The Journal of Development Studies
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Determinants of Private Tutoring Demand in Rural India
Ankush Agrawal, Parul Gupta & Debasis Mondal
To cite this article: Ankush Agrawal, Parul Gupta & Debasis Mondal (2024) Determinants of
Private Tutoring Demand in Rural India, The Journal of Development Studies, 60:1, 83-107, DOI:
10.1080/00220388.2023.2273798
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Published online: 15 Nov 2023.
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The Journal of Development Studies, 2024
Vol. 60, No. 1, 83–107, https://doi.org/10.1080/00220388.2023.2273798
Determinants of Private Tutoring Demand in
Rural India
ANKUSH AGRAWAL
, PARUL GUPTA ,
& DEBASIS MONDAL
Department of Humanities and Social Sciences, Indian Institute of Technology, Delhi, India;
Institution of Eminence, Dadri, Uttar Pradesh, India
Shiv Nadar
(Original version submitted May 2023; final version accepted October 2023)
ABSTRACT: Private tutoring participation is increasing in several developing countries, and this expansion has
attracted the interest of scholars spanning disciplines of economics, sociology and history. This paper presents a
theoretical model of private tutoring demand. The model incorporates the household and school characteristics
in a developing country context and demonstrates the source of gender gaps in access to private tutoring. Using
a recent database from India and employing a hurdle model approach, the paper also provides estimates of the
drivers of private tutoring participation and spending for pre-secondary students. Our results indicate evidence
of gender gaps in private tutoring access, and that the socio-economic profile of a student is positively correlated
with tutoring demand. Further, school quality indicators are negatively correlated with tutoring participation,
suggesting that students at ‘better’ schools rely less on tutoring. Overall, the findings suggest that tutoring
demand is influenced by a mix of demand-side (household, community drivers) and supply-side (school quality
and learning environment) factors. The results bring into focus the equity implications of tutoring growth and
the need to improve school quality in order to reduce the dependence on private tutoring.
KEYWORDS: private tutoring; gender bias; education; household bargaining; school quality; hurdle
model
JEL CLASSIFICATION CODES: C78; D31; I21; I28; I24
1. Introduction
Private tutoring has a significant presence in the Indian subcontinent, and in other developing
economies such as Vietnam, China and Egypt. It is estimated that more than a quarter of
school students in many of these countries attend private tutoring – the number even exceeds
60% in Vietnam, China and Sri Lanka (Bray, 1999; Dang & Glewwe, 2018; Ille & Peacey, 2019;
Liu & Bray, 2017; Mahmud & Bray, 2017; Pallegedara, 2018).1 Further, private tutoring
expenditure constitutes an important component of the household spending basket. For
example, 9% of household income in Myanmar (Ministry of Education, 1992), 20% in Vietnam
(Bray & Lykins, 2012), and 20 to 30% in Hong Kong (Bray & Kwok, 2003) is spent on private
tutoring. Similarly, the private tutoring expenditure constitutes more than 10% of the per capita
consumption expenditure in Sri Lanka (Pallegedara, 2018).
The growth of private tutoring has several implications for student achievement and allround development. While it is generally believed that parents resort to private tutoring to
Correspondence Address: Parul Gupta, Department of Humanities and Social Sciences, Indian Institute of
Technology, Delhi, India. Email: parul788@gmail.com
ß 2023 Informa UK Limited, trading as Taylor & Francis Group
84
A. Agrawal et al.
improve the learning outcomes of children, the evidence on the impact of private tutoring on
test scores and learning achievement in developing countries is mixed. For instance, Dang
(2007) finds a positive relation between private tutoring and academic achievement in Vietnam,
especially for lower secondary students, but Cole (2017) reports an absence of positive relation
for school students in Sri Lanka. On the other hand, a study by Zhang (2013) suggests a negative impact of private tutoring on scores in some subjects. Despite this ambiguity, private tutoring is popular and considered to be effective by the general populace.
Besides the above, the literature points to undesirable social implications of private tutoring
(Bray, 1999). Since supplementary tutoring is more easily accessible to the rich compared to the
poor, the growing culture of tutoring can potentially pose a threat to social stability by increasing educational inequalities, and diluting the prospects of socio-economic mobility for the less
privileged. Further, the burden of tutoring is often associated with stress in young children,
resulting in psychological pressure and affecting their all-round development (Bray, 1999;
Majumdar, 2014). These implications have led to attempts to ban and regulate private tutoring
by countries (for example, China), albeit with limited success (Wang, 2021, September 8).
Much like other countries, India is witnessing an expansion of the private tutoring phenomenon. In 2018, up to 30% of school students across the country availed private coaching for
academic courses (MoSPI, 2020) while this figure was less than 20% in 1986–87 (Azam, 2016).
Further, on average, high school students in 2018 spent more than `2500 annually on private
tutoring amounting to almost 15% of total household expenditure on education (MoSPI, 2020).
Many students and their parents consider private tutoring as indispensable to obtain a competitive edge in academics (Rana, Das, Sengupta, & Rafique, 2006), while others believe supplementary
help is taken only by ‘weak’ students. Teachers admit that tutors mostly teach students how to
score well on tests, inhibiting their creativity by providing quick ‘recipes’ to ‘solve’ questions
(Gupta, 2023). In today’s educational landscape, the emphasis on scoring well in ‘crucial’ exams
drives the demand for private tutoring, fueled by peer pressure.2 Private tutoring has emerged as a
pervasive system of education delivery in India, and can be viewed as a key element of the increasing privatization of education in the country. It is crucial to recognize the context in which such a
growth of private tutoring is situated, given the equity and policy implications of this phenomenon.
Findings from recent empirical literature reveal some clear stylized facts regarding the incidence and costs of private tutoring in India and other low-middle income countries: one, there
is a positive correlation between the socio-economic profile of students and their demand for
private tutoring; two, male students are likely to participate and spend more on tutoring compared to females and three, there is a negative correlation between school quality and tutoring
participation (Azam, 2016; Dang, 2013; Nath, 2008; Pallegedara, 2018; RMSA-TCA, 2016).
These findings hint towards a potential equity challenge posed by the private tutoring ‘culture’
due to two reasons. One, paid tutoring directly contradicts the notion of free and quality education envisaged by education policies such as the Right to Education Act (in India). Second, if
private tutoring indeed yields an academic advantage to its participants, it is apparent that educational and economic disparities are likely to be reinforced with the growth of private tutoring
because wealthier households who can afford private tutoring will be able to further improve
the academic performance and labour market prospects for their wards. Similarly, the gender
gap in educational and employment opportunities would increase if households prefer to invest
more on boys compared to girls.
This paper seeks to fill the gaps in the existing theoretical and empirical literature assessing
the determinants of private tutoring. Many of the studies (based out of India) exploring the
drivers of private tutoring are small-scale, and do not use nationally representative data. Also,
the literature proposing a theoretical model of the phenomenon, incorporating the gender
equity considerations is relatively scarce (Jayachandran, 2014; Kim & Lee, 2010). Specifically,
the drivers of the growth of private tutoring warrant a more careful study of the phenomenon
in the Indian context.
Determinants of Private Tutoring Demand in Rural India 85
Our theoretical results demonstrate that the gender gaps in private tutoring access arise due
to ability differences and a pro-male bias. We also show that tutoring demand increases with
income and decreases with improvements in school quality. Our empirical findings confirm the
presence of a gender gap in tutoring access and highlight the role of household socioeconomic
status, parental education, family size and peer effects on the demand for private tutoring. We
also find that tutoring demand is lower for students studying at schools with improved indicators such as infrastructure and class management.
The paper is structured as follows. We present a brief literature review in the following section. Next, we construct a theoretical model to describe the determinants of private tutoring. In
section 4, we describe the data and methodology we employ to examine the determinants of private tutoring. Empirical results and discussion are presented in the subsequent sections. The
last section concludes.
2. Literature review
The growing footprint of private tutoring and the implications of said growth have attracted
the interest of scholars exploring the economics and sociology of education. Empirical studies
for China, Vietnam, Bangladesh and Sri Lanka shed light on the determinants of private tutoring. These studies find student gender, household income and parental income to be key predictors of private tutoring participation and expenditure (Dang, 2013; Liu & Bray, 2017; Nath,
2008; Pallegedara, 2018). A limited but important strand of literature also looks at schoolrelated factors which influence tutoring demand. For example, Dang (2007) finds that the proportion of qualified primary school teachers is negatively associated with private tutoring
spending in Vietnam. Similarly, in Egypt, Elbadawy, Assaad, Ahlburg, and Levison (2004)
demonstrate that the student-teacher ratio is positively associated with participation in private
tutoring – a lower student teacher ratio indicates better attention per student, which could
reduce the need for private tutoring.
Some recent studies validate the above findings for India (Aslam & Atherton, 2012; Azam &
Kingdon, 2013; Kumar & Chowdhury, 2021; RMSA-TCA, 2016; Sujatha, 2014). This research
establishes some stylized facts on the determinants of private tutoring in India. One, there is a
positive relation between the household socio-economic profile and the demand for private
tutoring: students from wealthy households participate and spend more on tutoring. Figure 1
Figure 1. Income and private tutoring participation.
Notes: Each bar indicates the proportion of students attending private tutoring, in the respective income quintile.
Source: Authors’ calculations using IHDS (2012) data. The test of significance for the difference between means
is statistically significant at the 5% level of significance.
86
A. Agrawal et al.
illustrates this finding, using IHDS data: private tutoring participation is higher for those in the
richer quintiles. Two, there is a clear gender gap in favour of male students in tutoring participation and expenditure (Figures 2 and 3), indicating a household preference for additional
Figure 2. Proportion of students (aged 5 through 16 years) availing private tutoring, by gender and location of residence.
Notes: Gap indicates percentage point difference between males and females. The gender gap is statistically significant at the 5% level for rural, urban and overall sample.
Source: Authors’ calculations using IHDS (2012) data.
Figure 3. Average monthly expenditure (`) on private tutoring in rural areas, by gender.
Notes: Average has been taken over students taking private tutoring. Data for 2013 has been adjusted
using state-wise rural CPI for 2018 obtained from MoSPI (2018). The p-value for the test of significance
of absolute difference of means is mentioned in brackets along with the difference values.
Source: Authors’ calculations using ASER (2013, 2018).
Determinants of Private Tutoring Demand in Rural India 87
educational investments for their sons. Three, there appears to be a negative correlation
between school quality and demand for private tutoring – tuition demand is lower among students at schools with better indicators, for example, a favourable pupil-teacher ratio and sound
infrastructure (Figure 4).3
Figure 4. Private tutoring demand (participation, in percentage), by school characteristics.
Notes: The Right to Education (RTE) Act (2009) stipulates the pupil-teacher ratio (PTR) to be 30:1 at primary schools and 35:1 at upper primary schools. We used the respective pupil-teacher ratio. Each bar represents the proportion of students enrolled in private tutoring, within each category; for example, in panel
(a), of all students studying at schools satisfying RTE PTR norms, nearly 15% avail private tutoring.
Source: Authors’ calculations using ASER (2018) data. The test of significance for the difference between
the means is statistically significant at the 5% level.
88
A. Agrawal et al.
We contribute to this body of literature with an empirical analysis which investigates
the role of gender, household income, parental education and family size in determining
private tutoring demand. Our results support the evidence presented in recent studies for
India and other low or middle income countries. In particular, we find a gender gap in
tutoring participation and spending, and a positive correlation of tutoring demand with
household affluence and parental education. We also find evidence of a bandwagon effect,
wherein individual tutoring is positively correlated with the average tutoring participation
in the community.
Further, only a handful of large-scale studies explicitly explore the role of school quality
in determining private tutoring demand, and we attempt to fill this gap by making use of
pan-India data from ASER to investigate the influence of school characteristics on the demand
for private tutoring. We believe that our results are an important contribution to the empirical
literature on this theme. Our regression results broadly confirm the descriptive findings on
school quality and tutoring found in related literature.
The theoretical literature exploring the determinants of tutoring participation is considerably
limited and the existing literature employs game theoretic frameworks and neoclassical microeconomic analysis to model private tutoring demand (Choi, 2010; Ille & Peacey, 2019; Kim &
Lee, 2010; Yoo, 2002). These studies highlight the role of income, peer effects and competition
in determining the demand for private tutoring. Other studies (Ille & Peacey, 2019;
Jayachandran, 2014) explore the problem of teacher-provided tutoring (where a schoolteacher
moonlights as a tutor for her/his own students) in the context of distorted incentives and corruption. The gender differences in private tutoring access have not been formally demonstrated,
although some research has discussed the gender gaps in educational investments (Alderman &
King, 1998; Pasqua, 2005). Building on the work by Kim and Lee (2010), Yoo (2002) and
Pasqua (2005), we theoretically model the demand for private tutoring and confirm that private
tutoring is positively correlated with income and negatively correlated with school quality.
Further, we demonstrate the source of gender gaps in tutoring participation. Our results attribute gender gaps in private tutoring access to ability differences.
3. Theoretical model
Motivated by the descriptive results presented in the previous section (Figures 1–4), we develop
a theoretical model by extending the work by Yoo (2002), Pasqua (2005), Kim and Lee (2010)
and Agenor (2017) under the household utility maximization framework which illustrates the
gender gaps in tutoring access, and incorporates some additional determinants of private tutoring.4 Decomposing education into formal schooling and private components allows us to demonstrate the role of school quality, school costs and peer ability in shaping private tutoring
demand. Additionally, to demonstrate the determinants of differential spending on boys and
girls, we incorporate ability differences into our model.
In this model, a representative household consists of two adults, (a mother, m and a father
f), along with two children (a boy, b and a girl g). Parents allocate household resources to their
own consumption cm and cf, and each child’s education. Each child’s education consists of formal schooling (ei1 ) and private tutoring (ei2 ), where i ¼ b, g: Formally, households maximize
the objective function:
bUm þ ð1 − bÞUf
where, Uj ¼ ln ðcj Þ þ aj ln ðeg1 þ eg2 Þ þ ln ðeb1 þ eb2 Þ is the utility of parent j which is a function
of own consumption (cj) and each child’s education. The parameter b captures the relative bargaining power of the mother in the household. The parameter aj , ðj ¼ m, f Þ represents the
weight on girl’s education in the jth parent’s utility function. We assume that both am and af are
Determinants of Private Tutoring Demand in Rural India 89
strictly between 0 and 1. Thus, both parents value the girl’s education less than the boy’s education (which enters the parental utility function with a weight of unity). The constraints for the
maximisation problem are as follows:
cm þ cf þ pðeg2 þ eb2 Þ ¼ w − ge1
(1)
ei1 ¼ e1 þ hða − aki Þ,
(2)
i ¼ b, g
ei1 0, i ¼ b, g
(3)
In the above, household income, w, is exogenously given. The consumption of each adult is a
numeraire good, with a price of unity. Formal education obtained at school (for each i ¼ b, g)
is denoted by ei1 : In the first constraint, ge1 represents a lump-sum tax on education. The parameter h denotes the intensity with which an ability mismatch affects the level of formal education obtained. These parameters are explained below.
As mentioned above, ei1 is the formal education obtained at school (for each i ¼ b, g).
Formal education Equation (2) truly obtained depends on the difference between the average
ability of the peers (a ) in the classroom and the ability of the kth child (aki ) of gender i.5 For
given parameter, h > 0, an above average child is at a disadvantage in the school, and is unable
to achieve the minimum threshold education (e1 ). This could be because of the slow pace of the
class. Additionally, a below average student (aki < a ) enjoys positive peer effects, and thus
obtains ei > e1 , while a student of average ability receives (e1 ) amount of formal education.
The role of peer effects in influencing the received formal educational service has been modelled
differently by Kim and Lee (2010), who assume that a is the average ability of the peers who
have greater ability than the kth child. Further, Yoo (2002) models the ability differential term
as ð
a − aki Þ2 and assumes h < 0 : this suggests that students placed either at the upper or lower
tail of the ability distribution in the class suffer a loss or mismatch, and thus receive lower education compared to a student whose human capital level is equal to the mean of the class distribution. The author extends this argument and shows that these losses are the source of
differential tutoring demand across students.
Since all components of formal education (ei1 ) are exogenously determined (namely, ability
of child, ability of peers and minimum threshold education), parents cannot choose or alter the
level of formal education: if they want ‘more’ education for the child, this can be done only by
‘purchasing’ it through private tutoring, which costs p per unit for both boys and girls. School
education, on the other hand is financed through a lump-sum tax, ge1 : The parameter g also
captures the efficiency of formal schooling provided: lesser the g value, better is the schooling.
This is because with a lower g, the same number of units of education e1 are being provided at
a lower total cost. The choice variables in the above optimisation problem are cm , cf , eg2
and eb2 :
To solve the constrained problem, we construct the Lagrangian function:
L ¼ bUm þ ð1 − bÞUf − k cm þ cf þ pðeg2 þ eb2 Þ − w þ ge1
(4)
where, k is the Lagrange multiplier. The objective function is concave, and the constraint is linear, hence the Lagrange function is concave, and a maximum is guaranteed. Together with the
resource constraint, the first order conditions for an interior optimum are:
b
¼k
cm
(5)
90
A. Agrawal et al.
1−b
¼k
cf
(6)
ð1 − bÞaf
bam
þ
¼ pk
eg1 þ eg2 eg1 þ eg2
(7)
b
ð1 − bÞ
þ
¼ pk
eb1 þ eb2 eb1 þ eb2
(8)
Solving the above equations, we obtain the following expressions for the choice variables, cm,
cf, eg2 and eb2 (together with k).6
eb2 ¼
w − ge1 − peb1 ð1 þ XÞ þ peg1
pð2 þ XÞ
(9)
eg2 ¼
Xðw − ge1 Þ þ peb1 X − 2peg1
pð2 þ XÞ
(10)
bðw − ge1 þ peg1 þ peb1 Þ
2þX
(11)
ð1 − bÞðw − ge1 þ peg1 þ peb1 Þ
2þX
(12)
cm ¼
cf ¼
and
k¼
2þX
w − ge1 þ peg1 þ peb1
(13)
Here, X bam þ ð1 − bÞaf ; X 2 ð0, 1Þ:
Since tutoring expenditures can’t be negative, we obtain the following non-negativity conditions:
eb2 0ifw − ge1 peb1 ð1 þ XÞ − peg1
(14)
2peg1
− peb1
X
(15)
and
eg2 0ifw − ge1 Each of the sufficient conditions can be interpreted as a minimum income threshold which
determines whether each child will be sent to private tutoring or not.
3.1. Hypotheses
In this section, we propose a few hypotheses on the basis of the above solutions. These hypotheses provide a background for empirical work.
B.1 If the inherent ability of boy is weakly greater than girl’s, then the private tutoring
expenditure on boy will exceed the expenditure on the girl.
Formally, if ab ag , then peb2 > peg2 :
Determinants of Private Tutoring Demand in Rural India 91
Proof. First, using Equations (9) and (10), we write,
peb2 − peg2 ¼
w − ge1 − peb1 ð1 þ XÞ þ peg1 Xðw − ge1 Þ þ peb1 X − 2peg1
−
ð2 þ XÞ
ð2 þ XÞ
¼
ð1 − XÞðw − ge1 Þ − peb1 ð1 þ 2XÞ þ 3peg1
2þX
(16)
(17)
Since X 2 ð0, 1Þ, therefore 2 þ X > 0: Further, ð1 − XÞðw − ge1 Þ > 0, and we only need
3peg1 − peb1 ð1 þ 2XÞ > 0 to show that peb2 − peg2 > 0:
Let ab ag : Then, from (2), eg1 eb1 : It follows that
3eg1 3eb1 ) 3eg1 > ð1 þ 2XÞeb1 ; since 3 > 1 þ 2X
Thus, 3eg1 − ð1 þ 2XÞeb1 > 0 ) 3peg1 − peb1 ð1 þ 2XÞ > 0 ) peb2 − peg2 > 0
The relative ability of each child determines the magnitude of private tutoring. This is supported by the resource dilution hypothesis (Deshpande & Gupta, 2019; Fors & Lindskog,
2023), wherein parents would be willing to invest more in the child (believed to be) having better prospects, of which ability could be considered a weak proxy.
B.2An increase in income increases private tutoring expenditure on both boys and girls, but
@peg2
@peg2
@peb2
b2
the effect is larger for boys. Formally, @pe
@w > 0 and @w > 0 with @w > @w :
Proof. Using Equations (9) and (10), we write:
@peb2
1
>0
¼
2þX
@w
@peg2
X
>0
¼
2þX
@w
@pe
g2
b2
Since X < 1, @pe
@w > @w :
Thus, we can conclude that since the income effect is positive, private tutoring for both boys
and girls is a normal good. However, a relaxation of the income constraint benefits boys more
than girls. Thus, the gender gap in private tutoring expenditure is expected to increase in
income, ceteris paribus, assuming that the expenditure for boys is greater than girls to begin
with.
B.3 An increase in the average education obtained at school reduces the private tutoring
expenditure on both boys and girls.
i2
Formally, @pe
@e1 < 0, 8i ¼ b, g:
Proof :
@peb2 −g − pX
¼
2þX
@e1
which is negative, since X > 0
Similarly,
@peg2 −Xg − pð2 − XÞ
¼
2þX
@e1
which is also negative, since 0 < X < 1
92
A. Agrawal et al.
In this model, an improvement in school quality reduces private tutoring expenditure, other
things equal, since a better school (higher e1 ) is financed by greater lump sum taxes (ge1 ) which
tightens the household budget constraint. As a result, households shift away from spending on
private tutoring (since it is a normal good).
4. Materials and methods
4.1. Data
For our empirical analysis, we use data from the Annual Status of Education Report (ASER)
survey conducted by the ASER Centre.7 The annual survey assesses the schooling status and
learning achievement (in reading and arithmetic) of children of school-going age in rural India.
ASER is believed to be the only source of information about children’s foundational skills
across the country. We use the data from the 2018 round8 to examine the private tutoring
behaviour of school-going children in the age group 3 through 16.9
Previous studies have used the education schedule of the National Sample Survey (NSS) data
and the India Human Development Survey (IHDS) to analyse the tutoring phenomenon
(Azam, 2016; Chatterjee, 2018; RMSA-TCA, 2016). However, we explore the ASER data as it
has some advantages, and allows us to answer our research questions in a better manner.
First, ASER is the most up-to-date dataset focused narrowly on the education sector. Given
that trends in this sector are evolving rapidly, the use of a recent dataset is appropriate. Second,
the sample size is large (comprising of nearly 350,000 households, and 600,000 children), and
the survey is representative at the national, state and district levels (ASER Centre, 2019).
Finally, and most crucially, ASER’s sampling methodology and survey design allows surveyed students to be matched to the school they attend. Hence, we can assess the influence of
school characteristics on the educational choices of students. Our literature review reveals that
only small, localised surveys have been used to analyse the relation between school characteristics and observed behaviour of students (Ghosh & Bray, 2020; Sujatha, 2014) so far. To the
best of our knowledge, ASER is the only nationally representative survey that allows us to further explore the role of school-related factors at the all India level.10 Therefore, we prefer to use
ASER for the analysis.
4.2. Methodology
The solutions obtained in Equations (9) and (10) show that tutoring demand is a function of
household income (w) and school quality (e1 ). Further, tutoring demand differs for boys and
girls. Broadly, we propose that tutoring demand depends on child, household, school and community-level factors:
Tutoringi ¼ a þ bChildi þ cHouseholdi þ dSchooli þ wCommunityi þ i
Here, Tutoringi is the i’th child’s tutoring behaviour (participation or spending) which can be
linked to vectors of child-level correlates (Childi ), household-level correlates (Householdi ),
school-level correlates (Schooli ) and community-level (state and village) correlates (Communityi ).
Students who do not attend private tutoring incur zero tutoring expenditure. The tutoring
decision, thus, can be understood as being comprised of a two-stage process: in the first stage,
one decides whether to enrol for private tutoring, and in the second, how much to spend on private tutoring. Some studies use the Tobit model to estimate the relation between private tutoring and its determinants (Mitra & Sarkar, 2019; RMSA-TCA, 2016). However, the Tobit
model’s estimation strategy does not incorporate the two-tier process. In other words, the
Determinants of Private Tutoring Demand in Rural India 93
model does not allow separation of the determinants of the two decisions, and it is more appropriate to employ a methodology which incorporates the two-tier decision-making process.
As an alternative, the hurdle model (originally introduced by Cragg in 1971) allows a decoupling of the participation and intensity decisions, and has been commonly used to analyse
tobacco and alcohol expenditures (which can be zero for several individuals) (Eakins, 2016). In
the context of uncovering the determinants of car distance driven, Johansson-Stenman (2002)
fails to reject Cragg’s model in favour of the Tobit specification. Similarly, Azam and Kingdon
(2013) use the hurdle model to test gender inequality in education expenditures in India and
demonstrate that the hurdle model provides convincing estimates. Thus, the use of hurdle
model is well-justified to analyse a two-stage decision-making process.
To estimate the parameters of the hurdle model, the Maximum Likelihood Estimation is
employed in the first stage (to fit the probit model describing the participation decision), and
ordinary least squares (OLS) is used in the second (to fit the intensity equation), linking the estimates in the two stages (Greene, 2018). Formally, the hurdle model is described by the participation process (Equations (18) and (19) below), and the intensity process (Equations (20)
below) (Greene, 2018):
d ¼ Z 0 c þ u, where u Nð0, 1Þ
(18)
d ¼ 1, if d > 0, 0 otherwise
(19)
y ¼ X 0 b þ , where Nð0, r2 Þ
(20)
The observation mechanism specifies:
y ¼ 0 if d ¼ 0 and y ¼ y if d ¼ 1
(21)
Here, y is the latent variable, and y is the observed variable. Of note is the fact that Z and X
vectors need not be the same, that is, the correlates of the participation process may be different
from the correlates of the intensity decision: a feature that distinguishes the hurdle model from
other models used to treat censored observations. Further, the errors u and follow a joint normal distribution with correlation coefficient q.
In the next section, we present the results from the hurdle model to unpack the socioeconomic determinants of private tutoring participation and expenditure in India. The
summary statistics of the key variables are compiled in Table 1.
The dependent variable for the hurdle specification is the monthly expenditure on private
tutoring (in Rupees). In our sample, nearly 30% of students attend private tutoring and the
average per-capita monthly private tutoring spending is `90 (Table 1).
We follow the work by related studies (Aslam & Atherton, 2012; Azam, 2016; RMSA-TCA,
2016) and use child controls, household controls, school controls and community controls as
the regressors. Child controls include the gender of the child, type of school s/he attends (private or government) and the grade level s/he studies at (primary, middle or secondary). In the
sample, 50% of the students are male and 65% study in a government school (Table 1).
Household controls include family structure (such as, number of siblings in age group 0-5 years)
and education of each parent (Table 1). On average, the parents have between 5 and 7 years of
school education, and fathers are more educated compared to mothers. The student’s household has a graduate in the family in 23% of the sample.
Household controls also include a proxy for economic status. Since ASER data does not provide information on household income or expenditure, we construct an asset index using principal component analysis. The variables indicating ownership of tangible assets such as a
smartphone, television, motorized vehicle (four-wheeled or two-wheeled vehicle), house
94
A. Agrawal et al.
Table 1. Sample description, ASER (2018)
Characteristic
Individual tutoring demand
Student is enrolled in private
tutoring (¼1 if yes, 0 if no)
Monthly private tutoring expenditure (`)
Student characteristics
Male (¼1 if yes, 0 if female)
Studies at government
school (¼1 if yes, 0 if no)
Studies at English medium
school (¼1 if yes, 0 if no)
Household characteristics
Father’s highest education1
Mother’s highest education2
Household has a graduate (¼1 if yes, 0 if no)
Standardized asset index score
Family structure
No. of siblings of 0–5 age
Student is eldest child (¼1 if yes, 0 if no)
Village-level tutoring demand
Average private tutoring
enrolment (%) in village
Average monthly private tutoring
spending (Rs.) in village3
Observations
Mean
Std. Dev.
Min
Max
0.30
89.66
0.46
230.51
0
0.00
1
5000.00
0.50
0.65
0.50
0.48
0
0.00
1
1.00
0.23
0.42
0.00
1.00
6.99
4.71
0.23
−0.06
4.80
4.71
0.42
1.01
0.00
0.00
0
−2.19
20.00
20.00
1
1.40
0.27
0.46
0.50
0.50
0.00
0
3.00
1
23.18
20.19
1.43
100.00
301.18
282.78
0.00
5000.00
192970
Notes:
1,2
Highest education indicates the level upto which parent attended formal education. The variable takes
values 1 through 20; 1–12 being classes 1–12 (school), 13–15 indicate undergraduate education, 16–17
indicate post-graduate degree, and 18–20 indicate post-graduate diploma, ITI, and polytechnic
respectively.
3
Average has been taken over students availing private tutoring.
Source: ASER (2018).
construction type (pucca or not) and presence of electricity connection and toilet were considered for the asset index. We use the first component which explains 40% of the cumulative
variation.
Some empirical literature shows the correlation between private tutoring participation and
indicators such as schooling hours, class size and teacher’s qualifications but many of the studies (RMSA-TCA, 2016; Sujatha, 2014) do not use nationally representative data. Our analysis
uses the school survey conducted by ASER to include school-level controls. We include information on pupil-teacher ratio, student and teacher attendance, and provision of midday meals,
in addition to presence of blackboards and toilets.
Finally, since the community context can play a role in determining educational choices, we
include average private tutoring prevalence in the village (proportion of students attending private tutoring, and average private tutoring spending) to capture herd behaviour or peer effect.
The average private tutoring enrolment in the community is 23%, and the average monthly private tutoring spending in the village community is nearly `300. A recent study by Pan, Lien,
and Wang (2022) uses a similar strategy to uncover the role of peer effects for China. We also
control for village infrastructure (using availability of government and private school, health
clinic and internet cafe). In the absence of information on socio-economic categories (caste, religion and income), the village level variables also help us to control for average affluence of the
region, since there continues to exist a close association between the socio-economic status of a
village and its caste/religion composition (Thorat, 2009). Finally, since planning and allocation
Determinants of Private Tutoring Demand in Rural India 95
of resources for elementary education is done at the district level (ASER Centre, 2019), we
include district fixed effects in our model specification.
5. Results
5.1. Hurdle model results
As outlined above, the demand for private tutoring can be modelled as a two-stage process,
and Cragg’s hurdle model is an appropriate econometric framework for analysis. Results from
both the first (enrolment) and second (intensity) stage of the hurdle regression are presented in
Table 2.11 The first stage results show the effect of correlates on the probability of attending
private tutoring (dependent variable takes values 0 or 1), while the second stage results present
the marginal effect on private tutoring expenditure conditional upon participation. The log of
tuition spending variable appears to be approximately normal (Figure 5), hence we use the log
values as the dependent variable.12
Table 2. Private tutoring demand, hurdle model marginal effects (2018)
Student characteristics
Male
Attends government school
Attends middle school
Attends secondary school
Attends English medium school
Is the eldest child
Family characteristics
Father’s highest education
Mother’s highest education
Number of siblings of 0-5 age
Graduate in household
1st stage
2nd stage
0.0363
(19.98)
−0.0474
(-19.07)
0.0202
(9.28)
0.0926
(31.02)
0.0408
(12.34)
0.0434
(21.31)
0.0374
(8.66)
−0.140
(-21.85)
0.181
(34.60)
0.505
(78.97)
0.179
(22.71)
0.0473
(9.95)
0.00405
(16.73)
0.00568
(22.54)
−0.0186
(-9.46)
−0.00463
(-2.03)
0.00773
(13.24)
0.00879
(14.78)
−0.0297
(-6.31)
0.0182
(3.43)
0.0518
(18.44)
Standardized asset index score
Village characteristic
Average monthly tuition spending (Rs.) in village
Village infrastructure controls
District fixed-effects
N
Yes
Yes
192876
0.00139
(106.12)
Yes
Yes
57823
t statistics in parentheses.
Source: ASER (2018).
The 1st and 2nd stage results refer to participation and intensity equations, respectively.
The dependent variable in the 1st stage is the dummy indicating participation in private tutoring (yes ¼
1, no ¼ 0) and the dependent variable in the 2nd stage is the log of private tutoring spending.
Village infrastructure controls include dummies indicating whether village has govt and private schools,
govt and private health clinics and internet cafe.
p < 0.10, p < 0.05, p < 0.01.
96
A. Agrawal et al.
Figure 5. Distribution of log of monthly private tutoring spending.
Source: ASER (2018).
The first stage results suggest a presence of pro-male bias in private tutoring participation:
male children are about 3.6 percentage points more likely to attend private tutoring compared
to females.13 Further, government school students are less likely to attend private tutoring.
Also, even after controlling for additional socio-economic determinants, students are more
likely to attend private tutoring at higher classes (middle and secondary school) compared to
the primary school. This points to the high-stakes exams at higher grades, and increased difficulty of curriculum, warranting supplementary help. Moreover, until recently, a no-detention
policy was followed in India till class 8, hence it is possible that students in primary or middle
grades did not feel the need to attend extra classes, as promotion to the next grade was automatic and not linked to academic performance.14 Attending an English medium school is positively correlated with higher private tutoring enrolment.
Most of the household level determinants are correlated with private tutoring participation in
the expected direction. Wards of more educated parents are more likely to attend private tutoring, possibly suggesting a greater household preference for education. Having an additional sibling in the age group 0–5 is correlated with lower private tutoring participation. This finding
can be explained by the fact that having a young sibling may translate into greater domestic
responsibilities for elder children, thereby reducing the ability of the child to attend private
tutoring after school hours. On the other hand, the eldest child of the household is more likely
to attend tutoring, confirming the bias favouring first-born children in educational and health
outcomes reported in Indian literature. Moreover, younger siblings may receive homework and
other academic assistance from older siblings, reducing their dependence on private tutoring,
resulting in higher private tutoring incidence among eldest children. The presence of a collegeeducated family member is negatively correlated with private tutoring enrolment. Perhaps having a college-educated adult may reduce the need for private tutoring, as the said adult could
provide guidance on homework and studies in general. The asset index is positively correlated
with private tutoring participation. Finally, average private tutoring enrolment in village is
positively correlated with individual private tutoring participation, pointing to the presence of a
peer effect or bandwagon behaviour.
The semi-elasticity (stage 2) results are in the expected direction as predicted by the theoretical model and related studies: all the coefficients are positive except for government school and
Determinants of Private Tutoring Demand in Rural India 97
number of siblings. Having one more sibling in the age group 0-5 is correlated with lower private tutoring spending: this could be because of a greater financial constraint in a family with
larger size, thereby reducing discretionary spending on private tutoring. The asset index is positively correlated with private tutoring spending, confirming the results of our theoretical model.
However, we are unable to interpret this estimate as an income elasticity since we don’t have a
direct measure for household income or consumption. Private tutoring spending among peers
(average private tutoring spending in the village) is also positively correlated with an individual’s private tutoring spending. It is interesting to note that the coefficients of nearly all the covariates have the same sign in both the participation and intensity stages of the tutoring decision
(except for presence of college-education family member). Hence, it appears that the determinants influence the enrolment and spending decisions in the same direction, although the difference in magnitude validates the use of the hurdle model, ex-post.
One of the main contributions of our work to the empirical literature is the inclusion of
school-level characteristics into the analysis. Since tutoring is considered a ‘shadow’ of the education system, it is reasonable to hypothesize that the characteristics of the mainstream education structure would have a bearing on the demand for private tutoring. Table 4 presents the
results of the hurdle model estimation with school controls. All child, household and community controls presented in Table 2 have been included in this specification as well. The summary
statistics of the variables used for this regression are presented in Table 3.15 It is reassuring to
note that infrastructure quality (for example, toilets and blackboards) is reasonably good for
more than 75% of the sample, although nearly half the sampled students study at schools which
continue to lack in terms of satisfying pupil-teacher ratio norms. Moreover, the summary data
points to evidence of multi-grade teaching practices (students of several classes sitting together
with usually one teacher) in almost two-thirds of the sample. It will be especially useful to investigate the correlation of these indicators with private tutoring demand, as we will attempt in the
next section.
To reiterate, we would expect that private tutoring participation and spending would be
lower in schools of better quality. Many of the estimates conform to this hypothesis. Private
tutoring enrolment is lower in schools where pupil-teacher ratio satisfies RTE-stipulated norms,
although there is no statistically significant relation with private tutoring spending. The average
Table 3. Sample description with school controls: summary statistics (ASER, 2018)
School satisfies RTE norms for pupil-teacher ratio1
(Yes ¼ 1, No ¼ 0)
Multigrade teaching in school
(Yes ¼ 1, No ¼ 0)
No. of students per room
Proportion of students present on day of survey
Proportion of teachers present on day of survey
Midday meal provided in school
(Yes ¼ 1, No ¼ 0)
Usable blackboard, class 2
(Yes ¼ 1, No ¼ 0)
Usable girls’ toilet in school
(Yes ¼ 1, No ¼ 0)
Observations
Mean
Std. Dev.
Min
Max
0.51
0.50
0
1
0.66
0.48
0
1
42.73
67.73
83.88
0.88
34.45
19.82
23.67
0.32
0.60
0.00
0.00
0
454
100
100
1
0.95
0.21
0
1
0.80
0.40
0
1
42060
Notes:
1
This estimate indicates that half of the students in the sample study at schools satisfying RTE norms
for pupil-teacher ratio. Other figures can be interpreted similarly. Number of observations depicts number of sampled students. ASER conducts school survey in government schools, hence these estimates
do not reflect the average characteristics of private schools.
Source: ASER (2018).
98
A. Agrawal et al.
Table 4. Private tutoring demand, hurdle model marginal effects with school controls (2018)
School satisfies RTE norms for pupil-teacher ratio
(Yes ¼ 1, No ¼ 0)
Students per room
Proportion of students present
on day of survey
Proportion of teachers present
on day of survey
Usable blackboard, class 2 (Yes ¼ 1, No ¼ 0)
Usable girls’ toilet in school (Yes ¼ 1, No ¼ 0)
Male (Yes ¼ 1, No ¼ 0)
Attending Middle School (Base: Primary School)
Attending Secondary School (Base: Primary School)
Father’s highest education
Mother’s highest education
No. of siblings in age group 0-5
Eldest child
Standardized asset index score
Village Infrastructure controls
District fixed-effects
N
1st stage
2nd stage
−0.00668
(-1.72)
0.0000843
(1.67)
−0.000122
(-1.10)
−0.0000510
(-0.71)
0.00293
(0.31)
0.00331
(0.82)
0.019
(7.38)
0.0187
(5.74)
0.0418
(5.55)
0.00206
(5.02)
0.00387
(8.51)
−0.00693
(-2.3)
0.0329
(12.73)
0.0209
(10.56)
Yes
Yes
58787
0.00656
(0.40)
0.0000455
(0.27)
0.000141
(0.32)
0.000198
(0.62)
0.0293
(0.84)
0.00219
(0.14)
0.0350
(3.93)
0.173
(14.7)
0.494
(19.13)
0.00772
(5.19)
0.00978
(5.91)
−0.0244
(-2.31)
0.0557
(6.16)
0.0399
(5.80)
Yes
Yes
11915
ASER surveys one government school in each village, hence these results don’t speak to the correlation
between private tutoring and school characteristics for private schools. Also see notes to Table 2.
t statistics in parentheses.
p < 0.10, p < 0.05, p < 0.01.
Source: ASER (2018).
number of students per room, indicating overcrowding, is positively correlated with private
tutoring enrolment. Other studies (Liu & Bray, 2017) show that lack of individual attention
received at formal schools due to large class size encourages students to resort to private tutoring, and our result on overcrowding supports this hypothesis.
The coefficients on student and teacher attendance variables have the expected negative signs,
but are not statistically significant. That is, a school with higher student attendance sees less private tutoring enrolment (but not spending). Likewise, private tutoring enrolment is lower at
schools where teacher attendance is higher. It appears that students are satisfied with classroom
instruction (due to greater teacher presence and activity), hence are less likely to rely on private
tutoring. A similar finding has been reported by RMSA-TCA (2016): in Bihar government
schools, several students were found to be absent from school to attend private tutoring lessons.
Since government schools are not homogeneous with respect to quality, our result indicates
that improved schools may have better attendance and lower private tutoring enrolment by students.16 Kingdon and Banerji (2009) posit that high teacher absenteeism results in poorer learning outcomes. These results help to explain that in schools with higher teacher attendance,
students are satisfied with classroom teaching, hence private tutoring enrolment is lower.
Determinants of Private Tutoring Demand in Rural India 99
Finally, none of the infrastructure or amenities-related variables (blackboards, toilets, midday meal) have the expected sign or contribute a statistically significant effect on private tutoring participation or spending. This could be because infrastructure upgradation has taken place
in most schools – the U-DISE (2021) report reveals that more than 90% of government schools
have functional toilets and drinking water facility – hence there is not much variation with
respect to the infrastructure quality as also indicated in the summary statistics (Table 3).
Moreover, many of the school-level characteristics could be correlated with each other, hinting
towards the presence of multicollinearity in the model resulting in insignificant t-statistics. The
coefficients for the key socio-economic determinants at the child and household level have also
been presented, and the results are qualitatively the same as in Table 2 other than the estimates
for number of siblings and presence of graduate in household, which are not statistically significant once school characteristics are controlled for.
6. Discussion
Our econometric findings are in broad consonance with the theoretical results and descriptive
trends. A brief discussion of the results is furnished below.
We find strong and stable evidence of gender bias both in private tutoring participation and
expenditure. Given the inherent patriarchal norms in Indian society, this finding is in line with
the trends in lopsided allocation of household resources across genders in terms of nutrition,
health and education. In our theoretical model, we have ascribed the gender gap to the differences in ability, or its perception thereof (result B.1). One possible reason to observe a persistent
gender gap empirically in tutoring access is the greater value placed on boys’ education, compared to girls’, and better (presumed) confidence in the boys’ ability. To the extent that extra
tutoring could improve learning skills of students (Dongre & Tewary, 2015), denying this assistance to girls could further widen the already existing gaps in learning outcomes between genders. Thus, the equity challenges raised by the expanding tutoring phenomenon cannot be
ignored.
Further, school type appears to be an important determinant of private tutoring enrolment
and spending, with the results being robust across specifications. Even after controlling for
wealth differences, private school students are more likely to attend tutoring and spend more
on it, possibly indicating the role of factors such as peer pressure and personal aspirations as
noted in Sujatha (2014). Studies suggest that private schools, especially in rural areas, don’t
provide better education than government schools (Central Square Foundation, 2020; Goyal &
Pandey, 2012). Most private schools are only marginally better compared to government
schools, which makes parents opt for tutoring for their wards (Aslam & Atherton, 2012;
Sujatha, 2014). This indicates that school quality – a dimension that remains under-researched
in India – could play a role in influencing the private tutoring decision.
Further, we study the association between school characteristics and private tutoring demand
and find that private tutoring participation is lower in ‘better’ quality government schools, as
indicated by our theoretical result (B.3) as well. The empirical results indicate that an analysis
of the private tutoring expansion cannot be decoupled from the broader issues surrounding
schooling quality and adequacy in India.
The general attitude towards private tutoring is difficult to gauge. We have attempted to control for perception around tutoring through peer effects and the supply or availability of tutoring, by considering the average private tutoring participation in a village as an indicator of
acceptability and popularity of private tutoring. Our results show that the possibility of herd
behaviour cannot be ignored, as there is a statistically significant positive correlation between
average private tutoring participation in the village and the probability of enrolling in private
tutoring: thus, a child residing in a village where more students attend private tutoring is more
likely to attend tutoring. Average private tutoring expenditure in the village is also positively
100
A. Agrawal et al.
correlated with a given child’s private tutoring expenditure. Thus, peer effects and prevalence
or acceptance of availing private tutoring in the community are important determinants of private tutoring demand.
Although our data does not allow us to directly control for household income or expenditure,
we have constructed a proxy for affluence based on ownership of tangible assets and other
household characteristics. We find that there is a positive association between ownership of
assets and private tutoring participation as well as spending, in support of the theoretical
hypothesis B.2.
Sibship structure (the ordinal position of the child in the family) can help to describe the patterns of private tutoring demand in the household. First-born children are more likely to attend
private tutoring, and more resources are spent on their private tutoring. A similar pattern has
been observed in the intra-household allocation of other resources such as nutrition and health
(Jayachandran & Pande, 2017). The bias in favour of the eldest child is well documented, specially for India (Gupta, 2019; Kaul, 2018). Further, private tutoring demand is lower for a child
with a greater number of young siblings (in the age group 0–5). This can be explained in two
possible ways: one, a child with younger siblings is expected to assist in child care activities and
is unable to attend supplementary classes, and two, given the larger family size, parents need to
thinly spread resources across children and hence cut back on private tutoring spending of older
children.
Overall, we observe that the demand for private tutoring is influenced by a gamut of related
factors at the child, household, school and community level.
6.1. Our findings in relation to other studies
While our results corroborate the findings of the literature on private tutoring in India and
other low and middle income countries, we also provide evidence on the bandwagon effect and
the importance of school quality.
As with other developing countries in South Asia such as Pakistan and Bangladesh (Aslam &
Atherton, 2012; Nath, 2008), we find evidence of a pro-male bias in private tutoring participation and spending. Given that gender disparities in favour of sons are more pronounced in the
Indian subcontinent, it is not surprising to note that studies based out of China, Vietnam and
Indonesia do not report conclusive evidence on a lopsided pattern (Dang, 2013; Liu & Bray,
2017; Wahyuni & Susanti, 2016). The mechanism behind the gender differences has not been
explored in detail in the existing literature. For example, most empirical studies are unable to
conclude if higher household bargaining power for the mother translates into higher private
tutoring participation and spending for girls. It would be an interesting area of future research
to uncover the drivers of gender differences in private tutoring.
We find a positive correlation between socio-economic status and private tutoring participation. Although our results are not strictly comparable with other studies, several other papers
find evidence of a positive association between private tutoring and household status indicated
by income, assets and parental education (Aslam & Atherton, 2012; Dang, 2013; Pallegedara,
2018). The findings emphasise the fact that the growth of private tutoring could result in
increased educational inequality, translating into inter-generational inequality, since those from
privileged families have greater access to private tutoring, possibly yielding them a double
advantage. Further, some studies have found the income elasticity of private tutoring demand
to be less than unity (Azam, 2016; Tansel & Bircan, 2005), suggesting that private tutoring is a
necessity, thereby pointing to the popularity of and general attitude towards private tutoring in
society.
Our results suggest that private tutoring participation and spending among peers is positively
correlated with individual private tutoring, indicating the role of a ‘bandwagon’ effect. Pan
et al. (2022) report a similar finding for China and indicate that a student’s propensity to
Determinants of Private Tutoring Demand in Rural India 101
participate in extra tutoring is positively affected by the prevalence of that behaviour among
peers in the same classroom. Our results are in line with this observation. To the best of our
knowledge, other than Pan et al. (2022) the theme of peer effects and herd behaviour has not
been explored in detail in the existing literature and deserves more attention as a research
objective.
Finally, we find some evidence on the inverse relationship between school quality and private
tutoring. The literature also shows a negative correlation between private tutoring spending
and various indicators of school and teacher quality (or its perception) (Liu & Bray, 2017;
Mahmud & Bray, 2017).
7. Conclusion
Private tutoring, a relatively under-researched phenomenon, has witnessed a perceptible rise
over time. In this paper, we construct a theoretical model of the determinants of private tutoring, incorporating household, child, and school characteristics, including household income,
gender and relative academic ability of the child, and school quality and fees. Many of our
results, such as private tutoring being a normal good, resonate with the existing research. We
incorporate in the model the gender-bias in education, pervasive in several countries, by linking
the pro-male bias in tutoring spending to ability differences between boys and girls, and to the
inherent cultural preference for a male child.
In light of the model, using the 2018 round of the ASER data from India, we empirically
examine the drivers of the decision to avail private tutoring and spending thereof. We find that
private tutoring is popular across all stages of schooling, though its probability is higher among
secondary school students. Further, the children with a privileged socio-economic background
(more household assets and better educated parents) are more likely to attend private tutoring
and spend more on it. There is a statistically significant pro-male bias at both the participation
and spending stages of the private tutoring decision. These results point to the equity challenge
posed by the growing private tutoring ‘culture’. The equity implications have attracted the
interest of scholars from different disciplines (Hajar & Karakus, 2022), and provide grounds for
the regulation of private tutoring to prevent a worsening of educational inequalities.
A relatively novel contribution of this paper has been the inclusion of school-level controls.
It is expected that private tutoring participation would be lower for those studying in ‘better’
schools. Some of our findings confirm this hypothesis: private tutoring is lower among students
studying at schools with lower pupil-teacher ratio and higher student attendance. These findings
are in line with the argument that the rise in private tutoring could be attributed to the poor
quality of schooling (or instruction), inadequate facilities, misaligned curriculum and pedagogy,
and the lack of attention from teachers (Sieverding, Krafft, & Elbadawy, 2019; Song, Park, &
Sang, 2013; Sujatha, 2014). Recent rounds of ASER surveys (ASER Centre, 2021, 2023) offer
further support for this: the data suggests that the dependence on private tutoring increased in
the years 2020 and 2021, most likely due to school closures. Moreover, the gap between 2018
and 2021 figures for private tutoring participation was nearly 10 percentage points, and the
greatest increase was seen among less privileged households (as measured by parental education
levels) and for students whose schools hadn’t reopened. The expansion of private tutoring
could, therefore, be a response to the inadequate learning received at formal schools, and the
dependence on tutoring is likely to be more pronounced for less privileged students such as
first-generation learners.
The demand for private tutoring is fuelled by aspirations of socio-economic mobility
(Majumdar, 2014; Sharma, 2019). Empirical studies have shown that lower-income households
spend a greater proportion of their household incomes on private tutoring (Lakshmanasamy,
2017; Mitra & Sarkar, 2019), presumably to secure brighter prospects for the next generation.
However, the additional cost which households need to incur especially at the primary school
102
A. Agrawal et al.
level goes against the vision of ‘free and universal’ primary education enshrined in the Indian
Constitution and creates a wedge between the haves and have nots. While improvement in the
quality of schooling should be a priority policy objective in itself, its correlation with the
demand for private tutoring and its implications for socio-economic equity cannot be ignored.
Notes
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
While our focus has been on developing countries, a sizable number of school-going children in developed
countries also avail private tuition in some form. For instance, in several European countries the proportion of
such children was at least 40% (Irving, 2020). In Japan, in 1993, nearly ‘70% of all students had received
tutoring by the time they had completed middle school’ (Bray, 1999, Table 1).
In India, private tutoring broadly takes two forms: at the pre-secondary level, as a response to poor education
in schools, and at the secondary or post-secondary level, in response to the need for a competitive advantage.
Pre-secondary tutoring (or ‘tuitions’) mostly involves repetition of school syllabus and homework help, while
post-secondary ‘coaching’ is often geared towards preparation for entrance exams (for engineering, law,
medicine and other professional courses). Data constraints allow us to empirically analyse the phenomenon only
for pre-secondary students (ages 5 through 16).
Studies from West Bengal (Ghosh & Bray, 2020), Uttarakhand (Gupta, 2018), Bihar (Banerji & Wadhwa, 2015),
Uttar Pradesh, Kerala, Maharashtra, and Andhra Pradesh (Sujatha, 2014) provide a glimpse into the relation
between education quality and private tutoring demand, but these studies are not based on nationally
representative data.
Neoclassical household decision making models (Alderman & King, 1998; Thomas, Schoeni, & Strauss, 1996)
predict higher investment in boys’ education due to factors such as labour market returns, intrinsic preference
for son’s future earnings and expectation of positive remittances from the son.
We assume that average peer ability is independent of gender. While research on this matter is ongoing in the
psychology domain (Voyer & Voyer, 2014), we make this assumption for simplicity and to avoid any
unnecessary parameter restrictions.
The proof is provided in Appendix A.
ASER Centre is the research and assessment arm of Pratham, a non-governmental organisation working in the
education sphere in India.
We do not use the data from more recent rounds of ASER. In 2019, ASER’s ‘Early Years’ survey reported on
nearly 37,000 young children’s (age 4 to 8) pre-school and school enrollment status and their abilities on a range
of important developmental indicators but this data is not nationally representative (ASER Centre, 2019). The
2020 round was a phone-based survey due to the Covid-19 pandemic, and had limited information on
household indicators and school participation. The 2021 round retained the phone-based format (ASER Centre,
2023).
Since our analysis is limited to pre-secondary students, we do not comment on the drivers behind demand for
post-secondary tutoring.
Although the IHDS also collects information on school characteristics in each village/primary sampling unit, the
documentation suggests not to match surveyed students to surveyed schools (Desai & Vanneman, 2015).
In our analysis, we interpret the regressors as correlates rather than causing changes in the private tutoring
enrolment or expenditure. Since our data is not longitudinal, we are unable to make any claims for causality.
However, a comment on the sign of the coefficients or direction of association is not precluded.
Further, using the log values reduces heteroscedasticity, and regressions involving wages, income or
consumption routinely use the log form.
We are grateful to Geeta G. Kingdon who suggested using family fixed effects to detect whether the gender bias
exists only across households, or within-households as well. We followed Kingdon (2005) to estimate the family
fixed effects regressions and found that the coefficient on the gender dummy (Male ¼ 1) is still positive and
significant (for both outcomes, participation and spending), suggesting the existence of within-household
parental bias. These results have not been shown in the paper. Other studies have reported an intra-family
gender bias in education spending using the same methodology (Datta & Kingdon, 2019; Kingdon, 2005).
This policy was amended in 2019/2020, and states were allowed to reintroduce exams and detention in class 5 or
class 8. The no-detention policy which guaranteed automatic promotion to the next class might have diluted
motivation and incentives for students and teachers alike.
The reduction in sample size compared to the previous regression deserves a word of caution. We lose several
observations due to the matching of students to their respective schools. Further, there are many missing
observations for school variables such as girls’ toilet and blackboards, due to which the regression sample is
greatly reduced compared to the previous regression. However, the sample size remains reasonably large to
enable a meaningful analysis.
Determinants of Private Tutoring Demand in Rural India 103
16. It is possible for student attendance to be endogenous in this specification. We ran the regression again by
dropping the variable, but found that the coefficients were largely unchanged. Hence, we retain this variable.
Acknowledgements
We thank the journal’s editor and an anonymous reviewer for valuable suggestions to the initial draft. We are grateful to Abhiroop Mukhopadhyay, Geeta Gandhi Kingdon, Sisir
Debnath, Sourabh Paul, Jayan Jose Thomas and seminar participants at the Indian Institute
of Technology, Delhi and the Indian Statistical Institute, Delhi for their helpful feedback.
Any remaining errors are ours. We thank the team at ASER for sharing the survey data with
us. The data can be requested from ASER. The Stata code is available upon request.
Disclosure statement
No potential conflict of interest was reported by the author(s).
ORCID
Ankush Agrawal
http://orcid.org/0000-0001-6613-4392
Parul Gupta
http://orcid.org/0000-0003-4476-9295
Debasis Mondal
http://orcid.org/0000-0002-8316-9674
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Appendix A
The proofs of results of the model are presented in this Appendix. The Lagrange function and
first-order conditions for an interior optimum are given by:
L ¼ bUm þ ð1 − bÞUf − k cm þ cf þ pðeg2 þ eb2 Þ − w þ ge1
(22)
b
¼k
cm
(23)
106
A. Agrawal et al.
1−b
¼k
cf
(24)
ð1 − bÞaf
bam
þ
¼ pk
eg1 þ eg2 eg1 þ eg2
(25)
b
ð1 − bÞ
þ
¼ pk
eb1 þ eb2 eb1 þ eb2
(26)
From Equations (23) and (24), we have cm þ cf ¼ 1k :
From Equation (26), we have: pðeb1 þ eb2 Þ ¼ 1k
Together, from these equations, we get cm þ cf ¼ pðeb1 þ eb2 Þ: Using this, rewrite the constraint cm þ cf þ pðeg2 þ eb2 Þ ¼ w − ge1 as pðeb1 þ eb2 Þ þ pðeg2 þ eb2 Þ ¼ w − ge1 :
Collecting terms and rearranging, we get:
eb2 ¼
w − ge1 − peg2 − peb1
2p
(27)
Define X ¼ bam þ ð1 − bÞaf : From Equations (25) and (26),
X
1
eg1 þ eg2
− eb1
¼
) eb2 ¼
X
eg1 þ eg2 eb1 þ eb2
(28)
Equating the right hand sides of Equations (27) and (28):
ðw − ge1 − pðeg2 þ eb1 ÞÞ eg1 þ eg2
− eb1
¼
X
2p
Rearrange the above equation to get:
h
i
2pe
X w − ge1 − peb1 − Xg1 þ 2peb1
ðw − ge1 ÞX þ peb1 X − 2peg1
¼
eg2 ¼
pð2 þ XÞ
pð2 þ XÞ
Use (30) in (28) to get:
eb2 ¼
eg1 ðw − ge1 ÞX þ peb1 X − 2peg1
w − ge1 þ peg1 − peb1 ð1 þ XÞÞ
þ
− eb1 ¼
X
pXð2 þ XÞ
pð2 þ XÞ
From (26),
k¼
1
pðeg1 þ eg2 Þ
X
i
)k¼ h
ðw−ge1 ÞXþpeb1 X−2peg1
p eg1 þ
pð2þXÞ
2þX
)k¼
peg1 þ w − ge1 þ peb1
Finally, from (23),
cm ¼
b b½w − ge1 þ peg1 þ peb1 ¼
2þX
k
(29)
(30)
Determinants of Private Tutoring Demand in Rural India 107
and, from (24),
cf ¼
1 − b ð1 − bÞ½w − ge1 þ peg1 þ peb1 :
¼
2þX
k
Appendix B
In this Appendix, we describe another interesting theoretical finding on the relation between
schooling costs and private tutoring spending. We present descriptive evidence in support of
the result, however, we are unable to test the hypothesis using regression analysis due to data
limitations.
An increase in the price of public education reduces private tutoring expenditure for both
boys and girls.
i2
Formally, @pe
@g < 0, 8i ¼ b, g
Proof:
@peb2
−e1
¼
<0
@g
2þX
and
@peg2 −e1 X
¼
<0
@g
2þX
Thus, if public education becomes costlier, spending on private tutoring would reduce.
Conversely, a decrease in the cost of public education would increase expenditure on private
tutoring. This result finds empirical support: a study utilizing NSS 71st round data (RMSATCA., 2016) reports that students receiving government scholarship or stipend are more likely
to attend private tutoring. More precisely, the study finds that an increase in scholarship
amount is positively related to an increased probability of private tutoring participation for
both government school and private school students, although the effect on government
schools students is uniformly larger across scholarship amounts. An increase in scholarship
amount can be interpreted as a decrease in schooling costs, hence this finding supports the
above theoretical result, that households divert the additional resources towards private tutoring, indicating a positive ‘income effect’.