The Effect of After-School Classes on Private Tuition,Mental Health and Academic Outcomes: Evidence from Korea

Abstract

Using a natural experiment from South Korea’s high school equalisation policy area, we show that school-provided after-school classes reduce students’ time spent in private tuition and the associated household expenditure, as well as increase their likelihood of college attendance. Though high and low income groups use a different mix of unassisted study and private tuition to substitute for after-school class, both consume less private tuition as after-school class hours increase. Importantly, the likelihood of college attendance improves similarly for both high and low income groups. The findings suggest a role for after-school classes in improving the academic outcomes of students and reducing demand for private tuition, but their utility in reducing outcome inequality is less certain.

Keywords

after-school classes college attendance equalisation policy private tuition

Introduction

Numerous essays have been devoted to analysing the theory of social reproduction (Bourdieu and Passeron, 1977; Collins, 2009). Many (Aurini and Davies, 2005; Ball, 2003; Davies, 2004) have canvassed the motivations of parents for engaging in behaviours that assist their children in reaching or ascending from their own social class. Others (Ball et al., 1995; Gewirtz et al., 1994) have examined the role a more autonomous and choice-driven society plays in opening new avenues for social reproduction to take place. Given education’s positional nature, the interaction between school choice and social reproduction has assumed a high degree of interest.

Van Zanten (2005) identifies four means by which school choice can take place. Two of the more straightforward are private sector provision of schooling and the use of multiple providers within a state system. The rise of fee-charging private schools in Australia and India, charter schools in the USA and free schools in England are recent examples. Another manifestation is residential sorting, whereby families relocate to affluent neighbourhoods in order to access more desirable state-run schools. Finally, ‘colonisation’ of local state schools is an avenue parents can take to engineer superior in-school opportunities for their children by making specific demands (i.e. agitating for a ‘gifted’ stream of classes).

For those concerned by a trend towards more inequality, restricting these forms of school choice in order to limit social reproduction strategies is an attractive proposition. This sentiment is especially strong in the western systems where pro-school choice policies are growing in influence. The parallel shift towards increased use of private tuition has attracted less attention, despite it too presenting parents with new options to pursue social reproduction. This article investigates a possible course of action for governments interested in mitigating the extent to which further predicted increases¹ in the global market for private tuition will undermine equality of opportunity. The findings will also shed light on Bernstein’s (1970) question of whether education can compensate for society. In particular, our results will inform whether government efforts to influence households’ social reproduction strategies and their mix of educational inputs outside of school can lead to improved academic outcomes and equality.

South Korea (henceforth, Korea) is an ideal location to pursue this inquiry, as its experience suggests that rejection of school choice policies may drive parents to pursue social reproduction enhancing activities (primarily private tutoring) outside of the formal school sector (Dawson, 2010). Centralised control of the education system, widespread standardisation and the ‘high school equalisation policy’ (HSEP) of random student assignment to schools leaves little room for Korean parents to manipulate the formal school sector to sustain or advance their child’s social status. However, the motivation to compete in the positional arms race for academic success remains, manifesting in the creation of the world’s largest private tutoring market. The response of the Korean government to this phenomenon holds lessons for addressing the rise of private tuition as a tool for social reproduction worldwide.

This article examines whether school-provided after-school classes (ASCs) in Korea effectively reduced the demand for private tuition without deleterious impact on students. ASCs have evolved from basic day-care facilities to institutions staffed by qualified personnel trained to improve children’s academic outcomes (Halpern, 2002). Studies have shown that ASCs boost student literacy and numeracy test scores (Scott-Little et al., 2002), build positive attitudes to school, improve within-school behaviour (Grossman et al., 2002) and are beneficial for students regardless of socioeconomic background (Lauer et al., 2006; Posner and Vandell, 1994). However, their usefulness in reducing demand for private tuition is less established, as are the consequences that arise from doing so given that private tuition can improve student academic performance (Dang and Rogers, 2008; Ireson, 2004) and affect mental health (Ireson, 2004).

Our empirical setting is particularly useful to help understand the role that ASCs can play in reducing private tutoring and weakening the relationship between family advantage and educational success. We use cross-sectional data from the Korean Education and Employment Panel (KEEP) and rely on quasi-random variation in ASC hours across public general high schools in Korea to estimate the impacts of ASCs on the average and by household income group. Because students are assigned randomly into general high schools within a given district under Korea’s HSEP, students cannot self-select into schools with desired characteristics and the adoption of additional ASCs is effectively randomly distributed across schools. We can thus exclude factors that are correlated with ASCs and simultaneously influence students’ outcomes, and more confidently draw causal inferences on the impacts of ASCs. As we confine our analysis to public high schools that have limited capacity to vary their personnel and curricular decisions, other policy confounds are likely absent in our analysis.

Our results show that ASCs are effective in reducing consumption of private tuition and unassisted study hours. We also find evidence of a positive effect of ASCs on the likelihood of attending a college or undertaking a four-year university degree, and no significant effect on anxiety and suicidal ideation. The effects do not vary substantially with income, although there exists some difference in the mix of unassisted study and private tuition substituted for ASCs by high and low income households, with the former favouring the reduction of unassisted study. Collectively, these findings suggest that public school systems can use ASCs to reduce demand for private tuition, while improving student academic outcomes.

These findings are important, as the Korean private tuition boom is not an aberrant trend. In school systems in all states of development and centralisation, demand for private tutoring has soared (Bray, 2013). Education policy-makers should therefore consider publicly provisioned after-school classes as a potential means of limiting the pursuit of social reproduction strategies outside of the formal school sector, and a tool for promoting a more egalitarian education system.

Context: The Korean Education System

Before the 1970s, most school districts in Korea had elite high schools that took only students scoring in the upper tier of academic exams sat in middle school, creating a secondary school system highly stratified by academic ability (Byun, 2010). A desire to improve the odds of their children gaining admission to an elite school saw households spend heavily on private tuition (Kim and Lee, 2002, 2010), with some even going into debt in the lead-up to the exams (Byun, 2010). In response to equity concerns and the toll selection exams took on the well-being of students, the Korean government implemented the equalisation policy across middle schools between 1969 and 1971, and thereafter extended it to many high schools (the HSEP) (Kim and Lee, 2010).²

The Equalisation Policy

Explicitly, the equalisation policy aims to narrow the variance of school performance, reduce the use of private tutoring, and lower the financial burden of private education spending for households (Kim and Lee, 2002). It replaced high school admission exams with a lottery-based enrolment system that randomly assigns students to schools within a given district. Private schools are also subject to the HSEP, but they maintain autonomy in staffing matters (Hahn et al., 2014). Random assignment to schools removes much of the between-school variation in student academic ability seen under the prior system. Strict regulations concerning teacher salaries, fees, curricula and operating hours apply to both public and private schools, with funding centralised to reduce variability (Byun and Kim, 2010; Kim and Lee, 2002). The HSEP ensures parents have little direct control over which school their child attends, limiting the potential for self-selection into schools with particular types of policies in place. By the mid-2000s, roughly 70 per cent of all Korean academic high school students were subject to the HSEP (Wang, 2015).

The Rise of Private Tutoring

Household expenditure on private tuition has boomed, from 0.54 per cent of GDP in 1985 to 2.79 per cent in 2006 (Jung and Lee, 2010). Remarkably, a large part of this growth occurred despite heavy government regulation of the private tuition market, including a ban on some forms of private tuition introduced in 1980. While the ban was in place, an illegal and costly private tutoring market emerged, along with a strictly regulated but growing number of cramming schools known as hakwons (Lee et al., 2010). The ban was deeply resented and was eventually found to be unconstitutional in 2000 (Kim and Lee, 2010).

The top income decile of Korean households now outspends the bottom on private tuition by a factor of five (Kim and Lee, 2010). Kim and Lee (2010) interpret the growth in private tuition as a consequence of unmet demand for education among households competing for finite positions in prestigious universities. Though this seems at odds with the large proportion of Korean students now attending university (OECD, 2014), there is a long spectrum of quality in the university system, with graduates from top universities commanding significant wage-premiums over other graduates (Byun and Kim, 2010).

After-School Classes

At the turn of the century the Korean government began to fear the school curriculum was too fixed on acquiring content knowledge in narrowly defined areas (Bae et al., 2010). ASCs were seized on as a means of delivering a more holistic education that would enhance the creativity of students, which many educational experts saw as limited by conventional schooling (Hans, 2006 cited in Bae et al., 2010). ASCs were to provide enrichment activities unrelated to day-to-day schooling, often with an arts or cultural focus (Bae et al., 2010). As parents were given significant input as to the nature of ASCs provided, many high schools succumbed to pressure to operate them as academic tutorial programmes (Bae et al., 2010). In 2004, the Korean government acknowledged what had become the de facto purpose of ASCs in high schools, recasting them as a programme to reduce demand for private tuition within the state system (Lee et al., 2010). Recent research as summarised in Bae et al. (2010) finds a negative correlation between ASCs and household expenditure on private tutoring, but is yet to explore the exact substitution effect or measure the impact on student academic outcomes.

Data

To evaluate the impact of ASCs, we use data from the Korean Education and Employment Panel (KEEP) survey conducted in 2004 and 2005 by the Korea Research Institute for Vocational Education and Training (KRIVET). Two thousand final year general high school students in 100 schools across 15 regions of Korea were surveyed along with their parent or guardian, homeroom teacher and school administrator in 2004, based on a stratified cluster sampling method. In each sampled school, four classes and five students per class were randomly selected for the survey. We match these observations with a 2005 follow-up survey to investigate post-school outcomes.

To take advantage of the natural experiment conditions provided by the HSEP, we remove from our sample 872 students attending schools where the policy does not apply. To limit the potential for unobserved confounding influences, we also confine the sample to public general academic high schools, excluding 628 students who attend private schools. Though Korean private and public schools are subject to many of the same regulations, private schools face stronger pressure to deliver positive student outcomes and have greater freedom to alter school policies (Hahn et al., 2014; Kim and Lee, 2002). As a strategy for educational improvement, principals may raise the number of ASC hours in conjunction with other changes not accounted for in KEEP surveys. Keeping only the less autonomous public schools in the sample reduces the potential influences that these confounding factors may play. Public schools adhere to standardised staff qualification requirements, are funded on a uniform per-student basis and their teachers and principals rotate schools every four years. The rotation system means that public school principals: (a) have less incentive to improve the outcomes of students (Hahn et al., 2014); and (b) are more likely to inherit policies put in place by predecessors. In conjunction with the random assignment of students to high schools under the HSEP, our sample selection criteria ensure that the variation in ASC hours is as random as possible. After further removing 20 observations with missing values for one of the key variables used, we arrive at the final sample of 480 students. We present summary statistics of key variables in Table 1.

Table 1.

Descriptions and summary statistics of key variables.

Variable	Description	Mean	Std. Dev.	Min.	Max.
Metropolitan	Indicator for a student residing in a metropolitan area	0.713	0.453	0	1
Single-sex school	Indicator for a single-sex school	0.285	0.452	0	1
Household assets ($ million KRW)	Household assets, including bank deposits, residence & investments. We converted the 13 categorical responses into values at range mid-points, and the reference value specified for the highest and lowest ranges (i.e. less than 10m KRW becomes 10m KRW)	288.4	412.5	10	5000
Siblings	Indicator for a student who has any sibling	0.906	0.292	0	1
University graduate: father	Indicator for a student whose father has at least a 2–3-year college qualification	0.377	0.485	0	1
University graduate: mother	Indicator for a student whose mother has at least a 2–3-year college qualification	0.202	0.402	0	1
Frequent pre-school age reading	Indicator for a student whose parents read very frequently or definitely every day to the student prior to school age	0.363	0.481	0	1
After-school class hours	A composite measure created by multiplying the average hours of after-school classes per week by the proportion of participating students at a given school	4.566	2.901	0.8	12.6
Unassisted study	Total hours per week of unassisted study	11.33	10.32	1	30
Private tuition: all subjects	Total hours per week of any form of paid academic assistance in any subject	7.508	7.170	0	40
Private tuition: core subjects	Total hours per week of any form of paid academic assistance in Korean, mathematics or English	6.202	5.833	0	30
Premium tuition: core subjects	Total hours per week of one-on-one tutoring, group tutoring or lessons at a private institution in Korean, mathematics and English	5.681	5.568	0	30
Share of household income devoted to private tuition	Average monthly private tuition expense (September 2003–February 2004) divided by average monthly income	0.099	0.088	0	0.467
Leisure hours per week	Six ranges of values converted into values at range mid-points, and the reference value specified for the highest and lowest ranges. Resulting values for leisure hours on weekdays and weekends summed for weekly figure	6.115	2.456	1	11
Hours of TV per week	Six ranges of values converted into values at range mid-points, and the reference value specified for the highest and lowest ranges (i.e. more than 3 hours becomes 3 hours). Adjusted from days to weeks	6.643	4.044	3.5	21
Hours of sleep per week	Average hours of sleep per (calendar) week	38.73	6.279	21	70
College	Indicator for a student who is in college the year after the high school survey	0.742	0.438	0	1
Suicidal ideation	Indicator for a student who seriously considered suicide in the past	0.133	0.340	0	1
Serious anxiety	Indicator for a student who had experienced very serious or serious anxiety due to problems with their school grades	0.619	0.486	0	1
Four-year college	Indicator for a student who is enrolled in a four-year or more college degree	0.578	0.495	0	1
Ability grouping	Indicator for a school that uses ability grouping in any subject	0.648	0.478	0	1
Student–teacher ratio	Number of students per teacher in a school	15.21	2.420	5	17

Note: The total sample size is 480, except for variables college and four-year college, the sample size is 438.

The key explanatory variable, hours of ASC, is a composite measure created by multiplying the average hours per week devoted to ASCs by the proportion of students participating in ASCs at a given school.³ We draw data for both measures from school administrator responses. This is used in lieu of student responses as KRIVET did not ask individual students about information on school-provided after-school classes. Since most schools have the majority of students participating in ASCs (Figure 1A) and there is a large variation in average hours of ASCs per week across schools (Figure 1B), the variation in this school-centric measure of ASCs largely reflects the variation in individual students’ hours of ASCs.

Figure 1A.

Distribution of fraction of students participating in after-school classes.

Figure 1B.

Distribution of average hours of after-school classes per week.

Empirical Strategy

The HSEP imposes random assignment of students to schools at the school district level. It removes the possibility of students with particular types of characteristics, say wealth, academic motivation and strong parental support, to sort into schools with the desired average hours of ASCs, levels of resources, style of teaching and so on. We could draw causal inferences regarding the impact of ASCs on outcomes of interest using the following simple ordinary least squares (OLS) specification (1.1) and logit specification (1.2):

y_{i j k} = β_{0} + β_{1} A S C_{j k} + d_{k} + ε_{i j k}

(1.1)

P r o b (y_{i j k} = 1) = Λ (α_{0} + α_{1} A S C_{j k} + d_{k} + ε_{i j k} > 0)

(1.2)

The dependent variable in equation (1.1), $y_{i j k}$ , varies across students (level-1 variable). It measures student $i$ ’s weekly average hours of unassisted study, private tuition, leisure and sleep, or monthly tuition expenditure. The key explanatory variable is the weekly average of ASC hours ( $A S C_{j k}$ ) at school $j$ in district $k$ , which varies across schools (level-2 variable). We expect time spent on non-ASC activities to decrease as time spent on ASCs increases. Though in theory an additional hour of ASC could decrease private tuition by more than one hour, it is unlikely to occur in the Korean context as the use of private tutoring has become common. $d_{k}$ is a set of district dummy variables. Because the random assignment of students into schools is implemented within the district, the inclusion of $d_{k}$ ensures that the variation in weekly average ASC hours across schools is independent of other observed and unobserved factors captured in the error term $ε_{i j k}$ .

In the logit specification (1.2), $Λ$ denotes the logistic cumulative distribution function, and the dependent variable takes the value of one if student $i$ attends any college, and zero otherwise. We also use attendance at a more prestigious four-year university as an additional outcome measure. The effect of ASCs on college and university attendance is also most likely dependent on the extent to which students substitute between ASCs and other activities and the relative benefit of these activities. If $α_{1} > 0$ , then the net impact of ASCs on academic performance is positive.

Unfortunately, we are unable to include district dummy variables $d_{k}$ because school district information is unavailable in KEEP. To overcome this limitation, we incorporate a measure of household assets (level-1 variable) into our regression specifications to account for the ability of households to sort into school districts with the desirable average school quality, neighbourhood characteristics and amenities. Past studies show that the quality of public schools is capitalised in house prices as households sort into different neighbourhoods (Clapp et al., 2008; Fack and Grenet, 2010). Though relocating to a school district with higher average academic outcomes comes with the risk of assignment to the poorest performing school in that district, evidence suggests that Korean households still follow this pattern. For instance, house prices increased by 13 per cent when the HSEP was extended to a peripheral area of Seoul (Lee, 2012). Wealthier households may then congregate in better performing school districts. Controlling for household assets, which proxy for socioeconomic status and ability to sort into different districts, could potentially fill the role of school district dummy variables in equations (1.1) and (1.2). We therefore estimate the following OLS and logit specifications:

y_{i j} = β_{0} + β_{1} A S C_{j} + β_{2} a s s e t s_{i} + ε_{i j}

(2.1)

P r o b (y_{i j} = 1) = Λ (α_{0} + α_{1} A S C_{j} + α_{2} a s s e t s_{i} + ε_{i j} > 0)

(2.2)

The dependent variable, $y_{i j}$ , key explanatory variable, $A S C_{j}$ and error term, $ε_{i j}$ , remain as previously specified, but household assets now serves as a covariate.

Verifying Conditional Independence of ASCs

If after accounting for household assets, ASC hours become uncorrelated with factors that are largely determined prior to high school and that also influence outcomes, then we can argue that controlling for household assets adequately serves the role of district dummy variables. In other words, the variation in ASC hours across schools is conditionally exogenous if ASC hours are uncorrelated with a range of pre-determined influences on the outcomes of interest.

Because there are a limited set of variables that are unlikely to change during the three years of high school (during which the intervention occurred) available, we focus on four variables that the literature indicates influence academic results or take-up of private tutoring: paternal and maternal education background (Kim and Lee, 2010); early-childhood reading from parents (Whitehurst et al., 1999); and having a sibling (Byun, 2010; Park et al., 2011).

Table 2 reports the results of this test of conditional independence using the logit specification (2.2) with each of the following (level-1) dependent variables: (1) attainment of bachelor qualification or above: father; (2) attainment of bachelor qualification or above: mother; (3) frequent reading to student by parent prior to school age; and (4) one or more siblings.

Table 2.

Logit models of pre-determined observables.

	(1)	(2)	(3)	(4)
	University graduate: father	University graduate: mother	Frequent pre-school age reading	One or more siblings
Hours of after-school class	0.020	0.009	−0.061	0.001
Hours of after-school class	(0.041)	(0.047)	(0.043)	(0.066)
Household assets	0.004	0.002	0.0003	0.0002
	(0.001)***	(0.001)	(0.0004)	(0.0004)
Constant	−1.618	1.988	−0.395	2.233
	(0.285)***	(0.424)**	(0.249)	(0.352)***
Observations	480	480	480	480

Note: Heteroskedasticity-consistent standard errors in parentheses and estimates are adjusted for sampling weights. *p < .10; **p < .05; ***p < .01.

Table 2 shows that none of the socioeconomic and family background measures have a statistically significant relationship with ASCs after controlling for household assets.⁴ Thus, the claim that the variation in ASCs is conditionally exogenous likely holds in equations 2.1 and 2.2.

Results

As each additional hour of ASC by necessity displaces an hour of some other activity, we first focus on how time spent on unassisted study and several forms of private tuition is affected. If ASCs are an effective alternative to private tuition, time spent on private tuition should fall. If no reduction in private tuition is found, it suggests parents may not view ASCs as effective substitutes for private tuition.

Table 3 reports the estimated effect of ASCs on: (1) hours of unassisted study; (2) hours of private tuition in all subjects; (3) hours of private tuition in Korean, mathematics and English (the core subjects); (4) hours of more-expensive-form (premium) tuition in core subjects; (5) average monthly expense on private education; and average weekly hours of (6) leisure; (7) TV viewing; and (8) sleep.⁵

Table 3.

The effects of after-school classes; OLS estimates.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	Unassisted study	Private tuition: all subjects	Private tuition: core subjects	Premium tuition: core subjects	Share of income devoted to private tuition	Hours of leisure per week	Hours of TV per week	Hours of sleep per week
Hours of after-school class	−0.363	−0.531	−0.404	−0.333	−0.005	0.034	0.103	0.263
	(0.187)*	(0.100)***	(0.089)***	(0.086)***	(0.001)***	(0.043)	(0.087)	(0.123)**
Household assets	0.000	0.004	0.004	0.004	0.000	−0.0001	−0.0003	−0.0003
	(0.001)	(0.001)***	(0.001)***	(0.001)***	(0.00002)	(0.0003)	(0.001)	(0.0005)
Observations	480	480	480	480	480	480	480	480
R-squared	0.013	0.137	0.130	0.136	0.053	0.003	0.008	0.018

Note: The constant term in each specification is not reported above. Time measured in hours per week in columns 1–4 and 6–8. Heteroskedasticity-consistent standard errors in parentheses and estimates are adjusted for sampling weights. *p < .10; **p < .05; ***p < .01.

The results show that school-provided ASCs displace other after-school studying activities. Columns 1 and 2 indicate that attending schools with an additional ASC hour in a week, students reduce unassisted study by 22 minutes and private tuition by 32 minutes. On a narrower measure of private tuition involving only core academic subjects, the impact of an additional hour of ASC falls to 24 minutes (column 3). The reduction falls to 20 minutes per week when restricting private tuition to ‘premium’ one-to-one, small group or private class (hakwon) tuition (column 4).

In total, each additional hour of ASC displaces almost a combined hour of unassisted study (22 minutes) and private tuition (32 minutes). Thus, ASCs only partially substitute for private tuition. Though a one-to-one substitution would indicate greater effectiveness, households may be unwilling to more fully disengage with private tuition given the strong social norms that reinforce participation.⁶ Parents may also not see ASCs as equivalent to private tuition, especially the one-to-one type. It is also possible that because some ASCs are primarily cultural enrichment programmes as opposed to academic preparation programmes, they are not perceived as useful substitutes for private tutoring; unfortunately the KEEP surveys do not have the information for us to further examine this.

Column 5 in Table 3 indicates that each additional hour of ASC reduces the share of household income devoted to private tuition by 0.5 per cent. The mean number of ASC hours is 4.6, equating to an average monthly reduction of roughly 2.3 per cent. This result is not too dissimilar from a 2006 Ministry of Education and Human Resources Development study, which found average monthly private education expense fell by roughly 2.2 per cent of household income after ASCs were commenced in selected schools (Bae et al., 2010).

Next we turn to the impact on non-academic uses of time measured in the KEEP student survey. Columns 6–7 in Table 4 show no significant relationship between ASC hours and time spent engaging in leisurely activities or watching TV. With respect to sleep, column 8 notes an increase of approximately 16 minutes per week with each additional hour of ASC. This implies a student engaging in the mean number of ASC hours per week (4.6) would experience more than an additional hour of sleep (1.2 hours). Given a one-hour increase in ASC decreases unassisted study and private tuition by a total of 54 minutes per week, gaining an additional 16 minutes of sleep suggests there is a degree of inaccuracy in survey measurement. This is not surprising as students recalled average time spent on various activities rather than keeping a detailed diary. It is also possible that by taking school-provided ASCs and reducing private tutoring, the time saved from commuting to different places is used for sleeping.

Table 4.

The effects of after-school classes; logit estimates.

	(1)	(2)	(3)	(4)	(5)
	Suicidal ideation	Serious anxiety	Response rate	College attendance	Four-year college attendance
Hours of after-school class	−0.020	−0.046	−0.033	0.186	0.176
Hours of after-school class	(0.050)	(0.038)	(0.065)	(0.039)***	(0.046)***
Household assets	−0.002	−0.0002	−0.0002	−0.0001	0.001
	(0.001)***	(0.0003)	(0.0002)	(0.0002)	(0.0004)
Observations	480	480	480	438	438

Note: The constant term in each specification is not reported above. Heteroskedasticity-consistent standard errors in parentheses and estimates are adjusted for sampling weights. *p < .10; **p < .05; ***p < .01.

These results suggest a positive benefit to ASCs: students spend less time and money on private tuition, while sleeping more. Students also reduce hours of unassisted study which may not be as effective as school-provided ASCs in learning. To more fully evaluate the impact of ASCs, we next turn to analysing the impact on proxies for mental health and academic outcomes.

Table 4 measures the likelihood of a student reporting ‘serious’ or ‘very serious’ anxiety due to academic concerns (column 1) or having ‘serious’ consideration of suicide (column 2). Students attending schools with a higher level of ASCs do not appear to experience increased anxiety and suicidal ideation. The lack of change may occur as additional hours of ASCs substitute mostly for other academic activities, which are just as stressful. Alternatively, the negative impact of spending more time in school may be offset by additional sleep. Ultimately, there are many possible explanations for this result.

Last, we examine post-school outcomes in Table 4. Forty-two respondents in the 2004 sample did not respond in the 2005 follow-up survey, which raises the potential for non-random attrition. Nonetheless, column 3 in Table 4 rules out this possibility, showing no correlation between ASC hours and likelihood of response.

Both columns 4 and 5 in Table 4 suggest a significant improvement in the likelihood of attending college and undertaking a more prestigious four-year qualification, with the strength of the latter seemingly driving most of the improvement in overall college attendance (judging by the size of coefficients). The results imply that the activities displaced by ASC are likely less effective study aids. Time spent in ASCs instead of studying by oneself may provide better preparation for college admission exams, thus improving the college attendance rate. Overall, these results suggest ASCs are an effective means of reducing the demand for private tuition while improving academic outcomes.

Differences of Impact across Income Groups

As much of the contemporary policy rationale for ASCs rest on their ability to ease budget pressures for low income households, any difference in impact across income groups is of interest. We test whether the effects of ASCs differ by income using 2004 average monthly household income as reported by Statistics Korea to form high and low household income groups.⁷ Students from households earning above this figure (2,806,000 won per household per month) become the high income group, and the remainder form the low income group.

Column 1 in Table 5 shows a pronounced difference in how time is displaced by ASCs, with high income students recording a large reduction in unassisted study for every hour of ASC, and low income students not altering time spent on unassisted study by a statistically significant amount. Though the difference between high and low income students with respect to private tuition consumption is statistically insignificant, examining the size of coefficients suggests that low income students may respond to an additional hour of ASC by reducing private tuition only, whereas high income students reduce both unassisted study and tuition. This would accord with household income considerations, though the small samples available when splitting the sample by income prevent any firm conclusions from being made.

Table 5.

The effects of after-school classes by income group; OLS estimates.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	Unassisted study	Private tuition: all subjects	Private tuition: core subjects	Premium tuition: core subjects	Share of income devoted to private tuition	Hours of leisure per week	Hours of TV per week	Hours of sleep per week
Low income group
Hours of after-school class	0.188	−0.594	−0.517	−0.460	−0.004	0.066	−0.029	0.295
	(0.358)	(0.101)***	(0.093)***	(0.094)***	(0.002)**	(0.060)	(0.154)	(0.182)
Household assets	−0.003	0.014	0.010	0.011	0.0002	−0.002	−0.0001	0.004
	(0.005)	(0.004)***	(0.003)***	(0.003)***	(0.0001)***	(0.001)	(0.003)	(0.005)
Observations	164	164	164	164	164	164	164	164
R-squared	0.006	0.214	0.192	0.183	0.172	0.031	0.001	0.029
Mean of dependent variable	10.774	5.126	4.431	4.136	0.088	6.135	6.688	38.781
High income group
Hours of after-school class	−0.742	−0.446	−0.300	−0.219	−0.005	0.007	0.192	0.259
	(0.179)***	(0.143)***	(0.125)**	(0.122)*	(0.002)***	(0.059)	(0.108)*	(0.166)
Household assets	0.000	0.003	0.003	0.003	0.000	−0.0001	−0.0004	−0.0005
	(0.001)	(0.001)***	(0.001)***	(0.001)***	(0.00002)	(0.0003)	(0.001)	(0.0005)
Observations	316	316	316	316	316	316	316	316
R-squared	0.047	0.097	0.092	0.107	0.039	0.000	0.023	0.017
Mean of dependent variable	11.340	8.757	7.298	6.661	0.105	6.256	6.868	38.692
ASC impact difference	−0.930	0.148	0.216	0.241	−0.001	−0.059	0.221	−0.036
	(0.400)**	(0.175)	(0.156)	(0.154)	(0.002)	(0.084)	(0.187)	(0.246)

Note: The constant term in each specification is not reported. Time measured in hours per week in columns 1–4 and 6–8. Heteroskedasticity-consistent standard errors in parentheses and estimates are adjusted for sampling weights. ASC impact difference is the difference between the impact of ASCs on high and low income groups. *p < .10; **p < .05; ***p < .01.

Average monthly tuition expense as a share of household income falls similarly for high and low income background students as ASC hours increase (column 5). This is likely a product of high income households consuming more expensive forms of tuition. Though this shows larger absolute savings accruing to higher income households, ASCs are equally beneficial for high income and low income households in proportionate terms.

Table 6 shows there is no substantial difference in college attendance by income group. However, as ASC hours increase, low income students are less likely to experience suicidal ideation. It is unclear what might account for this difference. It is possible that ASCs benefit students who would have felt underprepared and stressed for exams if they relied primarily on unassisted study and low quality private tuition. Our analysis should be interpreted with caution given the small samples available after splitting by income.

Table 6.

The effects of after-school classes by income group; logit estimates.

	(1)	(2)	(3)	(4)	(5)
	Suicidal ideation	Serious anxiety	Response rate	College attendance	Four-year college attendance
Low income group
Hours of after-school class	−0.155	−0.061	−0.145	0.199	0.132
Hours of after-school class	(0.073)**	(0.060)	(0.124)	(0.071)***	(0.071)*
Household assets	−0.004	0.001	−0.002	0.010	0.006
	(0.002)**	(0.001)	(0.002)	(0.004)***	(0.002)***
Observations	164	164	164	155	155
High income group
Hours of after-school class	0.039	−0.031	−0.008	0.214	0.247
Hours of after-school class	(0.062)	(0.050)	(0.080)	(0.051)***	(0.054)***
Household assets	−0.002	−0.0003	0.0001	−0.0001	0.0003
	(0.001)**	(0.0003)	(0.0003)	(0.0003)	(0.0003)
Observations	316	316	316	283	283
ASC impact difference	0.194	0.031	0.138	0.015	0.115
	(0.096)**	(0.078)	(0.147)	(0.087)	(0.089)

Note: The constant term in each specification is not reported. Heteroskedasticity-consistent standard errors in parentheses and estimates are adjusted for sampling weights. ASC impact difference is the difference between the impact of ASCs on high and low income groups. *p < .10; **p < .05; ***p < .01.

Potential Confounding Variables and Robustness Checks

It is important to ensure that our main results do not inadvertently capture the effects of other variables, as we rely on controlling for household assets to provide conditional exogenous variation in ASCs. We now assess the sensitivity of our results by adding extra control variables that may vary by district. Table 7 reports the estimates by displaying the coefficient of ASC hours and associated standard error by each outcome in the row.

Table 7.

Robustness check: the impact of after-school class hours.

	(1) Single-sex		(2) Metropolitan		(3) Stud.–teacher ratio		(4) Ability grouping
	Coef.	Std. error	Coef.	Std. error	Coef.	Std. error	Coef.	Std. error
OLS estimates
Unassisted study	−0.358	(0.186)*	−0.355	(0.186)*	−0.340	(0.190)*	−0.383	(0.197)*
Private tuition: all subjects	−0.519	(0.102)***	−0.510	(0.101)***	−0.496	(0.103)***	−0.489	(0.102)***
Private tuition: core subjects	−0.396	(0.090)***	−0.389	(0.089)***	−0.367	(0.092)***	−0.359	(0.091)***
Premium tuition: core subjects	−0.330	(0.088)***	−0.324	(0.087)***	−0.306	(0.091)***	−0.310	(0.091)***
Share of income devoted to private tuition	−0.005	(0.001)***	−0.005	(0.001)***	−0.004	(0.001)***	−0.004	(0.001)***
Hours of leisure per week	0.030	(0.043)	0.031	(0.043)	0.032	(0.044)	0.053	(0.045)
Hours of TV per week	0.092	(0.086)	0.093	(0.087)	0.092	(0.083)	0.110	(0.091)
Hours of sleep per week	0.250	(0.123)**	0.256	(0.123)**	0.219	(0.121)*	0.231	(0.128)*
Logit estimates
Suicidal ideation	−0.017	(0.050)	−0.019	(0.050)	−0.006	(0.054)	0.020	(0.054)
Serious anxiety	−0.042	(0.038)	−0.042	(0.038)	−0.046	(0.039)	−0.034	(0.040)
Response rate	−0.028	(0.063)	−0.029	(0.064)	−0.025	(0.059)	−0.043	(0.061)
Attending college	0.186	(0.039)***	0.186	(0.039)***	0.181	(0.040)***	0.193	(0.043)***
Attending a four-year college	0.177	(0.045)***	0.177	(0.045)***	0.173	(0.045)***	0.153	(0.047)***
Observations	480		480		480		480

Note: Standard errors are heteroskedasticity-consistent and estimates are adjusted for sampling weights. The columns cumulatively add covariates for: (1) single-sex school; (2) metropolitan; (3) student–teacher ratio; and (4) ability grouping. *p < .10; **p < .05; ***p < .01.

One potential confounding factor is single-sex schooling. Single-sex schooling may improve students’ outcomes (Park et al., 2012) and its availability differs across districts (Ku and Kwak, 2013). Column 1 in Table 7 shows that the inclusion of single-sex schooling as a covariate does not materially change our estimates.

Local availability of private tuition and the influence of local government over school policy settings may also influence ASCs and outcomes. If metropolitan schools share characteristics with regard to access to private tuition and regulation of school policy, including metropolitan location as a covariate in addition to single-sex school attendance may reduce the scope for other unobservable differences to influence our estimates. Column 2 in Table 7 reports estimates after including both metropolitan location and single-sex schooling. Again, only very minor changes are visible, suggesting that the original estimates are fairly robust.

Other school resources and policies may also be correlated with ASCs and outcomes. In column 3, we include student-teacher ratio, measured by the number of students per teacher in a school, as an extra covariate to account for staffing differences between schools that may influence the extent to which they can offer ASCs. In column 4, we further include ability grouping – the streaming of students into classes stratified by academic ability – as a control variable as Wang (2015) shows that ability grouping influences students’ out-of-school time use and activities. The results in columns 3 and 4 are similar to those in Tables 3 and 4. As no estimates are materially different between columns 1 to 4, the estimated impacts of ASCs are unlikely to be confounded by single-sex schooling, metropolitan status, student–teacher ratio or ability grouping.

Conclusion

This article investigates the interaction between household and student level educational inputs, and the role that government plays in influencing the use of private tuition. By examining the impact of ASCs on the mixture of households’ educational inputs and students’ outcomes, we shed light on an important question within the sociology of education raised by Bernstein (1970): ‘can education compensate for society?’ We demonstrate that school-provided ASCs can reduce private tuition without sacrificing academic outcomes using data from Korea. In particular, students attending schools with greater hours of ASC decrease time spent on unassisted study and private tuition. High and low income households respond to an additional hour of ASC with a different substitution mix of unassisted study and private tuition, while both experienced similar reduction in the share of household income devoted to private tuition.

We also find that students attending schools with more hours of ASC have an increased likelihood of attending college, and are more likely to undertake a four-year university degree. Importantly, these changes do not differ by household income group. There is also no correlation between more ASC hours and measures of mental health on the average, while low income students experience less suicidal ideation as ASC hours increase. Our results do not pinpoint the exact channels through which additional ASC hours improve academic outcomes while not worsening students’ mental health, as the activities that ASC hours replace and the content of ASCs are all possible candidates.

Some caveats apply when drawing implications from this study. First, because our measure of ASC hours is school-centric, it is best to interpret the effects as the results of attending schools with different levels of ASC hours. Second, our results are drawn from survey data, which may suffer from measurement errors. As far as the imprecision in our measure of ASC hours is random, our estimates likely suffer from attenuation bias. This means that the magnitudes of our estimates are likely smaller than the true effects. Similarly, the lack of correlation between ASC hours and students’ predetermined background characteristics and the robustness of our estimates to various potential confounds suggest that misreporting bias due to the measurement errors in the dependent variables is likely small. Third, because of our small sample of schools available within each city, we are unable to systematically examine whether local regulations on private tuition provision influences the use of private tuition and hours of ASC available to students. Finally, worthy of more examination is whether the rate at which households substitute private tuition in response to ASC hours has changed since the Korean government adopted an explicit policy of academically orientated ASCs. When ASCs are more focused on academic preparation rather than cultural enrichment, the substitution rate with respect to private tuition may rise. Future research could gather data on how schools use ASCs to explore this point further.

Our findings demonstrate that ASCs present an effective means of reducing demand for private tuition and household competition outside the formal school sector, even in a context where the impetus for families to secure advantage through education is at the extreme. As private tutoring continues to enjoy strong growth in most corners of the world, our findings have significant implications beyond just the competitive Korean setting. ASCs may also be an effective tool to reduce the demand for private tuition and improve students’ outcomes in other less competitive settings. A definitive answer to Bernstein’s (1970) question of whether ‘education can compensate for society’ is more elusive: our results imply that while government-driven initiatives in schools can partially mitigate differences in how different types of households allocate their educational inputs outside of school, and improve students’ outcomes, their ability to reduce outcome inequality is less clear. Our findings suggest that though government policies may alter the process of social reproduction by influencing how households from different socioeconomic backgrounds allocate educational resources, they may not easily reduce outcome inequality. Thus, our results provide a new angle on this important question within the sociology of education.

Footnotes

Acknowledgements

We thank Simon Angus, Youjin Hahn, the anonymous referees and the seminar participants at Monash University for helpful comments.

Notes

Daniel Carr is an adviser at the Behavioural Insights Team and a recent first-class honours graduate of Monash University.

Liang Choon Wang is a senior lecturer in the Department of Economics of the Monash Business School, where his research focuses on development, education, health and labour market issues.

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