Arnaud Wolff
email: arnaud.wolff@malix.univ-paris1.fr.

Catherine Bros

Does Social Fragmentation Influence School Quality? Evidence from Indian Villages

Keywords: Collective action, Ethnic diversity, School quality JEL: O120, O150, I250, D020

The Indian society is characterized by its stratified social structure, based upon the caste system.

We investigate whether social fragmentation inside Indian villages can influence the quality of schools. We find that neither caste nor religious fractionalization significantly affect school quality. Rather, we find that caste segregation, measured as whether caste groups live in separate neighborhoods, and commitment to education, measured as the number of meetings held between parents and teachers as well as the number of parents that attend these meetings, are negatively and positively correlated with the quality of schools, respectively. We interpret these results as suggesting that the fractionalization indexes cannot consistently account for complex inter-group dynamics, and that more precise estimations are needed to capture the extent of collective action.

V Main Results

VI Robustness Checks

VII Conclusion

I INTRODUCTION

The crucial role played by education on human and economic development is undebated. 1 Yet, for a number of reasons, governments in developing countries have often failed to provide basic public goods such as education services. The poorest segments of societies have long been, and sometimes still are, deprived from access to school facilities. But as noted by Banerjee et al. [2007], access to public goods has always been highly unequal, both within and across national boundaries, and even after taking into account differences in income levels. This has motivated a considerable research on the determinants of collective action and access to public goods, focusing on the role played by the communities (bottom-up processes) in providing those public goods to overcome government failures. The literature has underlined several community characteristics that may explain why certain communities are more successful than others in providing public goods. This paper borrows from this large literature, but departs from it by studying how village2 characteristics influence the quality of school facilities in rural India, rather than focusing only on access. Indeed, through the last few decades, access to basic public goods, notably school infrastructure, has widely increased in most parts of the world. 3 In India, the average net enrolment ratio in primary schools increased from 61% in 1971 to almost 90% in 2012. 4 Following these worldwide improvements in access, the mainstream has switched its view from concerns about quantity to concerns about quality. Hanushek and Woessmann [2008] have found, in a classical study, that once the quality of schooling (measured as students' test scores) is taken into account, school attainment (measured as years of schooling) has no longer a significant impact on growth. This result underlines the necessity to look beyond simple access, and to investigate the determinants of school quality.

One possible determinant of school quality in India is collective action. India is a highly decentralized country, where states, districts and local governments (Gram Panchayat) share political power. As reported by Banerjee and Somanathan [2001], "a relevant feature of structure of government spending in India is that locally provided goods are not locally financed". That is, local governments largely depend on central funds provided by the states. They also note that the process through which the government funds are allocated is "not transparent and probably quite complex", implying that there may be scope for communities to politically compete against each other (by entertaining or writing to state officials for instance) to benefit from limited public resources.

Another major feature of the Indian society is its caste system, that still shapes social interactions between individuals, 5 even if discrimination based on caste membership has been prohibited since the enforcement of the Indian Constitution in 1950. Along caste differences, the Indian society is also religiously diverse, and this led to numerous conflicts, notably between the Hindus and the Muslims. 6 This study has the purpose to investigate if communities that are socially and economically diverse, and thus, initially, seem less likely to act collectively, only have access to schools that are of lower quality than more homogeneous communities.

Several papers have highlighted that socially diverse communities fare worse in terms of public goods provision (Alesina et al. [1999], Dayton-Johnson [2000], Miguel and Gugerty [2005]).

This negative relation has mostly been found to be driven by heterogeneous preferences 7 and the difficult enforcement of social norms across groups. Other studies found more nuanced results, emphasizing the importance of the historical, cultural and political context (Balasubramaniam et al. [2014], Banerjee and Somanathan [2007], Miguel [2004], Singh [2011]). The importance of the measure of diversity used has also been highlighted (Schaeffer [2013]). This suggests that the effect cannot be predicted a priori.

Studies investigating the link between community characteristics and publicly provided goods in India are not widespread at the village level. To our knowledge, the majority of the work that has been done on that subject has used the Indian Census, 8 thus conducting research by aggregating outcomes at the district level (Banerjee et al. [2005], Banerjee and Somanathan [2007] or 5 Anderson [2011] found that in villages dominated by a low caste, low-caste households have a significantly higher income than in villages where the dominant caste is a high caste. She attributes that finding to a trade breakdown in irrigation water across caste groups.

6 See Mitra and Ray [2014] for a detailed investigation. 7 Heterogeneity in preferences for a certain type of good implies that not all groups will exert the same level of effort to provide this good.

8 A notable exception is Bros [2010]. Balasubramaniam et al. [2014]). By using village level data, we are able to construct the different indexes of heterogeneity more precisely, which is an important step towards more consistent estimates. Moreover, we are not aware of any other study that has focused on the link between social heterogeneity and the quality of schools in India. 9 Hence, we expect our study to illustrate how community characteristics can impact the quality of public goods, such as schooling.

To conduct this study, we use the second wave of the nationally representative India Household Development Survey (IHDS-II) that covers the 2011-2012 period. 1410 villages are represented, in 384 districts and 33 different Indian states. We use data directly at the school and at the village level. There is detailed information on the caste and religious composition of villages that we use to compute our fragmentation indexes, as well as information on the level of infrastructure, population, village size, governance and land holdings. There is also detailed information on school facilities that we use to construct our index of quality. The latter depends on the presence of blackboards, chairs and desks, writing material as well as textbooks for all students. We wanted the study to concentrate on the material that is directly involved in student's learning process inside the classroom. Our index of school quality being a categorical variable, taking values ranging from 0 to 4, we run simple ordered probit regressions. 10 Our results suggest that neither caste nor religious fractionalization influence the quality of schools. 11 In all specifications, the coefficient on both indexes remains statistically insignificant, after controlling for state specific effects. Rather, we find that caste segregation, measured as whether the different caste groups inside the village live in separate neighborhoods, negatively impacts school quality. While researchers often proxy collective action using indexes of fragmentation, we interpret our results as evidence that the fractionalization indexes cannot capture the extent of collective action in a consistent way. Moreover, we find that commitment to education, measured as the number of meetings held between parents and teachers and the number of parents that attend these meetings, is positively correlated with school quality. Simple correlations tell us that commitment to education is not affected by the level of fractionalization, and only weakly affected by whether or not different groups live in different hamlets. We hypothesize that if strong 9 Miguel and Gugerty [2005]'s paper is closely related to ours, but their study takes place in Kenya. 10 Detailed discussion about the identification strategy can be found in Section IV. 1. 11 The results hold if we consider caste and religious polarization.

enough, commitment to education can overcome the negative effects of caste segregation. Overall, our results imply that future research will need to provide greater effort to consistently capture the magnitude of collective action.

The paper is organized as follows. Section II reviews experimental, observational and qualitative evidence on the relation between diversity, collective action and public goods provision.

Section III presents the data used as well as some descriptive statistics. Section IV presents the empirical methodology, while Section V introduces the main results. Section VI provides some robustness tests, and Section VII concludes.

II RELATED LITERATURE

As previously emphasized, a large literature has focused on the determinants of collective action and public goods provision, with overall conflicting results. We will review here two strands of the literature: the first one that uses experimental data, trying to capture the mechanisms through which diversity may undermine collective action, and the second one using observational and qualitative data.

II.1 EMPIRICAL FINDINGS USING EXPERIMENTAL DATA

The experimental literature typically tries to capture the channels through which diversity impacts collective action, and indirectly, public goods provision. Studies using observational data can only be suggestive about these channels, while running carefully designed experiments can provide strong evidence about the mechanisms at play.

In their influential paper, Habyarimana et al. [2007] found that individuals belonging to the same group cooperate more because they share the same set of norms and because norms are more easily enforced inside the group. Hence, ethnically diverse communities will find it difficult to cooperate because different groups share different sets of norms, and the enforcement of norms across groups is a tedious task. This result is also found in Bernhard et al. [2006], who run experiments in small-scale societies in Papua New Guinea, and find that egalitarian norms are more easily enforced and have a higher probability of being shared inside than across groups. Chakravarty and Fonseca [2014] show that the contribution to public goods mostly depends on who will benefit from the investment. They show that individuals from a certain ethnic group will tend to cooperate more if those who benefit are fellow members of their own group. Hence, if those who benefit from cooperation are mostly out-group members, as is often the case in ethnically diverse communities, individuals may decide not to cooperate in the first place. Finally, Waring [2012], running experiments in villages in Tamil Nadu, an Indian state, argues that ethnic dominance, more than ethnic diversity, undermines cooperation. He finds that low caste households are willing to cooperate with high caste ones, but that the latter constantly discriminate against the former ones, often using violence. This creates an inevitable breakdown in social interactions.

These findings are rather definitive. Indian villages are particularly diverse along caste and religious lines. If we believe the experimental evidence cited above, we would expect social fragmentation to have detrimental effects on cooperation and public goods provision.

II.2 EMPIRICAL FINDINGS USING OBSERVATIONAL AND QUALITATIVE DATA

Collective action is difficult to capture with observational data, as it cannot directly be observed.

Hence, researchers often proxy collective action by using fragmentation indexes, following the insights of the experimental literature described above.

Several papers have highlighted the fact that socially diverse communities fare worse in terms of public goods provision. In their seminal work, Alesina et al. [1999] found that the share of spending on education, roads and sewers and trash pickups is significantly lower in ethnically fragmented U.S. cities. They attribute this to differences in preferences across ethnic groups. Dayton-Johnson [2000], in his study of Mexican farmer-managed irrigation systems, found that social heterogeneity and economic inequality are consistently associated with a lower maintenance of irrigation systems. Miguel and Gugerty [2005] found that ethnic diversity inside communities in Kenya is associated with worse school facilities. They argue that the inability to impose social sanctions in diverse communities leads to collective action failures, and thus, lower primary school funding. Although their study is close to ours, they focus on the private provision of public goods (local school funding in Kenya depends mostly on the contributions of the community members), while in our case, the collective action has a more political dimension. In the case of India, Balasubramaniam et al. [2014] depart from the existing literature by using separate indexes for religious and caste heterogeneity instead of an aggregate one. They found that communities that are heterogeneous in terms of caste have lower access to tap water, while communities that are fragmented across religions have higher access.12 This result is important, as it stresses the necessity to separately take into account the different dimensions of diversity to avoid misleading results.

Others studies have more nuanced predictions about the impact of social heterogeneity. Miguel [2004] finds that ethnic diversity in Tanzania has no detrimental effect on public goods provision.

He attributes this finding to the policy of "nation-building" undertaken by the government after independence in the 1960s, fostering a sense of national identity and breaking existing cleavages across ethnic groups. Egel [2013] studies how tribes, the politically dominant structure in rural areas in Yemen, affect the provision of public goods. He finds that places with greater tribal heterogeneity experience a greater access to publicly provided goods, such as teachers and classrooms.

He suggests that this finding is due to political patronage, and to the importance of local influence groups in the provision of public goods. In her study of collective management of forests in Himachal Pradesh, Naidu [2009] found that at moderate levels, social diversity is associated with low collective management, but at higher levels, it is actually associated with higher collective management. 13 Finally, in her analysis of the highly ethnically diverse Indian state of Kerala, Singh [2011] notes that "exceptional levels of social development have been attained despite high fragmentation along religious and caste lines". She argues that a sense of "we-ness" is what mainly explains the level of public goods provision and social development, not merely "objective diversity".

While the early studies tended to emphasize the negative effect of social diversity on public goods provision, the more recent ones argue that diversity per se is not necessarily correlated with bad outcomes, and that researchers should be careful while measuring fragmentation. Our study follows that path, by explicitly differentiating caste, religious and economic diversity inside communities, to be able to get the clearest image about the effect of social heterogeneity on the quality of schools.

III DATA AND DESCRIPTIVE STATISTICS

We use data from the second wave of the nationally representative India Household Development Survey (IHDS-II) that covers the 2011-2012 period. The fieldwork was carried out by 20 collaborating institutions under the supervision of the National Council of Applied Economic Research (NCAER). 42.152 households are represented, and these are spread across 33 states and union territories, 384 districts, 1410 villages and 1042 urban blocks located in 276 towns and cities. Our analysis focuses on the rural part of India, that is, we only consider the 1410 villages, in 270 districts and 31 states. 14 As emphasized in the introduction, we use data directly at the school and at the village level. Detailed data are also available at the individual and household level, but these are not representative at the village level.

III.1 MEASURE OF SCHOOL QUALITY

We have detailed data on school infrastructure as well as on the learning material available for students. We have decided to focus on the latter to construct our index of school quality. Infrastructure (number of classrooms, presence of toilets, electricity, etc.) is without any doubt an important factor determining the quality of education, but we wanted our study to concentrate on the material that is directly involved in student's learning process inside the classroom. That is, we consider whether there are blackboards, writing material, chairs and desks as well as textbooks available for all students. 15 Every variable takes the value 0 or 1. The index is constructed as the simple sum of the four dummies. 16 Therefore, it can take the value of any natural number between 0 and 4. Table 1 describes the variables that we integrate in our school quality index and provides its distribution.

Almost all schools have blackboards in every classroom, but there is variation that we can exploit for the remaining variables. As already mentioned, our final sample gathers 1507 schools in 1088 villages. Table 2 shows the distribution of the types of schools in our sample. Public schools constitute the large majority of our sample. Private schools are divided along three categories: aided and recognized, recognized but not aided, and unrecognized. Government schools are mostly free.17 Private schools generally ask for registration fees, but these are negligible. 18 Government and private aided schools are similar in many respects. Pal [2010] argues that "both are entirely financed by the government and have little or no control over staffing (hiring/firing), curricula, teaching materials and budget allocation". The main difference between the two is that private aided schools are "nominally privately managed".

The remaining comprises private schools that are not funded by the government, either recognized or not, and convents.

We only consider schools that find themselves inside the villages. This of course is somewhat stringent,19 but helps us being confident about the consistency of our specification. As a robustness check, we follow Miguel and Gugerty [2005] who consider a sample of schools that are at most at 5km from the village.

III.2 MEASURE OF FRAGMENTATION

To measure the degree of fragmentation inside villages, we use the well-known ethnolinguistic fractionalization (ELF) measure. 20 As previously emphasized, we will compute indexes of fragmentation along caste and religious lines. We have detailed data on the shares of the different groups inside villages. Castes are categorized as follows: Brahmins, Forward Castes, Other Backward Classes (OBC), Schedulded Castes (SC), Schedulded Tribes (ST) and Others. Brahmins historically occupied the highest position in the Indian caste structure, even if they constituted a minority of the population. 21 They mostly were priests, teachers, and were seen as the repositories of written knowledge. This gave them privileges that they sustained through time, especially through networks. 22 Forward Castes are all the other castes that do not qualify for affirmation action policies undertaken by the government. Affirmative actions (or positive discrimination) in India are characterized by reservation measures, such as guaranteed positions in local governments, government jobs, or access to universities. These policies aim at favoring historically disadvantaged groups, which are the Schedulded Castes (SC) and the Schedulded Tribes (ST), to restore a sense of justice and equity. In the late 1980's, after a commission of inquiry, affirmative actions have also started to be directed towards 20 Following the literature on social and ethnic conflict (Esteban and Ray [1994] and Reynal-Querol [2002]), we also compute a Polarization Index (PI). Our ELF measure and the PI are highly correlated (see Appendix) and the results stay unchanged if we focus on the latter. We thus decide to focus exclusively on the ELF measure.

21 They represent no more than 5% of the overall population according to Indian Censuses. 22 See Munshi and Rosenzweig [2009].

Other Backward Classes (OBC). SCs, or Dalits, have been and still are discriminated against by higher castes. They often are called Untouchables, due to the activities they carry for living (ritually dirty and polluting jobs) which are seen as ritually impure. STs are communities that have been historically marginalized, living in forests and tribal areas. OBCs find themselves between the Forward Castes and SCs/STs in the Indian social structure. They historically are farmers living in rural areas, and are described as "socially and educationally backward classes" in the Indian constitution. Affirmative action policies thus aim at improving economical, social and political prospects for these communities.

Religions are categorized as follows: Hindus, Muslims, Christians, Sikhs, Buddhists, Jains, Tribals and Others. According to the 2011 Census of India, Hinduism is the main religion, and is practiced by around 80% of the population. Muslims account for 14% of the population. Christians, Sikhs, Buddhists, Jains and Tribals account for the small remaining part of the Indian population (less than 6%). This can be seen in Table 3.

Table 3 describes the different shares of the different groups in our sample. As expected, among caste groups, OBCs account for the major part of population in our sample, while Brahmins account for the smallest part. Indeed, as previously highlighted, OBCs are mostly farmers and live in rural areas, while Brahmins only represent a small part of the overall population and mostly live in urban areas. Among religious groups, Hindus account for around 83% of the sampled individuals, while Muslims are a little bit unrepresented, constituting less than 10% of the population in our sample. The other religions account for a negligible share.

Our measure of fragmentation is the widely used ethnolinguistic fractionalization (ELF) measure, which is closely related to the Herfindahl-Hirschmann index. It captures the probability that two individuals randomly chosen from the population belong to different groups (a value of one means that there is an infinite number of groups in the population), and is computed as follows:

ELF = 1 - n ∑ i=1 π 2 i (1)
where π i represents the population share of the ith group, with i the different caste or religious groups inside the village. This measure is supposed to capture social cohesion, as previously dis- et al. [2007], there is no universally accepted measure of cohesion inside groups or communities, and our ethnolinguistic fractionalization index is only a rough proxy for the possible complex dynamics that characterize inter-group relations. Also, it is based upon the controversial assumption that there are no interactions between the different ethnic groups. This will have to be kept in mind when we interpret our results.

Table 4 presents descriptive statistics for our indexes of fragmentation. 23 As described earlier, there is more diversity along caste than along religious lines, it is thus not surprising that the ELF measure is greater for castes than for religions. We also describe the Polarization Index (PI) we have computed, which is slightly high for castes. 24 Together, the ELF measure and the PI suggest that Indian villages are rather polarized along caste lines, which leaves the door open for potential tensions, having in mind the rather tumultuous history that characterizes the Indian society. 23 The correlation matrix can be found in the Appendix. 24 The Polarization Index (PI) is computed in the following way :

PI = 1 - n ∑ i=1 ( 0.5-π i 0.5 ) 2 π i .

III.3 OTHER CONTROLS

We include a wide range of variables that, beyond the effect of social fragmentation, could influence the quality of schools.

First, we create an index to capture the level of infrastructure inside the village. The level of infrastructure could either capture the level of development of the village, or the degree of commitment of the state in terms of public goods provision. In any case, we suspect villages having access to better infrastructure to have access to better school material. The index is computed in the same way as the school quality index. That is, we consider whether residents have access to a bus facility, whether the village is accessible by road, whether there is a telephone line, and whether 80% or more of the villagers have access to electricity. 25 All these variables take the value 0 or 1.

The index is thus the sum of the different dummies, and can take values ranging from 0 to 4, such as the school quality index. Table 5 describes these variables, and presents the distribution of the index.

As previously emphasized, India is a highly decentralized country. Gram Panchayats represent the lowest level of government. Each Gram Panchayat covers between one and five villages.

Members are elected by villagers. 26 The head of the Gram Panchayat, called Pradhan, is either directly elected by the villagers or elected by the members of the Panchayat, and elections are held every five years. The Gram Panchayat is mostly funded through government transfers, and has discretion over decisions than concern directly the village, such as the provision of public goods (such as schools, roads, health centers), the targeting of government programs, etc. Moreover, as a consequence of the affirmative action policies undertaken by the government, seats are reserved, in certain villages, to historically disadvantaged groups (SCs and STs) as well as for women. 27 It has been shown that reservation policies leading to women or SCs heading the local government have had an impact on the way the resources were spent. 28 Also, keeping in mind their historical attachment to education, 29 we may expect Brahmin Pradhans to have a preference for school provision, and thus directly influence school quality. Consequently, we need to control for the composition of the local government. Table 6 provides a detailed review of that composition in our sample. Other controls at the village level are the presence of an NGO, which can directly influence the 27 Around one half of the seats are reserved either for female contestants or for historically disadvantaged groups. 28 See for instance Chattopadhyay and Duflo [2004] and Krishnan et al. [2007]. 29 For a detailed discussion, see Banerjee et al. [2007].

quality of schools through the provision of material or personnel, population and village size, 30 distance to the district headquarters which captures the degree of remoteness of the village, and whether the different castes are mixed or live in different Hamlets. 31 Finally, we also introduce school characteristics in our specification. We control for the type of school, that is whether it is a public, private and recognized (aided or not aided), private and unrecognized or convent school. We also control for the year the school has opened. To try to capture the degree of commitment to education in the village, we also introduce the number of meetings that are yearly held between the parents and the teachers, as well as the number of parents that attend these meetings 32 . Table 7 describes these variables in detail. IV EMPIRICAL STRATEGY This section will describe our identification strategy as well as our empirical specification. The literature has encountered several endogeneity problems that we will try to manage. 30 Gathered in a Population Density variable, that can influence collective action. This information is drawn from the first wave of the IHDS-I, as population and village size (in hectares) are not available for the second wave. We make the hypothesis that there were no population shocks that affected the villages differently between the two waves, and thus consider that these measures are good proxies for their true level in 2012. That is, we have Population Density = Number of households/Hectares.

31 Most Indian villages are divided into hamlets, depending on their size. In our sample, the average number of hamlets in villages is 4. 32 Parents Attendance is divided by the number of students enrolled, that is, it takes values between 0 and 1. If Parents Attendance equals 1, all parents attend the meetings.

IV.1 IDENTIFICATION STRATEGY

First, we have to assure the exogeneity of the fragmentation index. Indeed, it may well be the case that people sort themselves to certain places especially because a certain type of public good is available there. This type of behavior is what is at the core of the Tiebout [1956] model. If the quality of schools determines the actual level of fragmentation inside the community, we would fail to infer a causal link from the fragmentation index to the quality of schools. Thankfully, there is very limited migration between states, districts, and even villages in rural India. Moreover, we have detailed information on the length of individuals' settlement inside communities. 78% of the households in our sample have resided their whole life in the same village, while only 3.25% of the interviewed have moved in the last 10 years. These statistics provide convincing evidence assuring that it is not the quality of schools that drives the level of fragmentation.

The second endogeneity problem comes from omitted variables. By using state fixed-effects, we are able to control for unobserved state specific effects. As emphasized above, local governments are mostly financed by the states. If there are unobserved characteristics influencing the way a state interacts with the different local governments, they will be captured by the fixed-effects. Also, if there is a strong state-specific level of social development, such as in Kerala, we can capture it. We also control, in our specification, for the different shares of the different groups inside the village. We do this to eliminate the possibility that the fragmentation index only captures differences in preferences for public goods between different groups. As noted by Banerjee and Somanathan [2001], it may be the case that different ethnic groups have different tastes for public goods, 33 and that the fragmentation index only picks up changes in the share of these groups. Moreover, as previously underlined, we control for the composition of the Gram Panchayat to account for differences in local governance between communities.

Finally, substitution between schools may be an issue. Indeed, it may be the case that community members substitute public schools that are of bad quality with private schools. This is indeed an issue in India, as the rise of private schools is mostly seen where government schools are failing. 34 It then wouldn't be relevant to study the quality of publicly provided schools if all 33 For instance, Brahmins are historically attached to schools and education. 34 See Muralidharan and Kremer [2006].

community members are sending their child to private schools. Moreover, the "type" of collective action needed is not the same. Private schools are club goods more than public goods, and their construction, financing and monitoring is not necessarily based upon collective action by the community, but more upon private or group specific incentives and capabilities. The dataset we work with has detailed information on both public and private schools inside villages. As described in the previous section, around 70% of the schools in our sample are public schools, while the remaining comprises private schools (recognized, unrecognized, convents). Contrary to public schools, private schools are not allocated randomly across villages. Pal [2010] has found that one of the best predictor of the presence of a private school in a village is its level of public infrastructure. 35 That is, our index of infrastructure allows us to control for the non-random allocation of private schools. We are then able to investigate the quality of both types of schools, to account for possible substitution between the two. We will also interact our fragmentation indexes with the different types of schools to account for heterogeneous effects.

IV.2 EMPIRICAL SPECIFICATION

Our dependent variable being a categorical variable, taking values ranging from 0 to 4, we run simple ordered probit regressions. We include in our specification the fragmentation index, as well as the village and school controls described above. Our first specification takes the following form:

Y i,v,s = β 0 + β 1 HHI Caste v,s + β 2 HHI Religion v,s + β 3 X v,s + β 4 Z i,v,s + φ s + ε v,s (2)
where Y i,v,s represents the index of school quality for school i, in village v, in state s. HHI are our fractionalization indexes. X v,s is a vector of village characteristics, such as the share of the different groups, the composition of the local government, the level of infrastructure, population density, whether the different groups live in different hamlets, whether there is an NGO and the distance to the district headquarters. Z i,v,s is a vector of school characteristics, which contains dummies for the type of school, the year the school has opened, the number of meetings held between parents and teachers as well as the number of parents that attend these meetings. φ s is a state fixed-effect. Errors are clustered at the level of the village to account for possible spatial correlation across schools.

Our second specification interacts the fractionalization indexes with the different types of school, to determine if social fragmentation has heterogeneous effects on school quality, depending on which type of school we focus on. This is important, because public schools are more easily accessed by the poor compared to private schools, which, as mentioned before, are not widespread and often ask for registration fees. Hence, the quality of public schools bears a larger weight in our development perspective. This second specification writes:

Y i,v,s = β 0 + β 1 HHI j v,s + β 2 HHI j v,s .Type i,v,s + β 3 Type i,v,s + β 4 X v,s + β 5 Z i,v,s + φ s + ε v,s (3)
where j ∈ {Caste, Religion} and Type ∈ {Government, Private}.36

V MAIN RESULTS

The results of our first specification (Equation 2) are displayed in two different tables, Table 8 andTable 9, for the sake of readability. 37 Both tables show the results of the same regressions. In Table 8, we display the coefficients of our fragmentation indexes, as well as the estimates of the school and village characteristics. In Table 9, we display the estimates of the group shares as well as those of the composition of the local government. 38 The results in Table 8 show that the coefficients on the caste/religious fractionalization indexes never enter significantly in our regressions. We add them one by one (Columns (1) and ( 2)), then together (Column (3)), but the coefficients both stay positive and not significantly different from zero. Rather, we find that having caste groups living in separate hamlets does have a negative and significant impact on school quality. This variable may better capture caste divisions inside communities. As already emphasized, the fractionalization index cannot take into account the probable complex dynamics that define inter-group relations. This further suggests that the ELF measure does not capture collective action in a consistent manner, at least in the context of Indian villages. 39 Population density, the level of infrastructure, the presence of an NGO as well as the distance from the district headquarters do not seem to influence the quality of schools. The non-significance of the level of infrastructure is rather surprising, as we may have expected that more developed villages have access to better schooling material. We interpret this result as providing further evidence about the discrepancy between quantity and quality. Our index of infrastructure only measures access to public infrastructure, but does not capture any dimension of quality.

Turning to school characteristics, we find that the younger the school, the worst its quality.

This probably is a byproduct of the policy of universal provision of schooling throughout the country. The objective was to provide all villages with access to schools. Our result suggests that not much attention was given to the provision of complementary material. We also find that private schools are generally of better quality. This result is not surprising, given that the rise of private schools in India is mostly due to the failure of public schools. Interestingly, only private schools in which the government does not intervene are of better quality. This suggests that private providers are more able to propose an education of quality, maybe through better monitoring, better financing or better commitment. Finally, and importantly, the number of meetings held between parents and teachers, as well as the number of parents that attend these meetings, are both positively and significantly related to school quality. 40 Simple correlations tell us that these two variables are not affected by the level of fractionalization, and only weakly affected by whether or not different groups live in different hamlets. 41 This suggests that these variables may be capturing a sense of collective action, which would here be represented by the commitment to education, and which is not (or weakly) influenced by social heterogeneity or caste segregation. Overall, we interpret these results as suggesting that while caste segregation can harm school quality by hampering collective action, this effect can be countered by a strong commitment to education inside the village. The remaining question would then be, how to enhance commitment to education in mostly poor rural settings. This is beyond the scope of our analysis. Table 9 presents the coefficients associated with the shares of the different caste groups and with the composition of the local government. 42 The results stay unchanged if we introduce each group share one by one. We see that the biggest the share of Schedulded Tribes inside the village, the better the school quality. This result is rather counter-intuitive. Indeed, as already discussed, STs are mostly living in remote places, often in forests, and far from the main business centers.

Moreover, the literature has shown that affirmative policies towards this historically disadvantaged group have had ambiguous effects, as STs often did not politically take advantage of the reservations they were entitled with. 43 All of this suggests that either villages with a higher share of STs are better targeted by the government or NGOs, or STs have started to make their voice heard. Both hypothesis can have their part of truth. Indeed, simple correlations show that NGOs tend to be more present in places where there is a high share of STs, while this is not true for any other caste group.

Secondly, we can see in Table 9 that villages in which there is a ST Pradhan or a Brahmin Pradhan fare better in terms of school quality than villages in which the Pradhan is from any other caste group, controlling for group shares. 44 Hence, different scenarios could possibly explain that unexpected result. Finally, we find that whether the Pradhan seat is reserved for women or for SC/ST does not influence the quality of schools.

Table 10 describes the results of our second specification (Equation (3)), which interacts our indexes of fractionalization with the type of school to assess if there are any heterogeneous effects. All the other controls are included, but are not shown for simplicity. We find that for caste fractionalization, there are no heterogeneous effects across the different types of schools: the effect of caste fractionalization on school quality always stays insignificant. However, for religious fractionalization, the effects differ depending on which type of school we focus on. Indeed, we do find that compared to public schools, private schools' quality is positively affected by religious fractionalization. Again, the effect comes out only for private schools in which the government does not intervene. This may indicate the presence of political patronage, as emphasized by Egel [2013], where more heterogeneous communities have better access to club goods which are partly excludable.

VI ROBUSTNESS CHECKS

Our results imply that neither caste nor religious fractionalization influence school quality. Rather, we find that caste segregation across hamlets does negatively impact school quality. We interpreted this as evidence that the fractionalization indexes can not capture the extent of collective action in a consistent way. Moreover, we find that commitment to education, measured as the number of meetings held between the parents and the teachers and the number of parents that attend these meetings, is positively correlated with school quality. We hypothesized that if strong enough, commitment to education can counteract and overcome the negative effects of caste segregation.

We, for now, focused on the sample of schools that find themselves inside the villages. By restricting the sample this way, we drop a non negligible amount of villages. 45 This could create concerns about selection, even if, as previously emphasized, the villages that are dropped do not significantly differ from the ones that stay in the sample. To expand the sample and relieve some concerns about selection, we, in a first step, consider schools that find themselves at most at 5km from the village, following Miguel and Gugerty [2005].

Moreover, one could ask whether commitment to education is only strong in places where individuals are educated. We could not directly test for that assumption, as the household data is not representative at the village level. As a robustness check, we still use these data to compute the average level of education in every community, which we integrate in our specification to assess if it trumps the effect of the two other variables. 46 These results should only be indicative, as they do not fully represent the level of education inside villages in our sample, but only proxy for it.

One could also ask whether the results would still hold if we introduced between group economic inequality, proxied by land inequality, into our specification. Indeed, we haven't, for now, dealt with that question. The reason is that the data we work with does not allow us to get detailed information on the shares of land of the different groups for all villages. We only have complete data on land for 35% of the villages in our sample. As a robustness check, we have decided to keep all the villages for which we have at least 70% of the shares of land recorded. This constitutes 88% 45 142 villages are dropped if we use the sample of schools inside the village. With the larger sample, we only loose 79 villages. These villages are not different from the ones that we keep, if we consider school quality (see Table 13 in the Appendix). 46 The average level of education in our sample is 7 years, with a standard deviation of 2.5 years.

of the previous sample. We construct a Gini index of land inequality, computed in the following way:

GI = |1 - n ∑ i=1 (X k -X k-1 )(Y k +Y k-1 )| (4)
which is a way of approximating the Gini coefficient when the entire Lorenz curve is not known.

X k is the cumulated proportion of the population variable, for k = 0, ..., n, with X 0 = 0 and X 1 = 1.

Y k is the cumulated proportion of the land variable, for k = 0, ..., n, with Y 0 = 0 and Y 1 = 1. Y k and X k are indexed in non-decreasing order. We have, however, to keep in mind that there are issues of selection, as the villages which are dropped here do differ from the ones that stay in the sample. 47 Hence, the estimations using the Gini index should only be suggestive.

Finally, one could worry that the non-significant relationship between our indexes of fractionalization and school quality is driven by omitted variables. In our case, the omitted bias would be positive, driving up school quality as well as the effect of fractionalization. One possible omitted variable could be land quality. It could be the case that villages in which social fragmentation is higher enjoy better land quality, which allows them to reap higher agricultural yields and invest more in education. To control for that, we introduce the wage rate of the low-skilled individuals working in agriculture. 48 We suggest that different wage rates can proxy for different agricultural yields. This variable, we hope, can then control for differences in land quality across villages.

We also control for whether schools have received any private donation in the year preceding the survey, to account for the possibility that the quality of schools is only driven by private, and not public provision.

The results of all these estimations can be found in Table 11. All the variables (except our fragmentation indexes) that stay statistically insignificant in all the regressions are not shown. 49 The first column presents the results of our regressions using the sample of schools at most at 5km from the village. This increases our number of observations from 1507 to 1898. We can see that the Hamlets, which becomes insignificant. We interpret this as evidence that caste segregation is less harming when the schools do not find themselves directly in the village. Indeed, if the school is built inside the village, it has to be located in one of the caste neighborhoods. If divisions across castes are strong, the involvement of the community to improve the quality of the school may be low. If the school does not reside in any caste neighborhood, tensions may be lower and a compromise could be found more easily.

In the second column, we introduce a variable that captures the average level of education in the village. The motivation is to assess if what we have interpreted as commitment to education only depends on the average level of education inside the village. In other terms, we want to test whether the number of meetings held as well as the number of parents that attend these meetings is only driven by educational attainment. If this is true, the introduction of the Education variable should trump the effect of the other two variables. This is not what we find. Education is positive and significantly correlated with school quality, while Parents Attendance and Number Meetings both stay positive and significant. We interpret this result as suggesting that commitment to education is not restricted to areas in which there is a high educational attainment, but can also be found in settings in which the latter is low. We also find that introducing the Education variable leads the coefficient on NGO to become statistically and significantly negative, while the same is true for Pradhan ST. This result is somewhat surprising, knowing that NGOs often provide material for schools and intervene to mitigate government failures. This should be scrutinized in further research.

The third column introduces the Gini index. This result should be taken with caution, as we face problems of selection. While the coefficients on the variables related to the commitment to education stay unchanged and are statistically precisely measured, the Gini index does not enter significantly in the regression. Moreover, the coefficient on Separate Hamlets becomes insignificant. This is due to the sample selection. Indeed, whether we introduce the Gini index or not, the coefficient on Separate Hamlets stays insignificant. This is why this specification is only suggestive about the effect of income inequality, and should not be considered as statistically robust.

Column 4 presents the results of our specification when we introduce the level of wage of unskilled workers working in agriculture, and whether there has been any private donation made to the school in the year preceding the survey. The former variable is supposed to capture land quality, as better quality of land should yield higher earnings. We show that this variable does not enter significantly in our specification, leaving all other coefficients unchanged. We argue that this implies that the non-significant relationship between our fractionalization indexes and school quality is not due to omitted variables. Moreover, the coefficient on Private Donation is not significant, implying that school quality is not driven by idiosyncratic and context-specific private provision of public goods.

VII CONCLUSION

The process of decentralization undertaken by the Indian central government, through the 73rd and 74th Constitutional Amendment in 1992 and 1993, has provided increasing power to local governments, called Gram Panchayats, which operate at the village level. While local governments have discretion on decisions such as the provision of public goods or the targeting of governmental programs, the decentralization process has not been accompanied by fiscal devolution. That is, local governments still largely depend on central funds provided by the states. In this context, local governments need to have privileged relationships with central states in order to benefit from transfers, since these transfers are "not transparent and probably quite complex" (Banerjee and Somanathan [2001]). Hence, to be heard by the states, local governments need to have strong political voices. It has been argued in the literature that a strong predictor of the capacity of local governments to voice their claims is the extent of collective action inside communities. A local government backed by a strong civil society will face a higher probability of getting transfers from the central state.

A large literature has studied the relationship between collective action and public goods provision, most often proxying collective action by social fragmentation. It has been argued that socially diverse communities would find it difficult to cooperate, because different groups share different sets of norms, and the enforcement of norms across groups is a tedious task. Social fragmentation, usually measured using indexes of fractionalization, has been found to harm public goods provision, but this relationship also depends on historical, political and cultural factors.

While the literature mostly focused on access to public goods, which has dramatically increased in the developing world in the recent years, little or no attention has been paid on the relationship between social fragmentation and the quality of public goods, especially in the Indian context. Our study aims at filling this gap, by investigating whether social fragmentation in Indian villages can influence school quality.

We construct caste and religious fractionalization indexes at the village level. We also use school facilities data to construct our index of quality. The latter depends on the presence of blackboards, chairs and desks, writing material as well as textbooks for all students. We wanted our study to concentrate on the material that is directly involved in student's learning process inside the classroom. Our results suggest that neither caste nor religious fractionalization significantly affect school quality. Rather, we find that caste segregation, measured as whether caste groups live in separate neighborhoods, and commitment to education, measured as the number of meetings held between parents and teachers as well as the number of parents that attend these meetings, are negatively and positively correlated with the quality of schools, respectively. While caste segregation tends to harm school quality, commitment to education enhances it. We interpret our results as suggesting that if strong enough, commitment to education can counteract and overcome the negative effects of caste segregation. Our estimations are robust to the inclusion of the average level of education inside villages, land quality, private donations or a proxy for economic inequality.

The constant statistical insignificance of our fractionalization indexes implies that they cannot consistently capture the extent of collective action inside communities. Indeed, we argue that these indexes cannot account for complex inter-group dynamics, and that their relevance as proxies for collective action may be limited. Future research will need more accurate estimations to better capture collective action, its determinants, and its scope. Moreover, our results suggest that commitment to education inside communities is a strong predictor of the quality of schools. We are limited by the dataset we work with, but future research should investigate the main drivers of commitment to education, as well as try to determine by which means it can be enhanced in poor rural settings.

  Findings Using Experimental Data . . . . . . . . . . . . . . . . . . . . II.2 Empirical Findings Using Observational and Qualitative Data . . . . . . . . . . . . III Data and Descriptive Statistics III.1 Measure of School Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III.2 Measure of Fragmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III.3 Other Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV Empirical Strategy IV.1 Identification Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV.2 Empirical Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Table 1 :

 1 School Quality

		Mean SD Min Max Sum
	Blackboards	.99	.11	0	1
	Writing Material	.85	.36	0	1
	Chairs and Desks	.34	.48	0	1
	Textbooks	.86	.35	0	1
	School Quality = 0 .002 .05	0	1	3
	School Quality = 1	.06	.25	0	1	97
	School Quality = 2	.11	.31	0	1	167
	School Quality = 3	.54	.50	0	1	813
	School Quality = 4	.28	.45	0	1	427
	School Quality	3.04 .82	0	4	1507
	Observations	1507

Table 2 :

 2 Types of School

		Sum Frequency
	Government	1040	69
	Private, aided and recognized	245	16.3
	Private, recognized, not aided 107	7.1
	Private and unrecognized	105	6.9
	Convent	10	.7
	Total	1507	100

Table 3 :

 3 Caste and Religious Shares

		Mean SD Min Max
	Share of Brahmins	.06	.12	0	.98
	Share of Forward Castes	.19	.24	0	1
	Share of OBCs	.42	.29	0	1
	Share of SCs	.22	.18	0	1
	Share of STs	.10	.22	0	1
	Share of Other Castes	.01	.08	0	.99
	Share of Hindus	.83	.28	0	1
	Share of Muslims	.09	.20	0	1
	Share of Christians	.03	.12	0	1
	Share of Sikhs	.03	.15	0	1
	Share of Buddhists	.01	.04	0	.65
	Share of Jains	.001 .008	0	.25
	Share of Tribals	.003	.02	0	.35
	Share of Other Religions .002	.03	0	.99
	Observations	1088

cussed in our review of the literature on diversity and collective action. But as noted by Banerjee

Table 4 :

 4 Indexes of Fragmentation

		Mean SD Min Max
	Caste Fractionalization	.49	.19	0	.81
	Religious Fractionalization	.13	.17	0	.73
	Caste Polarization	.71	.22	0	1
	Religious Polarization	.24	.31	0	1
	Observations	1088

Table 5 :

 5 Index of Infrastructure

		Mean SD Min Max Sum
	Coverage > 80%	.69	.46	0	1
	Road Accessibility	.99	.11	0	1
	Bus Facility	.62	.48	0	1
	Telephone Line	.65	.48	0	1
	Infrastructure = 0	.003	.06	0	1	4
	Infrastructure = 1	.11	.31	0	1	121
	Infrastructure = 2	.22	.41	0	1	240
	Infrastructure = 3	.25	.44	0	1	279
	Infrastructure = 4	.41	.49	0	1	444
	Infrastructure	2.95 1.05	0	4	1088
	Observations	1088

Table 6 :

 6 Local Government Composition

	Reserved seats	Sum Frequency
	No reservation	549	50.5
	For women	316	29
	For SCs or STs	223	20.5
	Total	1088	100
	Pradhan's caste
	Brahmin	48	4.4
	Forward	240	22.1
	OBC	435	40
	SC	196	18
	ST	162	14.9
	Other	7	.6
	Total	1088	100

Table 7 :

 7 Village and School Controls

	Village controls	Mean SD	Min Max
	NGO (No = 0 / Yes = 1)	.13	.34	0	1
	Population Density	1.4	3.3	.03	88.5
	Distance Headquarters	45.3 31.4	2	290
	Hamlets (Mixed = 0 / Separate = 1)	.64	.48	0	1
	Observations	1088
	School controls
	Year Open	1970	31	1840 2012
	Number Meetings	6.9	4.3	0	50
	Parents Attendance	.35	.25	0	1
	Observations	1507

Table 8 :

 8 Ordered Probit Regressions with School and Village Controls Note: the reference dummy for school type is Government.

		(1)	(2)	(3)
	VARIABLES	School Quality School Quality School Quality
	Caste Fractionalization	0.136		0.0841
		(0.198)		(0.200)
	Religious Fractionalization		0.241	0.223
			(0.207)	(0.209)
	Separate Hamlets	-0.130*	-0.124*	-0.125*
		(0.071)	(0.069)	(0.070)
	Population Density	0.001	0.001	0.001
		(0.008)	(0.008)	(0.008)
	Infrastructure	-0.025	-0.023	-0.025
		(0.040)	(0.040)	(0.040)
	NGO (No = 0 / Yes = 1)	-0.136	-0.135	-0.137
		(0.099)	(0.099)	(0.099)
	Distance Headquarters	0.001	0.001	0.001
		(0.001)	(0.001)	(0.001)
	Year Open	-0.003**	-0.003**	-0.003**
		(0.001)	(0.001)	(0.001)
	Parents Attendance	0.303**	0.309**	0.310**
		(0.142)	(0.141)	(0.141)
	Number Meetings	0.018**	0.019**	0.019**
		(0.008)	(0.008)	(0.008)
	School Type
	Private, aided and recognized	0.130	0.131	0.126
		(0.102)	(0.102)	(0.102)
	Private, recognized, not aided	0.343**	0.346**	0.342**
		(0.151)	(0.150)	(0.151)
	Private, unrecognized	0.323**	0.322**	0.319**
		(0.137)	(0.136)	(0.137)
	Convent	0.227	0.233	0.227
		(0.548)	(0.550)	(0.550)
	Observations	1,507	1,507	1,507
	Pseudo R 2	0.13	0.13	0.13
	Wald χ2	966.58	966.25	967.47
	State FE	YES	YES	YES
	Control for group shares	YES	YES	YES
	Control for local government	YES	YES	YES
	Robust standard errors in parentheses
	*** p<0.01, ** p<0.05, * p<0.1

Table 9 :

 9 Ordered Probit Regressions with Group Shares and Local Government Composition No Reservation and Pradhan Brahmin.

		(1)	(2)	(3)
	VARIABLES	School Quality School Quality School Quality
	Caste Fractionalization	0.136		0.0841
		(0.198)		(0.200)
	Religious Fractionalization		0.241	0.223
			(0.207)	(0.209)
	Share Brahmin	0.380	0.459	0.417
		(0.361)	(0.341)	(0.362)
	Share Forward	0.063	0.089	0.073
		(0.181)	(0.179)	(0.181)
	Share SC	-0.037	0.019	-0.016
		(0.229)	(0.216)	(0.230)
	Share ST	0.383**	0.405**	0.398**
		(0.177)	(0.176)	(0.177)
	Reservation Status
	Reserved Women	-0.001	-0.002	-0.002
		(0.073)	(0.073)	(0.073)
	Reserved SC/ST	-0.029	-0.019	-0.020
		(0.116)	(0.116)	(0.116)
	Pradhan Caste
	Pradhan Forward	-0.536***	-0.536***	-0.537***
		(0.168)	(0.168)	(0.168)
	Pradhan OBC	-0.354**	-0.363**	-0.362**
		(0.168)	(0.167)	(0.168)
	Pradhan SC	-0.378**	-0.389**	-0.387**
		(0.190)	(0.190)	(0.190)
	Pradhan ST	-0.274	-0.295	-0.294
		(0.192)	(0.192)	(0.192)
	Pradhan Other	-0.061	-0.058	-0.062
		(0.444)	(0.437)	(0.437)
	Observations	1,507	1,507	1,507
	Pseudo R 2	0.13	0.13	0.13
	Wald χ2	966.58	966.25	967.47
	State FE	YES	YES	YES
	Control for group shares	YES	YES	YES
	Control for local government	YES	YES	YES
	Robust standard errors in parentheses
	*** p<0.01, ** p<0.05, * p<0.1
	Reference dummies:

Table 10 :

 10 Ordered Probit Regressions with Interaction Terms Note: the reference dummy for school type is Government.

	(1)

Table 11 :

 11 Ordered Probit Regressions with School Quality as Dependent Variable Note: the reference dummy is Pradhan Brahmin. coefficients on Parents Attendance and Number Meetings stay positive and are statistically more precisely estimated than previously. The other coefficients stay unchanged, except for Separate

		(1)	(2)	(3)	(4)
	VARIABLES	Schools <= 5km With Education Gini Index With Wages
	Caste Fractionalization	0.011	0.013	0.0001	0.081
		(0.177)	(0.199)	(0.213)	(0.200)
	Religious Fractionalization	0.146	0.270	0.079	0.258
		(0.188)	(0.205)	(0.206)	(0.209)
	Separate Hamlets	-0.096	-0.125*	-0.068	-0.118*
		(0.063)	(0.068)	(0.072)	(0.070)
	NGO (No = 0 / Yes = 1)	-0.115	-0.198**	-0.0447	-0.142
		(0.090)	(0.098)	(0.109)	(0.099)
	Year Open	-0.003**	-0.003**	-0.003**	-0.003**
		(0.001)	(0.001)	(0.001)	(0.001)
	Parents Attendance	0.398***	0.293**	0.374***	0.302**
		(0.123)	(0.142)	(0.143)	(0.141)
	Number Meetings	0.022***	0.018**	0.022***	0.019**
		(0.007)	(0.008)	(0.009)	(0.008)
	Education		0.033**
			(0.016)
	Gini Index			-0.108
				(0.227)
	Unskilled Wage				0.0007
					(0.0005)
	Private Donation				0.039
					(0.048)
	Pradhan Caste
	Pradhan Forward	-0.458***	-0.600***	-0.520***	-0.535***
		(0.148)	(0.170)	(0.179)	(0.167)
	Pradhan OBC	-0.269*	-0.408**	-0.346*	-0.371**
		(0.145)	(0.166)	(0.177)	(0.167)
	Pradhan SC	-0.288*	-0.460**	-0.283	-0.382**
		(0.162)	(0.186)	(0.197)	(0.190)
	Pradhan ST	-0.241	-0.370*	-0.290	-0.297
		(0.168)	(0.195)	(0.209)	(0.191)
	Pradhan Other	-0.301	-0.157	-0.171	-0.086
		(0.432)	(0.426)	(0.475)	(0.429)
	Observations	1,898	1,445	1,393	1,503
	Pseudo R 2	0.132	0.125	0.143	0.130
	Wald χ2	1068.76	932.90	951.02	967.85
	State FE	YES	YES	YES	YES
	Other controls included	YES	YES	YES	YES
		Robust standard errors in parentheses
		*** p<0.01, ** p<0.05, * p<0.1

Table 14 :

 14 Correlation MatrixCaste.Frac Relig.Frac Caste.Polar Relig.Polar Gini Index p < 0.05, * * p < 0.01, * * * p < 0.001

	Caste.Frac Relig.Frac Caste.Polar Relig.Polar Gini Index	1 0.197 * * * 0.846 * * * 0.192 * * * 0.325 * * *	1 0.168 * * * 0.988 * * * 0.111 * * *	1 0.167 * * * 0.271 * * *	1 0.106 * * *	1

*

Table 15 :

 15 Ordered Probit Marginal Effects (1/4)

		(1)	(2)	(3)	(4)	(5)	(6)	(7)
	VARIABLES Cast.Frac. Relig.Frac. Sh.Brahm. Sh.For.	Sh.SC.	Sh.ST	Sep.Ham.
	Predict: SQ=0	-0.0005	-0.0014	-0.0026	-0.0005 9.62e-05	-0.0025	0.001
		(0.0013)	(0.0015)	(0.0026)	(0.001)	(0.001)	(0.0016)	(0.001)
	Predict: SQ=1	-0.009	-0.0242	-0.0453	-0.0079	0.0017	-0.0432**	0.0136*
		(0.0217)	(0.0227)	(0.0392)	(0.0197) (0.0250)	(0.0197)	(0.008)
	Predict: SQ=2	-0.009	-0.0235	-0.0440	-0.008	0.0016	-0.0420**	0.0132*
		(0.0211)	(0.0221)	(0.0384)	(0.0192) (0.0243)	(0.0188)	(0.007)
	Predict: SQ=3	-0.0048	-0.013	-0.024	-0.0042	0.001	-0.023**	0.0072*
		(0.0114)	(0.0123)	(0.0212)	(0.0104) (0.0132)	(0.0108)	(0.0042)
	Predict: SQ=4	0.0234	0.0618	0.116	0.0202	-0.004	0.111**	-0.0348*
		(0.0554)	(0.0582)	(0.100)	(0.0504)	(0.064)	(0.0493)	(0.0194)
	Observations	1,507	1,507	1,507	1,507	1,507	1,507	1,507
			Standard errors in parentheses
			*** p<0.01, ** p<0.05, * p<0.1

Table 16 :

 16 Ordered Probit Marginal Effects (2/4)

		(8)	(9)	(10)	(11)	(12)	(13)	(14)
	VARIABLES Res.Women Res.SC/ST	Pra.For.	Pra.OBC	Pra.SC	Pra.ST	Pra.Oth.
	Predict: SQ=0	1.73e-05	0.0001	0.003*	0.0014*	0.002	0.001	0.00015
		(0.0005)	(0.0007)	(0.0015)	(0.0008)	(0.001)	(0.0008)	(0.001)
	Predict: SQ=1	0.0003	0.0022	.0518***	0.052***	0.031**	0.034**	.00428
		(0.008)	(0.0127)	(0.0141)	(0.0121)	(0.0153) (0.0147) (0.0312)
	Predict: SQ=2	0.0003	0.0021	0.0544*** 0.0359** 0.0385**	0.0288	0.006
		(0.008)	(0.0123)	(0.0162)	(0.0157)	(0.0183) (0.0183) (0.0410)
	Predict: SQ=3	0.0002	0.001	0.0456*	0.0395	0.0409	0.0348	0.009
		(0.0043)	(0.0064)	(.0246)	(0.0246)	(0.0248) (0.0255) (0.0264)
	Predict: SQ=4	-0.001	-0.0055	-.155	-0.155*** -0.108** -0.115**	-.0193
		(0.0203)	(0.0321)	(0.0510)	(0.0519)	(0.0577) (0.0589)	(0.135)
	Observations	1,507	1,507	1,507	1,507	1,507	1,507	1,507
			Standard errors in parentheses
			*** p<0.01, ** p<0.05, * p<0.1

Table 17 :

 17 Ordered Probit Marginal Effects (3/4) Open Priv.Aid. Priv.Unaid. Priv.Unrec. Convent Pop.Dens. Infra.

		(15)	(16)	(17)	(18)	(19)	(20)	(21)
	VARIABLES Year.Predict: SQ=0 1.60e-05	-0.001	-0.002	-0.002*	-0.001	-2.42e-06	0.0002
		(1.08e-05)	(0.001)	(0.0011)	(0.001)	(0.0023) (5.10e-05) (0.0003)
	Predict: SQ=1 0.0003**	-0.0139	-0.033***	-0.031***	-0.024	-4.25e-05	0.003
		(0.0001)	(0.0107)	(0.0124)	(0.0116)	(0.0498)	(0.001)	(0.0044)
	Predict: SQ=2 0.0003**	-0.013	-0.035**	-0.033**	-0.0235	-4.13e-05	0.003
		(0.0001)	(0.011)	(0.0145)	(0.0134)	(0.0552)	(0.001)	(0.004)
	Predict: SQ=3 0.0002**	-0.007	-0.029	-0.026	-0.0157	-2.24e-05	0.002
		(7.49e-05)	(0.007)	(0.0189)	(0.0161)	(0.0552)	(0.0004)	(0.0023)
	Predict: SQ=4	-0.001**	0.0349	0.099**	0.092**	0.064	0.0001	-0.007
		(0.0003)	(0.029)	(0.046)	(0.041)	(0.162)	(0.0023)	(0.01)
	Observations	1,507	1,507	1,507	1,507	1,507	1,507	1,507
			Standard errors in parentheses
			*** p<0.01, ** p<0.05, * p<0.1

Table 18 :

 18 Ordered Probit Marginal Effects (4/4)

		(22)	(23)	(24)	(25)
	VARIABLES	NGO	Dist.Headqu. Parents.Att.	N.Meet.
	Predict: SQ=0	0.0008	-8.67e-07	-0.0019	-0.0001
		(0.0007)	(6.42e-06)	(0.0013)	(7.71e-05)
	Predict: SQ=1	0.0148	-1.52e-05	-0.0337**	-0.002**
		(0.0108)	(0.0001)	(0.0157)	(0.0009)
	Predict: SQ=2	0.0144	-1.48e-05	-0.0327**	-0.002**
		(0.0105)	(0.00011)	(0.0148)	(0.0008)
	Predict: SQ=3	0.0078	-8.02e-06	-0.0177**	-0.0011**
		(0.0059)	(5.93e-05)	(0.0086)	(0.0005)
	Predict: SQ=4 -0.0380	3.89e-05	0.0861**	0.0052**
		(0.0276)	(0.0003)	(0.0391)	(0.002)
	Observations	1,507	1,507	1,507	1,507
		Standard errors in parentheses
		*** p<0.01, ** p<0.05, * p<0.1

See for instance[START_REF] Duflo | Schooling and labor market consequences of school construction in indonesia: Evidence from an unusual policy experiment[END_REF] and[START_REF] Heckman | The economics of inequality: The value of early childhood education[END_REF].

I will use the words village and community interchangeably.

The world average net enrolment ratio in primary schooling was 72% in 1970, while it reached more than 89% in 2012. Source: UNESCO.

Source: UNESCO.

They argue that caste stratification inside communities deters cooperation, and thus, access to tap water, while religiously diverse communities have higher access because they are targeted by the government to avoid possible conflicts.

She argues that high levels of diversity makes it difficult for any caste group to capture power or dominate other caste groups, therefore potentially increasing interactions, communication and trust across individuals from different castes. She also found that moderate wealth heterogeneity is actually beneficial in terms of collective management.

Due to data misreporting, our final sample comprises

schools, in 1088 villages, 243 districts and 28 states. We discuss issues of selection further below, but simple comparisons of means show that the observations that are dropped are not significantly different from the ones that stay in our sample. See Table12and Table13in the Appendix.15 We also consider an index in which we add whether there are computers available for all students, and whether the class is indoors. The results stay unchanged.16 We thus consider that the four variables have an equal weight in our school quality index.

96% of the public schools in our sample do not ask for a registration fee.

The average registration fee in our sample is around 360 rupees (≈ 6 US$), which represents approximately two days of labour in rural areas.

We lose 142 villages if we restrict our sample to schools inside villages, these villages having no school built inside. But a simple test of equality of means shows that the villages that we lose are not significantly different from those that remain in the sample, considering the level of infrastructure (See Table12in the Appendix). If we consider the sample with schools that are at most at 5km from the village, we only loose 79 villages, and the remaining sample comprises 1898 schools.

The results stay unchanged if we consider different thresholds, as well as if we introduce either variable on its own in the specification.

The number of members depends upon the village population. There is usually between 7 and 17 members.

This is mostly because good public infrastructure decreases the marginal costs of building a new school.

Private contains: Private aided and recognized, Private recognized but not aided, Private unrecognized, and Convent.

 37 The marginal effects are displayed in the

Appendix. 38 The interpretation of the coefficients should be made in the following way: for a one unit increase in the predictor, the response variable level is expected to change by its respective regression coefficient in the ordered log-odds scale while the other variables in the model are held constant.

[START_REF] Bros | Social fragmentation and public goods: polarization, inequality and patronage in uttar pradesh and bihar[END_REF] finds that the fractionalization index has a positive impact on public goods provision. She attributes this finding to caste patronage.

When we interact these two variables with the different types of school, we find that parents' attendance only impacts the quality of private schools, while the number of meetings only affects the quality of public schools (Results not shown). This is somewhat surprising and is left for future research.

The correlation between Number Meetings and Separate Hamlets is 0.05 and significant, while the correlation between Parents Attendance and Separate Hamlets is -0.06 and significant.

We decided to focus on the different caste groups rather the religious groups, as in the latter case, the variation is rather low. Also, the share of OBCs is removed from the specification, due to multicollinearity concerns. The mean VIF without Share OBC is 1.12, while it is 4.09 if we introduce the latter.

See Krishnan et al. [2007].

It is not surprising to find that villages headed by a Brahmin Pradhan have access to better school quality, given their known preference for schooling.

The villages that are dropped have overall slightly worse school quality, are more diverse in terms of caste, are less divided across hamlets, and schools tend to be more private. To relieve the extent of selection, we only consider the sample of schools that are at most at 5km from the village.

The mean unskilled wage rate is 178 Indian Rupees, with a standard deviation of 70.

These comprise Infrastructure, Reserved Seat, Distance Headquarters and Population Density. The coefficients on the shares of the different groups and on the differents types of school stay unchanged compared to the baseline regression and are not shown for the sake of readability.

Table 12 shows simple comparisons of means between the villages that we keep in our sample (1257), and the ones that are dropped because there is no school built inside (142). We can see that these villages do not statistically differ in terms of level of infrastructure, nor in terms of religious fractionalization. However, they do differ in terms of caste fractionalization: the villages that are dropped are on average less diverse in terms of caste. If the villages that are dropped were more fragmented in terms of caste, we could be worried about issues of selection, since the literature has found that more diverse communities tended to have less access to school facilities. But we find the opposite. We interpret this as evidence that the policy of universalization of schooling undertaken by the government does not target more homogeneous communities first, and that the latter do not face a greater probability of having a school built inside the village. If we take the sample of schools built at most at 5km from the village, we only lose 79 villages, but the differences in means are about the same magnitude than above. Table 13 shows the comparison of means between our index of school quality, for the observations in our final sample (1507), and the ones that are dropped during the Ordered Probit estimations, because of missing values for the different variables that we use (203). We can see that the quality of the schools that we keep is on average not statistically different from the quality of the schools that are dropped from the estimation.


H Delebecque Supélec
email: henri.delebecque@supelec.fr

OPTIM: PEDAGOGICAL INTERACTIVE DOCUMENT DESIGN WITH MINIMAL PROGRAMMING

In this paper, we propose an open framework for teachers and lecturers in science, to help them write their pedagogical documents using both static textual subject matter, and interactive and multimedia content. Authors can focus their attention on their pedagogical goals, leaving all aspects (static or dynamic) of the GUI generation to our OPTIM engine. This framework will also allow them to factorize the structure common to all their documents into classes, and will free them from all other design considerations. Moreover, OPTIM allows authors to insert interactive parts into their documents without any GUI programming skills. They only have to provide the computational code that supplies the numerical values to be displayed by the GUI, and they can use whatever programming language they find the most convenient.

INTRODUCTION

We propose a general authoring framework, called OPTIM, which uses HDML [START_REF] Delebecque | HDML a lightweight authoring metalanguage with object-oriented features IADIS-WWW/Internet[END_REF], a metalanguage more suited to the definition of pedagogical documents than XML. Although one advantage of XML is the great amount of flexibility its tags offer, this flexibility also results in a great variety of semantics given to tags by the various authoring languages based on XML, and a lack of a standard in the pedagogical domain. On the contrary, the other extreme is a general authoring language like DocBook© which has its own drawbacks, since it defines only very general structural entities, like header, footer, body.

We want to help content authors, not always familiar with programming languages, and even less familiar with XML, to write their concrete documents, by defining an authoring language that will use only their domain specific terms. For example, a mathematician will greatly appreciate finding tags named «theorem», «premises», «conclusion», «proof» in the authoring language he/she uses. This is something that people have to code manually if they use languages like Latex.

Moreover, OPTIM will allow authors to insert interactive objects into their pedagogical documents. This is of great value in the pedagogical context, where students and learners can visualize the concepts described in an alternate graphical manner, while keeping an additional textual description.

The pedagogical domain helps us in our design, since the variety of graphical objects found in application is limited, as is the kind of interaction the user has with them. The purely graphical part of pedagogical software (even simulators) is commonly restricted to curves, animated schematics or graphics, that require minimal user interaction.

OPTIM Principles

The OPTIM acronym means «Open Platform for Teaching Interactively with Multimedia». OPTIM's goal is to give teachers a framework that allow them to focus on contents (in all its richness), to use their favorite editing tool, and to ignore presentation issues. This last point contributes to the openness, a key feature in the pedagogical world, where languages and tools are frequently specific. OPTIM uses HDML as a structuring language for the description of the document's static part.

Authors can add interactive parts to their documents using first OPTIM's GUI generator, and then its ability to link this GUI with the specific code the teacher supplies for the computation of the information displayed as curves or dynamic schematics.

To help teachers that contribute to a global pedagogical document to write their sections/chapters/books in a rather uniform manner, the editor of the whole document defines a structural model. This model defines all the mandatory or optional elements authors have to put into their documents. HDML is clearly a metalanguage, since this model is also used to define a markup language that we will call the content authoring language . This language defines tag names that are familiar to the content authors, since they are defined by the editor, using the words and terms authors currently use.

We will start with some definitions, to help us present OPTIM's general principles. For more clarity, lets define the following terms in the context of a large pedagogical document, which we will generalize below under the glo- bal document terms.

Definitions

HDML is used by the three different kinds of authors described in the next paragraph, while defining four kinds of documents. These are:

• The class (the structural model),

• The presentation model, • The concrete document (a class instance).

• The interactive objects (OPTIM objects)

During the meta-authoring phase, the class designer, who is also the global pedagogical document editor, defines the structural model common to all the pedagogical sub-documents that share either a common look or structure, or both. Since this class defines all the elements that can be displayed, a second author, with graphical skills, will then design a presentation model, that will explain how to display all the elements described by the class for a particular medium. He will also help the teacher in designing the look-and-feel of the GUI of all interactive parts, defined as OPTIM objects.

The HDML engine works then in three phases, using the four different kinds of documents described above.

At the end of the meta-authoring phase, the HDML engine generates a XSLT style-sheet for each pair of models, building a projection function from the class to the presentation medium. Moreover the HDML engine produces the grammar of the authoring language, which will be used by the content author in the authoring phase.

During this phase, the teachers will write the concrete document. They can use any editing tools they want, including tools that are not XML compliant, though obviously the great advantage of using XML compliant editing tools is the syntax sensitive help they can provide.

The pre-publishing phase allows the HDML engine to produce documents ready to be published. Contents documents, which combine semantically-rich items (supplied by content authors), and other items dedicated to presentation purposes like a logo or background (provided by the presentation model) are transformed using projection functions.

During the final phase, the documents are published. Authors can activate the new functionality OPTIM adds to HDML, and add interactively to their static documents. OPTIM objects are referenced by HDML documents, and activated at publishing time by the student. This activation will produce events that are filtered by the GUI defined in the OPTIM description file, and compiled as Java© objects by the OPTIM compiler.

All events are either completely managed by the Java code, or transformed into requests sent to the code that the content author supplied, to achieve the computation required. This code can, for example, compute a new 3D-curve to be displayed, the new state of a data path into a processor, or some animation, sound, etc. This method of separating purely graphical events (such as clicking, or filling in an entry field) follows the distinction Java makes between high-level "action" events, and low-level ones, like mouseUp or others.

OPTIM Architecture

Every interactive object included in a concrete document is described in two complementary ways. First, the graphical designer has to explain how the graphical sub-objects will answer to end-user stimuli (click, keyboard events,…). And second, the computation code should be able to send refresh orders to the GUI, when new data arrives. These two aspects are managed by a classical MVC architecture evangelized by Java. This very efficient and robust way to separate the Model (in charge of all computational tasks) and the View (that displays the values in the most convenient way for a given context or user) has proved its value since its first definition in the Smalltalk [START_REF] Goldberg | David Robson Smalltalk-80: The language and its implementation[END_REF] object-oriented language.

STRUCTURING ELEMENTS

The class designer builds the class using either generic blocks and objects or semantically qualified elements, such as those defining the author, the authoring platform, the document version, or others.

Every element of a class definition inherits the occurrence constraints that HDML defines for it. But the class author can modify them, according to the structural freedom he wants to give to his concrete document authors. In this way, he defines the occurrence constraints of the content authoring language elements.

The Elements

The Block Element

The block is the most versatile element, able to contain other generic elements, acting as subblocks or objects (the inclusion of semantically qualified elements is not currently supported, since they are limited to the whole page description).

A block definition in the class has a very important "name" attribute, that will give its name to the corresponding tag used by teachers when they write their concrete documents. An optional "type" attribute adds semantics by specifying the kind of block, such as: header, menu, footer, body.

The "name" attribute makes it possible to define the content authoring language so it is adapted for a specific domain. This language is fine-tuned for the content authors, since it names the structuring units they will use with the terms they are most familiar with.

Finally, a block can be structured using sub elements taken from the following non-exhaustive list: Block, Object, or OPTIM The recursive nature of the block element makes it possible to avoid defining a limited list of structuring levels, as in DocBook, while giving it, as the same time, more descriptive power.

The Object Element

The object element is always a leaf of the XML node tree. It describes either non-structured elements (such as a picture, sound or movie) included by reference, or very basically structured elements such as hyperlinks or the fields of a form.

The Optim Element

The Optim element is the link between the actual concrete document and the interactive objects built using the OPTIM platform. OPTIM keeps the content author in a familiar context, by using the authoring language that HDML extracts from the class, for all the static elements that surround the interactive ones. Moreover, by allowing the teacher to supply the computational code in various languages (including those that require a specific runtime), we insure the maximal adaptability to most scientific domains, and the openness teachers commonly require.

Example

Let us give an example, with few lines of HDML defining a class block named «Theorem», followed by an example of one theorem definition in a concrete document. <Blockname="Theorem" type="body"> <Object name="Name" type="text" Occurs="1" /> <Object name="Premises" type="text" Occurs="1+"/> <Object name="Conclusion" type="text"Occurs="1" /> <Object name="Proof" type="body" Occurs="1" /> <Object name="Keyword" type="text" Occurs="*" /> </Block>

The previous HDML fragment shows that the class designer has defined the generic structure for Theorem with the following sub-elements:

• a mandatory name (which is a text zone),

• a mandatory conclusion (as text zones),

• one or many premises (also as text zones),

• a mandatory proof (structured with sub-blocks) • some optional keyword., as in the following example: We can see here how the content authoring language uses domain centered entity names, instead of the generic ones (like block) that HDML defines. We can also appreciate how this authoring language can be easily used by mathematicians or teachers to write their documents.

RELATED WORK

HDML vs Tag Libs or XSLT

One can argue that our objectives can be fulfilled using tag libraries like Struts [3], which are based on the powerful capabilities such tag libraries add to HTML. We think that tag libraries are clearly an efficient solution in certain instances, but limited to HTML production, and probably more oriented towards dynamic HTML. Moreover, they tend to stress the lookand-feel, whereas we want to free the authors from such considerations, and stress the semantics and document structure. Finally, the customization of Struts features supposes skills that content authors and editors usually don't have.

The XSL tool is also very powerful, but has one major drawback for a teacher: the mastering of XSLT and X-Path. And if X-Path has to be used only rarely in our context, XSLT has to be used regularly for two reasons. First, the author has to write a separate XSL sheet for every presentation medium used and for every subset of the document. Then, this XSLT authoring must be redone every time the user changes the look and feel, for every presentation medium, and for every subset of the global document.

Moreover, this way of producing web pages does not allow any factorization and genericity, nor any possibility for handling interrelations between documents. It does not provide any way to manage the document versioning that the HDML engine supports, and which is of great help in managing the production of multiple content authors.

OPTIM versus current IDEs

One could think that existing Integrated Development Environments, like JBuilder©, or Visual Studio©, are a better choice for developing the interactive parts of our pedagogical documents. We think that these products are too complex for the rather simple graphical objects authors need. The complexity of the programming task should not be included in the GUI's design, but rather in the development of the simulation part. And we think that versatility and openness is crucial here, letting our teachers use their favorite programming languages. IDEs often impose the mastering of many specific programming skills, such as animation techniques and graphical algorithms, which are clearly not trivial, require a great investment and constant updates.

Programmingless Projects

Recently new products, like Gatonero have emerged that allow the quick design of interactive applications by non programmers. Gatonero offers the ability to describe sophisticated prototypes using its own specific language, or the Gatonero Studio tool with drag-and-drop capabilities. Gatonero's architecture relies on a framework, which is announced as "fully-programmable and object based". Gatonero obviously offers inheritance capabilities, but also the reusability of prototypes, as well as multiplatform deployment, since it is Java based.

We think that the goals of products like this one are slightly different from ours. They tend to propose Rapid Application Development tools rather than a framework that makes it possible to integrate data produced by a regular computational code into a pedagogical document.

Moreover, like the SimTool project mentioned below, Gatonero suffers from the lack of openness, since it offers only one programming language. Clearly, this gives it the ability to be multi-platform, but OPTIM can reach this goal in a different way, by allowing the client-side software, highly portable, to interact with a computational code located on a dedicated remote server.

E-Learning Development Tools

We have found active projects, like the one described in [START_REF] Matsuno | A Tiered Approach Utilizing Reusable Componentware Methodologies for Multimedia Educational Software Creation and Development[END_REF], that plans to give teachers a way to design interactive pedagogical documents without programming. It is a component-ware based project that has the same openness property as OPTIM, in that it offers links with databases, e-mail, etc. But this project is currently based on the Delphi© programming languages and seems to have no capability to cooperate with other programming language, for the computational requirements of the simulated objects. Moreover, it lacks the uniform model OPTIM defines with HDML, and which guaranties a excellent adaptability to new application domains.

Another project, SimTool [START_REF] Sassen | Instructional Design and Implementation of Interactive Learning Tools[END_REF], part of the more ambitious VORMS project, shares some of our principles. Experiments done using SimTool have shown that students greatly appreciate interactive counterparts to textual presentations. One of the major limitations of SimTool is the choice of Java for both the GUI construction and the computational tasks. We think that different authors will better implement these two complementary parts, and that one should leave more freedom in the choice of the computational language. To design a good graphical user interface it's necessary to master computer animation, graphic algorithms, and to have extensive knowledge of the GUI's object library. On the other hand, the teacher, which is, in this case, the knowledge reference should only describe the computations required to supply data to be displayed. This task is complex enough, and we have to let teachers use their favorite programming language to complete it. Moreover, SimTool's goal is the building of simulation applets, and it does not cover the whole design of a teacher's pedagogical document, as does HDML.