Private Investment in Education

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march 29, 2014 vol xlix no 13 EPW Economic & Political Weekly44

Private Investment in EducationEvidence across Castes and Religion from West Bengal

Kausik Gangopadhyay, Abhirup Sarkar

This article presents empirical evidence on educational

investments by members of different castes and religion

using household-level, cross-sectional data from West

Bengal. It finds that scheduled caste households invest

significantly less than other households in private

coaching of children, even after controlling for all

available socio-economic background variables.

This result is posited to arise from two possible sources:

from cultural factors and from positive discriminative

practices. The article develops an empirical strategy

to determine which type of factor is more significant

and finds that cultural factors are more likely than

positive discriminative practices to be the source of

the lower spending.

We thank Sandip Mitra and the seminar participants at IGC Bihar Growth Conference held at Patna. The usual disclaimers apply.

Kausik Gangopadhyay ([email protected]) is a member of the faculty at the Indian Institute of Management, Kozhikode. Abhirup Sarkar ([email protected]) is a member of the faculty at the Indian Statistical Institute, Kolkata.

1 Introduction

The backwardness of certain groups within a country or a region seems to be a global phenomenon and can be attributed to various historical factors. To correct the

historical inequities often modern communities decide to embark upon positive discriminatory policies. Examples of such policies include affi rmative action for blacks in the United States (US) and South Africa or the practice of reservation in places of higher learning and public sector jobs in India. While the objectives of such policies cannot be questioned, doubts arise as to whether positive discrimination is the right weapon to combat historical differences. More so for the Indian con-text where, after decades of reservation, huge differences in economic and social achievements tend to persist between advantaged and disadvantaged groups.

The purpose of the paper is to understand the effect of posi-tive discrimination on Indian society. To fi nd out the extent to which positive discrimination has been useful in curbing social and economic inequalities we narrow down our focus on a survey data from West Bengal. It is well accepted that on an average affl uence goes hand in hand with educational achievements. We, therefore, narrow down our focus further on educational investment as revealed by the survey data. The fi rst question we ask is whether there is any signifi cant differ-ence in private educational investment across castes and reli-gion. We fi nd that the scheduled castes (SCs) tend to invest signifi cantly less than other caste and religious groups, con-trolling for all possible socio-economic factors. Our fi nding passes certain robustness tests. Therefore, we try to fi nd the reason behind this empirically well-founded phenomenon. In parti cular, we focus our attention on two possible factors lead-ing to underinvestment in education by SCs. First, we posit that positive discrimination itself could have acted as a disin-centive to invest privately on education. Second, we look at possible cultural factors behind this underinvestment. We fi nd that our data tend to support the latter as a possible cause as opposed to the former.

The system of positive discrimination, usually referred as “reservation”, was put in place in 1951 and has been main-tained up to the present, with commitment towards it growing gradually over time.1 The reservation system is characterised by better opportunities for targeted subgroups, namely, SCs and scheduled tribes (STs), to receive advanced education compared to the general population (described as “general category”).2 This system also specifi es that a certain fraction

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Economic & Political Weekly EPW march 29, 2014 vol xlix no 13 45

of jobs in the public sector are reserved exclusively for members of the targeted populations, where the specifi ed proportions far exceed the proportions of the targeted groups that have historically held such jobs. Therefore, it seems plausible that members of the targeted subgroup are more likely than non-members to fi nd jobs in the public sector, controlling for other socio-economic factors. Given this policy, one might expect members of the targeted groups to invest in education at least on a par with the general popula-tion, if not at a higher rate, controlling for socio-economic background. This was also the hope of architects of the Indian Constitution – that through such measures, a level of parity would be achieved, in a reasonable period of time. It was expected that the reservation system would provide the subgroups a channel to partake in education and thereby escape their backwardness.

This system of reservation can only succeed in improving educational outcomes among targeted groups if there is no systematic underinvestment in education on the part of the targeted communities themselves. Private coaching, a common method of instruction for students in West Bengal, enabled us to track private educational investments of households. Using household-level data to investigate educational spending patterns, we observed that, controlling for all other socio-economic factors, SC households invest signifi cantly less than other households in human capital. This result is noteworthy, as it clearly shows that the mere presence of reservation may not suffi ce to bridge the gap in educational attainment between the general population and communities targeted by the reservation system. If investment in education is less among the backward communities – for whom positive dis-crimination in job placement is practised – than among the general population, then we are led to the conclusion that the existing policies of positive discrimination are not fully effec-tive in bridging the education gap between backward commu-nities and the general population. At a minimum, this fi nding necessitates a re-examination of the policies of positive discrimination prevalent in India.

Our empirical fi ndings could be rationalised by two alter-native types of explanation. The fi rst stems from the possibility that reservation itself can act as a disincentive to invest privately in education. If the competition for getting jobs is less intense, the targeted community might fi nd it worthwhile to spend less on education which under normal circumstances is the main vehicle to compete for jobs. The second possible explanation is related to the cultural paradigm. According to this paradigm education carries less social value to the back-ward or targeted groups which induces them to spend less on education. Such explanations become especially relevant if economic explanations fail to account for the relevant obser-vation. Below we articulate an empirical strategy designed to distinguish between these two channels. The power of our strategy lies in the assumption that an increase in the propor-tion of backward-community population households in a given village makes all members of that community more receptive to the norms of their culture. Our analysis largely supports the

explanation in terms of cultural paradigm over the view that low educational investment is an unintended consequence of reservation policy itself.

The paper is structured as follows. Section 2 describes our data and empirical strategy. Section 3 discusses our main results, in particular, that the SC households invest less in education, controlling for other factors, than general population house-holds. Section 4 discusses the implications of our results, relat-ing them to the existing literature and to economic theory. The article then constructs and carries out a test to differentiate a culture-based from a reservation-based explanation for our data. Section 5 concludes.

2 Data and Empirical Strategy

2.1 Data Description

We use the data collected by Bardhan et al (2009) from a survey conducted in 2003-05, of rural and urban households of West Bengal. The rural part of the survey involved 2,402 households in a sample of 89 West Bengalese villages. In addition, there are also 1,000 urban households in this survey from fi ve urban agglomerations of West Bengal, namely, Durgapur, Howrah, Kolkata, Malda and Siliguri. The rural sample is a subsample of an original stratifi ed random sample of villages selected from all major agricultural districts of the state (only Kolkata and Darjeeling are excluded) by the socio-economic evalua-tion branch of the Department of Agriculture, Government of West Bengal, for the purpose of calculating the cost of cultiva-tion of major crops between 1981 and 1996. Typically, a random sample of blocks was selected in each district, and within each block one village was selected randomly, followed by random selection of another village within an eight kilometre radius of the fi rst village. A survey team visited these villages between 2003 and 2005, carried out a complete enumeration of landholdings of every household, and then selected a stratifi ed random sample (stratifi ed by landowner-ship) of approximately 25 households per village on average. Selected households were subsequently administered a survey questionnaire to record various demographic and economic variables.3

The ideal data set, to test the impact of caste on educational investment, could be generated through a socio-economic experiment. In that hypothetical experiment, sets of house-holds, with identical economic and educational backgrounds, but from different caste-based study groups, would be chosen. The children from those households would receive identical opportunities for education. They would then be observed over a period of years to record their educational attainment. If one study group invests less in education than other study groups, that would be indicative of the appropriateness of differential educational investment by different groups. Since an ideal experiment as such cannot be conducted, we rely on survey data to try to detect any discernible differences in educational expenditure between various communities. As the socio-economic backgrounds of households from different study groups are disparate, we

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chose statistical techniques in order to control for the availa-ble background variables.

2.2 Variable for Educational Investment

How can we account for educational investment of a household? This question is particularly pertinent in India, as education is free in public schools until high school, and most households send their children to public schools. Nevertheless, private tuition is common in West Bengal, as also in some other states, and indeed it is now assumed to be the de facto mode of education in these states. In West Bengal, the public education system suf-fers from resource defi ciency and ineffi ciency. For example, in 2005-06, 50,522 primary and junior basic schools catered to the educational needs of children between 6 and 14 years of age. Total enrolment in primary schools (grades four and below) stood at approximately 1,04,89,000, and the total number of teachers at the primary school level was 1,53,220 (Bureau of Applied Economics and Statistics 2007). Therefore, on average each school had 208 students and three teachers. Each teacher then, on average, managed 69 students, perhaps typically spread over two classes. The story is essentially the same for high schools.

We note, however, that the high stu-dent-to-teacher ratio is just one part of the overall picture. Teacher competence and the absence of any effective super-vision are also signifi cant concerns in the public school system (Bandyopadhyay 2008). Given these circumstances, it is something of an understatement that private tuition is rampant, cutting across caste, creed and the economic positions of households. Indeed, the system of private tuition has become the princi-pal mode of education, replacing public schools. Students, though offi cially reg-istered in the government education system, spend considerable portions of their time availing themselves of private tuition classes. Expenditures incurred by households for private tuition of their children are substantial. A statistical report (National Sample Survey Organi-sation 1998) shows that the ratio of tui-tion expenditure to total education ex-penditure for students aged 5-24 years, for an average household, was 45% in rural West Bengal in 1995-96 against an all-India average of 10%. For urban households these proportions were 35.8% in West Bengal and 12.6% for India. All these fi gures indicate the predomi-nance of private tuition in West Bengal, well above the all-India average.

In our survey data, too, the average annual expenditure on private tuition was Rs 1,673 for a rural household in 2003-05, which is 54.98% of total educational expenses. For an urban household, the corresponding fi gure was Rs 2,230. These fi g-ures demonstrate the rising importance of the institution of private tuition in the West Bengal educational system. There-fore, we choose spending on private tuition as a measure of educational investment.

2.3 Empirical Strategy and Selection Bias

We run the following baseline regression to obtain our principal result:yi = Xi . β + Di

ST δST + DiSC δSC + Di

Muslim δMuslim + εi ...(1)where yi is average annual expenditure on private tuition of the ith household, Xi is the set of socio-economic variables for this household, including variables related to income and

Table 1: Summary Statistics: Rural Sample Obs Mean Std Dev Min Max

Number of household members 2,398 5.213 2.577 1 23

Age (heads) 2,398 49.244 14.404 3 104

Age (entire sample) 12,527 28.450 19.558 0 104

Age-squared (heads) 2,398 2,632.397 1,519.975 9 10,816

Age-squared (entire sample) 12,527 1,191.872 1,444.278 0 10,816

Years of education (heads) 2,398 4.782 4.446 0 17

Maximum years of education (household-wise) 2,397 8.213 4.242 0 17

Maximum years of education for females (household-wise) 2,375 5.908 4.225 0 17

Male dummy (heads) 2,397 0.894 0.308 0 1

Male dummy (entire sample) 12,532 0.516 0.500 0 1

Scheduled tribe dummy (heads) 2,398 0.035 0.184 0 1

Scheduled tribe dummy (entire sample) 12,532 0.036 0.186 0 1

Scheduled caste dummy (heads) 2,398 0.319 0.466 0 1

Scheduled caste dummy (entire sample) 12,532 0.319 0.466 0 1

Muslim dummy (heads) 2,398 0.201 0.401 0 1

Muslim dummy (entire sample) 12,532 0.213 0.409 0 1

Distance to the primary school 2,398 0.033 0.146 0 1

Distance to the secondary school 2,398 0.961 1.431 0 7

Distance to the college 2,398 10.287 8.906 0 38

Amount of household owned land (in cents) 2,398 1.306 2.508 0 21.33

Annual household income 2,398 36,604 73,858 0 25,99,000

Annual total educational expenditure 2,398 3,041 8,061 0 2,07,100

Annual tuition expenditure 2,398 1,671 4,228 0 84,000

Number of children up to age 6 2,398 0.628 0.890 0 6

Number of schoolgoing children up to age 6 2,398 0.136 0.365 0 4

Number of male children up to age 6 2,398 0.313 0.586 0 5

Number of schoolgoing male children up to age 6 2,398 0.070 0.267 0 4

Number of children between ages 7 and 12 2,398 0.678 0.868 0 6

Number of schoolgoing children between ages 7 and 12 2,398 0.615 0.824 0 6

Number of male children between ages 7 and 12 2,398 0.347 0.615 0 4

Number of schoolgoing male children between ages 7 and 12 2,398 0.317 0.582 0 4

Number of minors between ages 13 and 18 2,398 0.682 0.881 0 5

Number of schoolgoing minors between ages 13 and 18 2,398 0.392 0.688 0 4

Number of male minors between ages 13 and 18 2,398 0.340 0.602 0 3

Number of schoolgoing male minors between ages 13 and 18 2,398 0.195 0.469 0 3

Number of persons between ages 19 and 22 2,398 0.390 0.633 0 4

Number of schoolgoing persons between ages 19 and 22 2,398 0.075 0.289 0 2

Number of males between ages 19 and 22 2,398 0.201 0.445 0 2

Number of schoolgoing males between ages 19 and 22 2,398 0.043 0.212 0 2

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educational attainment, and DiST, Di

SC and DiMuslim are dummy

variables for the ST, SC and Muslim communities, respectively. In the above regression, signifi cant positive or negative values for the estimates of the parameters associated with these dummy variables would indicate behavioural differences between the community in question and the general population.4

A potential issue with this regression is selection bias. If the error term, εi, is systematically correlated with the independent variables, we will not have an unbiased estimate of the para-meters of our regression equation (1), using the method of ordinary least squares. For example, educational investment may be determined by children’s aptitude, in addition to the variables included in the regression equation; that, however, would be recorded as an ingredient in the error term. More generally, if the error term is correlated with some independent variable, our estimates will be biased.

We argue, however, that there should not be selection bias in regression equation (1). Selection bias here could occur in

two ways: as omitted variable bias; or as a result of correlation between the error term, εi,, and independent variables. We considered a large number of socio-economic variables in this regression, to minimise the chance of omitted variable bias. All salient variables explored in the literature that concern educational investment are included in our list of independent variables. The sample households were randomly chosen, using a proper statistical framework, from rural and urban sectors of West Bengal. Given this randomness, our sample is not biased with respect to any observable attribute. The error term is uncorrelated with observable attributes. Therefore, it is quite unlikely that any omitted variable is both correlated with the ST, SC or Muslim dummy variables and determinative of educational investment.

2.4 Reduction of Noise in the Data

The data set contains a considerable amount of noise. For example, the income data for households have been collected

both monthly and yearly, and there is a substantial discrepancy between these two numbers for a large number of households. The monthly fi gures suffer from under-reporting bias, so we choose to use the yearly fi gures in our analysis. It should here be noted that use of the monthly fi gures would not make any qualitative difference to the analysis. Similarly, the data on educational expenses has three components – total educational expenses, expenses on books and expenses on private tuition, all taken annually. Ideally, the fi rst component should be obtained by add-ing together the second and third. However, this is not observed in a very large number of cases.

Therefore, if total expenses are less than the sum of the two other compo-nents, we view the remainder as other expenses unaccounted for by the cate-gories of tuition and books. Adding together all three, we compute total expense on education. Also note that if the reported tuition expense is less than Rs 30, we consider it a monthly rather than a yearly fi gure. Sometimes, a household reports the age of an individual at the time one is attaining one’s highest educational level rather than years of education completed by the individual. Accordingly, the maxi-mum number of years of education has been restricted to 17 in the rural sample and 18 in the urban sample. Even after these modifi cations, there are some anomalies in the data. However,

Table 2: Summary Statistics: Urban Sample Obs Mean Std Dev Min Max

Number of household members 1,000 4.547 2.055 1 19

Age (head) 1,000 46.022 13.092 13 92

Age (entire sample) 4,547 30.346 18.800 0 115

Age-squared (heads) 1,000 2,289.266 1,259.771 169 8,464

Age-squared (entire sample) 4,547 1,274.260 1,370.475 0 13,225

Years of education (heads) 1,000 7.031 5.557 0 18

Maximum years of education (household-wise) 1,000 9.645 4.820 0 18

Male dummy (heads) 1,000 0.809 0.393 0 1

Male dummy (entire sample) 4,547 0.530 0.499 0 1

Scheduled tribe dummy (heads) 1,000 0.003 0.055 0 1

Scheduled tribe dummy (entire sample) 4,547 0.003 0.055 0 1

Scheduled caste dummy (heads) 1,000 0.286 0.452 0 1

Scheduled caste dummy (entire sample) 4,547 0.307 0.461 0 1

Muslim dummy (heads) 1,000 0.069 0.254 0 1

Muslim dummy (entire sample) 4,547 0.075 0.263 0 1

Annual household income 1,000 99,385.600 1,80,841.600 0 36,48,000

Annual total educational expenditure 1,000 5,943.624 30,464.290 0 9,10,000

Annual tuition expenditure 1,000 2,230.002 5,293.640 0 96,000

Number of children up to age 6 1,000 0.429 0.738 0 4

Number of schoolgoing children up to age 6 1,000 0.383 0.365 0 2

Number of male children up to age 6 1,000 0.229 0.517 0 3

Number of schoolgoing male children up to age 6 1,000 0.085 0.289 0 2

Number of children between ages 7 and 12 1,000 0.485 0.770 0 6

Number of schoolgoing children between ages 7 and 12 1,000 0.416 0.694 0 6

Number of male children between ages 7 and 12 1,000 0.257 0.523 0 4

Number of schoolgoing male children between ages 7 and 12 1,000 0.222 0.480 0 4

Number of minors between ages 13 and 18 1,000 0.549 0.809 0 6

Number of schoolgoing minors between ages 13 and 18 1,000 0.291 0.590 0 3

Number of male minors between ages 13 and 18 1,000 0.297 0.560 0 3

Number of schoolgoing male minors between ages 13 and 18 1,000 0.153 0.409 0 3

Number of persons between ages 19 and 22 1,000 0.345 0.608 0 4

Number of schoolgoing persons between ages 19 and 22 1,000 0.079 0.284 0 2

Number of males between ages 19 and 22 1,000 0.178 0.420 0 2

Number of schoolgoing males between ages 19 and 22 1,000 0.043 0.208 0 2

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we do not drop any data points because it is infested with noise components.

3 Results

3.1 Descriptive Features

Socio-economic and cultural conditions, in general, differ signi-fi cantly between rural and urban parts of West Bengal. In terms of educational aspiration and infrastructure, rural agglomera-tions and urban agglomerations are not comparable, as indi-cated in the descriptive statistics tabulated in Table 1 (p 46) and Table 2 (p 47). Therefore, we analysed the rural and ur-ban samples separately. Nonetheless, the general results are qualitatively similar in both types of social environment.

We use economic variables to compare the SCs, STs and Muslims with persons (classifi ed as “Others”) not belonging to any of these categories (as shown, for the rural sample, in Table 3). It should be clarifi ed here that the SCs and STs are mutually exclusive populations. The Muslims, by contrast, constitute a group based on religious affi liation and could

overlap with SCs and STs. For example, we found one house-hold in our sample that is both Muslim and SC.

Evidently, discrepancies between these underprivileged groups and others are quite large. Average annual income for non-un-derprivileged households was Rs 46,233 in the rural sample, whereas the corresponding fi gures for SCs, STs and Muslims were Rs 27,882, Rs 26,234 and Rs 30,775, respectively. Educational attainment of the head of a household differs across the groups in a similar manner. In the rural sample, a non-underprivileged head of the household had, on average, 6.21 years of education to his credit. The most highly-educated members of such house-holds possessed, on average, 9.62 years of education. The corresponding fi gures for SC households were 3.56 and 7.18 years, respectively. STs and Muslims do not differ in this respect.

Table 4 (p 49) records this comparison for the urban sample. As there are only three ST households in the urban sample, we do not report these fi gures for the STs. Generally, households in the urban sample had higher incomes than their rural coun-terparts. However, while non-underprivileged individuals in the urban sample are better educated than their rural counter-

parts, there is no such pattern between SCs and Muslims.

3.2 School Enrolment

School enrolment indicates educational investment in an extensive margin. We divided children into different age groups. The fi rst age group consists of children up to six years of age. The second and third age groups consist of young per-sons aged between 7 and 12 years and 13 and 18 years, respectively. The fi nal group consists of young adults between the ages of 19 and 22. Households belonging to any of these underprivi-leged groups possessed signifi cantly more children, on average, than others for all age groups.

In the rural sample, the proportion of schoolgoing children up to age 12 is somewhat similar for all the population subgroups. For young persons over 13 years of age, non-underprivileged house-holds defi nitely achieved a higher pro-portion of school enrolment compared to their SC, ST or Muslim counterparts. In the urban sample, “Others” had dis-cernibly higher enrolment rates for all age groups, compared to their SC and Muslim counterparts. Furthermore, the SCs showed higher enrolment compared to Muslims.

We also computed these proportions, restricting our attention to male children for all the subgroups, to record the extent of gender bias among them. We did not

Table 3: Population-wise Mean of Some Economic Variables: Rural SampleAll non-SC, non-ST and non-Muslim households are clubbed in the general category.Standard deviations of the variables are reported in the parenthesis.Variable Scheduled Caste Scheduled Tribe Muslim General

Age (head) 48.32 49.71 45.75 51.47 (14.18) (15.22) (13.81) (14.42)

Years of education (heads) 3.56 3.11 3.83 6.21 (3.79) (4.20) (4.35) (4.52)

Years of highest education (household-wise) 7.18 6.29 7.06 9.62 (3.98) (4.24) (4.20) (4.01)

Years of highest female education (household-wise) 4.74 4.20 5.06 7.26 (3.98) (3.90) (3.98) (4.13)

Average distance to the primary school 0.05 0.02 0.04 0.02 (0.16) (0.11) (0.17) (0.13)

Average distance to the secondary school 1.10 1.50 1.24 0.70 (1.52) (1.85) (1.60) (1.18)

Average distance to the college 10.88 10.46 12.28 8.95 (10.25) (8.89) (7.79) (8.09)

Average amount of household owned land (in cents) 1.04 1.99 1.16 1.52 (2.30) (3.37) (2.30) (2.68)

Average annual household income 27,882 26,234 30,775 46,233 (34,056) (35,265) (34,413) (1,02,870)

Average annual total educational expenditure 2,019 2,524 2,776 3,916 (3,617) (6,921) (6,556) (10,561)

Average annual tuition expenditure 1,115 1,709 1,448 2,157 (2,048) (5,760) (3,350) (5,379)

Average number of children up to age 6 0.66 0.64 0.86 0.50 (0.89) (0.83) (1.01) (0.80)

Proportion of schoolgoing children up to age 6 0.22 0.19 0.20 0.23

Proportion of schoolgoing male children up to age 6 0.26 0.15 0.17 0.24

Average number of children between ages 7 and 12 0.79 0.68 0.83 0.53 (0.91) (0.81) (0.99) (0.75)

Proportion of schoolgoing children between ages 7 and 12 0.89 0.86 0.90 0.94

Proportion of schoolgoing male children between ages 7 and 12 0.91 0.86 0.88 0.95

Average number of minors between ages 13 and 18 0.70 0.87 0.88 0.57 (0.87) (0.89) (1.02) (0.80)

Proportion of schoolgoing minors between ages 13 and 18 0.54 0.45 0.47 0.69

Proportion of schoolgoing male minors between ages 13 and 18 0.57 0.41 0.43 0.70

Average number of persons between ages 19 and 22 0.37 0.43 0.44 0.38 (0.63) (0.61) (0.68) (0.61)

Proportion of schoolgoing persons between ages 19 and 22 0.13 0.19 0.14 0.26

Proportion of schoolgoing males between ages 19 and 22 0.16 0.21 0.16 0.28

Number of households 766 84 482 1,072

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fi nd evidence of gender bias in school enrolment for children aged 18 years or less. For young adults between 19 and 22 years, there was some gender disparity in school enrolment. Nonethe-less, there were no discernible trends among the different popu-lation groups, in one direction or the other.

A systematic way to verify our fi ndings on variations in school enrolment would be to use a linear probability model, where the dependent variable is the proportion of children, between the ages of 7 and 12, per household enrolled in school. For example, the distance of the nearest secondary school or college can be a determinant of enrolment, and we included it as an independent variable in our analysis of the rural sample. The results tabulated in Table 5 indicate that, in the rural sam-ple, there is no signifi cant difference in enrolment between children of various backward communities and others, after controlling for socio-economic background parameters. How-ever, we could fi nd signifi cant differences in the urban sample, in the case of Muslims, and to some extent, that of SCs.

3.3 The Principal Findings

In the intensive margin, tuition expenditure represents educa-tional investment for a household. We ran an ordinary least squares regression using equation (1). The result is tabulated in

column 1 of Table 6 (p 50), for the rural sample. As expected, annual household income and landholding, representing the income and wealth of a rural household, are positively associ-ated with tuition expenditure. A more educated decision-maker should value a ward’s education more, compared to a less-educated decision-maker. Unsurprisingly, therefore, we found educational attainment of the household-head to be statistically signifi cant in this regression. With respect to numbers of schoolgoing children for the different age groups, 7-12, 13-18 and 19-22, distance to the nearest secondary school and to the nearest college are signifi cant, as expected. Most importantly, the coeffi cient on the SC dummy variable reveals that an average SC household spent approximately Rs 344 less, annually, on tuition, which is statistically signifi cant at the 5% level.

It could be argued that local effects drive our result and that, unless we control for them, the generalised conclusions of our results might not be valid. To answer this objection, we performed an analysis with fi xed effects for the villages (column 2, Table 6). A random effects model would be more appropriate in this scenario, where sampling units (villages) were chosen at random from a population. We estimated a random effects model, reported in column 3 of Table 6. Inclu-sion of village specifi c effects did not render the coeffi cient on the SC dummy variable statistically insignifi cant. We found similar results for our urban sample, reported in Table 7 (p 50), where it is shown that an SC household spent, on average,

Table 4: Population-wise Mean of Some Economic Variables – Urban SampleAll non-SC, non-ST and non-Muslim households are clubbed in the “Others” category.Standard deviations of the variables are reported in the parenthesis.Variable Scheduled Muslim General Caste

Age (head) 42.89 45.09 47.53 (13.85) (11.82) (12.66)

Average years of education (heads) 3.49 3.88 8.93 (4.16) (4.68) (5.28)

Average highest education (household-wise) 6.98 6.13 11.20 (4.00) (5.02) (4.43)

Average annual household income 57,932 53,910 1,22,770 (2,18,909) (88,382) (1,65,080)

Average annual total educational expenditure 3,064 3,020 7,535 (5,649) (7,132) (37,649)

Average annual tuition expenditure 1,517 1,741 2,594 (2,317) (5,052) (6,176)

Average number of children up to age 6 0.56 0.51 0.36 (0.83) (0.76) (0.69)

Proportion of schoolgoing children up to age 6 0.30 0.20 0.41

Proportion of schoolgoing male children up to age 6 0.31 0.24 0.43

Average number of children between 0.73 0.77 0.34 ages 7 and 12 (0.89) (0.99) (0.64)

Proportion of schoolgoing children between ages 7 and 12 0.83 0.58 0.94

Proportion of schoolgoing male children between ages 7 and 12 0.84 0.56 0.96

Average number of minors between 0.70 0.78 0.45 ages 13 and 18 (0.82) (1.07) (0.76)

Proportion of schoolgoing minors between ages 13 and 18 0.41 0.35 0.64

Proportion of schoolgoing male minors between ages 13 and 18 0.39 0.31 0.64

Average number of persons between 0.44 0.41 0.30 ages 19 and 22 (0.69) (0.71) (0.55)

Proportion of schoolgoing persons between ages 19 and 22 0.10 0.14 0.32

Proportion of schoolgoing males between ages 19 and 22 0.13 0.00 0.35

Number of households 286 69 643

Table 5: Role of Caste Dummies in Explaining School EnrolmentLinear probability model with proportion of schoolgoing children aged 7-12 years as the dependent variable.Standard errors of the estimates are reported in the parenthesis.Independent Variables Rural Sample Urban Sample

Annual household income 1.87e-08 2.22e-06*** (1.57e-06) (7.69e-07)

Annual household income squared -1.52e-11 -6.52e-12***

(1.71e-11) (2.37e-12)

Annual household income cubed 6.70e-17 5.29e-18**

to the power fourth (5.69e-17) (2.07e-18)

Annual household income -6.44e-23 -1.00e-24** (5.06e-23) (4.09e-25)

Age of the head 0.007* -0.007 (0.004) (0.007)

Age of the head squared -6.83e-05 -3.86e-05 (4.25e-05) (7.47e-05)

Amount of household owned land (in cents) 0.004 (0.005)

Years of education of the head 0.007** 0.024*** (0.003) (0.004)

Scheduled tribe dummy -0.051 0.138 (0.065) (0.218)

Scheduled caste dummy 0.001 -0.070* (0.030) (0.039)

Muslim dummy -0.029 -0.171** (0.032) (0.062)

Distance to the secondary school (in kms) -0.085*** (0.026)

Distance to the secondary school squared 0.019*** (0.006)

City-specific fixed effect - Yes

Number of observations 995 568* Significant at 10%; ** Significant at 5%; *** Significant at 1%.

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Rs 730 less on private tuition of children, after controlling for all available factors.5

4 Test of Two Paradigms

We asked the question: do households of some backward communities incur signifi cantly less expenditure for educa-tion of their children than those of other communities, con-trolling for all other socio-economic factors? Our empirical investigation reveals that the answer is yes for the SC commu-nity. It is sometimes argued that the observed low educational investment of SC households is due to a paucity of schools and other infrastructure. We have already controlled for distance to school and college in our regression, along with local effects. Therefore, the next pertinent question is: why should SCs invest less than others in the education of their children? We explore two avenues in search of an answer to this question.

4.1 Cultural Paradigm

The existence of intrinsic differences, related to educational attainment, among various communities is well known. For example, Cochran, Hardy and Harpending (2006) argue that,

owing to various historical factors, Ashkenazi Jews in the US have signifi cantly higher levels of educational and intellectual attainment than average Americans.

In the context of individual economic decision-making, a systematic, statistically signifi cant pattern not explained by socio-economic factors should be attributable to cultural fac-tors. Fernández and Fogli (2009) examine women’s labour market participation and fertility decisions, as these vary across various immigrant communities in the US. They fi nd that immigrant household decisions are signifi cantly related to the culture of the country of emigration. Fernández, Fogli and Olivetti (2004) demonstrate that wives of American men whose mothers had participated in the labour market are signifi cantly more likely to participate in the labour market. This phenomenon could be explained by preference formation during the second world war.

Other examples of a signifi cant role for cultural paradigm include the gender gap in labour force participation (Antecol 2000), the propensity to shirk (Ichino and Maggi 2000), social capital (Guiso, Sapienza and Zingales 2004), and living arrangements (Giuliano 2007). Our fi ndings might also be viewed as an illustration of the importance of cultural para-digm. In particular, cultural differences between SCs and the

Table 6: Role of Caste Dummies in Explaining Tuition Expenditure – Rural SampleDependent Variable: Annual expenditure on tuition for a household.Standard errors of the estimates are reported in the parenthesis.Independent Variables (1) (2) (3)

Annual household income 0.019*** 0.019*** 0.019***

(0.002) (0.002) (0.002)

Annual household -4.20e-9*** -4.27e-9*** -4.28e-9***

income squared (9.96e-10) (1.03e-9) (9.96e-10)

Age of the head 36.929 48.049 36.546

(29.744) (30.415) (29.763)

Age of the head squared -0.360 -0.472 -0.356

(0.281) (0.288) (0.281)

Amount of household owned land 62.314* 114.056*** 58.399*

(in cents) (33.364) (36.119) (32.920)

Years of education of the head 80.698*** 68.847*** 79.929***

(19.035) (19.492) (19.050)

Scheduled tribe dummy 219.924 184.672 156.049

(402.049) (468.895) (400.795)

Scheduled caste dummy -343.995** -320.321* -359.860**

(174.847) (188.821) (173.184)

Muslim dummy -231.572 -2.976 -242.906

(201.975) (274.097) (198.972)

Distance to the secondary school -112.270** – –

(in kms) (52.716)

Distance to the college (in kms) 14.489* – –

(8.442)

Number of schoolgoing children 279.584 283.965 283.244

up to age 6 (198.652) (202.874) (198.778)

Number of schoolgoing children 419.010*** 419.758*** 424.017***

between ages 7 and 12 (89.399) (90.978) (89.453)

Number of schoolgoing minors 1,406.816*** 1,398.897*** 1,413.993***

between ages 13 and 18 (107.867) (109.770) (107.916)

Number of schoolgoing persons 4,234.174*** 4,362.273*** 4,270.905***

between ages 19 and 22 (261.861) (266.790) (261.419)

Village Effect No Control Fixed Effects Random Effects

R-Square 0.317 0.314 0.316

Number of observations 2,398 2,398 2,398* Significant at 10%; ** Significant at 5%; *** Significant at 1%.

Table 7: The Role of Caste Dummies in Explaining Tuition Expenditure – Urban SampleDependent Variable: Annual expenditure on tuition for a household.Standard errors of the estimates are reported in the parenthesis.Independent Variables (1) (2) (3)

Annual household income 0.020*** 0.019*** 0.020***

(0.006) (0.006) (0.006)

Annual household -4.12e-08** -4.33e-08*** -4.12e-08**

income squared (1.64e-08) (1.64e-08) (1.64e-08)

Annual household 2.74e-14** 2.99e-14** 2.74e-14**

income cubed (1.34e-14) (1.34e-14) (1.34e-14)

Annual household income to -4.80e-21* -5.31e-21** -4.80e-21*

the power fourth (2.57e-21) (2.57e-21) (2.57e-21)

Age of the head 9.663 8.350 9.663

(67.476) (67.678) (67.476)

Age of the head squared -0.355 -0.278 -0.355

(0.698) (0.698) (0.698)

Maximum years of education 17.348 41.194 17.348

(45.506) (46.182) (45.506)

Scheduled tribe dummy -1,256.745 -1,444.213 -1,256.745

(2,666.590) (2,664.355) (2,666.590)

Scheduled caste dummy -729.538** -790.799** -729.538**

(362.959) (381.596) (362.959)

Muslim dummy -241.338 -286.144 -241.338

(605.285) (645.028) (605.285)

Number of schoolgoing children -314.012 -262.300 -314.012

up to age 6 (389.253) (388.464) (389.253)

Number of schoolgoing children 1,112.918*** 1,149.540*** 1,112.918***

between ages 7 and 12 (218.562) (219.724) (218.562)

Number of schoolgoing minors 3,633.512*** 3,636.648*** 3,633.512*** between ages 13 and 18 (253.078) (253.579) (253.078)

Number of schoolgoing persons 2,198.813*** 2,136.944*** 2,198.813***

between ages 19 and 22 (537.782) (536.714) (537.782)

City Specific Effect No Control Fixed Effects Random Effects

R-Square 0.263 0.262 0.263

Number of observations 1,000 1,000 1,000* Significant at 10%; ** Significant at 5%; *** Significant at 1%.

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non-underprivileged population are refl ected in differential educational investments of corresponding households.

4.2 Positive Discrimination and Educational Investment

We can also view our empirical fi ndings as a consequence of the reservation system itself. The impact of affi rmative action on targeted communities has been extensively studied in the economics literature, for example, in Lundberg and Startz (1983), Lundberg (1991), Moro and Norman (2003), Chan and Eyster (2003) and Rotthoff (2008). Holzer and Neumark (2000) provide a detailed assessment of various empirical studies, some of which report defi ciencies of performance of one or more minority groups compared to the general population.

Coate and Loury (1993) present a model that shows that taste-based discrimination can be observed in equilibrium, even when markets are perfectly competitive. In their model, the impact of affi rmative action is ambiguous. A mild imposi-tion of affi rmative action encourages increased investment in human capital by the targeted groups, as well as by the general population. An aggressive implementation of affi rmative action leads to an equilibrium in which the targeted group invests less in skills.

Sanders (2005) argues that affi rmative action could be the reason there are relatively few black lawyers. The argument

goes like this: Affi rmative action enables many blacks to enrol in premium law schools. Many, however, are compelled even-tually to discontinue their studies, as they fi nd themselves unable to meet the high standards of the prestigious institution. If, instead, they had enrolled in less prestigious schools, more would have been able to complete their studies and become lawyers. We have formulated an economic framework in which the provision of reservation lowers the incentive of targeted communities to invest in education.6 This mechanism per-fectly explains the gap in educational investment between SCs and the general population.

There are two competing channels to explain educational underinvestment of SC households. We tested the hypothesis of cultural paradigm against a reservation-driven paradigm by creating a proxy for cultural forces. We measured the in-tensity of cultural pressure on households of a particular community by the proportion of the household’s neighbours who are of that community. In other words, we considered the proportion of the population that is SC, in a given village, to be a proxy for the strength of cultural forces acting on SC households of that village. As values for this variable are missing for three villages in our sample, we used all house-holds residing in the remaining 86 villages of our rural sample. If underinvestment in education by SCs is driven in-stead by the reservation system, neighbourhood composition would not be critical to the decision-making of SC households. Therefore, our fi ndings suggest the dominance of cultural factors over the reservation system as an explanation of SC underinvestment in education.

We ran the baseline regression (equation 1) for our (modi-fi ed) sample and found a signifi cantly negative coeffi cient on the SC dummy (Specifi cation 1, Table 8), as expected. This confi rms that attrition of the sample was not crucial to our main fi ndings. We included a new variable – the product of the proportion of the SC population in a household’s village and the SC dummy – in our regression, with village-specifi c random effects (Specifi cation 2, Table 8). We found the coeffi cient on this variable to be negative and statistically somewhat signifi cant (with a p-value of 6.7%). However, the SC dummy itself was rendered statistically insignifi cant by this specifi cation. This suggests that cultural paradigm is the primary factor behind SC underinvestment in children’s edu-cation. We also ran this regression without the SC dummy, to confi rm the negative magnitude and the statistical signifi -cance (signifi cant at 1% level) of our newly created variable (Specifi cation 3, Table 8).

5 Conclusion

Using a statistically well-designed data set, we investigated the educational investments of various communities in India. Households belonging to the SC community, a targeted group under positive discrimination, spend less on their children’s education than their non-underprivileged counterparts, con-trolling for other socio-economic background variables. Our fi nding is robust to the inclusion of all relevant variables present in the data set. In general, urban agglomerations in

Table 8: Cultural Paradigm Test – Rural SampleDependent Variable: Annual expenditure on tuition for a household.Standard errors of the estimates are reported in the parenthesis.Independent Variables (1) (2) (3)

Annual household income 0.019*** 0.019*** 0.019*** (0.002) (0.002) (0.002)

Annual household -4.24e-09*** -4.21e-09*** -4.21e-09*** income squared (1.01e-09) (1.01e-09) (1.01e-09)

Age of the head 41.921 41.041 41.181 (30.788) (30.776) (30.768)

Age of the head squared -0.414 -0.407 -0.409 (0.292) (0.292) (0.291)

Amount of household 71.910** 78.788** 77.842** owned land (in cents) (34.141) (34.330) (34.223)

Years of education of the head 78.541*** 78.941*** 78.020*** (19.752) (19.743) (19.574)

ST dummy 367.189 364.123 348.875 (415.415) (415.205) (412.969)

SC dummy -372.665** 115.850 – (178.929) (321.209)

SC dummy • Proportion – -942.337* -788.138*** of the SCs (514.689) (286.506)

Muslim dummy -232.813 -232.277 -248.694 (205.914) (205.808) (200.674)

Number of schoolgoing children 283.803 289.784 290.330 up to age 6 (206.978) (206.9) (206.893)

Number of schoolgoing children 421.298*** 419.375*** 420.980*** between ages 7 and 12 (92.226) (92.185) (92.060)



Village Effect Random Effects Random Effects Random Effects

R-Square 0.429 0.427 0.429

Number of observations 2,303 2,303 2,303

Number of villages 86 86 86* Significant at 10%; ** Significant at 5%; *** Significant at 1.

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India offer signifi cantly greater opportunities for education and employment. However, we fi nd that our result is equally valid for our urban sample.

It is well known that the backward communities lag the general population in educational attainment. However, underinvestment in wards’ education within the backward communities, after controlling for socio-economic back-ground variables, is neither logically entailed nor empirically observable a priori. The idea behind positive discrimination (the reservation system) is to create opportunities for backward communities to bridge the educational gap be-tween the backward communities and the general popula-tion. Thus our empirical fi nding brings into question the effi cacy of the reservation system as a means of bridging the education gap between targeted communities and the general population.

Educational underinvestment among the SC could be driven by a cultural paradigm – the SCs may be less motivated to invest in education for historical reasons. Additionally, underinvestment in education could be a consequence of the reservation system itself, in that SC households may have less incentive than others to invest in education. A test that sepa-rates out these two channels offers evidence in favour of cul-tural paradigm as the dominant factor. Thus, reservation does not appear to be directly responsible for discouraging educa-tional investment by the targeted communities. Nevertheless, it is clear that reservation fails to address a key factor, cultural outlook, responsible for the educational gap between back-ward communities and the general population. Reservation seeks only to create opportunities, whereas new policy meas-ures that promote a culture of education among the backward communities are required.

Notes

1 A more detailed historical insight into reserva-tion is provided in appendix, Section A, availa-ble from authors on request.

2 The initiation of Other Backward Classes as a separate category also to receive special bene-fi ts was completed after the survey was conducted and therefore does not fi gure into our analysis.

3 The religion of a household was not asked in the questionnaire. This variable was imputed using the name of the head of the household, as religion can be inferred with near certainty from a person’s name, given the cultural con-text of the study area. In three cases, we found names to be too ambiguous to be indicative of a particular religious group and therefore ex-cluded them from our sample.

4 The sample does not record whether a house-hold belongs to the category of OBC. This is of minimal consequence, as OBCs have been in-cluded in reservation only since 1993.

5 Our result is quite robust subject to change in specifi cations in regression equation as dis-cussed in appendix, Section B, available from authors on request.

6 The model is presented in appendix, Section C, available from authors on request.

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REVISITING SECULARISATIONDecember 14, 2013

Reassessing Secularism and Secularisation in South Asia – Humeira Iqtidar, Tanika Sarkar

Secularisation and Partition Emergencies: Deep Diplomacy in South Asia – Joya Chatterji

Defining Self and Other: Bangladesh’s Secular Aspirations and Its Writing of Islam – Samia Huq

Desecularisation as an Instituted Process: National Identity and Religious Difference in Pakistan – Sadia Saeed

Secularising the ‘Secular’: Monumentalisation of the Taj Mahal in Postcolonial India – Hilal Ahmed

Reimagining Secularism: Respect, Domination and Principled Distance – Rajeev Bhargava

Languages of Secularity – Sudipta Kaviraj

Secularism and Secularisation: A Bibliographical Essay – Mohita Bhatia

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email: [email protected]

Private Investment in Education

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