World Development Vol. 38, No. 2, pp. 195–204, 2010
Ó 2009 Elsevier Ltd. All rights reserved
0305-750X/$ - see front matter
www.elsevier.com/locate/worlddev
doi:10.1016/j.worlddev.2009.10.013
Political Market Characteristics and the Provision
of Educational Infrastructure in North India
BENJAMIN CROST
University of California, Berkeley, USA
and
UMA S. KAMBHAMPATI *
University of Reading, UK
Summary. — In this paper, we are concerned with the provision of schools in rural North India, particularly with whether such provision
is determined by the demographic and economic characteristics of the region or whether local democracy also plays a role. We find that
the probability that a governing party loses an election has a positive effect on the provision of schooling infrastructure, while the margin
of victory of the governing party has a negative effect. Political reservation for members of the Scheduled Castes (SCs) has a positive
effect on schooling infrastructure in villages with a large SC population, but a negative effect overall.
Ó 2009 Elsevier Ltd. All rights reserved.
Key words — schooling, India, public goods, political economy, political competition, caste
cess in increasing literacy (male literacy increased by 11.8 percentage points and female literacy by 15 percentage points
since the beginning of the 1990s (Borooah & Iyer, 2005)), India’s performance relative to other countries, particularly China, has been very poor. Thus, the adult literacy rate in India
was only 61% in 2003, as opposed to almost 91% in China
and virtually 100% in the UK. The difference between the male
and female literacy rate was also highest in India, with 73.4%
for males and 47.8% for females, while it was 95.1% for males
and 86.5% for females in China in 2003.
But a closer look at the data shows that educational attainment varies considerably across states within the country (see
Table 2). For example, literacy rates were 49.2% in Bihar,
58.1% in Uttar Pradesh, but 90.9% in Kerala in 2001. To what
extent are these correlated with the socio-economic conditions
of these two states? Table 1 indicates that these two states are
outliers in terms of many of these characteristics. Both have
very low per capita state domestic product levels, with Bihar
at Rs. 5,108 in 2000–1 and UP at Rs. 9,721. This compares
unfavorably with a per capita income of Rs. 16,373 in Andhra
Pradesh, Rs. 16,072 in West Bengal, and Rs. 25,048 in Punjab
(a very prosperous state). Similarly, the proportion of population living below the poverty line in 1999–2000 was 42.6 in Bihar and 31.15 in UP compared to an all India average of 26.
The low level of economic development is also reflected in
the states’ per capita consumption of electricity (Bihar
(146.7) and UP (179.1) compared to an all India average of
364.5) and their credit–deposit ratios (23.2 in Bihar, 30.9 in
UP and an average of 56.0 for India overall) in 1999–2000.
Bihar and UP have also lagged with respect to investments
in education and health as compared to other states in India.
Table 3 reveals that while Kerala has fewer primary schools
per 1,000 people, its primary schools are accessible to a higher
proportion of its population. Its schools also seem to be better
1. INTRODUCTION
The positive effects of education on economic and social
development have been extensively documented and include
increased productivity, accelerated adoption of new technologies, reduced child and adult mortality, increased gender
equality, and increased accountability of governance institutions. Since the returns to education and health are at least
partly public in nature there is an economic rationale for public provision or subsidization of education to offset private under-investment. Indeed, in many developing countries the state
plays a large role in the provision of education. If properly
allocated, public expenditures could help to overcome market
failures in these sectors but evidence seems to indicate that
such market failures are simply reinforced by political market
imperfections (Keefer & Khemani, 2005).
In this paper, we are concerned with the provision of rural
schools in two states in Northern India—Bihar and Uttar Pradesh. Our main question is the extent to which the allocation
of schools in these states is related to political factors as opposed to the demographic and economic characteristics of
the region. Is school allocation merely an outcome of the size
of the village and its prosperity or does its ethnic composition
also play a role? What role does local democracy (in terms of
voter involvement, reservation of seats for minority communities, etc.) play in this allocation? In trying to answer these
questions, the paper contributes to the growing political economy literature on the public provision of goods and services
and on elite capture. The analysis is at village level and therefore allows us to consider the issues at a more disaggregated
level than previous studies (e.g., Banerjee & Somanathan,
2007; Betancourt & Gleason, 2000).
In India the public sector has made large investments in education in the past decade. Thus, public expenditure on education in India as a proportion of total government expenditure
was 12.7% in 2002, which compares well with 12.8% in China
in 1990 and 11.5% in the UK. While there has been some suc-
*
195
Final revision accepted: December 3, 2008.
196
WORLD DEVELOPMENT
Table 1. Socio-economic indicators for a selection of Indian states Source: (1) Per capita electricity consumption: Government of India (2002c). (2) For
urban population and population below the poverty line: Government of India (2002b). (3) Reserve Bank of India (2001). (4) Per capita net state domestic
product: Government of India, 2002a.
States
Ratio of urban Population
(%, 1999–2000)
Per capita consumption
of electricity (1999–2000)
Per Capita net state
domestic product at
current prices (2000–01, Rs)
Credit–deposit ratio
(utilization) 2000
% Living below
poverty line
(1999–2000)
27.1
10.5
37.4
26.0
42.4
33.9
20.8
28.0
27.8
434.0
146.7
840.9
315.0
571.6
924.1
179.1
206.9
364.5
16,373
5,108
19,228
19,463
23,726
25,048
9,721
16,072
NA
65.5
23.2
53.6
41.7
83.4
40.9
30.9
44.9
56.0
15.77
42.6
14.07
12.72
25.02
6.16
31.15
27.02
26.1
Andhra Pradesh
Bihar
Gujarat
Kerala
Maharastra
Punjab
Uttar Pradesh
West Bengal
India
Table 2. Variation in educational attainment by state—2001. Source: Government of India (2001).
State
Uttar Pradesh
Bihar
Kerala
Andhra Pradesh
Gujarat
West Bengal
Literacy (%)
Literacy SC (%)
Literacy ST (%)
Age specific enrollment
rates (6–11 years) (%)
Age specific enrollment
rates (11–14 years) (%)
Male
Female
All
Male
Female
All
Male
Female
All
Male
Female
All
Male
Female
All
70.9
62.2
94.2
70.9
76.5
77.6
43.9
35.2
87.9
51.2
55.6
60.2
58.1
49.2
90.9
61.1
66.4
69.2
59.0
38.7
87.2
60.6
79.2
69.1
28.3
14.1
76.4
39.8
51.2
44.5
44.5
26.9
81.7
50.3
65.6
57.1
46.7
37.6
70.2
46.1
58.1
56.6
18.3
13.3
57.3
24.5
34.6
27.9
33.0
25.9
65.7
35.4
46.5
42.4
79.9
98.2
87.7
105.2
137.9
110.9
50.3
60.5
86.5
102.9
113.6
103.3
65.7
79.9
87.1
104.1
126.2
107.2
48.1
40.7
99.8
53.5
73.0
59.7
25.2
20.7
94.8
44.3
59.5
44.4
37.4
31.3
97.3
49.0
66.5
52.2
Table 3. Inputs into education by state Source: UNDP (2001).
Variable
All India average
Uttar Pradesh
Bihar
Kerala
85.13
85.5
45
29
5.75
66.8
73.15
58
55
4.44
87.44
77.51
52
37
4.88
85.81
84.07
32
30
2.19
Population with primary schools (up to 0.5 kms) in 1978
Population with primary schools (up to 0.5 kms) in 1993
Pupil–teacher ratio in primary school (1992–93)
Pupil–teacher ratio in secondary school (1992–93)
Number of primary schools per 1,000 population (1992–93)
equipped, with pupil–teacher ratios of 32 as opposed to 50 or
above in Bihar and UP. Dreze and Gazdar (1996) found that
almost two-thirds of the teachers employed in the sample
schools that they visited in the state of Uttar Pradesh were absent at the time of the visits. However, problems of this kind
are not confined to Bihar and UP. Similar patterns have been
identified at the all India level by Chaudhury, Hammer, Kremer, Muralidharan, and Halsey Rogers (2006) who found that
one quarter of government primary school teachers in India
were absent from school and only about half of the teachers
present were actually teaching when enumerators arrived at
the schools.
Dreze and Sen (1996) argue that the contrast between UP
and Kerala is caused by different political incentives with regard to the provision of social services in the two states. Keefer
and Khemani (2005), extend this point, arguing that while the
quality of public services is hard to target, it is clear that the
quality and effectiveness of public spending are higher in Kerala. They argue that since the formal political and legal institutions in the two states are the same, the differences must
arise from the “dynamics of political competition (i.e., the
availability of information to voters, the extent of social polarization, and the credibility of political promises) rather than in
differences in the political institutions themselves” (p. 18).
Thus, in UP, the Congress Party dominated electoral politics
and did not confront vigorous competition from other credible
and well-organized alternatives. While some caste and ethnicity-based political parties did emerge—like the Bharatiya
Janata Party, the Bahujan Samaj Party, and the Samajwadi
Party—these parties competed on explicitly clientelist platforms and so were not judged even by the electorate on their
performance with respect to the provision of broad public services like schools and health services.
In this paper, we empirically analyze whether such political
factors have influenced the provision of schools and schooling
infrastructure in UP and Bihar. We argue that, despite these
states being at one end of the socio-economic spectrum, many
of the problems they face are similar in kind (if not degree) to
those elsewhere in India. Therefore, while the results in the paper pertain particularly to two states, they are relevant to
many other less developed states and regions in India.
2. LITERATURE
The question of how public goods are allocated by the public sector in developing countries has recently received increasing attention (see Keefer and Khemani (2005) for a review).
POLITICAL MARKET CHARACTERISTICS AND THE PROVISION OF EDUCATIONAL INFRASTRUCTURE IN NORTH INDIA 197
Much of the literature has concentrated on why public provision in developing countries is rarely as good as might be expected. Is it because too much centralization of decisionmaking and resource allocation results in governments being
unaware of the needs of citizens at a local level? Such centralization of service delivery might also result in cost padding,
service diversion, limited responsiveness to local needs, limited
access, and higher prices (Bardhan & Mookherjee, 2006). On
the other hand, the decentralization of resource allocation
decisions also has its problems, in particular, the “greater capture of these programs by local elites” (Bardhan & Mookherjee, 2000). Thus, with limited political contestability of local
elections, leaders may be susceptible to capture by special
interest groups, slacken effort to improve public services, or
may be incompetent, without facing any risk of losing their
positions. In this case, “accountability, efficiency, and equity
in service delivery may worsen under decentralization” (p.
102).
The literature is generally divided over this issue. While Lieten (1996) and Matthew and Nayak (1996) find that some local village groups in India were subverted by local elites,
Galasso and Ravallion (2005), find that intra-district targeting
failures in schooling programs in Bangladesh are less severe
than inter-district failures, leading them to conclude that local
government is more redistributive than central government.
Bardhan and Mookherjee (2000) argue that special interest
groups find it harder to co-ordinate at the national level, which
decreases their influence and makes centralized decision-making more effective. They also argue that the level of homogeneity of party loyalties, differences in intra-district inequality,
and levels of political awareness may shape the effect of decentralization.
Keefer and Khemani (2005) argue that broad public services
are most vulnerable to political market imperfections (which
include lack of information among voters, social fragmentation of voters, and lack of credibility of political promises),
since it is hard for citizens to evaluate the quality of services
or identify those responsible for improvements. It is therefore
easier for politicians to promise narrowly targeted public
goods where their responsibility is more visible and verifiable.
In our context, this would imply that politicians would promise to build new schools rather than to improve the quality of
existing schools. Keefer and Khemani (2005) also argue that
social services suffer when societies are ethnically divided because people are more likely to vote for candidates that they
identify with, regardless of their performance record.
Probably fueled by the availability of good data and the
large regional variation in socio-economic and political conditions, India has become a favorite subject of study in this area
of research. Besley and Burgess (2002) have examined the effect of newspaper circulation on the responsiveness of Indian
states to shocks in food production. Chattopadhyay and Duflo (2004) and Pande (2003) analyze the effect of mandated
political representation on investment in publicly provided
goods at the state and village levels, respectively. Pande
(2003) finds that mandated reservation in state legislatures increased public sector job quotas for Scheduled Castes (SCs)
but decreased resource allocation to education. In addition,
Besley, Pande, and Rao (2007) in a study of public resource
allocation in South India find that voters are aware of political
discretion in policy making and use their electoral clout to ensure that resources are directed toward themselves. They also
find that low caste households are significantly more likely to
be allocated the Below Poverty Line (BPL) cards when the
village Pradhan (or Head) shares their group identity. They
therefore argue that politicians’ group identity and self-inter-
est are important in the allocation of public resources. Banerjee and Somanathan (2007) and Betancourt and Gleason
(2000) have examined the differences in allocation of several
public goods (among them, the number of teachers and
schools) between Indian districts. Betancourt and Gleason
(2000) find that districts with a high proportion of Muslims
have significantly fewer teachers and districts with a higher
Scheduled Caste and Muslim population receive lower public
inputs into education and health in India. Banerjee and Somanathan (2007) find inter alia that fewer villages have middle
and high schools in districts that are more fragmented along
the lines of caste and in districts with a high proportion of
Muslims and Scheduled Tribes.
While Betancourt and Gleason (2000) and Banerjee and
Somanathan (2007) use the district as their level of analysis,
much of the literature suggests the incentives for, or against,
school provision work at a highly localized level. It is therefore
possible that aggregating to the district level hides distributional aspects that may be important for our understanding
of the issue. Consequently, we take a micro-approach and analyze the allocation of schools across villages in the two North
Indian states of Uttar Pradesh and Bihar. In particular, we
want to understand which village characteristics determine
the presence of schools and teachers, and the quality of schooling infrastructure.
3. INSTITUTIONAL BACKGROUND
The Indian education system is divided into pre-primary,
primary, middle, and secondary schools. Within this, primary
schools include children between 6 and 11 years, while middle
schools include children between 11 and 14 years (though
there is some variability across states regarding whether 11year olds are in primary or middle schools). The number of
primary and upper primary schools in India increased from
0.223 million in 1950–51 to 0.775 million in 1996–97.
Until 1976, education was largely the responsibility of the
states, with the Central Government only specifying standards
and co-ordinating technical and higher educations. However,
since 1976, education has become the joint responsibility of
the states and the center. The 8th five year plan put forward
the target of universalizing elementary education in India
along three lines—universal access, universal retention, and
universal achievement. In its National Policy on Education
(first put forward in 1986 and revised in 1992), the Government of India committed itself to increasing spending on education to 6% of GDP by 2000. Part of this expenditure was
meant to be on improving access to schools, on improving
the quality of schools, and on improving enrollment in
schools.
This culminated in the launch of the District Primary Education Program (DPEP) in 1994 by the Government of India
in collaboration with the World Bank, European Commission,
the Government of Netherlands, UNICEF, and the Department for International Development (UK). These external
donors provided 85% of funding for this program, while
15% came from the state governments. Phase I concentrated
on seven states—Assam, Haryana, Karnataka, Kerala, Maharashtra, Tamil Nadu, and Madhya Pradesh. It therefore
excluded both our sample states, which were included in later
phases in the DPEP (UP in Phase II and Bihar in Phase III
started in 1997).
Having said this, the precursor to the DPEP in Bihar was
the Bihar Education Project (BEP), which was set up in
1992 in seven districts. There were also a number of other
198
WORLD DEVELOPMENT
initiatives and schemes at both the state and the central levels,
including the National Literacy Campaign launched in 1988,
Operation Blackboard (1986), the Mid-day Meal Scheme
(1982), and the Shiksha Karmi Program (1987). These would
have had some effect on the supply side outcomes that we
see in the districts that we are analyzing in this paper. They
are however very difficult to identify at the village level in
our dataset because there is no information about whether
the specific village benefits under these schemes or not. While
five states—Andhra Pradesh, Bihar, UP, Maharashtra, and
Rajasthan—were especially backward on the educational
front in 1991, Bihar became the worst off state in India in educational terms by 2001 (Schmid, 2007).
What determines the allocation of schools across villages in
India? It seems to be a combination of the ability of the village
to lobby for resources and the district and state governments’
incentives to allocate resources to it. Thus, we would expect
village characteristics as well as district level political institutions to play a role. Betancourt and Gleason (2000) identify
three main stylized facts in this decision-making framework.
First, Indian states are the main decision makers in the allocation of resources to health and education at a local level (Thakur, 1995, Chapter 3), since they control both the finances and
the administrative bureaucracies devoted to these activities.
Second, elected members of the state legislature play key roles
in the allocation decisions. Third, the district is the next most
important administrative level after the state, with district officers who have jurisdiction over the district. While the broad
decisions over resource allocation are made by the states, they
are implemented by the districts, which also monitor the provision of public services. As Betancourt and Gleason (2000)
conclude, “local outcomes with respect to the availability of
health and education inputs at the district level are determined
by decision makers in each state, who are politicians elected by
constituencies to the state legislatures, and they do so through
the mediation of state bureaucracies and subject to constraints
imposed by the center,” (p. 2171). The state distributes the
education budget across districts in line with the number of
schools, the number of teachers, enrollment rates, and other
such factors. In addition, there are off-budget items like the
mid-day meal scheme for which almost all funding comes from
the central government based on the number of enrollments.
The state government only provides the premises in which
the scheme operates.
pends on the individuals’ prospects in the labor market and
this might reflect their returns to education (the evidence
regarding differential returns to education by social group is
mixed, see Das & Dutta, 2008; Madeshwaran & Attewell,
2007; Unni, 2001). The elasticity of income with respect to
the public goods is denoted by l (>0, <1), and f denotes the
level of the public goods provided in the village. 2
Following Bardhan and Mookherjee (2006) we assume that
all individuals have concave and homothetic utility functions
over their incomes
uc ¼
y 1q
c
:
1q
ð2Þ
We further assume that the officials in charge of allocating the
public goods do not maximize the aggregate utility of the villages, but rather assign them different welfare weights xv.
These weights are determined by the characteristics of the village, particularly by the political influence of its inhabitants.
In our sample, these weights may depend upon the proportion
of upper castes, Scheduled Castes (SCs), or Muslims in the village, or on the wealth of its inhabitants. For different theories
of how local elites can “capture” the process of political decision-making see for instance Grossman and Helpman (1996)
or Bardhan and Mookherjee (2000).
The demographic weights of the classes within
P a village are
denoted by bcv, which we normalize so that cv bcv ¼ 1. If we
assume that, once a public good is present in the village, nobody can beP
excluded from its use, the whole village receives
utility uv ¼ c bcv ucv from the public goods. This need not,
however, be true. There are a number of cultural and social
factors that may, for instance, prevent Scheduled Caste children from attending schools to which upper caste children
go. In some villages, they may be physically prevented from
accessing public goods. In this case the village utility is just
the sum of the weighted utilities of all classes that benefit from
the school. Consequently, utility received from schools will be
smaller, the larger the fraction of excluded classes in the village. Given the village utility levels, officials choose the allocation to maximize
"
#
"
#
X
X
X
X
X
ðhv Ac fvl Þ1q
xv uv ¼
xv
bcv ucv ¼
xv
bcv
1q
v
v
cv
v
cv
X
fv ¼ f :
subject to the constraint
v
4. THEORETICAL MODEL
We analyze the provision of public services in rural India by
building on a model proposed by Bardhan and Mookherjee
(2006), which considers the allocation of direct government
transfers to villages and individuals. We begin by dividing
the population into classes, which differ in their potential to
benefit from the public goods; schooling in our case. 1 The
classes can be interpreted as representing ethnic, religious, or
economic sub-groups of the population. This categorization
is not meant to be exclusive; the same household could fall
into several classes, for example, it could be Scheduled Caste
as well as landless. The income generated by a member of class
c is
y c ¼ hv Ac fvl ;
ð1Þ
where hv is the village-specific productivity of the public goods,
in our case, for example, the productivity of human capital
manifest in the difference between skilled and unskilled wages.
Ac is the class-specific productivity of the public goods and de-
The relative allocations of the public goods to two villages, a
and b, can then be described as
1
0P
11lð1qÞ
1q
1
1lð1qÞ
1lð1qÞ
bca A1q
c
fa
xa
ha
B ca
C
¼
:
ð3Þ
@P
A
fb
xb
hb
bcb A1q
c
cb
We further assume that the welfare weight of the village is
itself a function of the demographic composition of the
village (b), the demographic composition of the district (B),
and the political factors at both the village and district levels
(P)
xv ¼ xðb; B; P Þ:
As pointed out by Bardhan and Mookherjee (2006), it is
impossible to directly infer the relative welfare weights of the
villages from their relative allocations of the public goods.
For example, a low allocation of the goods can arise due either
to the lower welfare weight assigned to the villagers (i.e., their
political disempowerment), or to their lower returns to the
POLITICAL MARKET CHARACTERISTICS AND THE PROVISION OF EDUCATIONAL INFRASTRUCTURE IN NORTH INDIA 199
public goods (and, as a consequence, their lower preference for
it). The evidence that the members of disadvantaged groups
(Scheduled Castes, Muslims, and women (see Kingdon,
1998) have lower returns to education and tend to prefer direct
income transfers over public goods (see Pande, 2003) shows
that this is indeed a problem in the Indian context.
Nevertheless, it is possible to make some empirical inference
on how the political process allocates public goods, by analyzing the welfare weights in detail. Particularly, we are interested
v
v
in the signs of the partial cross-derivatives, @b@x
and @b@x
,
cv @Bv
cv @P v
which describe how changes in district level demographic composition and political variables affect the political welfare
weights of different classes. The north of India (and particularly Bihar) is well known for the high salience of caste in
the political process. There are influential lobby groups, which
represent caste interests and try to maximize government
transfers to their members. In such a setting, it is likely that
the political influence of a caste in a particular district rises
with the number of its members. We would therefore expect
the welfare weights of a particular caste to be higher in a district in which it represents a larger share of the population
v
(i.e., @b@x
> 0). We would also expect the welfare weight of
cv @Bv
Scheduled Castes to increase through political reservation of
v
parliamentary seats for their members (i.e., @b@x
> 0; see
cv @P v
Pande (2003) for a possible mechanism).
5. EMPIRICAL ESTIMATION
As indicated earlier, we are primarily concerned with the
allocation of public schools across villages in India. We apply
the theoretical framework outlined above using the data from
the village section of the Uttar Pradesh and Bihar Survey of
Living Conditions, which was conducted by the World Bank
in 1997–98. The survey collected detailed information on 120
villages located in 25 districts of the two states. For our analysis we counted government and government-aided schools as
public. The villages in the sample have a total of 124 public
primary schools. Twenty-seven villages have no public primary school and 93 have one or more; the maximum being five
in one village. Twenty-seven of the villages have a public middle school and the other 93 have none.
Unfortunately, for a few villages in our sample, the data on
variables of interest is missing. The unskilled wage is missing
for 1 village, the skilled wage for 3 villages, and the proportion
of households with significant off-farm income is missing for 4
villages. If we were to use only complete observations, we
would have to exclude 7 villages. In order to salvage the
incomplete observations we substituted the district level averages of the variables for the missing values.
(a) Dependent variables
We analyze the factors influencing the number of public primary and middle schools in a village, as well as some characteristics of primary schools, 3 such as the number of teachers,
the presence of usable blackboards, and the physical structure
of the school.
The determinants of the number of public primary and middle schools and the number of teachers within the primary
schools are analyzed by estimating Tobit models. On the other
hand, since the schooling infrastructure variables (the floor
material, physical structure, and the presence of usable blackboards) are not continuous, we analyze the factors influencing
them by estimating ordered Probit models. For these models,
our dependent variables take on values between 0 and 2. For
floor material we assign a value of 2 for schools with cement,
stone, or tile floors, 1 for schools with mud or brick floors, and
0 if there is no primary school present in the village. For physical structure we assign a value of 2 for Pucka schools, 1 for
other schools, and 0 if there is no school in the village. For
blackboards we assign a value of 2 for schools with usable
blackboards, 1 for schools without, and, again, 0 if there is
no school present. The definitions of the dependent variables
are summarized in Table 4.
(b) Factors influencing school infrastructure
In order to keep the empirical model close to the theoretical
one, we include variables that capture its relevant terms: x (the
political welfare weight of the village), h (the productivity
of
P
1q
human capital in the village as a whole), and
ca bca Aca
(the part of the productivity of human capital influenced by
the demographic composition of the village). To achieve this,
we consider demographic, economic, and political variables,
measured at the village and district levels and estimate their effect on the provision of education-related public goods (which
we refer to as schooling infrastructure). It is clear from the discussions in the theoretical section that some of the variables
may affect schooling infrastructure through more than one
mechanism. This makes the interpretation of their coefficients
somewhat ambiguous and we discuss possible mechanisms in
this section.
(c) Demographic variables
We begin by including the number of households in the village to capture the size of each village. We expect that larger
villages are likely to have better schooling infrastructure. This
could be both because large villages are likely to have greater
demand for such infrastructure and because they are likely to
have greater power to lobby for resources and therefore have a
higher welfare weight (x) in the objective function of policy
Table 4. Definition of dependent variables
Variable
Definition
Number of public primary schools
Self-explanatory
Number of public middle schools
Self-explanatory
Number of teachers in public
Takes the value 0 if there is no public primary school, otherwise
primary school
the number of teachers in the school
Blackboards present in pps
Takes the value 0 if there is no public primary school in the village, 1 if there is one without
usable blackboards, and 2 if there is one with usable blackboards
Physical structure of pps
Takes the value 0 if there is no public primary school in the village, 1 if its physical
structure is Katcha or Semi-Pucka, and 2 if it is Pucka
Floor material in pps
Takes the value 0 if there is no public primary school in the village,
1 if its floor material is mud or brick, and 2 if it is cement, stone, or tile
Estimation model
Tobit
Tobit
Tobit
Ordered probit
Ordered probit
Ordered probit
200
WORLD DEVELOPMENT
makers. To capture possible non-linearities in the effect of village size, we also include a quadratic term in this variable.
In addition to this broad size variable, we include a number
of variables that capture the characteristics of the village population and its ability to lobby for resources. In this context,
we include the fraction of households in the village, who belong to the upper castes, which we expect will have a positive
effect on schooling infrastructure in the village. We also include the proportion of Scheduled Caste households in the village, which we expect will have a negative effect, possibly
because these households tend not to have significant power
to lobby officials at the district level. To the extent that these
variables capture the lobbying capacity of the village, they
can also be seen as influencing x. It is also possible that Scheduled Caste households prefer direct monetary transfers to public goods (Pande, 2003). In both Bihar and UP, the Scheduled
Castes and Tribes have begun to dominate the political scene
through the influence of certain explicitly caste-based political
parties like the Bahujan Samaj Party and the Samajwadi
Party. In this context, we may expect that the Scheduled
Castes use political power to direct public resources toward
their members. To allow for this, we include an interaction
term between the number of SC households in the village
and a variable indicating whether the constituency is reserved
for SC candidates. While we cannot be sure whether the village
level
Pfraction of SC affects schooling infrastructure through x
or ca bca A1q
ca , it is likely that the interaction with reservation
works only through its effect on the welfare weight, x. 4 Since
we also expect ethnic groups to have more political power in
areas where they are present in larger numbers, we include
an interaction between the village and district level fractions
of SC. We expect this to have a positive effect if the Scheduled
Castes can translate greater numbers at the district level into
political power and use this power to direct resources toward
their members. In a similar vein, we include the proportion of
Muslim households in the village and an interaction between
village and district level Muslim populations. We hypothesize
that school infrastructure will be poorer in majority Muslim
villages, but that villages with a large Muslim population will
fare better in districts with a large Muslim population.
To capture the possible effect of ethnic diversity, we include a
caste fractionalization index, which is constructed using upper
caste, middle caste, SC, backward agricultural and other
castes, and Muslims as its basis. It is calculated as
P i 2
1 ni¼1 NNtotal
; where Ni is the number of households belonging to caste group i and Ntotal is the number of households in
the village. Since previous studies have found that fractionalized communities are less able to lobby effectively for public
goods (Alesina, Baqir, & Easterly, 1999), we hypothesize that
caste fractionalization has a negative effect on the political welfare weight (x) and will therefore lead to lower allocations of
schooling infrastructure. In addition to these variables, we also
include the population density of the district as a proxy for the
degree of urbanization of the district.
(d) Economic variables
In addition to the demographic variables discussed above,
we include a number of economic variables, reflecting the potential prosperity of the households in these villages. We include the average skilled and unskilled wages in the villages
(the skilled wage is reported in the dataset; we define the unskilled wage as the average of wages in agriculture and construction). These variables are likely to influence schooling
infrastructure through their effect on the returns to education.
A high unskilled wage (and a low skilled wage) means that the
returns to human capital are low in the village and the gains
from keeping children out of school and letting them work
are high. Of course, it is also possible that high wages positively affect the welfare weight of the village, since wealthier
villages may be better connected politically.
As additional proxies for village prosperity, we include the
proportion of households with a significant source of off-farm
income, as well as the proportion of households, which have
members working as rural laborers. Households with a high
fraction of non-farm income may be wealthier and politically
better connected, or they may have a higher return to human
capital (if the return is higher outside agriculture), while rural
laborers are likely to be less wealthy. We also include the proportion of households that are completely landless. We expect
that schooling infrastructure will improve as households have
more access to off-farm income, and decrease with the proportion of rural laborers and landless households.
Finally, we include two variables that help to control for
other specific village level factors and may influence both the
welfare weight (x) and the return to education (h). The first
is an indicator of whether the majority of land in the village
is irrigated. Irrigated land is likely to be more productive
and therefore to sustain more prosperous households. We also
include the ratio of irrigated land to total agricultural land in
the district to indicate whether the district in which the village
is located is prosperous or not. Second, we include an indicator for villages that have access to electricity, which may also
proxy for the quality of infrastructure in general. In addition,
both irrigation and electrification may reflect an organizational ability within the village, which enables it to obtain such
facilities from the landlords and/or the state.
(e) Political variables
The political variables we include relate to the extent of
political competition in the district (as reflected in the margin
of victory of the incumbent party, the extent of party fractionalization, and the probability that an incumbent party loses an
election); political awareness of inhabitants of the village (as
reflected in turnout at elections) and empowerment of minority communities (as reflected in reservation of seats for such
communities in assembly elections).
The political variables are based on the results of state elections. For Bihar, we use the elections of 1977, 1980, 1985,
1990, and 1995; for UP the ones of 1977, 1980, 1985, 1989,
1991, 1993, and 1996. While the variables are available at
the level of the assembly constituency (AC), the rest of our
variables are only available at administrative district level.
We therefore matched assembly constituencies to parliamentary constituencies (PC) using a matching scheme available
from the Election Commission and manually mapped the parliamentary constituencies on to the districts. For each district,
we take the weighted averages over all parliamentary constituencies and all elections in the relevant period.
We include five political variables, reflecting political reservation, electoral turnout, the margin of victory, party turnover, and party fractionalization. For political reservation,
we include a dummy variable that equals 1 if the constituency
is reserved for a candidate from the Scheduled Castes. As indicated earlier, this variable is likely to have a negative impact
on a village within it unless the village happens to be largely
of Scheduled Caste people. Two effects are possible. Pande
(2003) finds that in districts where seats are reserved for SC
candidates, less is spent on education. If this holds in our case,
we would expect schooling infrastructure to be poorer in
POLITICAL MARKET CHARACTERISTICS AND THE PROVISION OF EDUCATIONAL INFRASTRUCTURE IN NORTH INDIA 201
districts where political reservation exists. However, as
pointed out before, it is possible that a predominantly SC village will do better in a district in which seats are reserved for
SC candidates. We attempt to capture this effect by including
an interaction between political reservation and the proportion of Scheduled Caste households in the village. We also include electoral turnout, which is the number of voters as a
fraction of the electorate. This indicates the political activism
of the electorate and may therefore indicate its ability and willingness to lobby for public goods. It is likely that regions with
higher levels of political activism are given greater weight by
policy makers as they attempt to keep voters content.
We include three variables reflecting political competition in
the district. First, the margin of victory is the difference between the winner and the runner-up as a proportion of the voters. This reflects the extent of political competition in the
district and therefore also the amount of pressure on the existing candidates to keep voters happy. There is more pressure on
incumbents with smaller margins of victory and they are therefore more likely to provide public services liberally. This is
particularly true for services that can be easily traced back
to the candidate like the number of schools in a village. It is
less likely to be true for school infrastructure variables, for instance, which are less easy to quantify and harder to trace
back to the politician (see Keefer & Khemani, 2005).
Second, we include party turnover, that is, the probability
that an incumbent party loses an election. Again, as in the
case of the margin of victory, this measures the pressure on
the incumbent party to keep voters in the constituency content. Third, we include a party fractionalization index, which
is calculated in the same way as the caste fractionalization index but here N is the number of votes cast for each political
party. This index measures fractionalization among parties
in the constituency with respect to the fraction of votes cast
for each party. Again, it is a measure of competition amongst
the parties.
A look at the summary statistics in Table 5 shows that
21.3% of constituencies in our sample are reserved for the
Scheduled Castes, which is only marginally larger than the
proportion of SCs in the population as a whole (20.3%). The
average turnout in the sample constituencies is 56.8%, which
is roughly average for India and the average margin of victory
is 12.0%. With 52.5%, the probability that an incumbent party
loses the election appears quite high, but is not unusual for India. In fact, incumbents tend to be at a disadvantage in India
and if an incumbent runs for re-election his/her average probability of winning is only 50% (Uppal, 2009). Overall therefore, the level of political competition in the districts in our
sample is relatively strong.
6. RESULTS
Table 6 presents the results relating to the number of primary and middle schools, as well as the number of teachers
in a village. Table 7 presents the results relating to the provision of schooling infrastructure in a village. In what follows
we will discuss these results in more detail.
Beginning with the village level demographic variables, we
find that the size of the village is one of the most consistently
significant variables in our models. Thus, larger villages tend
to have a larger number of schools and teachers and better
school infrastructure. This is a heartening result and indicates
that the allocation mechanism seems to be working as it is
meant to, directing schooling resources to regions where there
is likely to be greater demand for these resources.
Table 5. Summary statistics of the explanatory variables
Village level variables
Public primary schools
Private primary schools
Public middle schools
Private middle schools
Village size (households)
Caste fractionalization index
Upper caste (fraction of
households in village)
Scheduled Caste (fraction
of households in village)
Muslim (fraction of
households in village)
Skilled wage
Unskilled wage
Access to off-farm income
(fraction of households in village)
Rural laborers (fraction of
households in village)
Landless (fraction of
households in village)
Village irrigated
Village access to electricity
District level variables
Irrigated land (district level
fraction of agricultural land)
Population density
Scheduled Caste population
(district level fraction)
Muslim population
(district level fraction)
Political variables
Political reservation (for SC)
Margin of victory
Party fractionalization
Party turnover
Turnout
Mean
Standard deviation
1.033
0.283
0.233
0.142
260.0
0.688
0.137
0.819
0.611
0.425
0.350
162.9
0.215
0.200
0.274
0.208
0.119
0.227
64.5
29.0
0.392
13.5
6.4
0.256
0.399
0.221
0.236
0.209
0.683
0.525
0.467
0.501
0.418
0.181
6.10
0.203
2.44
0.055
0.123
0.086
0.213
0.120
0.715
0.525
0.568
0.126
0.042
0.034
0.096
0.066
We also find that the presence of primary schools, teachers,
and the quality of infrastructure is significantly positively related to the extent of irrigation (IRRIG) in a village. This result might reflect two effects. First, irrigation may reflect the
prosperity of a region, and the regions that are more prosperous are likely to demand more schooling services than poorer
regions. Second, irrigation may well reflect the organizational
capabilities and expectations of a village. Villages that are able
to organize to procure irrigation infrastructure (either via
grassroots democracy or through a benevolent landlord) are
likely to be able to make use of the same organizational infrastructure to procure good quality schools.
Our results also indicate that predominantly upper caste villages are likely to have more primary schools and also to be
better equipped with teachers and blackboards. The finding
that caste and ethnicity matter for the allocation of educational infrastructure is reinforced by the result that predominantly Muslim villages are less likely to have schools with
blackboards.
Does economic prosperity have an impact on school allocation? Our results indicate that while skilled wages do not
significantly influence school infrastructure in a village,
unskilled wages do. Surprisingly, the effect is contrary to
expectations. We find that higher unskilled wages in a village
202
WORLD DEVELOPMENT
Table 6. Factors Influencing the number of schools and teachers in a village
Number of public
primary schools
Number of public
middle schools
Number of
teachers
Marginal effect
Standard error
Marginal effect
Standard error
Marginal effect
Standard error
8.08*
4.28
7.47
9.74
10.2
12.3
0.27
0.0087***
6.8 * 106***
0.059
1.00**
1.01
0.13
6.7 * 104
0.046**
0.66**
0.66
0.58
0.72***
0.31*
0.42***
–
0.51
0.0019
2.3 * 106
0.43
0.42
1.40
0.72
0.0075
0.017
0.32
0.40
0.48
0.20
0.18
0.13
–
1.57
0.013***
1.1 * 105**
1.48
0.76
1.70
2.69
0.0099
0.027
0.64
1.16
0.58
0.30
0.43
0.36
1.01
0.0049
5.2 * 106
1.02
0.78
3.14
2.27
0.017
0.035
0.65
0.93
0.98
0.42
0.38
0.42
0.50
0.023***
2.1 * 105***
0.092
3.19***
1.23
2.18
0.0055
0.090*
1.11
1.31
1.03
1.05*
0.38
0.076
–
1.47
0.0055
6.6 * 106
1.24
1.20
4.07
2.05
0.022
0.050
0.90
1.15
1.41
0.55
0.52
0.37
–
District variables
Irrigated land
Population density
Scheduled Caste
Muslim
2.02**
0.068
8.70**
0.89
0.94
0.050
3.88
1.54
4.36**
0.012
9.55
5.19
2.10
0.10
8.60
3.58
4.44
0.11
15.5
0.60
2.70
0.14
11.2
4.45
Political variables
Political reservation
Margin of victory
Party fractionalization
Party turnover
Turnout
5.64**
1.72
8.89**
3.22**
2.80
2.27
2.74
4.10
1.36
3.55
2.99
14.2*
0.25
1.59
11.6
4.72
7.80
8.85
3.00
7.84
3.17
11.9
8.71
4.49
7.01
6.50
7.93
11.8
3.88
10.2
2.18
5.78
27.0**
2.90
7.29
12.0
21.3
13.9
2.14
16.9
16.1
23.9
5.57
9.51
4.73
8.72
21.1
34.2
Constant
Village variables
Bihar dummy
Village size
Village size squared
Caste fractionalization
Upper caste
Scheduled Caste
Muslim
Skilled wage
Unskilled wage
Off-farm income
Rural laborers
Landless households
Irrigated
Access to electricity
Private primary schools
Private middle schools
Interaction variables
Muslim pop. (district village)
Scheduled Caste pop. (district village)
Political reservation Scheduled
Caste pop. (village)
*
Denotes statistically significant at the 10% level.
Denotes statistically significant at the 5% level.
***
Denotes statistically significant at the 1% level.
**
are correlated with worse schooling infrastructure. It is possible high unskilled wages signal that education is not crucial for
earning a living, that is, h is smaller than in villages with low
unskilled wages. Parents in this situation are less concerned
about educating their children and therefore less concerned
about the availability of schooling.
Other village level factors—the availability of off-farm
employment and having a large proportion of landless laborers—also have a significant impact on the different dependant
variables, which is in the expected direction.
Turning to consider whether political competition has any
impact on schooling infrastructure in a village, we look at
the results to the five political variables. We find that one variable—party turnover—has a significant positive effect on the
number of primary schools and the quality of their infrastructure. It therefore seems that political competition is important
in determining schooling provision in rural India. This reinforces Bardhan and Mookherjee’s (2000) argument that “with
limited political contestability of local elections, leaders may
be subjected to capture by special interest groups.” Such elite
capture is less likely when party turnover is high, making the
party more accountable to the electorate. This finding is further reinforced by the fact that a higher margin of victory results in fewer middle schools in the village, which suggests that
politicians’ greater confidence regarding their political position leads to a lower allocation of resources to schooling.
Party fractionalization and party turnover are positively correlated with the presence of primary schools indicating a significant beneficial effect of political competition at the party level.
While party turnover has a significant positive impact on
schooling infrastructure (presence of both blackboard and
pucka structure), party fractionalization has no significant impact on these variables at all.
Our results for the political reservation variable seem to confirm Pande’s (2003) argument that reservation of seats actually
worsens, if anything, the number of public primary schools and
their infrastructure: where significant, political reservation has
a negative effect on both schooling availability and infrastructure. The positive and highly significant coefficient of the interaction term between political reservation and the proportion of
POLITICAL MARKET CHARACTERISTICS AND THE PROVISION OF EDUCATIONAL INFRASTRUCTURE IN NORTH INDIA 203
Table 7. Factors influencing schooling infrastructure in a village
Blackboard
Village variables
Bihar dummy
Village size
Village size squared
Caste fractionalization
Upper caste
Scheduled Caste
Muslim
Skilled wage
Unskilled wage
Off-farm income
Rural laborers
Landless households
Irrigated
Access to electricity
Private primary schools
*
Pucka structure
Cement floor
Coefficient
Standard error
Coefficient
Standard error
Coefficient
Standard error
0.44
0.0088***
6.3 106
0.59
1.63**
0.62
2.53**
0.0030
0.069**
0.63
1.06
2.49**
0.38
0.26
0.28
0.91
0.0033
4.0 106
0.76
0.75
2.49
1.20
0.013
0.031
0.56
0.72
0.89
0.33
0.32
0.23
0.062
0.011***
9.5 106**
1.06
0.68
0.84
0.072
0.017
0.056*
0.91*
0.23
1.88**
0.57*
0.50
0.37*
0.88
0.0034
4.0 106
0.78
0.68
2.54
1.23
0.014
0.031
0.54
0.68
0.86
0.33
0.31
0.22
0.42
0.0079***
5.5 106
0.46
0.39
2.66
1.05
0.011
0.059**
0.65
0.016
1.39*
0.50
0.20
0.23
0.82
0.0031
3.8 106
0.67
0.65
2.24
1.15
0.012
0.028
0.50
0.64
0.78
0.31
0.29
0.21
District variables
Irrigated land
Population density
Scheduled Caste
Muslim
Political variables
Political reservation
Margin of victory
Party fractionalization
Party turnover
Turnout
4.03**
0.098
16.4**
3.72
1.72
0.088
7.15
2.82
2.20
0.12
6.51
0.46
1.58
0.083
6.93
2.71
1.99
0.15*
13.6**
1.04
1.50
0.082
6.49
2.53
8.19**
4.84
10.1
6.02**
6.43
4.09
5.01
7.27
2.43**
6.30
5.74
3.54
7.39
3.87*
1.69
3.92
4.60
7.09
2.29
5.98
6.64*
3.64
6.91
2.03
4.21
3.75
4.38
6.60
2.16
5.65
Interaction variables
Muslim pop. (district village)
Scheduled Caste pop. (district village)
Political reservation Scheduled Caste pop. (village)
Threshold 1
Threshold 2
14.4***
3.51
40.4*
9.17
11.9
5.03
12.7
21.7
7.45
7.49
1.03
3.72
21.5
5.21
6.05
5.27
12.9
21.1
7.22
7.22
2.10
12.5
31.6
4.96
6.10
4.86
11.6
19.6
6.70
6.70
Denotes statistically significant at the 10% level.
Denotes statistically significant at the 5% level.
Denotes statistically significant at the 1% level.
**
***
SC households in the village indicates that, whereas reservation
for SC does not improve schooling infrastructure in general, it
leads to improvements in villages which are of the same caste
identity as the reserved candidate. Thus, reservation works to
the benefit of the group for whom the reservation is being
made. Since the result indicates that Scheduled Caste politicians try to direct educational resources toward SC villages,
the lower allocation of schools to villages with a large fraction
of SC households cannot be explained by their lower preference
for education alone (though that may still play a role). This
group seems to use political power to increase its allocation
of educational infrastructure, when it has the opportunity.
Once again, we find that the probability of having a school with
a blackboard is higher for villages that have a high proportion
of Muslims, which are also located in districts with a large
Muslim population. Thus, while a Muslim village on its own
does not seem to be significantly advantaged or disadvantaged,
Muslim villages which are in districts with a large Muslim
population have an advantage at least in terms of some
schooling infrastructure like blackboards. Again, this is related to the impact of minorities when they are in a strong position politically. Thus, Muslims do seem to value educational
infrastructure and use the political power they receive from
larger numbers to direct educational resources toward themselves.
7. CONCLUSION
In this paper, we considered the provision of schools in rural
India, particularly the influence of demographic and economic
characteristics as well as of local democracy. Is school allocation an outcome of the size of the village and its prosperity?
Does ethnic composition also play a role? What role does
the local democracy play in this allocation? To consider these
issues, we analyzed the impact of these variables on a range of
school availability and infrastructure variables. Political competition is proxied by including the margin of victory of the
governing party in a constituency, the turnover amongst the
governing parties in the last few elections, party fractionalization, and the reservation of a seat for a minority community
candidate.
Our results indicate that one political variable—turnover
amongst the governing parties—has a significant positive effect
on the number of primary schools and the quality of their
infrastructure. This result is reinforced by the fact that a higher margin of victory results in fewer middle schools in the
village. Thus, political competition, as reflected in the ease
with which the incumbent party can be thrown out of power,
has a significant effect on the provision of educational infrastructure. Our results also indicate that the reservation of
political seats for the Scheduled Castes decreases the number
204
WORLD DEVELOPMENT
of primary schools in the village. This may indicate that, as
Pande (2003) suggests, SC households tend to prefer monetary
transfers to the provision of public goods. This might be at
least partly because of uncertainty regarding whether schools
and other such public services will be accessible to Scheduled
Castes. However, we also find that when a village with a high
proportion of Scheduled Caste households is in a constituency
with political reservation, then it improves the number of pri-
mary schools and some quality indicators in that village. In the
latter case, of course, politicians are aware that in a majority
SC village, fair access to public goods is more likely and therefore they use their political power to procure public goods
rather than merely monetary resources. Expanding political
reservation is therefore likely to increase the Scheduled Castes’
access to educational infrastructure, though it may do so at
the cost of reducing access for other groups.
NOTES
1. This could be seen as differential returns to schooling for different
classes of the population.
2. Since it’s a Cobb–Douglas specification the model does not allow for
zero-levels of the goods, since then the income would drop to zero, but one
could argue that theoretically every village has access to some non-zero
level of education, even if the closest school is located quite far away.
3. The public primary school whose characteristics are included in the
data set is chosen at random, if the village had more than one public
primary school.
4. Though it is true that political reservation may increase the return to
education for SC/ST households by giving them better access to well-paid
public-sector jobs, this would in itself indicate an increase in their political
power.
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