The Journal of Development Studies ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/fjds20 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 To link to this article: https://doi.org/10.1080/00220388.2023.2273798 Published online: 15 Nov 2023. Submit your article to this journal Article views: 398 View related articles View Crossmark data Citing articles: 1 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=fjds20 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. 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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’.
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