high school graduates who lived in the city where university was located and had enough money to attend preparatory courses within university and/or private classes with tutors (who usually worked as professors in the target university and could ‘help’ their students at entry exams) had a far greater chance of being admitted compared to students from other regions or from disadvantaged backgrounds. Moreover, such a system of admission created possibilities for corruption and unfair access, which worked against students from poor backgrounds who might not know how to deal with the system (Osipian, 2009). USE, which was introduced nationwide in 2009, represents a new system of university entrance and in most cases the results of USE are the only criteria for admission to state-subsidized places irrespective of income and other socio-demographic characteristics.1 Students take USE in high schools (it is also the final school exam), and USE results are comparable across schools and students. Then hopefuls can apply to up to 5 different universities by sending an application form with their USE results. Universities (with some few exceptions) do not have a right to set up their own admissions procedures. They collect student 1 Aside from the USE, there are still a few opportunities of admission. One of them is taking part in Olympiads for pupils. However, not more than 4% of students are admitted on this basis.
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Lochner (2005), using data from the National Longitudinal Survey of Youth, showed that an increase in total family income by US$1000 (after tax) improves the Mathematics results by 2.1%, and in literacy by 3.6% of the standard deviation. The positive effects of income have been shown in information gathered during a series of experiments (Morris et al., 2004). The nature of this relationship between income and achievement is discussed in several studies. Leibowitz (1977) gives the following explanation: families with a higher level of income can afford to buy more goods than less advantaged households. If the consumption of these goods positively affects a student’s performance, then there is a direct link between income and results. Davis-Kean (2005) in her conceptual model concluded that family income (and parental education) significantly and positively influences children’s school achievement. She tested the link between income, parental expectations and beliefs, parental behaviour, and child’s achievement and showed an indirectly positive relationship between income and achievement. As stated above, most studies do not focus on pre-entry coaching in explaining university choice but there is evidence that more affluent students attend preparatory courses and hence, do better during exams. Preparatory programs exist in many countries with different levels of economic and educational development. The existence of such services has two main justifications: the insufficient quality of school education (Davies, 2004), or the need for preparation to the university (Lee, 2007; ESP, 2006). In developing countries the second reason is especially important because of the barriers for entry into higher education. Such programs are widespread in Hong Kong (Bray and Kwok, 2003), Vietnam (Dang, 2007), Bangladesh (Nath, 2008), Turkey (Tansel and Bircan, 2006), Brazil (Guimarães and Sampaio, 2013), and in Eastern Europe (ESP, 2006). Russia is no exception. The results of empirical studies show that the decision to attend such courses or tutors is closely linked to family background: parental education and income (Bray, 2006; Tansel and Bircan, 2006). Income status can serve as a main factor for investment in pre-entry coaching, as in most cases there is a fee for additional preparation. Hence, most affluent students have more opportunities for such coaching than students from disadvantaged backgrounds. This investment in pre-entry coaching can increase the academic performance, e.g. test or examination results (Becker, 1990; Powers, 1993; Powers and Rock, 1999). This violates the idea of equality of opportunities of being enrolled to the university, as wealthier students being coached increase their relevant exam scores and have a better chance to be admitted (Bray, 2006; Guimarães and Sampaio, 2013). Russian evidence shows that the variation in the pre-entry coaching programs can lead to unequal opportunities in university access (Loyalka and Zakharov, 2016). In the absence of mechanisms of student financial support in many developing countries, poorer students can be excluded from good quality higher education and thus decrease their lifetime earnings and opportunities. This problem is very important for Russia where getting into highly selective universities requires high scores, so even with USE students from high income families can have an advantage in terms of higher USE results through additional investment in pre-entry coaching (Prakhov, 2016, 2017). In this paper we provide a link between income, preparation, and USE results, and we examine efficiency of ‘old’ methods of preparation under new institutional settings for student admission.
admission procedure became unified and standardized, pre-entry programs became less oriented towards a certain higher education institutions, but provide general training for USE (Prakhov, 2017). However, due to institutional inertia pre-entry courses and classes with tutors are still popular mostly because parents believe that preparation within classes in high schools is not enough to successfully pass USE (Androushchak et al., 2010). Hence, parents believe in the efficiency of pre-entry coaching. While many researchers (Coleman et al., 1966; Baird, 1984; Dahl and Lochner, 2005; Datcher, 1982) have analysed the relationship between social status and educational outcomes in stable systems of admission (when parental and student expectations were formed) and did not focus on pre-entry coaching, we offer explanations of the effects of income in Russia shortly after introduction of the new admissions system, but when students use strategies for preparation which were appropriate under the previous system. We use a combined model of university choice as a framework for our analysis (Vossensteyn, 2005; Hossler et al., 1999) and analyse students’ multistep decisions of (Chapman, 1981; Litten, 1982). 3. The impact of income on educational outcomes: the results of empirical studies The problems with the accessibility of higher education and issues concerning the equal opportunities for students from different SES have attracted the attention of many researchers in different countries, and there is a significant amount of research devoted to the effect of income on educational opportunities. This influence was examined using models of university choice, which represent the multistage process of choices before going to university (Vossensteyn, 2005). There are different conceptual approaches, which set up frameworks for the analysis of university choice. According to Hossler et al. (1999), the models of university choice can be classified into sociological models focusing on student motivation, expectations and background, economic models focusing on the analysis of costs and benefits from education, and combined models gathering and analysing information concerning universities and other features of higher education and labour market. It is assumed that student background matters, and family characteristics, such as income define the decisions made at later stages. Chapman (1981) proposed a multistep model of university choice. Based on the results of previous research, he argued that the family and its characteristics significantly affect the choice of educational institution. Income, says Chapman, has a direct influence on educational decisions, as it determines the ability to cover tuition fees at university. Litten (1982) extended this model. He showed that SES (in particular, income) are significant determinants in this process: university aspirations, the search process, the decision on where to apply, the application and enrolment. Those models were based on US evidence and corresponding educational statistics. The stage of pre-entry coaching was often neglected. In Russia USE scores serve as the basis for university choice. Empirical studies find a significant positive correlation between income and exam scores, and between income and the level of university selectivity. As a starting point we may consider Coleman report (Coleman et al., 1966), which stated that student educational achievement (expressed in scores) is to a large extent defined by family SES. In subsequent years, researchers tested various hypotheses on the influence of family SES several times, and in most cases they concluded that the level of income is an important determinant of a student’s academic success, in that income status positively affects achievement (e.g., White, 1982). In subsequent decades, research continued, Hill and O'Neill (1994) found a positive relationship between income and student academic performance. Orr (2003) showed that income is a significant predictor of the differences in test results of black and white Americans (and ethnicity is often correlated with SES, including income). Dahl and
4. Data and methodology of the study We use the results of the Russian Longitudinal Panel Study of Educational and Occupational Trajectories, run by the Higher School of Economics. This is a representative sample, which includes data provided by high school students. We use only those cases where students were applying to universities in 2014 (several years after the official introduction of the USE). The data includes information on household 3
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Table 1 Descriptive statistics. Variable
N
Minimum
Maximum
Mean
Std. Deviation
Family income (rubles per month) Mother’s education (=1 if higher education) Father’s education (=1 if higher education) Incomplete family Books at home Gender (=1 if male) Class specialization (=1 if yes) Investment in extra classes in educational centre (rubles) Investment in pre-entry courses at university (rubles) Investment in classes with tutors (rubles) Investment in extra classes at school (rubles) Total investment in pre-entry coaching SFE result in Russian SFE result in Mathematics USE result in Mathematics USE result in Russian
874 874 874 874 874 874 874 874 874 874 874 874 855 817 874 874
10000.00 0.00 0.00 0.00 5.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2 2 12.00 12.00
95000.00 1.00 1.00 1.00 650.00 1.00 1.00 12000.00 19000.00 40000.00 8000.00 40000.00 5 5 91.00 100.00
29519.21 0.47 0.41 0.14 160.80 0.39 0.70 94.89 257.27 2463.19 89.45 2904.80 4.34 4.25 50.41 68.57
22826.99 0.50 0.49 0.35 179.12 0.49 0.46 718.94 1285.20 4268.46 452.96 4521.46 0.67 0.79 15.72 13.15
Table 2 Mean USE results depending on level of income, with fixed achievement in the 9th grade. Achievement in the 9th grade Level of income
Low achievers
Subject Russian Mathematics
Low 55.3 41.5
Medium achievers Medium 56.6 41.2
High 60.5 41.5
Low 62.6 46.8
Medium 64.8 45.1
High achievers High 66.4 46.5
Low 71.5 55.3
Medium 77.9 59.2
High 75.3 60.9
The differences are significant at 1%-interval, based on chi-square test.
According to the proposed framework, we test the following hypotheses:
SES (income per person per month, parental education), student achievement (USE scores in Russian and Mathematics which are the obligatory subjects, average USE score, and average high school grade before pre-entry coaching) and their application strategies which covers time and financial investments, sets of universities and disciplines under consideration and the availability of information. Households were divided into three groups according to their level of income: First group: a monthly income of less than 20,000 rubles (under US $572), the proportion of this type of households totalling 26.4% (231 observations); Second group: a monthly income from 20,000–29,000 rubles (US $572–US$830); 30.7% (268 observations); Third group: the monthly income is higher than 30,000 rubles (over US$830); 42.9% (375 observations).2 Descriptive statistics are presented in Table 1. We used a combined multistep model of university choice. We argue that the level of income can positively influence USE results via investment in pre-entry coaching: higher levels of investment lead to higher USE scores. Higher USE scores give more opportunities for successful enrolment in university, however in this paper we concentrate only on USE scores as indicators of student performance. High income students get better results and may apply to more selective universities. Finally, the percentage of students with higher levels of income will be higher in more selective universities. Hence, we predict that income affects scores and university choice even under the unified examination system:
Hypothesis 1. High income students have higher USE scores than less affluent students. Hypothesis 2. High income students choose relatively more efficient ways of pre-entry coaching in terms of increasing their USE scores. To test these hypotheses, we examine the correlations between income and USE scores, and run several regressions which reflect the relationship between investments in pre-entry coaching and USE results. 5. The effects of income on student achievement Income is correlated with USE scores for Russian and Mathematics: with an increase in the level of income, these USE results improve. This is true for the means as well: when the level of income rises, the mean USE score in Russian increases from 65 to 69 points, and in Mathematics from 48 to 52 points. The Pearson correlation between monthly income (in rubles) and USE scores is high and positive. The coefficient of the correlation for USE results in Russian is 0.062 (significant at 10% level); for Mathematics 0.068 (significant at 5% level). In order to understand why wealthier students achieve better results, one need to address two questions: (1) how USE results are different under equal achievement before the beginning of the preparation process; and (2) how investment in the coaching process varies for students from low, medium and high income groups. Let us consider how average USE results differ in Russian and Mathematics for students from different families, but with the same achievement at the end of 9th grade. For this purpose, we split the sample into several categories according to the marks on State Final Examination (SFE, an analogue of USE which is taken two years prior to high school graduation) in Russian and Mathematics at the end of 9th grade.
Income → Pre-entry coaching → USE scores.
2 The logic of grouping households by income is as follows: monthly household income per capita is 23221 rubles, and we have identified a group whose income is significantly lower than the average per capita income (low-income group), middle-income group and the group with significantly higher income (high-income group), maintaining a balance of observations in the three groups.
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0.334 360 0.277 253 0.260 368 0.368 260 0.295 227 0.255 817 0.305 855 R2 Observations
−5.496*** (0.803) 1.976** (0.842) 1.983** (0.830) −1.025 (1.091) 0.003 (0.002) 2.028** (0.832)
Tis = αs + βs ln(1 + Totalinvestment i) + γs ln(Incomei) + Xi′ δs + εis, where Tis is USE result of student i for subject s (we consider results in Russian and Mathematics); ln(1 + Total investmenti) is the natural logarithm of total investment in pre-entry coaching plus one; ln(Incomei) is the natural logarithm of monthly family income; Xi is a vector of other independent variables: they include family factors (mother’s and father’s education, family structure, number of books at home), personal characteristics (student’s gender, SFE results in Russian or Mathematics according to the dependent variable) and class specialization in high school. The results of the regression analysis are presented in Table 3. First two columns contain estimations of regression coefficients for USE results in Russian (1) and Mathematics (2). Investment in pre-entry coaching is positively related to the result in Russian, but is not significant for USE score in Mathematics. One possible explanation for this is that USE scores in Mathematics are not required by all universities during the process of admission. Hence, even though this exam is obligatory for USE, not all the applicants need the best scores in Mathematics, as their desired universities do not require it. In this case USE scores in Russian are more important, as this subject is required by all universities. Other significant factors are parental education (both mother’s and father’s): students whose parents have higher education gain more USE points; gender (girls are more successful in Russian, while boys have higher USE results in Mathematics), SFE results in Russian and
Standard errors in parentheses. Significance level: * −10%, ** −5%, *** −1%.
7.134*** (1.276) 0.084 (2.180) 1.276 (2.403) 2.013 (2.425) 1.564 (2.684) −0.007 (0.006) −0.374 (2.197) −3.209*** (1.115) 2.068* (1.204) 2.667** (1.164) −1.774 (1.723) 0.000 (0.003) 0.580 (1.247) −7.525*** (1.621) 2.253 (1.546) 1.227 (1.536) −0.996 (2.134) 0.005 (0.004) 2.422 (1.628) −6.265*** (1.616) 0.851 (1.798) 2.255 (1.794) −0.324 (1.934) 0.004 (0.005) 3.337* (1.605)
7.450*** (0.819) 9.549*** (1.176) 6.921*** (1.159)
8.969*** (0.611) 2.025** (0.987) 2.390** (1.052) 2.650*** (1.029) 0.019 (1.373) 0.003 (0.003) −0.191 (1.054)
0.538 (0.677) 1.119** (0.542) 8.007*** (0.582)
25.999*** (5.628) 0.199 (0.200) 32.705*** (5.282) 0.427** (0.206) 4.432 (7.154) −0.043 (0.125) 20.048*** (6.031) 0.269*** (0.100)
(Constant) Ln (1+Total investment in pre-entry coaching) Ln (Income) SFE result in Russian SFE result in Mathematics Gender Mother’s education Father’s education Incomplete family Books at home Class specialization
According to 9th grade SFE results, 10.6% of students achieved fair marks (3 out of 5) in Russian, more than 44% of students have received good marks (4 out of 5), and 45.1% achieved excellent marks (5 out of 5) For Mathematics, 20.8% achieved fair marks, 32.3% got good marks, and 46.6% had excellent marks. For further analysis we marked students with fair marks as ‘low achievers’, students with good marks are ‘medium achievers’. Those students who have received only excellent marks are ‘high achievers’. The relationship between the level of income and USE scores is still positive and significant even for groups with the same achievement at the end of 9th grade, i.e. for each group of the low achievers, medium achievers and high achievers (see Table 2). However, the differences between the closest groups tend to be smoothed out (the difference between their achievement reflected in USE scores is minimal), but high income students have highest marks in 3 cases out of 6, and medium and high income students outperform low income students on the basis of the USE results in 5 cases out of 6. We argue that, with their prior achievements being equal, students from richer households pass USE exams more successfully than students from lower income families. How can these discrepancies in USE results be explained, if by the end of the 9th grade the overall achievement is the same? A possible explanation for this fact is that wealthier families can invest in preentry coaching more efficiently.3 Although the admission system under USE does not require any university-specific investment in the pre-entry process, the majority of students (more than 60%) still believe that high school education is insufficient for success in USE. Such a lack of confidence in the quality of high school education forces students (even before the introduction of the USE) to attend additional pre-entry classes, which is what they actively do. To evaluate the impact of income and investment in pre-entry coaching on USE results, we propose several regression models. First of all, we estimate the following model:
0.153 204
9.958*** (0.860) 4.931*** (1.366) 1.732 (1.494) 2.916** (1.428) −0.304 (2.090) 0.005 (0.004) 1.002 (1.544) 9.184*** (1.169) −0.605 (1.836) 4.031** (1.856) 2.448 (1.833) −1.948 (2.570) 0.007 (0.005) −0.472 (2.015)
3.948 (3.951) −0.071 (0.170) 8.347 (5.124) −0.039 (0.239) 19.386*** (5.442) −0.082 (0.279) 35.147*** (3.695) 0.155 (0.137)
(8) (7) (5) (4) (3) (2) (1)
High income Sample Sample
Medium income
(6)
High income Low income Low income
Medium income
Dependent variable: USE result in Mathematics Dependent variable: USE result in Russian Dependent variable: USE result in Mathematics Dependent variable: USE result in Russian Independent variables
Table 3 Results of regression analysis: impact of income and investment in pre-entry coaching on the USE results (OLS method).
I. Prakhov, M. Yudkevich
3 Certainly, more affluent families can invest more in pre-entry coaching, and may have higher returns from such an investment in terms of the USE scores. However, we cannot say that low income families do not invest in pre-entry coaching at all. For example, the average investment in pre-entry courses is 2233.08 rubles for low income families, and 3398.55 rubles for high income families. The average investment in classes with tutors is 3237.52 rubles for low income families, and 5198.46 for high income families.
5
6
0.304 855
R2 Observations 0.265 817
Standard errors in parentheses. Significance level: * − 10%, ** − 5%, *** − 1%.
−5.686*** (0.802) 1.922** (0.847) 2.017** (0.833) −0.989 (1.094) 0.003 (0.002) 2.263** (0.836)
0.000** (0.000) 0.001 (0.001) 0.559 (0.679)
0.000 (0.000) 0.000 (0.001) 1.017* (0.548) 8.086*** (0.548) 8.969*** (0.607) 1.964** (0.978) 2.595** (1.051) 2.171** (1.025) 0.100 (1.367) 0.002 (0.003) −0.039 (1.052)
0.001* (0.000)
0.001* (0.000)
(10)
(9) 4.202 (7.166) 0.000 (0.001)
Sample
Sample
21.609*** (6.088) 0.000 (0.001)
Dependent variable: USE result in Mathematics
Dependent variable: USE result in Russian
(Constant) Investment in extra classes in educational centre Investment in pre-entry courses at university Investment in classes with tutors Investment in extra classes at school Ln (Income) SFE result in Russian SFE result in Mathematics Gender Mother’s education Father’s education Incomplete family Books at home Class specialization
Independent variables
0.284 227
−6.911*** (1.611) 0.842 (1.814) 2.183 (1.810) 0.201 (1.933) 0.005 (0.005) 3.869** (1.610)
7.333*** (1.159)
0.000 (0.001)
32.709*** (5.334)
(11)
Low income
0.366 260
−7.688*** (1.613) 2.121 (1.552) 1.326 (1.536) −0.877 (2.134) 0.005 (0.004) 2.554 (1.636)
9.616*** (1.175)
0.000 (0.001)
26.731*** (5.512)
(12)
Medium income
Dependent variable: USE result in Russian
Table 4 Effects of investment in different types of pre-entry coaching on USE results for students with different levels of income.
0.277 368
−3.558*** (1.103) 2.486** (1.195) 2.588** (1.150) −2.053 (1.700) −0.001 (0.003) 1.278 (1.246)
7.441*** (0.806)
0.001*** (0.000)
35.103*** (3.637)
(13)
High income
0.153 204
7.049*** (1.268) 0.161 (2.158) 1.277 (2.404) 2.004 (2.427) 1.459 (2.661) −0.007 (0.006) −0.408 (2.196)
0.000 (0.001)
19.393*** (5.475)
(14)
Low income
0.280 253
9.129*** (1.164) −0.557 (1.821) 3.849** (1.855) 2.363 (1.822) −1.926 (2.556) 0.007 (0.005) −2.234 (2.022)
0.001 (0.001)
8.629* (5.041)
(15)
Medium income
0.343 360
9.692*** (0.857) 4.768*** (1.355) 2.052 (1.490) 2.843** (1.419) −0.465 (2.077) 0.004 (0.004) 1.556 (1.556)
0.001** (0.001)
3.922 (3.890)
(16)
High income
Dependent variable: USE result in Mathematics
I. Prakhov, M. Yudkevich
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bigger cities (with best ones concentrated in Moscow and St.Petersburg) and are associated with higher costs of living and housing for students from other cities. Low-income households normally cannot afford that: according to the data of the Federal State Statistics Service,4 at the beginning of 2014, average monthly expenditures per person among households in Moscow were 2.06 times higher than in all Russia, and in St. Petersburg 1.30 times higher. In the absence of additional financial support not all students can move to these cities and study there. All these contribute to the existing inequality in the admission process and should be addressed separately. Programs of additional preentry coaching still exist and attract new customers. Does it mean that high school education is not enough for successful matriculation? The significant role of pre-entry coaching for some social groups might prompt discussion on how to improve the quality of secondary education. In other words, one direction of smoothing the inequality of access to higher education is the use of the resources of high schools, as secondary education mechanisms are not heavily influenced by cultural capital and income status. If coaching mechanisms are effective for high income students, those students would have an ‘unfair disadvantage over others’ (Powers, 1993). This fact would contradict the idea of equality of access to higher education, which prompted the introduction of USE. If access to higher education is still unequal, other options of making higher education more accessible, especially for students from disadvantaged backgrounds need to be found. For example, educational loans could be a mechanism that stimulates the demand for education and provides more opportunities for students from less affluent families. However, existing programs of student lending in Russia have high interest rates (7.5%–28.9% per year) making this mechanism of student support unpopular, especially among poor families.
Mathematics. Class specialization (extra school classes in certain subjects) is significant for USE results in Russia. In general, these results are in line with the findings of previous studies. Income is positively correlated with investment in pre-entry coaching (the coefficient of the correlation is 0.154 and it is significant at 1% level). In other words, wealthier households tend to spend more on pre-entry coaching. Consequently, there can be a multicollinearity problem when estimating the model described above. To correct for possible increased variation of the regression coefficient, we run a set of regressions for each level of income. We estimate the model:
Tis = αs + βs ln(1 + Totalinvestment i) + Xi′ δs + εis for low income, medium and high income households and look at the coefficients concerning investment in pre-entry coaching. Table 3 contains the results of regression analysis for the USE score in Russian (models (3)-(5)), and for the USE score in Mathematics (models (6)(8)). The results are ambiguous and show that investment in pre-entry coaching is significant only for low income families and only for the USE result in Russian, while such investment is not significant for other model specifications. What does this mean? One possible explanation for this result is that our variable of interest reflects total investment in pre-entry coaching, i.e. the sum of investments in various preparatory programs: classes with tutors, pre-entry courses within university, classes in educational centres and extra classes in high school. That is why we separate different types of investment in pre-entry coaching in further econometric specifications. Table 4 represents the results of the regression analysis for different types of pre-entry coaching. Models (9) and (10) include both income and different investments in extra coaching. The results show that only investment in pre-entry courses at university can improve USE results both in Russian and Mathematics (however, expenditure on private tutors is also significant for Mathematics, but its impact is lower than the effect of pre-entry courses). That is why in the next specifications we concentrate on investment in pre-entry courses at university. Next, we run regression models for every level of income. Models (11)-(13) have the USE result in Russian as the dependent variable, and models (14)-(16) are for the USE result in Mathematics (Table 4). The results of the regression analysis show that investment in pre-entry courses is significant and positively influences USE results in Russian and Mathematics only for students from the most affluent families. Even under the standardized system of examinations the most affluent families benefit by investing in more efficient types of pre-entry coaching and, hence, improve USE results.
Acknowledgements The article was prepared within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE) and supported within the framework of a subsidy granted to the HSE by the Government of the Russian Federation for the implementation of the Global Competitiveness Program. The financial support from the Government of the Russian Federation within the framework of the implementation of the 5–100 Programme Roadmap of the National Research University Higher School of Economics is acknowledged. References
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We have shown that access to higher education in Russia under the system of standardized exams can be limited for lower income students, as richer families are the only income group which can benefit by investing in pre-entry courses at university which improves USE results. Pre-entry coaching is not beneficial for low income students, but preparatory effort is not the only mechanism that supports inequality; we identify two more. First, even with equal USE scores, students from different income groups may make different decisions about where to apply: in particular, richer households feel more secure about the result of applying to a university, and if unsuccessful in getting a state-subsidized place richer students have sufficient financial resources to cover the tuition fees, whereas poorer students do not and are forced to choose a lower quality institution, but with a higher probability of enrolment given the lower level of competition between applicants. In general, richer households can use USE scores more effectively which is consistent with an existing literature (see Baird, 1967; Datcher, 1982; Hearn, 1991; Cabrera and La Nasa, 2001). Second, better and more selective universities are usually situated in
4 Data from the Federal State Statistics Service: http://www.gks.ru/wps/wcm/ connect/rosstat_main/rosstat/en/main/. In the 1st quarter of 2014 average expenditures per person in households in Russia were 13895.0 rubles per month (US$398 per person per month), while the same indicator in Moscow was 28677.3 rubles (US$821 per person per month), and in St. Petersburg − 18045.6 rubles (US$516 per person per month).
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Ilya Prakhov Born in 1986 in Moscow. Bachelor of Economics (2007; Higher School of Economics), Master of Economics (2009; Higher School of Economics), Candidate of Sciences (PhD, 2013; Higher School of Economics). Research interests: institutional economics, economics of education, agency theory, economic analysis of student loans. Maria Yudkevich Born in 1974 in Moscow. Diploma in Mathematics (1996; Moscow State University), Master of Economics (1999; Higher School of Economics), Candidate of Sciences (PhD, 2003; Higher School of Economics). Research interests: institutional economics, economics of education, contract theory, game theory.
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