Targeting Public Goods to the Poor in a Segregated Economy: An Empirical Analysis of Central Mandates

in Rural India

Anjini Kochar,+ Kesar Singh++ and Sukhwinder Singh++

October 2006

Abstract

A striking feature of many developing economies is the substantial disparity in the quality of public goods used by the poor and the non-poor. This is possible because of the extensive residential segregation of the village population along socio-economic and ethnic characteristics correlated with poverty, which induces a distinction between the public goods consumed by the poor and the non-poor. However, residential segregation also provides the potential for governments to reduce these disparities by targeting funds for public goods towards habitations in which the poor reside. This paper studies the effectiveness of such targeting in rural India. It finds that targeting infrastructural investments towards under-privileged castes does increase investments in sub-habitations where such castes reside. However, it also reduces investments in public goods shared by all village households. We explain this as a consequence of the desire for segregation of public goods by caste, and provide evidence in support of this hypothesis. Key words: Public goods, central mandates, segregation, social fragmentation

We thank the Ministry of Panchayati Raj, Government of India, for their generous financial support of this project. This project would not have been possible without the overall guidance and support of Mr. Rashpal Malhotra, Director, and Mr. Satyapal, of CRRID. Our thanks, too, to the field investigators and computer programmers at CRRID. Anna Levine provided excellent research assistance. This paper has benefited from very helpful comments by Eva Meyersson-Milgrom and Christina Gathmann.

+ Stanford University, Stanford, USA ++ Centre for Research in Rural and Industrial Development, Chandigarh, India.

1 Introduction

A striking feature of many developing economies is the substantial disparity in the

quality of public goods used by the poor and the non-poor. This difference reflects not just

geographic concentrations of the poor in specific regions, but also the extensive residential

segregation of the village population along socio-economic and ethnic characteristics closely

correlated with poverty. In India, for example, segregation takes the form of segregation by caste,

with upper caste households residing in separate sub-habitations of the village with substantially

better quality public goods. However, residential segregation and the accompanying separation in

the public goods consumed by different social groups also provide the potential for governments

to target public goods used by the poor; they can mandate minimum investment levels for

habitations of a village, rather than for the village as a whole. If successful, such policies could

reduce disparities in public good access and, through this, socio-economic inequalities which

have sometimes persisted for generations. It is an open question, however, whether local elites

will allow such changes in the nature of the village community. Particularly in economies where

village governments have been entrusted with control over funds intended for local public goods,

changes in the relative socio-economic status of village households may be difficult to affect.

This paper examines the effectiveness of mandates introduced by the Government of

India in its primary rural development program, the Sampoorna Grameen Rozgar Yojana, in

2001. These mandates require a certain proportion of funds intended for infrastructural

development in villages to be spent exclusively on households on the lowest rung of the caste

hierarchy, the “scheduled” castes and tribes (SCs and STs). Using data we collected from a

survey of villages in the North Indian state of Punjab, we find that mandates are effective,

increasing infrastructural investments in SC habitations, but not in those habited by upper castes.

This suggests that, even when village governments control the disbursal of government funds,

local elites do not obstruct policies intended to equate the quality of habitation-specific public

goods. There is, therefore, potential to use central mandates to redress existing inequalities.

However, not all public goods can be effectively delivered at the level of sub-habitations

of the village. For public goods such as schools, economies of scale require the good to be shared

by all village citizens. We find that program funds which target investments towards habitation-

1

specific goods reduce investments in village-wide public goods such as schools. This negative

effect suggests that central mandates may not improve the welfare of the poor, despite their

positive effect on habitation-specific public goods; their overall effect will depend on the relative

importance of these two types of public goods. This result also cautions against drawing policy

implications based on the effect of central mandates on the goods they directly govern.

In an extension of the main results of this paper, we explain the negative effect of central

mandates on village public goods as a natural consequence of the same factors which generated

the extreme residential segregation which characterizes village India. Upper caste households

refuse to share public goods with scheduled castes, believing that the quality of public goods is

adversely affected by caste fractionalization in the user population. In the past, this was

accomplished through residential segregation which confined scheduled caste households to the

use of drinking water and sanitation facilities in their own habitation. In recent years, and

particularly for public goods which are provided only at the level of the village, segregation in the

use of public goods has taken the form of the growth of a parallel private sector.

As long as beliefs regarding the negative effects of caste fractionalization on the quality

of public goods persist, policies which improve the welfare of scheduled castes and hence their

use of village public goods will cause further segregation in the form of exit to the private sector.

Improvements in SC infrastructure, particularly health-related infrastructure such as sanitation

and drinking water, to the extent that they increase SC demand for schooling, may simultaneously

reduce upper caste demand for government schools. In turn, any reduction in the proportion of

elite households using government schools will diminish their willingness to support local

government expenditures on schools. We provide empirical evidence which supports this model.

Taken together, our results suggest that while policies which improve the relative welfare

of scheduled castes can be effective, this may only be true of policies which maintain segregation

in public good use. Reduction in the segregation of public goods use appears far more difficult to

accomplish. Moreover, when segregation takes the form of an increase in the private sector, it

reduces local support for public goods shared by the village community, such as government

schools. In this context, policies which entrust local governments with responsibility for

improving village-level public goods are unlikely to be successful.

2

Empirical work on this topic is scant, perhaps because of the difficulty in disentangling

the effect of central mandates from that of government income. This is particularly problematic in

economies where village governments have few “own” resources, and rely primarily on funds

provided to them by higher level governments. In addition to the need to separate government

revenue into “controlled” and local finance, fully under the control of the village government,

such an analysis also requires dealing with the endogeneity of both sources of income. This is

difficult, particularly for local finance. In most economies, local finance represents income earned

from local taxation. This raises difficult endogeneity issues, because both the level of taxation and

the amount of tax collections are likely to depend on socio-economic conditions in the village

economy. In this context, there may be few credible instruments to identify the effects of local

finance on the investment choices of village governments.

Conditions in the north Indian state of Punjab provide a rare opportunity to separate the

effects of central mandates from locally controlled funds. A large component of the income of

village governments in this state represents own income from common village land, known as

Shamlat land. This land dates to the Independence of India (1947), when the state of Punjab was

divided between India and Pakistan. The vast ensuing migration of Punjabi households between

the two countries resulted in abandoned land holdings. Rather than being redistributed, this land

was maintained as common village land.1 Income obtained from the annual rental of Shamlat

land accrues primarily to the village Panchayat, and is solely under the Panchayat’s control.

Shamlat land ensures that village governments in Punjab have access to income other

than that received from higher level governments, making it possible to separate centralized from

local finance. More important, inter-village variation in local finance in Punjab primarily reflects

differences in the amount of historically determined Shamlat land, rather than differences in

current socio-economic conditions within the village economy. We use this variation in Shamlat

land to test the effectiveness of central mandates.

To identify the effects of centralized funds, we follow Galasso and Ravallion (1999) in

exploiting the two-stage targeting procedure commonly used by Governments. Specifically, the

1In 1953, the government enacted legislation which stipulates that this land serve as common property (The Punjab Village Common Lands (Regulation) Act of 1953).

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central government allocates SGRY funds to districts on the basis of a well-specified set of

district-level indicators. These funds are then divided amongst villages by district governments.

This two-stage process implies that allocations to village governments reflect an interaction

between specific district-level variables, those which determine funding to district governments,

and the village level variables which guide the division of district funds amongst villages.

The analysis of this paper is related to several theoretical and empirical literatures. It

draws heavily on the theoretical framework developed by Bardhan and Mookherjee (2000,

2006a) to assess the effects of local capture on allocations in a decentralized village economy.

This literature argues that socio-economic characteristics of the village economy, such as the

extent of wealth inequality, can affect allocations, through their effect on the ability of local elites

to influence the decisions of village governments. We combine this model with research that

emphasizes the effect of racial, ethnic and social fractionalization on public goods (Alesina, Baqir

and Easterly 1999) and on social segregation (Bénabou 1996), arguing that the positive benefits

of programs that improve the welfare of the poor can be undone, because they adversely affect

the extent of fractionalization in the user population of village public goods, and through this,

increase segregation and reduce local government support for public goods.

On the empirical side, our work contributes to a literature that examines the determinants

of investments in public goods, much of it based on the Indian economy. Bardhan and

Mookherjee (2006b) analyze the determinants of targeting of programs. Foster and Rosenzweig

(2002) assess how village governments’ choice between different public goods is affected by

decentralization and democratization. For Bolivia, Faguet (20004) similarly assesses the

effectiveness of decentralization on investments by municipal governments. Finally, Banerjee,

Iyer and Somanathan (2005) examine the influence of colonial power, land tenure, and caste

fragmentation on investments in public goods in rural India.

Much of this literature ignores the specific institutional context which governs public

goods allocations in economies such as India’s. For example, few of these studies address the

very limited extent of decentralization in the Indian economy and the pervasiveness of central

mandates which stipulate not just the distribution of funds across broad socio-economic groups of

households, but also the purposes for which they can be used. Additionally, the distinction

4

between public goods which are caste specific and those which are shared by all village

households is rarely made. Yet, particularly given that it is in the use of public goods that caste is

particularly salient, we would expect the success of policies to differ, depending on whether they

affect habitation-specific or village public goods. Our framework, which allows for this

difference in public goods, can help explain Banerjee et al’s (2005) finding that the effect of caste

fractionalization differs across public goods: it decreases investments in schooling, while

increasing investments in water wells and hand pumps. This finding is consistent with our results.

The remainder of this paper is structured as follows. Section 2 describes the Indian

context, reviewing social sector policies in India and Punjab. It also provides details of the

program we focus on, the SGRY. Section 3 describes the survey data, providing summary

statistics on socio-economic characteristics of the village, and on the income and expenditure of

village panchayats. We outline the theoretical framework which guides our empirical analysis of

the effectiveness of central mandates in section 4. Section 5 discusses the empirical methodology

we adopt for this exercise, while Section 6 presents the main results. Section 7 extends the

theoretical framework to incorporate caste fractionalization and its effects on public goods. It also

provides regression evidence in support of the view that public good allocations reflect the

demand for segregation. The last section concludes.

2 Decentralization, Centralized Schemes and Private Provision of

Public Goods in India and Punjab

2.1 Decentralization and Centrally Sponsored Schemes

India has long been characterized by very poor quality local public goods such as government

schools and health centers, and by the high levels of waste associated with its anti-poverty

programs. To remedy this, and to enhance local control and democracy, the Government

announced a sweeping program of political, fiscal and administrative decentralization in 1992,

though the 73rd Constitutional Amendment. This Amendment constitutionalized a three-tier

structure of government below the level of state governments, known collectively as the

Panchayati Raj Institutions (PRIs), and required state governments to devolve administrative

5

responsibility for local public goods and social services to these local governments.2 It also

recommended fiscal decentralization, in the form of both expenditure and revenue

decentralization. In their areas of responsibility, PRIs were to control the disbursal of central and

state government funds

Fiscal decentralization, it was believed, would improve the efficiency of funds intended

for social programs. However, fiscal decentralization, particularly to village governments known

as Gram Panchayats, remains weak.3 Though state governments are supposed to pass on a

stipulated percentage of their revenues to local governments, this is not always done. For

example, the Government of Punjab has, since 1996-97, failed to devolve its committed share of

revenues to PRIs because of its poor fiscal condition.4 And, though local governments have been

granted the authority to raise revenue through local taxes, village governments in Punjab are loath

to exercise this authority. The only tax being collected by the Gram Panchayat, a tax on

residential buildings commonly referred to as the House Tax, generates hardly any revenue.

With weak fiscal decentralization, PRIs remain heavily dependent for income on grants

from the central government. These grants are primarily for Centrally Sponsored Schemes (CSS),

which fund investments which reflect the priorities of the Central Government. The Central

Government dictates the use of these funds; local governments serve only to monitor their use.

The high level of central control of village governments reflects, perhaps, the concern that

decentralization will increase inequality across and within villages. That this is a concern of the

Government is evident from the introduction, in recent years, of central mandates which require a

stipulated proportion of funds received under Centrally Sponsored Schemes to be spent

exclusively on the poor. We describe the mandates which accompany India’s major rural

development program in the next sub-section.

Data for 1999-2000 on the revenue of all Gram (village) panchayats in the state

(Government of Punjab 2002) confirm the insignificant level of revenue from local taxes, and the

2 Specifically, PRIs were given authority over 29 subjects listed in the Eleventh Schedule. These included primary and secondary schools, health, sanitation, family welfare, welfare of Weaker Sections including scheduled castes and tribes, rural housing, drinking water, and other local infrastructure. 3 A survey (World Bank 2000) of six states found that expenditures by Gram Panchayats accounted for only a very small share of total government expenditure in rural areas, ranging from 0.6% in Rajasthan to 1.5% in Uttar Pradesh. 4 The state fiscal deficit averaged 5.25% between the years 1985-86 to 2001-02.

6

dependence of the village government on income from Centrally Sponsored Schemes. The house

tax generated only 0.58% of the total revenue of gram panchayats in this year. In contrast,

Centrally Sponsored Schemes accounted for 47% of total revenues. Transfers from the State

Finance Commission generated 9% of total revenues, while income received from a scheme

which provides Members of Parliament with funds to invest in villages in their constituency (the

Member of Parliament Area Development Fund) generated 4% of total revenues.

Punjab and Haryana are unique amongst India’s states in the importance to the village

government of income from common property land, known as Shamlat land. In 1999-2000,

averaging across all villages in the state, income from Shamlat land accounted for 30% of the

total revenue of Gram Panchayats. State and Central governments place no restriction on the use

of income earned from Panchayat assets; they can be used as wanted by village governments.

This is in sharp contrast to funds received from Centrally Sponsored Schemes, of which the two

most important are the Sampoorna Grameen Rozgar Yojana (SGRY), a rural employment and

infrastructural development program, and the Indira Awaas Yojana (IAY), a housing program.

Since our evaluation of central mandates focuses on the SGRY, we provide details of it below.

2.2 Sampoorna Grameen Rozgar Yojana (SGRY):

The SGRY, initiated in September 2001, merged three infrastructure and employment programs

of the Indian Government.5 The scheme is the largest program currently implemented by the

Department of Rural Development. In the 10th Plan Period (2002-2007), it received a budget of

Rs. 300 billion, accounting for more than half of the Department’s funds (Rs. 567 billion). Its

objectives are two-fold: to create wage employment and durable community, social and economic

infrastructure in rural areas.

Under the scheme, the Central Government retains 10% of the funds, to meet emergency

needs. The remaining 90% is distributed to village governments, but through a process which

gives each level of the three tier system of local government some control over available funds.

This is done by dividing available funds into two “streams.” The first stream distributes 50% of

5 These were: Jawahar Rozgar Yojana, an infrastructure program; the Employment Assurance Scheme; and the Food for Work program.

7

funds to district level governments, of which 20% is retained by the district government and 30%

passed on to the second tier of government (the block or intermediate level). The second stream

distributes the remaining 50% of funds directly, and equally, amongst village panchayats.6

Allocations of first stream funds to district governments follow a two-stage process,

with available funds first divided amongst states and subsequently allocated to districts within

each state, according to a set of well-specified rules. The division amongst state governments is

based on the proportion of the rural poor in the state to the total rural poor in the country, while

the division of the state allocation amongst districts is based on an index of backwardness, which

gives equal weight to the proportion of rural scheduled castes and tribes in the district to the state

population, and the inverse of production per agricultural worker in the state.

SGRY funds provided to district and block governments do not stay with these

governments; they are required to distribute them amongst village governments. Government

regulations stipulate that allocations by district and block governments to village governments are

to be based on local conditions, primarily in the labor market. Specifically, district governments

are instructed to target villages suffering from endemic labor migration and villages in

historically backward regions. District and block governments, however, specify the use of these

funds. Thus, though they show up as revenue in the accounts of the village Panchayat, the

Panchayat actually has no discretion in determining their use. The role of the village government

in this case is simply to “pay the bill” as and when the work gets completed

The rules which divide available funds amongst districts are basically the same as those

followed under the earlier infrastructural program, the Jawahar Rozgar Yojana. However, the

ratio in which funds are divided amongst village, block and district governments has changed,

from an earlier 70:15:15 division to the current 50:30:20 rule, reducing the funds directly

controlled by village governments. Rules determining the division of district and block funds to

village governments have also changed; it was earlier based on the total population of the village

Panchayat and the share of scheduled castes and tribes in this total.

6 In 2004-05. the program was changed to an integrated scheme. The division of funds between the three levels of PRIs as well as the criteria for division, remain unchanged.

8

Perhaps the biggest change, however, is the introduction of mandates dictating that some

percentage of program funds must be spent on investments which directly benefit scheduled

castes and tribes. Specifically, of the funds provided directly to the village government, 50% must

be spent on the creation of village infrastructure in scheduled caste and tribe sub-habitations of

the village. Funds provided to the district and block level government are also subject to these

mandates; 22.5% of the annual allocation received by district and intermediate level panchayats is

to be spent on schemes for SC/ST households

Additionally, village governments are also issued a list of “priority investments” to be

undertaken with SGRY funds. This list emphasizes investments in water and sanitation projects,

and in community infrastructure such as primary schools, health centers and link roads.

Discretion in the choice of investments is further limited by the stipulation that only labor-

intensive projects, which do not require any technical expertise beyond that available in the

village, and which can be completed within a two year period, are to be selected.

In summary, even though the Government of India has publicized the decentralization of

poverty and social sector programs, village governments have limited control over their income.

Their primary income source remains Centrally Sponsored Schemes, such as the SGRY. Funds

from these programs are accompanied by mandates intended to ensure that the government’s

equity objectives are met. Most mandates target funds towards SCs and STs. Over time, central

control has increased, with a smaller share of funds accruing directly to village governments.

2.3 Private provision of public goods in Punjab

The excessive centralization of the village economy may explain the widespread

prevalence of private markets for the provision of goods that are traditionally thought of as

belonging in the public domain. By 1994, Punjab (20%) ranked second only to Uttar Pradesh

(27%) in terms of the proportion of students in the 6 to 14 age group enrolled in private schools.

Data from the Directorate of Education reveal a steady increase in enrollments in recognized and

unrecognized private schools, with the fastest growth occurring in unrecognized schools. By

2000, 25% of enrollment at the primary level was in unrecognized private schools, 9% in

recognized private schools, and only 66% in government schools. The rapid growth in private

9

schooling is apparent in the fact that enrollments in government schools fell from 72% in 1996 to

66% in just five years.

Reflecting the high incidence of private schooling, Punjabi households spend more than

double the national average on school education. The average expenditure per child in general

education by Punjab is Rs. 1,394 in rural areas, as compared to only Rs. 570 in India.7 In urban

areas, Punjab spends Rs. 2,786 per child as compared to Rs. 1,686 at the all-India level.

3 Theoretical Framework

3.1 The General Model

The empirical work of this paper draws on a variant of the Downsian model of political outcomes

developed by Grossman and Helpman (1996) and further adapted by Bardhan and Mookherjee

(2000, 2006a) to assess the consequences of granting village governments control over

developmental funds. The model assumes that political candidates commit to policy platforms

before elections. In the absence of frictions, this assumption implies that all candidates will chose

the same party platform, reflecting the preferences of the average voter. However, if policy

choices can be influenced by elites (through their financial contributions to campaigns, for

example), Bardhan and Mookherjee show that the policies announced by different candidates will

differ, and will generally favor the preferences of local elites. That is, policy outcomes reflect the

maximization of a welfare function which weights the preferences of different groups by their

population shares, but places an additional weight on the preferences of the elite. This additional

weight represents the “local capture” of the political process by village elites.

Building on this framework, we assume there are two social groups in the village

economy, indexed by j, representing scheduled (j=s) and upper castes (j=u). The economy is

characterized by residential segregation, with scheduled castes living in one habitation and upper

castes in another. The proportion of scheduled castes in village i is given by πi. For ease in

7 These statistics are from the National Sample Survey Organization, 52nd round (1995-96).

10

exposition, we restrict our attention to two public goods, infrastructural investments and schools.

Infrastructural investments (y), such as sanitation and local roads, are specific to a habitation and

directly increase the incomes of households in that habitation. Infrastructural investments are

therefore indexed by caste (y=yj). In contrast, government schools (z) are local to the village; the

single (government) primary school in the village is shared by scheduled and upper castes.8

Households gain utility from the consumption of a private good (c) and from schooling.

We start by assuming that schooling is provided by the government and is free. Through a

standard schooling production function, attained levels of schooling are a function of government

schooling expenditures, z. Households spend their income on the consumption of the private

good. Household income, in turn, increases with infrastructural investments (y). We therefore

represent the indirect utility function of a member of the upper caste in village i by

, and that of a scheduled caste household by the function . ),( zyvU uui = ),( zyvU ssi =

We model a decentralized economy in which the village government determines

expenditure allocations subject to its budget constraint and to any mandates imposed by higher

level governments. The village government’s budget constraint requires total expenditure to equal

total income, the sum of income from common property resources (G) and from higher level

governments (I). The latter comes with the requirement that some stipulated amount, I , be spent

on infrastructural investments in scheduled caste habitations. Specifically, the expenditure

decisions of the village government reflect the maximization of the following objective function:

(1) ),()1(),( zyvzyv uuissi ϕππ −+

subject to the budget constraint,

(2) IGzyy su +=++

a “distributional constraint” which stipulates a minimum expenditure on scheduled castes,

8 This is true of Punjab, but not of all of India’s states.

11

(3) Iys ≥

and non-negativity constraints on investment, yu,z >0, which we assume are never binding. The

solution to this problem generates the following first order conditions for investments in SC and

upper caste habitations (ys, yu) and for government schooling expenditures, z:

(4a) 0),( ≤+−∂∂

sss

si zy

yv

μλπ

(4b) 0),()1( ≤−∂∂

− λϕπ zyyv

uu

ui

(4c) 0),()1(),( ≤−∂∂

−+∂∂

λϕππ zyzv

zyzv

uu

iss

i

where λ is the Lagrange multiplier on the budget constraint (2), while μs is the multiplier on the

distributional constraint (3).

3.2 Analysis if the distributional constraint (3) is not binding

The constraint which requires a minimum level of expenditures on scheduled caste

habitations will not be binding if village governments optimally choose expenditures on

scheduled castes to exceed the government mandated minimum.9 If this is the case, then the

Lagrange multiplier, μs, in the first order conditions (4a) will equal zero. Assuming that the budget

constraint, (2), binds, the set of first order conditions will be met as equalities.

In this case of “pure” decentralization, funds controlled by the village government (G)

will have the same effect as those provided by higher level governments; expenditure out of both

local and centralized funds will reflect the priorities of the village government. Decentralization

to village governments is complete, in that central mandates will have no independent effect on

9 From (4a), this will be the case if 0),( ≥−

∂∂ λπ zIyv

si

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infrastructural investments (in SC habitations or in those habited by upper castes), or on

schooling expenditures, in regressions which also control for total government income.

However, a prediction of this model is that, because of local capture, infrastructure will

be over-provided to elite households. In this simple model, over-provision to elites comes at the

cost of investments in the public good, z. The optimal allocation will equate marginal returns for

these two types of investments. In general, the effect of own income on the distribution of

investments across SC and other caste habitations will reflect preferences as well as local capture.

3.3 Analysis if the distributional constraint (3) is binding:

If the distributional constraint (3) binds, investment in scheduled caste habitations, , will equal sy

I . The village government’s optimization problem can then be written as follows:

(5) max ),()1(),( zyvzIv uuisi ϕππ −+

subject to the budget constraint:

(6) IIGzyu −+=+

This generates the following first order conditions for yu and z:

(7a) 0),()1( =−∂∂

− λϕπ zyyv

uu

ui

(7b) 0),()1(),( =−∂∂

−+∂∂

λϕππ zyzv

zIzv

uu

is

i

The “constrained” model carries several implications for investment decisions within the

village. First, infrastructural investments in scheduled caste localities will be determined by the

constraint (3). In contrast to the unconstrained model previously described, they will therefore

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reflect the amounts mandated by higher level governments for investments in SC habitations.

Second, the relevant income measure which determines investments in upper caste localities, as

well as investments in schools, is total government income )( IIG −+ ; binding central

mandates reduce the “unconstrained” funds available for local governments to invest as they

choose. Finally, in this model, binding central mandates which stipulate expenditure on SC

habitations may also affect investments in upper caste habitations and in schools, but only if

infrastructural investments in SC habitations and schooling are complements or substitutes in

utility functions. These effects are likely to be weak; there is no reason to expect substitution

effects, if they exist, to be large. Effects of I on infrastructural investments in upper caste

habitations will be even weaker, since they can occur only through z.

3.4 Local capture with binding distributional constraints

While this simple model does not allow any direct effects of targeting towards scheduled castes

on upper caste habitations, many believe that such effects may exist. They are suggested by

models of social status (Fershtman, Murphy and Weiss 1996) and relative power (Rajan and

Zingales 1995), which opine that the utility of sub-groups of the population depends not just on

their own consumption levels but on their status, as measured by their consumption relative to

other sub-groups. If local elites care about relative welfare, they may counter mandated

improvements in scheduled caste welfare by using unrestricted funds to increase investment in

their own habitation. For example, the participation of the non-poor in political lobbies may

increase with policies which target relative income levels in the village economy.

To incorporate such effects, assume that the coefficient of local capture depends on the

ratio of investments in upper caste and scheduled caste habitations. With binding constraints on

the minimum level of investment in scheduled caste habitations, this implies that

0),( <′= ϕϕϕIyu . Under this assumption, the first order condition for yu is:

(8) 0)](),(

),()()[1( =−′+∂∂

− λϕϕπIy

IZyv

zyyv

Iy uuu

uu

uui

14

If 0<′′ϕ , increases in I will induce compensating increases in infrastructural investments in

upper caste habitations. This is also true if 0>′′ϕ , as long as the rate at which changes in

relative income affect local capture is modest. Under these conditions central mandates will

increase investment in both scheduled and upper caste habitations. Their effect on relative

investments levels across habitations is therefore ambiguous.

4 Data and Socio-economic Profile of the Survey Area

In January 2006, we conducted a survey of 300 villages in Punjab, selected from all of Punjab’s

17 districts on the basis of proportional representation, with the distribution of sample villages

across districts reflecting the population distribution. Within each district, sample villages were

randomly chosen. Our survey comprised two modules. The village module provided information

on village socio-economic characteristics as well as detailed panchayat expenditure and income

accounts. The school module collected data on village government and private schools.

Punjab is one of India’s most prosperous states; in 2000-2001, its per capita state

domestic product (Rs. 25,048) was the highest amongst India’s major states, far higher than the

national average (Rs. 16,707). It is also one of the most heterogeneous in terms of caste

population, with scheduled castes accounting for 30% of its population (as opposed to the

national average of 24%).10 Despite its wealth, Punjab’s record in the provision of public goods

and related human development indicators is poor. Punjab ranked 12th (1991) amongst all states

and union territories in the value of its Human Development Index.11 In access to rural health

sub-centers, Punjab was ranked last amongst the major states. Literacy levels in the state (2001)

are close to the national average (69.9%, relative to a national average literacy rate of 65.4%), and

enrollment of children between the ages of 6 to 14 is below the national average (73.42%, versus

an average of 81.58% in 2001).

10 There are no scheduled tribes in Punjab. 11 These data are Centre for Research in Rural and Industrial Development, 2002.

15

4.1 Socio-economic conditions in survey villages

Village populations in Punjab are relatively large, with the mean population size in our

sample villages being 1,421 (249 households). This means that almost all villages are “single

panchayat” villages, in which the panchayat only serves the village in which it is located. Five of

our survey villages had multiple panchayats, while 9 additional villages shared a panchayat with a

neighboring village. Our empirical analysis excludes the 5 villages with multiple panchayats.

Summary data on socio-economic conditions in the villages are presented in table 1. On

average, scheduled castes account for 37% of the village population (36% of village

households).12 Despite the relatively high incidence of landlessness, Punjab’s prosperity is

reflected in the low proportion of the population which falls below the official poverty line. 74%

of sample villages have a separate scheduled caste habitation, locally referred to as a “vehra.” In

villages where a SC vehra exists, residential segregation is almost complete: there are very few

SC households who live in the main village.

Because we identify the effects of the village government’s own income using the

amount of village Shamlat (common property) land as an instrument, it is worth establishing that

variations in the amount of Shamlat land across villages, determined through a historical process,

are unrelated to socio-economic characteristics. Table 1 provides data on total households, SC

households, proportion of below-poverty-line households and proportion landless, separately for

villages with and without Shamlat land. There is almost no difference in the mean level of these

variables across these two sub-samples. Formally testing the difference in outcomes across

villages with and without Shamlat land results in a rejection of the null hypothesis that these

differences are significant.

Table 1 also includes information on the popularity of private schooling, and of the caste

composition of students in government and private schools. Of school-age children (ages 6-14),

approximately 35% are enrolled in private schools within and outside the village. But, private

schools appear to cater primarily to upper caste households. Drawing on data on private schools

12 There are no scheduled tribes in Punjab. The proportion of scheduled castes in the state is amongst the highest of all states in India.

16

in our survey, scheduled castes comprise only 17% of their student population. In contrast, as

much as 58% of the students in government primary schools are members of scheduled castes.

4.1 Panchayat Income and Expenditure

Table 2 provides data on the income of surveyed Panchayats, for the years 2004-05 and

2005-06. The data confirm the complete lack of local taxation in the state. The only tax collected,

the house tax, generates less than 1/100th of Panchayat income (0.0004 and 0.0006 percentage of

the total, in 2004-05 and 2005-06 respectively). In contrast, income from Shamlat land is

considerable. Indeed, averaging across the villages in our sample, it constitutes the single most

important source of income for the Panchayat, accounting for 26% of income in 2004-05 and

34% in 2005-06. Other significant sources of income are the SGRY (21% of income in 2004-05

and 17% in 2005-06), state government grants and transfers (16% and 22% in each of the two

years), and grants from the Member of Parliament Local Area Development Scheme (MPLADS)

which generated 21% of average Panchayat income in 2004-05 and 15% in 2005-06

The data confirm the limited control the Panchayat has over its income; the only fully

unrestricted income comes from Shamlat land. Income from the MPLADF, the IAY, and other

Central Government grants is “tied,” in that it is provided for a pre-specified investment project.

Panchayats do control SGRY funds provided directly to them. But, “unrestricted” SGRY funds

constituted only 5% of village income in the two years for which we gathered data; the remainder

merely “passes through” the Panchayat; its use is dictated by district and block governments.

Table 3 provides data on total Panchayat expenditures for 2004-05 and 2005-06 and,

separately, for investments in SC vehras for the sample of 223 villages with an SC vehra. In other

villages, because of the lack of residential separation between SC and other caste households, we

did not attempt to collect data separately for investments in SC localities. The data reveal the

clear distinction between public goods that are habitation-specific and those that are used by all

village households. Habitation specific goods are sanitation and drinking water projects, and local

roads. Village public goods are schools, health centers and investments in electricity works.13

13 A small component of expenditures on SC vehras is for schooling, representing transfers of subsidies to members of scheduled castes.

17

Investment in sanitation projects dominates; its share in total expenditure was as high as

48% in 2004-05, and 51% in 2005-06, while it represented as much as 66% of expenditures in SC

vehras in 2004-05 and 53% in 2005-06. Other important investments in terms of the level of

expenditure include local roads (14% of total expenditures in 2004-05, and 16% in 2005-06), and

schools (10% and 8% of expenditures in the two years respectively). Balancing the relatively

larger share of expenditure on sanitation projects, expenditure on roads constitutes a relatively

smaller share of total expenditures in SC vehras. There is almost no investment in irrigation in

these localities, due to the very few members of scheduled castes who own agricultural land and

are engaged in own cultivation.

To assess how much of total expenditure occurs on SC vehras, we restrict our attention to

villages in which such vehras exist. These tend to be relatively more prosperous, with average

expenditure of Rs. 179,155.9 and Rs. 228,330.3 in 2004-05 and 2005-06 respectively. As a

percentage of these totals, investment in SC vehras amounts to 44% and 31% of total

expenditures in these two years, similar to the average 35% share of scheduled castes in village

population. It is not possible to infer whether Central Government mandates requiring a stipulated

level of investment in SC localities, or the programs which completely restrict investment to such

localities, bind the allocative decisions of Panchayats, because these mandates apply only to some

part of total village income. For example, the percentage expenditure in SC localities is, of

course, less than the Central Government mandate that 50% of SGRY funds provided directly to

the village be spent on scheduled castes. However, as noted above, SGRY funds constitute only

about 20% of total Panchayat income.

In addition to the amounts which are spent on each of these items, it is also worth

examining the incidence of expenditure. These data are provided in table 4, which records the

proportion of village Panchayats reporting investment in each of the different types of projects,

separately for total expenditures and for expenditures in SC vehras (in the case of villages where

they exist), in either 2004-05 or 2005-06. Not surprisingly, almost all Panchayats (87%) report

investments in sanitation projects. In terms of incidence, other important projects are schools,

roads and drinking water. Even though the amount of expenditure on drinking water projects is

small (approximately 3 to 5% of total expenditure), 31% of village Panchayats report such

investments. The same is true for investments on electricity projects and irrigation, both of which

18

amounted to only 2-3% of total expenditures. 17% of villages report expenditures on electricity

projects, and 12% report investment on irrigation, which almost exclusively benefits other caste

households. 35% of Panchayats report investments in Panchayat buildings.

5 Empirical Framework

5.1 The Effectiveness of Central Mandates

We start the empirical analysis of this paper by examining whether central mandates,

which specify a minimum level of infrastructural investments in SC localities, constrain the

allocative decisions of village governments. Our test of this hypothesis derives from the

theoretical result (Section 3) that, in an unconstrained economy, funds intended exclusively for

investments on scheduled castes ( I ) will have no independent effect on investments in SC

habitations in regressions which control for total village income.

I includes funds provided under the SGRY program, but also mandated investments for

scheduled castes under the MPLADF and other state government schemes.14 We do not know the

exact value of I . However, we do know that some component of SGRY funds must be spent on

SC localities; that is, ),( scYSGRYfI = , where Ysc represents other village income intended for

investments in SC habitations. Our test for whether central mandates constrain village

governments is therefore based on testing the significance of SGRY income in the following:

(9) jiiiiji uXSGRYYy +′++= 210 ααα j=s,u

Our basic strategy is to regress infrastructural expenditures on village and SGRY income,

separately for expenditures in SC and upper caste habitations. The null hypothesis that central

mandates have no effect on allocations by the village government implies that, in regressions

which control for total village income, Yi, α1=0.

14 As, for example, state government funds to improve sanitation in scheduled caste habitations.

19

There are no similar predictions regarding the coefficient on village income, αo. Even

with binding central mandates, village income may affect investments in SC habitations. One

reason is that SGRY specifies employment targets for each village, separating out funds for labor

costs from those meant for capital expenses. It is therefore possible that the scheme constrains

village governments to spend more than they would like on employment, but provides insufficient

funds for working materials such as bricks, expenses for which must then be borne by the village

government. Alternatively, the effect of own income could reflect the requirement for matching

funds, which characterizes many of the state government programs targeted towards SCs.15

OLS estimation of equation (9) runs into two problems. The first relates to measurement

error in village income, Yi. If any measurement error in village income is correlated with SGRY

income, then the coefficient on SGRY income will be significant, even if central mandates are not

binding. The second problem stems from the endogeneity of SGRY income. If SGRY allocations

to a village are correlated with unobserved village characteristics which independently affect

infrastructural investments, then, again, we may erroneously fail to reject the null hypothesis.

To control for measurement error in village income, we instrument it by the village’s

endowment of Shamlat land. Shamlat land would be an inappropriate instrument if it

independently determined other types of income subject to central mandates. This would be the

case if allocations from higher governments to village Panchayats reflected endowments of

Shamlat land. This is a testable assumption; we start with first stage regressions which examine

the effect of Shamlat land on CPR income, total income, and total income net of CPR income.

We similarly control for the potential endogeneity of SGRY income through instrumental

variables. Our instruments exploit the two-stage allocation of SGRY funds by the central

government to village government detailed in Section 2 of this paper. Specifically, village SGRY

funds reflect the provision of funds to the respective district government and their subsequent

division to village governments. Since district allocations are made on the basis of the proportion

of scheduled castes in the district population and the (district average) agricultural productivity

per agricultural laborer, this suggests that village SGRY funds reflect the interaction of village 15 For example, the State govt scheme, “Rural Sanitation for Scheduled Caste” and the scheme for the Construction of Toilets in Rural Areas. Both schemes require matching contribution by the Gram Panchayat (Government of Punjab 2006).

20

level variables, which determine allocations from the district to the village, with a specific set of

district level variables. As we have data on district SGRY funds for our survey years, we use the

actual funds provided to the districts as the basis for our instrument set. Because allocations of

district funds to village governments are generated by village labor market conditions, one natural

choice of an interaction variable is the village wage rate. We also interact district SGRY funds

with the village’s endowment of Shamlat land, a proxy variable for village wealth, guided by

empirical evidence that targeting across villages by higher level governments is prone to local

capture and reflects local socio-economic conditions (Bardhan and Mookherjee 2006).

Our instruments for village SGRY funds are therefore: total district SGRY funds, district

funds per panchayat, the interaction of per panchayat funds with village wage rates (male harvest

wage rate) and with the amount of shamlat land, and the number of panchayats in the district. It is

possible, of course, that district SGRY funds may be correlated with unobserved village variables

such as agricultural productivity. This would make it an invalid instrument if these unobserved

variables affect infrastructural investment. Because the regression is over-identified, we test the

validity of district-level instruments through standard over-identification tests.

In (9), X is a set of additional regressors including village population, village SC

population, the distribution of land in the village (the proportion of households with land of

different size categories), village female literacy rate, village area, and the distance to the nearest

town. Infrastructural investments are the sum of investments which are local to a habitation; these

are investments in sanitation, drinking water and local roads. Both village income and

infrastructural expenditures represent the total of these figures for the two years of information in

our survey (2004-05 and 2005-06).

The regression is run only on the sub-sample of survey villages which have an SC vehra

(approximately 75% of the sample). To facilitate comparison, we also restrict our analysis of

investments in other caste communities to villages with a SC vehra. Even though this excludes

only 25% of the sample, omitting a sample selection correction term may nevertheless generate

biased estimates if residential segregation reflects unobserved preferences for public goods.16

While this may be so, IV regressions eliminate any bias due to this omitted variable, as long as

16 We omit a sample selection term since we can identify if only through functional form assumptions.

21

the instruments are uncorrelated with residential segregation. Since current segregation patterns in

the state are old, pre-dating independence, there is little reason to expect them to be a

consequence of either Shamlat land or SGRY funds. Again, however, this is a testable

assumption. In Appendix A we report results from a probit regression on an indicator variable for

whether the village has a SC vehra. Though the proportion of SC households, the proportion of

poor SC households, village population, village area and distance to town are significant

determinants of residential segregation by caste, the set of instruments for both own and SGRY

income are not. These results suggest that our instruments are valid, even with endogenous

sample selection, and that our results are therefore not biased as a consequence of such selection.

Because much of the decision making regarding social programs and public goods is

coordinated at the district level, it is likely that regression errors are correlated across villages

within a district. To account for this, all standard errors are clustered at the level of the district.

6 Results of the Effectiveness of Central Mandates

6.1 First stage regressions

We start our empirical analysis with a series of regressions which establish the validity of

our instruments for total and SGRY funds, commencing with an analysis of the correlation

between Shamlat land and different types of village income. Summary statistics in Table 1

provide prima facie evidence to support the hypothesis that Shamlat land is uncorrelated with

village socio-economic conditions; these conditions do not significantly differ across villages

with and without shamlat land. We now directly test the correlation between Shamlat land and

various sources of income, through regressions of Shamlat land on CPR income, total Panchayat

income, total income net of CPR income and SGRY funds, in addition to the set of auxiliary

regressors detailed in the previous section (table 5). The regressions are based on the sample of

villages with a SC vehra. Regressions reported in Table 6 expand the set of regressors to include

instruments for SGRY funds (district SGRY funds, per panchayat SGRY funds, interactions of

per panchayat funds with village wage rates Shamlat land, and the number of panchayats in the

22

district). These regressions (table 6) form the first stage regressions for our subsequent

instrumental variable regressions which test the effectiveness of central mandates.

Regression results confirm that CPR income reflects ownership of Shamlat land,

supporting the use of Shamlat land as an instrument for own income. They also confirm that

Shamlat land is not correlated with other sources of village income: The effect of Shamlat land on

village income net of CPR income, and on SGRY income, is statistically insignificant. These

regressions thus establish that the instrumentation of total income by Shamlat land identifies only

that part of village income which is under the full control of the village government.

The first stage regressions reported in table 6 confirm the explanatory power of our

instruments for SGRY funds. Village SGRY funds increase with per panchayat district funds.

Further, the distribution of district funds across villages varies with labor market conditions; more

funds are provided to villages with lower wage rates. But, wealthier villages with larger

endowments of Shamlat land also receive more funds. These results suggest that even though

central government targeting rules to states and districts ensure that the distribution of funds

across villages does not favor wealthier villages, the allocation of district government funds does.

The rules which guide central government allocations to states and to district governments appear

to be instrumental in ensuring that program funds are not captured by wealthier villages.

6.2 The effectiveness of Central Mandates

The first stage regressions in table 6 provide the basis for the instrumental variable

regressions of table 7. These regressions support the hypothesis that central mandates effectively

constrain the decisions of village Panchayats: They cause investments in SC communities to

increase significantly, even after controlling for the effect of total village incomes (Regression 1).

Further support for the hypothesis that central mandates determine infrastructural investments in

SC habitations comes from the insignificance of village socio-economic conditions in this

regression. Even though factors such as the extent of poverty and the literacy rate determine

investments in other-caste communities, they have no significant effect on SC investments. This

lack of sensitivity to local conditions supports one argument commonly made in favor of

decentralization: it allows investment choices to more closely reflect local priorities.

23

Similar regressions on infrastructural investments in upper caste habitations test whether

central mandates reduce inequality in public good investments across habitations. Their positive

effect on SC investments suggests that this should be the case, both because of their direct effects

and because they reduce the amount of unrestricted income available for investments in upper

caste habitations. However, their effect on inequality depends on whether they increase

investments in upper caste habitations. Regression 3 in table 7 provides no evidence of local

capture. While total income is an important determinant of infrastructural investments in other

caste habitations, SGRY funding has no significant effect. These results imply that central

mandates do reduce inequality in infrastructural investments within a village; local elites do not

divert central funds to other purposes and also do not match the increase in SC investments by

offsetting investments in their own habitations.

Our instruments for SGRY income can be questioned on the grounds that several of them

are district level variables. Even though SGRY allocations to the district are not based on village

outcomes, it is possible that district SGRY allocations are correlated with other omitted district

level determinants of village outcomes. Because our regression is over-identified, we test the

validity of district allocations as instruments by including them as regressors, thereby excluding

them from the set of instruments. Results using this expanded regressor set (regressions 2 and 4 in

the table) suggest no independent effect of these regressors on investments in SC or OC

habitations. The explanatory power of the instrument set and hence of SGRFY income is,

however, reduced by this exclusion.

6.3 The magnitude of SGRY effects

The coefficient on SGRY income in these regressions reflects the additional effect of

SGRY income, controlling for any effect through total income. Correspondingly, the marginal

effect of SGRY income on infrastructural investments in SC habitations reflects the summation of

the coefficient on total and SGRY income. Our estimate of 0.36 implies an elasticity of 0.37. This

is far less than the elasticity of infrastructural investments in other caste communities with respect

to village income, estimated at 0.85. Thus, though central mandates reduce inequality in

infrastructure, forcing village governments to invest more in SC habitations than they otherwise

would, these mandates are not sufficient to eliminate infrastructural differences.

24

It is not possible to use our regression results to contrast infrastructural investments in SC

habitations in economies subject to central mandates to those in a decentralized economy where

the village government has full control over the allocation of income. Though it is tempting to

infer the effect of decentralized income from the coefficient on own income in the SC regression

in table 7, this is not valid; income effects in an economy subject to central mandates may bear

little relation to the coefficient one would obtain in an unobserved counterfactual situation, when

allocations are determined by the village government without any central influence.

However, because central mandates force village governments to invest more than they

would like in SC habitations, a comparison of investment levels across SC and other caste

habitations, even when SC investments are centrally mandated, provides a lower bound on the

inequality that would result if the economy were fully decentralized. Thus, comparisons of

expenditure on SC and other caste habitations are still instructive.

Figure 1 uses the results of table 7 to graph predicted SC and upper caste investments

against village income. We standardize for differences in the population size of different castes

by setting the proportion of SC households in each village to 0.5. Even though investments in

other caste habitations are not subject to central mandates, binding expenditures on scheduled

castes nevertheless reduce investments in upper caste habitations, because the need to devote

some share of income to scheduled castes implies a reduction in unconstrained village income.

Investments in upper caste habitations in an economy subject to central mandates will therefore

differ from those in an unconstrained, fully decentralized economy. To gauge this difference, we

use two estimates of investments in other caste habitations. The top-most graph estimates

investments using full village income, and hence represents outcomes in a fully decentralized

economy. The second graph defines income as village income net of expenditures on SC

habitations, and applies to an economy subject to central mandates. The bottom graph is predicted

investments in SC habitations with binding central mandates.

Figure 1 reveals the substantial inequality in infrastructural expenditures in SC and OC

habitations that maintains even with effective centralized mandates (a comparison of the lowest

two graphs). This inequality increases with decentralization, as the government’s unconstrained

income increases (top graph). As previously noted, we are only able to provide a lower bound of

25

the effect of decentralization on infrastructure inequality; it will be larger, because investments in

SC communities will be lower in the absence of mandates.

6.4 Effect of SGRY mandates on village public goods

Even though centrally mandated expenditures in SC habitations do not increase investments in

upper caste habitations, their effects on village public goods such as schools may be stronger.

Because village public goods are critical for the long-term welfare of scheduled castes, any

evaluation of central mandates must include an assessment of their effect on these goods.

Table 8 reports results from probit regressions of village government funding for

electricity projects and elementary schools on total and SGRY income, using the same

instruments and regressors as in previous regressions on habitation-specific investments. The

regressions reveal that SGRY income reduces the probability that the village government invests

in either village schools or electricity projects. This negative effect counteracts the positive effect

of SGRY mandates on habitation-specific infrastructural investments, generating an ambiguous

effect of central mandates on overall SC welfare. It also supports the need for caution when

evaluating the effects of any particular program; any direct positive effects may well be negated

through negative spillover effects on other goods not covered by the program in question.

7 Explaining the Effect of Central Mandates on Village Public Goods

It is unlikely that the independent negative effect of SGRY incomes on village public goods is a

consequence of non-separabilities in preferences between infrastructural goods and schooling or

electricity projects. On the contrary, one would expect that investments in sanitation, the primary

item of expenditure in infrastructural investments, would be complementary to schooling

expenditures, so that SGRY-mandated increases in sanitation would increase investments in

schooling. Nor is it likely to be a consequence of the effect of mandated transfers on capture of

village funds by local elites. If this were so, SGRY funding should increase infrastructural

investments in upper caste habitations. Our previous regression results provide no support for this

hypothesis. Given that SGRY funding does not increase investments in upper caste habitations,

26

explanations for its effect on schools and other shared public goods must be sought in features

specific to the production of these goods.

Restricting our attention to schooling, we hypothesize that such effects operate through

the schooling production function, with schooling outcomes varying with the extent of caste

fractionalization in the student population. Our explanation emphasizes changes in the caste

profile of the user population of village public goods caused by effective central targeting, the

effect of this on exit to the private sector, and the effect of exit on village support for public goods

which are local to the village.

We augment the theoretical framework of Section 3 to allow schooling outcomes to be

affected not just by government spending on schools, z, but also, adversely, by the extent of caste

fragmentation in the student population (Fr). Households choose between government and private

schools. Government schools are free, but private schools charge a fixed fee, q, which we assume

that scheduled castes cannot afford.17 The only decision facing scheduled castes, therefore, is

whether or not to enroll their child in school. Upper castes choose between government and

private schools by comparing expected utility under each of these options. Thus, school choice

outcomes (for both castes) reflect expectations of government schooling expenditures in the

upcoming school year, the extent of caste fractionalization in government schools, private school

fees, and other determinants of household utility including income.

Because the income of SC households increases with infrastructural investments in SC

habitations and hence with I ( if constraint (3) is binding, as we assume for the remainder of this

section), increases in I imply an increase in the proportion of SC students in government schools.

I does not, however, independently affect upper caste incomes. Thus, under the assumption that

government schools are initially dominated by upper caste students, an increase in I increases

caste fractionalization in the student population of government schools ( 0)(),( >′= IfIfFrg ).

In private schools, because entry is restricted to upper caste students, Frp=0.18

17 This assumption is made only for the purpose of simplicity in exposition. 18 This assumption can be relaxed; we only require fractionalization to be lower in private schools.

27

We make the following assumptions regarding the timing of decisions, following closely

the actual timing of events in the Indian economy.19 Each year (t) is divided into three periods.

The Central Government announces its programs and allocations for the year, including tI , in

period (s-1). Households subsequently choose schools at the start of the school year in period s,

based in part on expectations of their own income, government resources for schooling in the

upcoming year, and the caste composition of the student body. We assume that Et(zt)=zt-1 , while

household expectations regarding the nature of the student body are updated with news of

),()(, 1 ttttt IFrfFrEI −= . These decisions yield the proportion of scheduled caste students

who attend government schools, ),,( 11 tttt IFrzp −− , and the proportion of upper caste students

who choose government schools over private schools, ),( 1,11 ttt IFrz −−δ . Finally, in period (s+1),

the village government announces its budget, based on the division of households into those who

attend government schools and those who do not. Figure 2 illustrates this time line.

Central Govt announces tI

Households make school choice decisions based on tI -

period (s-1) period s Period (s+1)

Village govt determines investments zt, based on pt, δt and Frt

Figure 2

The division of the village population into households who send their children to private

schools and those that remain in the government sector modifies the village government’s

objective function, (6). For expositional purposes, we suppress the dependence of school choice

outcomes on lagged variables (zt-1, Frt-1), retaining only the dependence on I . As before, π

represents the proportion of scheduled caste households in the village population. Let Rt

determine school resources in private schools. To determine schooling investments, the

government of village i now maximizes the following function:

19 The central government operates on a April-March fiscal year, with allocations announced in April. The school year starts in August. Panchayats make their decisions after receiving central and state funds.

28

(10) max +))(,,()( ttttsttii IFrzIvIpπ

)],())(1())(,,()([)1( tttuuttttttuutti RqyvIIFrzyvI −−+− δδϕπ

Subject to the budget constraint (8). This generates the following first order conditions for z:

(11) 0),,()()1(),()( =−∂∂

−+∂∂

λϕδππ ttutt

uttitt

t

stiti Izy

zv

IIzzv

Ip

Comparing (11) to 7(b) reveals that I affects the government’s schooling allocation not

just through a conventional substitution effect with z (as in 7b), but also because of its effect on

the schooling choices of both scheduled and other caste households. While there is no reason to

expect conventional substitution effects to be large, the effect through school choice may well be.

Though I increases enrollments of SCs in government schools, it also reduces government

school enrollments by upper castes. If local capture is pervasive, so that government allocations

primarily reflect the preferences of upper castes, then this latter effect will dominate. Thus,

increases in I can generate reductions in village government support for schools.

A prediction of this model is that SGRY income increases caste fractionalization in the

student population of government primary schools and enrollments in private schools. We test

these hypotheses using data we collected on the caste composition of students in government

schools and on private school enrollments. Importantly, this includes data on the number of

students enrolled in private schools outside the village; while only 43% of villages reported

private schools within the village, the market for private schools extends beyond the boundaries

of the village, with many students enrolled in schools in other villages.

`We measure caste fractionalization in government schools by the conventional measure

, where s∑∑ −=−=j

jjj

j sssFr 21)1( j is the proportion of caste group j in the student

population. This statistic reflects the probability that two randomly drawn students from the

school population are from distinct caste groups. We construct it using the three political caste

groups in the village economy: scheduled castes, other backward castes and upper castes.

29

As before, regressors include total income and SGRY income, and we instrument both by

the instruments previously described. Additional regressors are also the same, except for the

inclusion of caste_het, the extent of caste fractionalization in the village population, and the size

of the school-age population (children ages 6-14). Because of the inclusion of these additional

regressors, we re-run the regression on village government schooling expenditures, to confirm

that the negative effect of SGRY remains, even in this expanded regression.

The regression results (table 9) confirm the basic prediction of this model; effective

central mandates which improve the welfare of scheduled castes increase the extent of

fractionalization in government schools. They also increase private school enrollments.

Of other regressors, village caste fractionalization is, as expected, an important

determinant of caste fractionalization in government schools. It does not, however, directly

increase private school enrollments. This does not contradict the hypothesis that caste

fractionalization reduces the demand for government schools; while this is a statement about the

demand for schools, table 10 reports results from reduced form regressions on the determinants of

both the demand for, and supply of, private schooling. It is possible that villages with greater

caste fractionalization, while characterized by a greater demand for private schools, are also less

likely to have a private school locate in them, increasing private schooling costs and hence

reducing enrollments. If so, the negative effect on supply may offset any positive demand effect.

The reduced form regressions reported in table 9 provide broad support for our thesis;

effective central mandates which improve the welfare of scheduled castes increase

fractionalization in the student population of government schools and simultaneously increase

exit to private schools. They do not, however, link exit from government schools to the caste

composition of their students. Nor do they demonstrate that the negative effect of SGRY funds on

village government support for schools is a consequence of changes in caste composition and the

number of village households who benefit from government schools.

Testing these hypotheses is harder; it requires regressions on school funding which

condition on village income, SGRY allocations, school caste fractionalization and the proportion

of students in private schools, and which treat the endogeneity of all four variables. IV

30

regressions of school funding on caste fractionalization and private school enrollments

accompanied by standard over-identification tests which examine whether the determinants of

SGRY income affect school funding only through these variables will produce misleading results,

unless we are willing to maintain that preferences for schooling are separable from the

infrastructural investments which SGRY supports. Consequently, we require independent

instruments for all four variables.

Lacking instruments for the caste composition of the student body,20 we test a weaker

hypothesis, namely, that I affects government schooling allocations through its effect on private

school enrollments (without addressing the question of whether this is through its effect on caste

fragmentation or not). To do this, we instrument private school enrollments with private school

fees. We use a district average of reported private school fees for this purpose, recognizing that

the market for private schools extends beyond the village. Doing so reduces the possibility that

our measure of private fees is correlated with village conditions. However, as before, we remain

concerned that this district level variable may be correlated with other unobserved district level

determinants of village outcomes.

Stronger identification comes from interacting district private school fees with

determinants of government school quality: private school fees will matter less in villages where

government quality is very low. Since government spending on schools is determined primarily

on the basis of school enrollments, and this is in turn determined by the village school-age

population (children ages 6-14), we use an interaction of private fees with the school-age

population. 21 We expect the coefficient on this interacted term to be positive; larger student

bodies reduce the quality of government schools, since many government programs provide

funding for school at a uniform rate for all schools.22 We cannot, however, use over-identifying

tests to test the validity of our instrument. Given this, we regard our results as suggestive,

requiring confirmation based on stronger instruments.

20 Using village caste heterogeneity as an instrument for school fractionalization would generate biased results, because it will also affect investments in other public goods which directly affect school funding. 21 This is true of teachers, classrooms, and also for government financial grants to schools. 22 For example, the Government of India’s premier program for schools, the Sarva Shiksha Abhiyan, provides a standard school grant of Rs. 2000 per school, and an additional repairs and maintenance grant of Rs. 4000 for schools with less than 3 classrooms, and Rs. 7500 for larger schools.

31

Regression results are reported in table 11, along with the results of first stage

regressions. The first stage regressions confirm the explanatory power of private school fees in

explaining enrollments. However, private school fees also affect village income, validating

concerns regarding the use of a district-level variable as an instrument. On the other hand, the

interaction of school fees with the child-age population, while it is a significant determinant of

private school enrollments, has no effect on either village income or SGRY allocations.

The results confirm that private school enrollments do reduce village government support

for schools. Further, conditioning on private enrollments reduces the coefficient on SGRY funds,

so that they are no longer a significant determinant of village government schooling expenditures.

Thus, the results confirm that the negative effect of SGRY partly reflects its effect on exit to the

private sector and the reduced support for government schools which follows increased private

sector enrollments.

The analysis of this section therefore suggests that though village elites may not oppose

improvements in the welfare of scheduled castes, they are unwilling to share public goods with

them; central mandates increase investments in the goods they target but simultaneously increase

schooling segregation. We also show that increased schooling segregation in the form of exit to

the private sector reduces village government support for schools. This reduction may have far

greater repercussions on the welfare of scheduled castes then the positive effect of central

mandates on infrastructural investments in scheduled caste habitations.

8 Conclusion

The governments of many developing economies are increasingly resorting to a mix

of centralized mandates and decentralized administration to improve the quality of public

goods while simultaneously ensuring equity objectives. There is little evidence on whether

such strategies work. This paper evaluates one such program implemented by the

Government of India. The program is accompanied by mandates requiring some proportion of

program funds to be spent exclusively on investments that benefit members of scheduled

32

castes. Targeting to scheduled castes is possible because of the extensive residential

segregation by caste which exists within villages.

We solve the difficulty of separating the effect of centrally controlled funds from that

of other sources of village income by exploiting conditions in the North Indian state of

Punjab. In this state, village governments have significant “own” resources as a consequence

of historical endowments of common property land. Using these land endowments, in

combination with rules that dictate the allocation of government funds to village economies,

we find that central mandates do achieve their objectives of increasing infrastructural

investments in scheduled caste habitations and reducing existing levels of inequality in the

availability of infrastructure by caste. This suggests that central mandates could be effectively

used in segregated communities to target funds towards public goods utilized primarily by the

poor. However, even with binding central mandates, our results show that the extent of

inequality in infrastructural investments across habitations remains substantial.

Moreover, the overall effect on the welfare of scheduled castes is unclear because not

all public goods are provided at the level of sub-habitations of the village; an important class

of goods, such as schools and health facilities, must be shared by all village citizens. We find

that investments under this program reduce village government support for such goods. This

finding suggests that the direct effects of many welfare programs can be negated through

indirect spillover effects.

We suggest that the negative effect on village public goods of allocations intended to

improve the welfare of minorities may be a consequence of the same factors which, in the

past, created the residential segregation of village households along caste lines. If households

are averse to the sharing of public goods, then they will resist this by increased levels of

segregation. For goods which must be provided at the level of the village, this will take the

shape of increased demand for private substitutes. As hypothesized by many, exit from

government provided public goods will make it difficult for governments to rely on a

decentralized strategy, which entrusts the functioning of these goods to village governments.

Our results provide broad support for this hypothesis.

33

References

Alesina, Alberto, Reza Baqir and William Easterly. 1999. “Public Goods and Ethnic Division.” Quarterly Journal of Economics 114(4):1243-1284. Banerjee, Abhijit, Lakshmi Iyer and Rohini Somanathan. 2005. “history, Social Division and Public Goods in Rural India.” Journal of the European Economic Association 3(2-3): 639-647. Bardhan, Pranab and Dilip Mookherjee. 2006a “Decentralization and Accountability in Infrastructure Delivery in Developing Countries.” Economic Journal 116(508):101-127. Bardhan, Pranab, and Dilip Mookherjee. 2006b. “Pro-poor Targeting and Accountability of Local Governments in West Bengal.” Journal of Development Economics 79:303-327. Bardhan, Pranab and Dilip Mookherjee. 2000. “Capture and Governance at Local and National Levels.” American Economic Review 90(2):135-39. Bénabou, R. 1996. “Equity and Efficiency in Human Capital Investment: The Local Connection.” Review of Economic Studies 63:237-264. Centre for Research in Rural and Industrial Development. 2002. State Development Report of Punjab. Report prepared for the Government of India, Planning Commission. Faguet, Jean-Paul. 2004. “Does Decentralization Increase Government Responsiveness to Local Needs? Evidence from Bolivia.” Journal of Public Economics 88: 867-893. Fershtman, Chaim, Kevin M. Murphy and Yoram Weiss. 1996. “Social Status, Education, and Growth.” Journal of Political Economy 104(1):108-132. Foster, Andrew and Mark Rosenzweig. 2002. “Democratization, Decentralization and the Distribution of Local Public Goods in a Poor Rural Economy.” Manuscript. Galasso, Emanuela and Martin Ravallion. 1999. “Distributional Outcomes of a Decentralized Welfare Program.” Washington, D.C.: The World Bank, Working paper. Government of Punjab. 1006. Department of Planning. Annual Plan 2006-07. Government of Punjab. 2002. “Report of the Public Expenditure Reforms Commission, Punjab.” Grossman, Gene and Elhanan Helpman. 1996. “Electoral Competition and Special Interest Politics.” Review of Economic Studies 63:265-286. Rajan, Raghuram and Luigi Zingales. 1995. “The Tyranny of the Inefficient: An Enquiry into the Adverse Consequences of Power Struggles.” Cambridge, Massachusetts: National Bureau of Economic Research, Working Paper #5396. World Bank. 2000. Overview of Rural Decentralization in India. Manuscript.

34

Table 1. Socio-economic characteristics of sample villages Variable Full sample

(mean) Villages with shamlat land

Village without shamlat land

Total village population (census 2001)

1394.78 (1295.60)

1392.87 (1251.77)

1402.05 (1462.18)

Proportion SC population (census 2001)

0.34 (0.21)

0.34 (0.20)

0.33 (0.26)

Total village households (survey)

257.63 (235.47)

258.09 (226.95)

255.87 (267.58)

Proportion SC households (survey)

0.37 (0.23)

0.38 (0.21)

0.36 (0.27)

Number of villages with SC vehra 223

Proportion of below-poverty-line households in village

0.13 (0.17)

0.14 (0.16)

0.12 (0.18)

Proportion of landless hholds 0.46

(0.20) 0.46

(0.18) 0.46

(0.23)

Land area of village (acres) 1031.01 (1162.85)

Shamlat land (acres) 22.00 (48.67)

Schooling:

Proportion school-age children in private schools

0.35 (0.20)

Ratio of SC children to total students in govt. primary school

0.58 (0.26)

Ratio of SC children to total students in village pvt schools

0.17 (0.17)

Note: Data are from the survey of 300 villages.

35

Table 2.—Panchayat Income, 2004-05 and 2005-06, Survey Villages

2004-05 2005-06 Income source Mean Std. Dev Mean Std. Dev

House tax 110.07 (0.0004)

767.55 119.81 (0.0006)

899.79

Shamlat land 60,579.25 (25.61)

138,991.70 64,496.98 (33.96)

151.555.3

Other rental income from own property

4,512.17 (1.91)

20,181.22 4,774.34 (2.51)

22,140.02

SGRY (total) 50,521.87 (21.36)

63,615.00 33,092.51 (17.42)

46,496.43

Of which, SGRY direct to Panchayat

13,094.48 (5.53)

24,023.29 8,613.05 (4.53)

21,258.74

Other Central Govt. grants

10,746.1 (4.54)

55,607.64 5,498.88 (2.90)

32,155.88

MPLADF 50,606.78 (21.39)

246,217.7 28,366.1 (14.94)

69,928.84

State Govt. grants 36,868.47 (15.58)

145,422.40 42,444.07 (22.35)

117,266.9

Total Income 236,578.0

(100.00) 358,416.2 189,923.0

(100.00) 220,636.1

Sample size 295 295 Note: Figures in brackets are percentages to total income. All amounts are in Rupees.

36

Table 3.—Panchayat Expenditure, 2004-05 and 2005-06, Survey Villages, by location

2004-05 2005-06 Item Total In SC Vehra Total In SC Vehra

Electricity projects 2,321.57 (20,240.39)

1.48%

-- 1,814.05 (10.735.5)

0.92%

--

Irrigation 4,471.19 (18,317.82)

2.85%

-- 3135.59 (15,311.85)

1.59%

134.53 (2,008.95)

0.22%

Drinking water 4944.89 (18,958.19)

3.15%

2,145.29 (7,938.73)

3.11%

8,914.58 (54,617.18)

4.51%

5,402.24 (34,015.46)

8.73%

Sanitation projects 75,971.9 (149,596.5)

48.39%

45,514.33 (143,439.9)

66.04%

101,372.6 (400,660.1)

51.28%

32,511.26 (53,725.46)

52.53%

Local roads 21,297.63 (77,257.56)

13.56%

3,117.94 (14,283.42)

4.52%

31,562.46 (119,878.1)

15.97%

7,673.77 (31,347.75)

12.40%

Schools & schooling items

15,152.54 (47,522.65)

9.65%

721.97 (6064.51)

1.05%

15,233.66 (46,258.74)

7.71%

753.36 (6540.74)

1.22%

Health centers 2,671.19 (40,882.99)

1.70%

-- 1,545.09 (11,459.63)

0.78%

--

Street lighting 1,953.73 (14,665.9)

1.24%

713.00 (5,619.96)

1.03%

575.25 (6,579.92)

0.29%

165.92 (1,460.23)

0.27%

Panchayat Building 10,685,76 (36,262.94)

9.34%

-- 13,762.27 (34,659.68)

6.96%

--

Other projects 17,540.43 (40,101.31)

11.17%

16,708.52 (49,816.75)

(24.24)

19,778.87 (47,685.3)

10.0%

15,252.91 (36,779.79)

24.64%

Total Expenditure 157,010.4 (215,521.5)

100.00%

68,921.06 (153,239.4)

100.00%

197,694.4 (450,527.5)

100.00%

61,894.00 (83,020.8) 100.00%

Note: Figures in brackets are standard deviations. Percentages reported are percentages to total expenditure in the respective column. All amounts are in Rupees. Sample villages=295.

37

Table 4.—Proportion of Village Panchayats Reporting Expenditure by Item, 2004-05 and 2005-06 combined.

Total SC Vehra Item Mean Std. Dev Mean Std. dev

Electricity projects 0.17 0.18 - -

Irrigation 0.12 0.33 0.005 0.07

Drinking water 0.31 0.46 0.21 0.41

Sanitation projects 0.87 0.34 0.82 0.39

Local roads 0.29 0.45 0.16 0.36

Schools & schooling items

0.31 0.46 0.03 0.18

Health centers 0.07 0.27 - -

Street lighting 0.07 0.25 0.04 0.18

Panchayat Building 0.35 0.48 - -

Sample size 295 223

38

Table 5. Effect of Shamlat land on Different Sources of Panchayat Income Sample: Villages with SC habitation

Variable CPR income Total Income Total – CPR income

SGRY Income

Shamlat land (acres)

6.57*

(1.39) 6.49*

(1.89) -0.09 (1.49)

0.628 (0.374)

Shamlat land square -0.028*

(0.009) -0.023*

(0.01) 0.005

(0.006)

-0.002 (0.001)

Village Wage (Rs.)

0.33*

(0.14) 0.37

(0.62) 0.04

(0.51)

0.491+

(0.270)

Population (2001) 0.03 (0.03)

0.19*

(0.06) 0.16*

(0.05)

0.033*

(0.015)

Population square -1.37 e-6 (4.12 e-6)

-1.19 e-5 (0.90 e-5)

-0.00001 (8.5 e-6)

-3.07 e-6 (2.62 e-6)

Prop. SC pop 4.93 (35.95)

148.18 (129.11)

143.25 (133.13)

42.57+

(23.93)

Prop. BPL -26.96 (69.72)

281.07 (170.82)

308.03+

(180.031)

96.97+

(49.68)

Prop SC population below poverty line

0.03 (0.43)

-0.97 (1.05)

-1.00 (0.96)

-0.184 (0.331)

Female literacy rate -0.42 (0.71)

0.27 (1.44)

0.70 (0.99)

0.104 (0.488)

Village area -0.02 (0.02)

-0.03 (0.03)

-0.012 (0.020)

-0.006 (0.007)

Sample size 213 213 213 213 Regression R2 0.37 0.295 0.22 0.16 Note: Regressors include measures of land distribution in the village (proportion of households with land in 6 different size categories). All standard errors are clustered at the district level. Income is in Rs. ‘000.

* Significant at a 5% level + Significant at a 10% level

39

Table 6. First Stage Regressions for Total and SGRY income Sample: Villages with SC habitations

Variable Total Income (Rs. ‘000) SGRY income (Rs. ‘000)

Shamlat land 5.525* (2.484) -0.804 (0.646)

Shamlat land square -0.020* (0.009) 0.0004 (0.002)

District SGRY income 0.030 (0.465) -0.194 (0.121)

District SGRY per panchayat -92.248 (309.226) 155.28* (80.45)

SGRY per panch*shamlat land 0.255 (1.439) 0.982* (0.374)

SGRY per panch*village wage 0.275 (1.665) -1.019* (0.433)

# panchayats -0.148 (0.441) 0.187+ (0.115)

Village wage -0.178 (1.724) 1.521* (0.449)

Population 0.219+ (0.056) 0.041* (0.015)

Population square -0.00002+ (0.00001) -5.23 e-6* (2.61 e-6)

Prop. SC 120.30 (128.78) 77.25* (33.51)

Prop. BPL 251.78 (271.23) 83.15 (70.57)

Prop SC who are BPL -0.833 (1.50) -0.098 (0.390)

Female literacy rate 0.233 (1.897) 0.444 (0.493)

Village area -0.035 (0.032) -0.006 (0.008)

Number of observations 213 213 Regression R2 0.2871 0.2483 Regression F(21,191) 3.66 3.01 Note: Regressors include measures of land distribution in the village (proportion of households with land in 6 different size categories). All standard errors are clustered at the district level. Income is in Rs. ‘000.

* Significant at a 5% level + Significant at a 10% level

40

Table 7. Testing the Effect of Central Mandates (Sample: Villages with SC habitations)

Investments in SC habitation Investments in Other Caste habitations

Variable

Regression 1 Regression 2 Regression 3 Regression 4 Total income 0.125*

(0.053) 0.198*

(0.079) 0.361*

(0.088) 0.382*

(0.082)

SGRY income 0.242*

(0.114) 0.187

(0.166) -0.132 (0.286)

0.102 (0.317)

Village wage 0.146

(0.108) 0.266+

(0.153) -0.227 (0.239)

-0.293 (0.380)

Population -0.027

(0.020) -0.039+

(0.021) 0.027

(0.026) 0.020

(0.027)

Population square

4.98 e-6+

(2.69 e-6) 5.58 e-6+

(2.73 e-6) -4.69 e-6 (4.35 e-6)

-3.84 e-6 (3.79 e-6)

Prop. SC 37.04

(36.13) 48.26

(37.76) -153.64 (102.79)

-188.06 (118.74)

Prop. BPL 35.53

(85.36) 34.33

(88.94) 531.26+

(283.25) 521.48+

(285.34)

Prop SC below poverty line

-0.108 (0.298)

-0.019 (0.346)

-2.59+

(1.29) -2.40*

(1.33)

Female lit rate 0.156 (0.718)

0.159 (0.688)

1.262+

(0.687) 0.872

(0.860)

Village area -0.002 (0.008)

0.005 (0.011)

-0.019 (0.013)

-0.017 (0.017)

District SGRY income

-- -0.015 (0.108)

-- 0.344 (0.367)

Distr. SGRY per panchayat

-- -17.024 (45.916)

-- -132.29 (124.76)

# panchayats -- 0.062

(0.108) -- -0.310

(0.359)

Regression R2 0.156 0.158 0.214 0.1970 Note: IV regressions. See text for instruments and for additional regressors. All standard errors are clustered at the district level. Income is in Rs. ‘000.

* Significant at a 5% level + Significant at a 10% level

41

Table 8. Testing the Effect of Central Mandates on Village Public Goods

Electricity Schools Variable IV probit Marginal effects IV probit Marginal effects

Total income 0.003*

(0.0007) 0.001 0.002*

(0.0006)

0.001

SGRY income -0.007*

(0.002) -0.002 -0.004*

(0.002)

-0.001

Village wage 0.006*

(0.002) 0.002 -0.001

(0.002)

0.0004

Population -0.0002 (0.0004)

-0.0001 -0.0009*

(0.0003)

-0.0003

Population square

-1.62 e-08 (5.64 e-08)

-4.59 e-9 5.44 e-8 (4.07 e-8)

1.90 e-8

Prop. SC 0.239 (0.487)

0.068 -0.423 (0.444)

-0.148

Prop. BPL -0.081 (0.670)

-0.023 0.057 (0.926)

0.020

Prop SC below poverty line

0.007*

(0.003) 0.002 0.003

(0.004)

0.001

Female lit rate 0.0001 (0.006)

0.00004 0.002 (0.007)

0.001

Village area 0.0002 (0.0001)

0.00005 0.0002*

(0.0001)

0.0001

Log likelihood -3756.78 -3616.03 Sample size 290 276 Wald χ2 test of exogeneity

14.01 8.23

Note: IV regressions. See text for instruments and for additional regressors. All standard errors are clustered at the district level. Income is in Rs. ‘000.

* Significant at a 5% level + Significant at a 10% level

42

Table 9. Effect of SGRY funds on caste fractionalization in schools, private schooling and govt funding for schools

Variable School caste fractionalization

(log) Number children in private schools

Village govt funding for schools

Total income (Rs. ‘000)

-0.00003 (0.0001)

-0.0006+

(0.0003) 0.003*

(0.001)

SGRY income (Rs. ‘000)

0.0005*

(0.0002) 0.003*

(0.001) -0.004+

(0.002)

# children, 6-14 0.00001 (0.00004)

0.002*

(0.0003) -0.0004 (0.0005)

Caste fractionalization, village population

0.606*

(0.102) 0.228

(0.383) -0.144 (0.702)

Village wage -0.00005

(0.0002) -0.00007 (0.0009)

-0.001 (0.002)

Population -0.00003

(0.00004) 0.00007 (0.0002)

-0.0008*

(0.0003)

Population square 7.89 e-9 (3.84 e-9)

-2.94 e-08 (2.88 e-08)

5.11 e-8 (4.71 e-8)

Proportion SC -0.323*

(0.058) -0.207 (0.399)

-0.401 (0.457)

Proportion BPL -0.224*

(0.100) -0.867 (0.629)

0.041 (0.928)

Prop. SC who are BPL

0.001+

(0.0006) -0.003 (0.004)

0.003 (0.004)

Female lit rate 0.002*

(0.0006) -0.002 (0.004)

0.003 (0.006)

Village area -7.76 e-6

(7.29 e-6) 0.00009+

(0.00005) 0.0003*

(0.0001)

Number observations 272 272 272 R2 0.4171 0.4139 -3551.71

Note: First two regressions are IV regressions. Regression for school funding is IV-probit (Log likelihood is reported instead of R2 at the bottom of the table). See text for additional regressors. Samples for all regressions are villages with government primary schools. All standard errors are clustered at the district level. * Significant at 5% level + Significant at 10% level

43

Table 10. Government funding for schools, conditioning on private enrollments First stage regressions Govt funding Variable

(log) Pvt. Enrollment

SGRY Income Regression 4

Pvt school fees (district)

-0.002*

(0.001) 0.150

(0.148) -2.322*

(0.777) --

Pvt school fees*school age population

3.22 e-6* (1.57 e-6)

0.0001 (0.0002)

0.001 (0.001)

--

Shamlat land -0.001 (0.003)

-0.308 (0.359)

6.346*

(2.659) --

Shamlat land sq. -6.47 e-6 (4.23 e-6)

-0.001*

(0.001) -0.012*

(0.004) --

District SGRY -0.001+

(0.0008) -0.107 (0.077)

-0.787*

(0.276) --

District SGRY per panchayat

0.218 (0.435)

127.12*

(63.33) 97.912

(172.08) --

District SGRY per panchayat*Shamlat

0.003+

(0.002) 0.86*

(0.236) -0.868 (1.296)

--

District SGRY per panchayat*wage

-0.0.002 (0.002)

-0.85 (0.393)*

0.716 (1.523)

--

Village income --

--

-- 0.002*

(0.0004)

SGRY --

-- -- -0.001 (0.002)

Pvt enrollments -- -- -- -0.858*

(0.294)

Child 1.089*

(0.098) 9.92

(10.36) 16.744

(63.658) 0.892*

(0.399)

Caste fractionalization in population

0.080 (0.232)

-90.54*

(35.13) -39.643 (218.79)

0.040 (0.439)

Sample size 276 276 276 276

Regression R2 0.7265 0.2845 0.2771 -3990.77**

Note: Govt funding regression is IV probit. See text for instruments and for additional regressors. All standard errors are clustered at the district level. Income is in Rs. ‘000. * Significant at a 5% level + Significant at a 10% level ** value of Log Likelihood

44

0

200

400

600

0 500 1000 1500 2000

Village income (Rs. ‘000)

Other caste (full income)

Other caste (income –SC exp)

SC expenditure

Infr

astru

ctur

al e

xpen

ditu

re (R

s. ‘0

00)

Figure 1: Predicted SC and other caste (OC) infrastructural investments, by income Note: Graphs for investments in other caste habitations use two different measures of income, total village income and village income net of SC expenditures.

45

Appendix A. Probit Regression on Probability of SC vehra in village

Variables Coefficient Standard Error

Shamlat land -0.014 (0.015)

Shamlat land squared -0.00002 (0.00002)

District SGRY funds 0.0005 (0.002)

District SGRY funds per panchayat -2.457 (2.239)

District SGRY per panchayat*shamlat 0.018 (0.014)

District SGRY per panchayat*wage 0.011 (0.017)

Number of panchayats in district -0.0009 (0.0017)

Village wage -0.003 (0.017)

Population 0.001* (0.0003)

Population square -1.15 e-7* (3.92 e-8)

Proportion scheduled caste 1.75* (0.438)

Proportion below poverty line -0.966 (0.652)

Proportion SC below poverty line 0.014* (0.005)

Female literacy rate 0.009 (0.008)

Village area -0.0003+ (0.0002)

Distance to town -0.055* (0.019)

Log likelihood -113.48

Sample size 285

Note: All standard errors are clustered at the district level. * Significant at 5% level + Significant at 10% level

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