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
1
In 1953, the government enacted legislation which stipulates that this land serve as common property (The
Punjab Village Common Lands (Regulation) Act of 1953).
3
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 laborintensive 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
U ui  v( yu , z ) , and that of a scheduled caste household by the function U si  v( y s , z ) .
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)
 i v s ( y s , z )  (1   i )  vu ( yu , z )
subject to the budget constraint,
(2)
yu  y s  z  G  I
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)
ys  I
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:
vs
( ys , z)     s  0
y s
(4a)
i
(4b)
(1   i )
(4c)
i
vu
( yu , z )    0
yu
vs
v
( y s , z )  (1   i ) u ( yu , z )    0
z
z
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
i
v
(I , z)    0
y s
12
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, y s , will equal
I . The village government’s optimization problem can then be written as follows:
 i vs ( I , z)  (1   i )  vu ( yu , z)
(5) max
subject to the budget constraint:
(6)
yu  z  G  I  I
This generates the following first order conditions for yu and z:
(7a)
(1   i )
(7b)
i
vu
( yu , z )    0
yu
vs
v
( I , z )  (1   i ) u ( yu , z )    0
z
z
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
13
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 (G  I  I ) ; 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
  (
yu
(8)
(1   i )[ (
I
),    0 . Under this assumption, the first order condition for yu is:
yu vu
v (y , Z)
y
)
( yu , z )  u u
 ( u )]    0
I yu
I
I
14
If    0 , increases in I will induce compensating increases in infrastructural investments in
upper caste habitations. This is also true if    0 , 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 subcenters, 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
11
There are no scheduled tribes in Punjab.
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, I  f (SGRY , Ysc ) , 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)

y ji   0Yi  1 SGRYi  X i  2  u ji
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
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).
15
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 ( Frg  f ( I ), f ( I )  0 ).
In private schools, because entry is restricted to upper caste students, Frp=0.18
17
18
This assumption is made only for the purpose of simplicity in exposition.
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 I t , 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
I t , Et ( Frt )  f ( Frt 1 , I t ) . These decisions yield the proportion of scheduled caste students
who attend government schools, pt ( zt 1 , Frt 1 , I t ) , and the proportion of upper caste students
who choose government schools over private schools,  1 ( z t 1, Frt 1 , I t ) . 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 I t
period (s-1)
Households
make school
choice
decisions
Village govt
determines
investments zt,
based on pt, δt
and Frt
based on I t determines pt ,
δt and Frt.
Period (s+1)
period s
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
 i pi t ( I t )vs ( I t , zt , Frt ( I t )) 
(1   i ) [  t ( I t ) vu ( yu t , zt , Frt ( I t ))  (1   t ( I t ))vu ( yu t  qt , Rt )]
Subject to the budget constraint (8). This generates the following first order conditions for z:
 i pit ( I t )
(11)
v s
v
( z t , I t )  (1   i ) t ( I t ) u ( yut , z t , I t )    0
z t
z t
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
Fr   s j (1  s j )  1   s 2j , where sj is the proportion of caste group j in the student
j
j
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
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Adverse Consequences of Power Struggles.” Cambridge, Massachusetts: National Bureau of
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World Bank. 2000. Overview of Rural Decentralization in India. Manuscript.
34
Table 1. Socio-economic characteristics of sample villages
Variable
Full sample
Villages with
(mean)
shamlat land
Total village population
1394.78
1392.87
(census 2001)
(1295.60)
(1251.77)
Village without
shamlat land
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)
Shamlat land (acres)
1031.01
(1162.85)
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
Income source
2004-05
2005-06
Mean
Std. Dev
119.81
899.79
(0.0006)
Mean
110.07
(0.0004)
Std. Dev
767.55
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
House tax
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
Item
2004-05
Total
In SC Vehra
2,321.57
-(20,240.39)
1.48%
2005-06
Total
In SC Vehra
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%
Electricity projects
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 200506 combined.
Item
Total
SC Vehra
Mean
Std. dev
-
Electricity projects
Mean
0.17
Std. Dev
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
Health centers
0.31
0.46
0.03
0.18
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
SGRY Income
6.49*
(1.89)
Total – CPR
income
-0.09
(1.49)
-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)
213
0.37
213
0.295
213
0.22
213
0.16
Variable
CPR income
Total Income
Shamlat land
(acres)
6.57*
(1.39)
Shamlat land square
Sample size
Regression R2
0.628
(0.374)
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)
0.030
(0.465)
-0.194
(0.121)
(309.226)
155.28*
(80.45)
District SGRY income
District SGRY per panchayat
-92.248
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)
-0.00002+
(0.00001)
-5.23 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)
Population square
(2.61 e-6)
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)
Variable
Investments in SC habitation
Investments in Other Caste
habitations
Regression 3
Regression 4
*
0.361
0.382*
(0.088)
(0.082)
Regression 1
0.125*
(0.053)
Regression 2
0.198*
(0.079)
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)
SGRY
--
-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)
Total income
District
income
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
Variable
Total income
IV probit
0.003*
(0.0007)
Electricity
Marginal effects
0.001
IV probit
0.002*
(0.0006)
Schools
Marginal effects
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
290
276
14.01
8.23
Sample size
Wald χ2 test of
exogeneity
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
-0.00003
(0.0001)
(log) Number children
in private schools
-0.0006+
(0.0003)
Village govt funding
for schools
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)
0.606*
(0.102)
0.228
(0.383)
-0.144
(0.702)
-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)
272
0.4171
272
0.4139
272
-3551.71
Total
‘000)
income
(Rs.
Caste
fractionalization,
village population
Village wage
Number observations
R2
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
Variable
First stage regressions
Govt funding
(log) Pvt.
SGRY
Income
Regression 4
Enrollment
Pvt school fees
-0.002*
0.150
-2.322*
-(district)
(0.001)
(0.148)
(0.777)
Pvt school
fees*school
population
Shamlat land
3.22 e-6*
(1.57 e-6)
0.0001
(0.0002)
0.001
(0.001)
--
-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
Sample size
0.080
(0.232)
-90.54*
(35.13)
-39.643
(218.79)
0.040
(0.439)
276
276
276
276
Regression R2
0.7265
0.2845
0.2771
-3990.77**
age
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
Infrastructural expenditure (Rs. ‘000)
600
Other caste
(full income)
400
Other caste
(income –SC exp)
200
SC expenditure
0
0
500
1000
1500
2000
Village income (Rs. ‘000)
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
-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)
-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
Shamlat land
Population square
Sample size
285
Note: All standard errors are clustered at the district level.
*
Significant at 5% level
+
Significant at 10% level
46