Heriot-Watt University
Economics
Discussion Papers
Discrimination in Accessing Cleaner
Cooking Fuels and Electricity in North
India: The Role of Caste, Tribe, and
Religion
Prabir C. Bhattacharya
Vibhor Saxena
Heriot-Watt University
Department of Economics
Edinburgh
EH14 4AS, UK
Working Paper
No. 2015-02
January 2015
1
Discrimination in Accessing Cleaner Cooking Fuels and Electricity
in North India: The Role of Caste, Tribe, and Religion
Prabir C. Bhattacharya (Heriot-Watt University)
Vibhor Saxena (Heriot-Watt University)
Abstract
This paper studies the socio-economic determinants of access to cleaner cooking fuels and
electricity by households in seven north Indian states in a multivariate framework. Together
these states account for about 40 per cent of India’s population. We investigate, in particular,
the role of any possible discrimination against the three major disadvantaged groups in the
country – viz., the scheduled castes, scheduled tribes, and Muslims – in their accessing these
goods. The results of our analysis suggest that discrimination against these groups,
particularly against the Muslim households, does play an important role in their poorer access
to both cleaner cooking fuels and electricity. The paper concludes with some policy
suggestions.
Key words: Cooking fuels, electricity, discrimination, north India.
JEL classification: O13, Q40.
2
1. Introduction
The purpose of this paper is to study the socio-economic determinants of access to cleaner
cooking fuels and electricity by households in the seven north Indian states. The states are;
Punjab, Haryana, Rajasthan, Madhya Pradesh, Chhattisgarh, Uttar Pradesh and Uttaranchal
(see fig. 1). Together, these states account for about 40 per cent of India’s population. We
investigate, in particular, the role of any possible discrimination against the three major
disadvantaged groups in the country – viz., the scheduled castes, scheduled tribes, and
Muslims – in their accessing these goods. Formerly known as ‘untouchables’, a term applied
to a wide range of Hindu groups belonging to the lowest rung in the caste hierarchy, the
schedule castes comprise about 16 per cent of India’s population. They continue to suffer
from persecution even though discrimination on the basis of caste has been declared illegal in
the Indian constitution. The scheduled tribes include most so-called tribal or indigenous
communities throughout India. Considered to be outside the Hindu caste system, they
comprise about 8 per cent of India’s population. Both scheduled castes and scheduled tribes
are at the lowest level of the social scale. Muslims in India, who comprise about 13 per cent
of India’s population, are also considerably deprived in many dimensions. The results of our
analysis suggest that discrimination against these groups, particularly against the Muslim
households, does play an important role in their poorer access to both cleaner cooking fuels
and electricity.
The importance of energy in achieving economic development in low income countries is, of
course, widely recognised. 1 However, estimates suggest more than 1.5 billion people in
developing countries do not have access to electricity and approximately 2.5 billion lack
access to clean cooking fuels. 2 India contains the largest number of energy deprived people in
the world. Although the total population of India is approximately one fifth of the world’s
total, the energy consumption of India is approximately only 4 per cent of the world’s total. 3
The two most important forms of energy for Indian households are cooking fuels and
electricity. In contrast to the developed countries where air pollution is generally regarded as
an urban problem, in the developing countries, the indoor air pollution is probably of greater
consequence for the health and well-being of the population. The primary reason for the
indoor air pollution is the type of cooking fuels used by the households in the developing
1
WEC(2000, 2001), IEA(2002), UNDP(2000, 2003, 2006).
Chaurey, Ranganathan and Mohanty (2004).
3
Raha, Mahanta and Clarke (2014).
2
3
countries. The biomass fuels and coal – both of which are widely used – are not only
relatively inefficient in energy production, they also emit high levels of carbon-dioxide (CO 2 )
and other hazardous substances, contributing to respiratory problems, cancer, blindness, and
other illnesses. 4 These necessitate increased medical expenditures and time-off work for the
affected households and erode their productivity.
In India, the only widely used clean cooking fuel available is the Liquefied Petroleum Gas
(LPG) in cylinders. These are heavily subsidised by the government when targeting poorer
households. However, the informal and illegal use of LPG in the motor vehicles, small
industries and lavish wedding parties hinders the uninterrupted supply to the targeted groups.
Motor vehicles, industries and the gas distribution agencies are all usually owned by
members of social groups with high incomes and they often prioritise the distribution of LPG
cylinders to suit their own economic interests. In the urban areas of India, only 60 per cent of
the households have access to LPG. The situation is much worse in the rural areas, especially
in some north Indian states, where more than 90 per cent of the households use
unconventional and hazardous cooking fuels. 5
4
5
Mishra, Retherford, Smith (1999), Ellegard (1996), Zhang and Smith (2007).
D’sa and Murthy (2004), UNICEF, TERI and AIIMS combined report (2011).
4
While the usage of LPG at the household level is limited to cooking, the use of electricity is
more widespread. Indeed, it is possible to argue that the access to electricity is probably of
greater consequence than the access to LPG for immediate economic outcomes. However,
while the official estimates indicate that more than 80 per cent of the rural areas and 100
percent of the urban areas were electrified in the decade of 2000-2010, the uninterrupted
delivery of electricity is frequently not realised. It is not uncommon for many villages and
urban areas to be supplied with electricity for only a few hours a day.
So far as the three marginalised groups are concerned, their places of residence would appear
to pose particular problems in their accessing both LPG and electricity. Scheduled caste
households are often segregated in hamlets outside the main perimeters of the villages where
they live and this makes it easy to discriminate against them. Members of the scheduled tribe
communities often live in the relatively remote areas. And while the Muslim communities
mostly live in the urban areas, they live within the city boundaries in Muslim ghettos, 6 where
households are not always properly accounted for by the official authorities. There are also
cases of illegal access to electricity in many urban slums, where one connection is shared by
many households. These then lead to the electricity interruptions by the authorities to reduce
the loss to the government owned electricity boards.
Most studies on energy access in developing countries, as Kanagawa and Nikata (2007) have
noted, can be classified into three broad types: descriptive, experimental and analytical. They
also noted that while the descriptive and experimental studies are quite common in the
literature, the analytical studies are less so. In the Indian context, while a few studies have
looked at the determinants of access to electricity in a multivariate framework, 7 even fewer
have looked at the socio-economic determinants of access to cleaner cooking fuels in such a
framework. 8 And while a few researchers have examined the impact of the scheduled caste
and scheduled tribes status in the context of accessing electricity, 9 the impact of the Muslim
status in accessing electricity (and LPG) has received relatively little attention. 10 The aim of
this paper is therefore to address some of these issues.
6
Gayer and Jafferlot (2012).
Oda and Tsujita (2011), Kemmler (2007).
8
Gupta and Kohlin (2006), Rao and Reddy (2007).
9
Of these studies, Kemmler’s (2007) is the most relevant in the context of discussions in the present paper.
Kemmler’s study suggests that the scheduled caste and scheduled tribes face discrimination in accessing
electricity. However, Kemmeler does not estimate the proportion of discrimination or unobservable factors in
determining the outcomes. Kemmler’s study is also restricted to analysing data from the rural areas only, while
our sample includes data from both the rural and urban areas. We also consider access to LPG and additionally
include Muslim households in the analysis.
10
See, however, Thorat (2009), and Oda and Tsujita (2011).
7
5
The plan of the rest of the paper is as follows. Section 2 describes the data source and the
variables used. Section 3 provides a brief description of the data in terms of the percentage
distribution of LPG and electricity access by some of the key household characteristics.
Section 4 presents the regression estimates and provides a discussion of the results. Section 5
concludes.
2. Data and Variables for the Analysis
The data used in the analysis come from The National Election Study (2004). This is a
sample survey conducted by the Centre for the Study of Developing Societies (CSDS), New
Delhi. Although the dataset is primarily motivated towards political research, the survey
collects ample information on the socio-economic backgrounds of the respondents’
households in the sample. 11 As these data have not been much used by the applied
economists, it is important to mention that the external research on the Indian databases has
considered these datasets to be of excellent quality. 12 The number of households in the
sample that we use is 7197.
2.1 Dependent variables
As already indicated, the dependent variables are Liquid Petroleum Gas (LPG) and electricity
access, respectively.
LPG access. The first dependent variable is the access to LPG by a household. The sample
provides seven categories of cooking fuels. Of these, only electricity and LPG are considered
as cleaner cooking fuels and the rest (such as coal and kerosene) as hazardous. In the sample
less than 0.5 per cent of the households reported the use of electricity as a cooking fuel. For
the purpose of this paper, we therefore consider LPG as the only non-hazardous and clean
cooking fuel available to households in India. In the sample, 29 per cent of the households
reported having access to LPG.
Electricity access. The second dependent variable is the electricity access by a household in
the sample. In India, electricity is primarily used for lighting (in substitutions of kerosene and
other biomass for lighting). 13 In the sample lighting access is classified into four categories:
electricity, kerosene, no lighting and others. 68 per cent of the households in the sample
reported having access to electricity.
11
Lokniti Team’s NES (2004).
Smith, Pellissery, Rajan and Dubuc (2007).
13
Modi (2005), Khandker, Barnes and Samad (2010)
12
6
2.2 Explanatory variables
The explanatory variables included are as described below.
Caste and religion. This is the set of binary variables in the regression equations. Each
binary variable represents a particular social group. The estimation includes four categories.
Scheduled caste is the binary variable representing the scheduled caste households and
scheduled tribe is the variable representing the scheduled tribe households. Muslim is the
dummy variable for the Muslim households. All other households are the base category for
comparisons in the estimations. The base category (‘others’) includes the non-scheduled caste
Hindu households and households belonging to the other minority religions: Sikhs, Jains,
Buddhists, etc., and is referred to in this paper as the upper caste households.
Education. Information on education is available only for the respondent (one for each
household) in the survey. However, as the respondent is an adult, the respondent’s education
is taken as an indicator of the overall level of education of the respondent’s household. In the
sample, the education levels are provided by nine categories. For the regression estimations
here, however, three levels of education are constructed. The base category is the respondent
without any formal education and is considered illiterate. Respondents with a level of
education between primary and matric pass (grade 10 pass) are taken to belong to the low
education category, while the households where respondents had attained or were pursuing
college level education are considered to belong to the category of high education.
Location. The location of the household is represented by two different dummy variables.
The first categorisation is on the basis of rural-urban classification. The base category is the
urban area in the rural-urban classification. In the second regional classification, we include a
dummy variable to represent the states of Madhya Pradesh, Chhattisgarh and Uttar Pradesh.
These states are the poorest of the seven north Indian states and have relatively poor
infrastructure facilities. They are also culturally somewhat distinct from the other north
Indian states and form a distinct geographical sub-cluster within the region.
Poverty categories.
Full information on average household income is employed in
constructing the relative poverty lines. Instead of the traditional method of estimating poverty
level by a binary variable, conditional on the two outcomes of poor or not poor, four poverty
levels are constructed from the sample information. 14 The poverty levels constructed define
14
The binary approach is of course equivalent to assuming that the rest of the information about the
household’s income is unavailable. See, among others, Diamond, Simon and Warner (1990), Borooah (2005),
and Sen (1976).
7
the classification points by applying the median income methodology. In this method, the
poverty lines are constructed by multiplying the median income by 0.75, 0.50 and 0.25
respectively. In other words, if M is the median income in the sample then the three points
distributing the sample into four categories are L 1 = 0.75 M, L 2 = 0.5 M, L 3 = 0.25 M.
Other covariates. In addition to the variables discussed above, interaction terms of the
scheduled castes, scheduled tribes and Muslim households with a positive level of
educational attainments (i.e., with non-illiteracy), and with the dummy for the states of
Madhya Pradesh, Chhattisgarh and Uttar Pradesh (the relatively backward states) are also
controlled for. These interaction covariates estimate the impact of education and the states
they reside in on accessing LPG and electricity by these marginalised groups within their
respective group affiliations. A final variable included is the size of the household. The
reason for including this variable is that in India knowing the right persons and those who can
be persuaded to intercede on one’s behalf (i.e., having the right ‘contacts’) can often be
important in accessing a number of different services. A larger-sized household, with a larger
number of members, other things being equal, will have more ‘contacts’ than a smaller-sized
household. We therefore expect larger sized households to have better access to both LPG
and electricity.
3. Percentage Distribution of LPG and Electricity by Household Characteristics
Having discussed the dependent and explanatory variables, it may be useful, before
proceeding to estimations, to present the data in terms of the percentage distribution of LPG
and electricity by household characteristics discussed above. This is done in Table 1. As can
be seen from the table, approximately 17 per cent, 5 per cent, 29 per cent, and 36 per cent of
the scheduled caste, scheduled tribe, Muslim, and upper caste households, respectively, have
access to LPG. Without controlling for the other factors, Muslim households thus have
significantly better access to LPG than the scheduled caste and scheduled tribe households.
Scheduled tribe households are in the worst position in this respect.
So far as the electricity access is concerned, approximately 55 per cent, 64 per cent, 59 per
cent, and 73 per cent of the scheduled tribe, scheduled caste, Muslim and upper caste
households, respectively, have access to electricity. Once again, the scheduled tribe
households are in the worst position.
As would be expected, there exists significant rural-urban divide: while 74 per cent of urban
households have access to LPG and 92 per cent access to electricity, only 19 per cent of rural
8
Table 1. Proportional distribution of LPG and Electricity by household characteristics
LPG
0.177
(0.382)
Electricity
0.642
(0.480)
Scheduled tribe households
0.056
(0.231)
0.551
(0.498)
Muslim households
0.296
(0.457)
0.591
(0.492)
Upper caste households (‘Others’)
0.360
(0.480)
0.733
(0.442)
Rural households
0.194
(0.396)
0.630
(0.483)
Urban households
0.747
(0.435)
0.923
(0.266)
High education households
0.656
(0.475)
0.880
(0.325)
Low education households
0.368
(0.482)
0.760
(0.427)
Illiterate households
0.129
(0.335)
0.559
(0.497)
Households in Madhya Pradesh, Chhattisgarh and Uttar Pradesh
0.177
(0.381)
0.560
(0.496)
Households in Haryana, Punjab, Rajasthan and Uttaranchal
0.420
(0.494)
0.818
(0.386)
Very poor households (poverty category 1)
0.039
(0.196)
0.424
(0.495)
Poor households (poverty category 2)
0.051
(0.221)
0.482
(0.500)
Mildly poor households (poverty category 3)
0.109
(0.312)
0.617
(0.486)
Non-poor households (poverty category 4)
0.445
(0.497)
0.806
(0.396)
Scheduled caste households
mean coefficients; sd in parentheses
households have access to LPG and 63 per cent access to electricity. The divide is clearly
much greater for LPG than for electricity access.
9
High education households have significantly higher access to both LPG and electricity (66
and 88 per cent, respectively) compared to the households with illiterate respondents (13 and
56 per cent). Households in the states with relatively poor infrastructure facilities – Madhya
Pradesh, Uttar Pradesh and Chhattisgarh – have lower access to LPG and electricity (17 and
56 per cent, respectively) compared to the households in the other states in the sample (42
and 82 per cent).
Distribution of LPG and electricity by poverty categories show that household income levels
also play an important role in accessing LPG and electricity. Only around 4 per cent of the
households in the very poor category have access to LPG and 42 per cent access to
electricity, while the figures for the highest income category in the sample (non-poor
households) are 44 per cent for LPG and 80 per cent for electricity.
4. Estimations and Results
4.1 Probit models and estimates
We use binary dependent variables for estimating LPG and electricity access by the
households. 15 It is assumed that the chances of accessing electricity and LPG by a household
depend on the caste, tribe and religious affiliation of the household, even after controlling for
the other household characteristics (such as education, poverty levels, and place of
residence), 16 and probit models are estimated.
In equation (1), 𝒀𝒊∗ , represents both of the dependent variables, considering that the equation
𝑞
can be used for estimating electricity as well as LPG access. ∑𝑚=1 Ω𝑚 𝑋𝑖𝑖 is the sum of
𝒒
𝒀∗𝒊 = 𝜷 + ∑𝒎=𝟏 Ω𝒎 𝑿𝒊𝒊 + ∑𝒌𝒋=𝟏 𝜶𝒋 𝑿𝒊𝒊 + 𝜺𝒊
(1)
and
𝒀𝒊 = �
𝟏 𝒊𝒊 𝒀𝒊∗ > 0
𝟎 𝒊𝒊 𝒀𝒊∗ ≤ 𝟎
(2)
dummy covariates representing scheduled caste, scheduled tribe and Muslim households and
∑𝑘𝑗=1 𝛼𝑗 𝑋𝑖𝑖 is the set of other control variables. Therefore, 𝛽 + ∑𝑘𝑗=1 𝛼𝑗 can be defined as the
coefficients of the upper caste households. Equation (2) is the measurement equation that
15
16
Long and Freese (2001), Cameroon and Trivedi (2005, 2010), Wooldridge (2002), Hendrickx (2002).
Gangopadhyay, Ramaswami and Wadhwa (2003), Heltberg (2004), Kemmler (2007).
10
connects the latent dependent variable in equation (1) – where the variable takes value 1 if the
household has access to LPG or electricity, zero otherwise. The error term in equation (1) is
assumed to be normally distributed with zero mean and variance of one.
The results of the estimates are presented in Table 2. Table 2 shows four different estimated
equations. Models 1 and 3 show estimates without controlling for the poverty categories,
while models 2 and 4 control for these categories. All models estimate a negative and
significant coefficient for each of the scheduled caste, scheduled tribe, and Muslim
households variables (in comparison with the base category of the ‘other’ households).
However, for the scheduled caste, scheduled tribe and Muslim households, the size of
negative coefficients declines in models 2 and 4 after controlling for the poverty categories.
This suggests that the poverty alleviation certainly increases the chances of accessing LPG
and electricity by the households in the sample.
Models 2 and 4, which control for the poverty categories, are the models of our main interest
and hereafter we concentrate on discussing the results of these estimates. In accessing both
electricity (model 2) and LPG (model 4), the scheduled tribe households variable has the
largest negative coefficient. The coefficient of the Muslim and scheduled caste households
variables are not (statistically) significantly different from each other, however.
Of the other variables, both high and low education households have positive and significant
coefficients (in comparison to the households with illiterate respondents) in both models 2
and 4. The high education households has a significantly larger coefficient in comparison to
the low education households in both models. However, the coefficients of the non-illiterate
Muslim, scheduled caste and scheduled tribe households are all statistically insignificant.
Rural households has negative and significant coefficients in both the models. The dummy
variable for the states of Madhya Pradesh, Uttar Pradesh, and Chhattisgarh – the relatively
backward states – has negative and significant coefficients in both the models. It is interesting
to note that the coefficients of the Muslim, scheduled caste, and scheduled tribe households
are all positive and significant in these three states for the LPG access (showing, in other
words, that these marginalised groups in these three states have better access to LPG
compared to their counterparts in the better-off states in the sample). The reason for this, we
believe, is that there are considerably more scheduled caste, scheduled tribe and Muslim
households in these three states than in the other north Indian states, and this gives these
households more bargaining power and ‘contacts’ in these three states, both of which
facilitate access to LPG.
11
Table 2. Regression estimates for binary dependent variables of electricity and LPG
Model 1
Model 2
Model 3
(Electricity)
(Electricity)
(LPG)
***
***
***
Scheduled caste households
-0.361
-0.270
-0.571
(-4.93)
(-3.60)
(-6.64)
***
-1.132
(-10.21)
***
-1.119
(-6.47)
***
-0.237
(-1.89)
*
-0.748
(-5.29)
***
-0.931
(-14.96)
***
0.548
(10.20)
***
0.415
(7.53)
***
0.964
(20.71)
***
0.227
(5.39)
***
Scheduled tribe households
-1.183
(-10.86)
Muslim households
-0.371
(-3.03)
Rural households
-1.040
(-17.19)
High education households
Low education households
0.259
(6.24)
Total household size
0.0128
(2.61)
Uttar Pradesh, Madhya Pradesh and
Chhattisgarh
Model 4
(LPG)
***
-0.461
(-5.10)
***
-1.035
(-5.74)
***
***
-0.627
(-4.15)
-1.501
(-30.88)
***
-1.384
(-27.54)
***
0.834
(17.40)
0.294
(6.93)
***
0.265
(6.03)
***
-0.000431
(-0.08)
0.0247
(4.14)
***
***
***
***
***
***
0.0341
(6.33)
-0.899
(-20.52)
***
-0.782
(-17.37)
-0.925
(-20.35)
-0.803
(-16.86)
Scheduled caste households in Uttar
Pradesh, Madhya Pradesh and Chhattisgarh
-0.0553
(-0.64)
-0.0419
(-0.48)
0.150
(1.38)
0.233
(2.03)
Scheduled tribe households in Uttar
Pradesh, Madhya Pradesh and Chhattisgarh
1.369
(11.40)
***
1.486
(12.17)
***
0.307
(1.58)
0.409
(2.00)
Muslim households in Uttar Pradesh,
Madhya Pradesh and Chhattisgarh
0.151
(1.13)
0.0565
(0.41)
0.604
(4.16)
***
0.570
(3.71)
Non-illiterate scheduled caste households
0.166
(1.94)
-0.115
(-1.07)
Non-illiterate scheduled tribe households
Non-illiterate Muslim households
*
***
**
**
***
*
0.146
(1.68)
-0.0366
(-0.36)
0.246
(2.12)
**
0.155
(1.31)
0.321
(1.66)
0.185
(0.90)
-0.0456
(-0.36)
-0.120
(-0.94)
0.171
(1.20)
0.0982
(0.64)
0.209
(1.77)
***
0.381
(3.01)
***
1.199
(11.50)
7197
0.245
(3.99)
Mildly poor households (poverty category 3)
0.436
(5.87)
Non-poor households (poverty category 4)
0.796
(13.43)
7197
7197
12
*
***
Poor households (poverty category 2)
Observations
t statistics in parentheses
*
p < 0.1, ** p < 0.05, *** p < 0.01
***
***
*
***
***
7197
The coefficients of the poor, mildly poor, and non-poor households (i.e. poverty categories 2,
3, and 4) are all positive and significant (in comparison to the very poor households), and
they are significantly different from one another in both the models. The coefficient of the
total household size is also positive and significant in both models, suggesting that ‘contacts’
facilitate accessing of both electricity and LPG by the households in the sample.
4.2 Probability outcomes
Although the probit coefficients have been discussed above, they do not, of course, explain
the probability of accessing LPG or electricity by different social groups in the sample. A
negative coefficient in the probit estimates only suggests a declining z-score (the area under
the standard normal distribution) of the dependent variable with respect to the control
variable’s negative coefficient. In this sub-section we discuss the marginal effects or
predicted probabilities of accessing LPG and electricity by the households by their
characteristics.
The results are presented in Table 3. The results show that the Muslim households have, on
average, a 13 per cent chance of accessing LPG and the scheduled caste households a 14
percent chance. Of the three marginalised groups, however, it is the scheduled tribe
households which have the lowest predicted probability of accessing LPG (at 5 per cent).
As in the case of LPG, so in the case of electricity access, the scheduled tribe households
have the lowest predicted probability at 39 per cent. The predicted probabilities of the
Muslim and scheduled caste households of accessing electricity (at 70 and 68 per cent,
respectively) are also lower than that for the upper caste households at 78 per cent.
The results also show that the predicted probabilities of accessing LPG and electricity
increase with the increase in household incomes: the higher a household is in the income
categories, the greater are its probabilities of accessing both LPG and electricity. Thus, while
the very poor households have a predicted probability of 5 per cent in accessing LPG and 52
per cent in accessing electricity, the non-poor households – poverty category 4 households –
have a predicted probability of 33 per cent in accessing LPG and 80 per cent in accessing
electricity.
13
Table 3. Predicted probabilities for LPG and Electricity access by key control variables
LPG
Scheduled caste households
0.144***
(8.47)
Electricity
0.684***
(31.63)
Scheduled tribe households
0.051***
(2.95)
0.392***
(10.45)
Muslim households
0.131***
(4.46)
0.708***
(18.03)
Upper caste households
0.283***
(7.67)
0.784***
(31.13)
Rural households
0.137***
(23.50)
0.679***
(99.66)
Urban households
0.608***
(33.71)
0.917***
(101.09)
High education households
0.435***
(24.31)
0.832***
(65.30)
Low education households
0.241***
(23.51)
0.772***
(85.13)
Illiterate households
0.0992***
(9.53)
0.639***
(27.21)
Households in Madhya Pradesh, Chhattisgarh and Uttar Pradesh
0.110***
(16.68)
0.600***
(60.62)
Households in Haryana, Punjab, Rajasthan and Uttaranchal
0.336***
(28.24)
0.850***
(111.61)
Very poor households (poverty category 1)
0.049***
(4.79)
0.522***
(24.75)
Poor households (poverty category 2)
0.075***
(8.53)
0.618***
(44.97)
Mildly poor households (poverty category 3)
0.103***
(7.15)
0.688***
(34.30)
Non-poor households (poverty category 4)
0.328***
(36.16)
0.803***
(117.83)
t statistics in parentheses; delta std. errors
**
***
p < 0.1, p < 0.05, p < 0.01
*
14
Similarly, the predicted probabilities of accessing both LPG and electricity are the highest for
the high education households. The illiterate households have a predicted probability of 10
percent in accessing LPG and 64 per cent in accessing electricity; the high education
households, by contrast, have a predicted probability of 43 per cent in accessing LPG and 83
per cent in accessing electricity.
The households in the states of the Madhya Pradesh, Uttar Pradesh and Chhattisgarh – the
states with relatively poor infrastructure facilities – have lower predicted probabilities of
accessing both LPG and electricity (at 11 and 60 per cent, respectively) than those in the
other states in the sample (34 and 85 per cent). And, as one would expect, urban households
have greater predicted probabilities of accessing both LPG and electricity than the rural
households (60 and 90 per cent vs. 13 and 68 per cent).
4.3 Estimates of non-linear decomposition
In large parts of India, particularly in the rural areas of north India, members of the scheduled
caste communities are often denied access to common public facilities. Scheduled tribes
usually live in areas with poor facilities and Muslims are largely concentrated in urban
ghettos. In this sub-section we estimate the proportion of unobservable discrimination, if any,
in predicting the outcomes of LPG and electricity access by these three marginalised groups.
The pair wise decomposition methodology in regression estimation was first proposed by
Oaxaca (1973) and Blinder (1973). This methodology is primarily for the linear models and
the estimation of decomposition methods were primarily used for the linear decomposition. 17
However, each of the dependent variables in this paper is a binary variable and non-linear
probit estimations have been employed. For decomposition of the non-linear models, several
methods have been applied on the basis of Oxaca-Blinder decomposition in the literature. 18
We employ the methodology proposed by Bauer and Sinning (2008). Tables 4a and 4b show
the decomposition of total differences in predictions due to the characteristic and unexplained
part for the scheduled caste, scheduled tribe and Muslim households in the sample.
The estimates show that all the three marginalised groups have significant share of the
unexplained part in determining the predicted outcomes vis-à-vis the upper caste households
in the sample. For the Muslim households, the total differences are lower than the scheduled
caste and scheduled tribe households in the predicted outcomes. The tribal households are
worst off in terms of the total differences.
17
18
Neumark (1988), Oaxaca and Ransom (1988, 1994).
Gomulka and Stern (1990), Fairlie (2006), Yun (2004).
15
Table 4a. Decomposition for LPG access
Total Difference
Scheduled caste households
0.18***
(0. 013)
Explained Part
0.07***
(0.009)
Unexplained Share
0.11***
(0.014)
Scheduled tribe households
0.30***
(0.009)
0.14***
(0.015)
0.16***
(0.019)
Muslim households
0.06***
(0.018)
- 0.01
(0.023)
0.07***
(0.025)
Explained Part
0.08***
(0.008)
Unexplained Share
0.06***
(0.012)
S.E in parentheses, Bootstrap S.E
*
p < 0.1, ** p < 0.05, *** p < 0.01
Table 4b. Decomposition for electricity access
Total Difference
Scheduled caste households
0.14***
(0.014)
Scheduled tribe households
0.18***
(0.019)
0.10***
(0.017)
0.08***
(0.021)
Muslim households
0.09***
(0.022)
0.01
(0.016)
0.08***
(0.027)
S.E in parentheses, Bootstrap S.E
*
p < 0.1, ** p < 0.05, *** p < 0.01
Although the Muslim households have least differences in the predicted probability outcomes
in a pair wise comparison, their share of unexplained part is significantly largest among all
the three marginalised social groups. And the share of explained part in determining the
outcomes against the upper caste households is not significantly different from zero.
These estimates suggest that the Muslim households, which do not have significantly
different characteristics effect, exhibit the largest amount of unexplained part in determining
the predicted outcomes in this analysis. If this unexplained part is termed as discrimination,
then Muslim households suffer the most discrimination in the equality spaces of electricity
and LPG distribution.
5. Conclusions
In this paper we have studied the socio-economic determinants of access to cleaner cooking
fuels and electricity by households in seven north Indian states. The results show that higher
education and lower poverty levels are associated with an increased probability of accessing
both LPG and electricity by the households in the sample. Households in the states of
Madhya Pradesh, Uttar Pradesh and Chhattisgarh – the relatively backward states – have a
lower predicted probability of accessing both LPG and electricity than those in the other
16
states in the sample. Similarly, after controlling for the other factors, the scheduled caste,
scheduled tribe, and Muslim households have lower predicted probabilities of accessing these
energy goods compared to the upper caste households. For the Muslim households, the
contribution of the discrimination in their lower probability of accessing these goods is the
largest, even though the total differences for the Muslim households are less than those for
the scheduled caste and scheduled tribe households. As already mentioned, the Muslims in
India mainly reside in the urban areas, but in isolated ghettos, and this, we believe, makes it
easier to discriminate against them in poorly administered urban blocks.
At the policy level, there are a number of large scale interventions and policies by the
government to overcome inequalities and poverty in general, but very few for dealing with
the issue of accessing cleaner energy sources by marginalised groups. Rajiv Gandhi Grameen
Vidyutikaran Yojna (RGGVY) – which aims to provide free electricity connection to below
poverty line households – only targets the rural areas. The problem of isolation of Muslim
households in the urban areas without electricity has not received any attention in any policy
documents or initiatives. Equally, while subsidy is provided on LPG cylinders, the
administrators rarely take the monitoring of the distribution of these cylinders seriously.
Surely, more can be done in these areas.
References:
Alam, M., Sathaye, J. and Barnes D., 1998, Urban household energy use in India: efficiency and
policy implications, Energy Policy, 26(11), 885-891.
Bauer, T. K., and Sinning, M., 2008, An extension of the Blinder–Oaxaca decomposition to nonlinear
models, Advances in Statistical Analysis, 92(2), 197–206.
Bhat, P. N. Mari and Zavier, A. J. Francis, 2005, Role of religion in fertility decline: the case of
Indian Muslims, Economic and Political Weekly, 40(5), 385-402.
Bhattacharya, P. C., 2006, Economic development, gender inequality, and demographic outcomes:
Evidence from India, Population and Development Review, 32(2), 263-291.
Bhattacharyya, Subhes C., 2006, Energy access problem of the poor in India: Is rural electrification a
remedy?, Energy Policy, 34(18), 3387-3397.
Blinder, Alan S., 1973, Wage discrimination: reduced form and structural estimates, The Journal of
Human Resources, 8(4), 436 – 455.
Borooah, Vani K., 2005, Caste, inequality, and poverty in India, Review of Development Economics,
9(3), 399-414.
Cameron, A. Colin and Trivedi, Pravin K., 2010, Microeconometrics Using Stata, Revised Edition,
Stata Press Books, StataCorp LP.
Chaurey, A., Ranganathan, M. and Mohanty, P., 2004, Electricity access for geographically
disadvantaged rural communities – technology and policy insights, Energy Policy, 32(15), 1693 –
1705.
17
Diamond, C., Simon, C. and Warner, J., I990, A multinomial probability model of the size
distribution of income, Journal of Econometrics, 43, 43-6I.
D'Sa, A. and Murthy, K. V., 2004, LPG as a Cooking fuel option for India, Energy for Sustainable
Development, 8(3), 91 – 106.
Ellegard, A. and Egneus, H., 1993, Exposure to biomass fuel pollution in Lusaka, Energy Policy,
21(5), 615 – 622.
Ellegård, A., 1996, Cooking fuel smoke and respiratory symptoms among women in low-income areas
in Maputo, Environ Health Prospect, 104(9), 980–985.
Fairlie, Robert W., (2006), An extension of the Blinder-Oaxaca decomposition technique to Logit and
Probit models, IZA Discussion Papers, 1917, Institute for the Study of Labor.
Filippini, M. and Pachauri, S., 2004, Elasticities of electricity demand in urban Indian households,
Energy Policy, Elsevier, 32(3), 429-436.
Gangopadhyay, S., Ramaswami, B. and Wadhwa, W., 2003, Access of the Poor to Modern
Household Fuels in India, SERFA Report for the World Bank, (Report available at
http://www.cleancookstoves.org/resources_files/access-of-the-poor-to-clean.pdf).
Gayer, L. and Jafferlot, C., 2012, Muslims in Indian Cities: Trajectories of Marginalisation, C Hurst
& Co Publishers/Columbia University Press.
Gomulka, J. and S., Nicholas, 1990, The employment of married women in the United Kingdom
1970-83, Economica, 57(226), 171-99.
Government of India, 2005, Sachar Committee Report, Report available at (Report available at, Last
access December 2013, http://www.minorityaffairs.gov.in/sachar).
Guillot, M. and Keera, A., 2010, Hindu-Muslim differentials in child mortality in India, Genus, 66,
43-68.
Gupta, G. and Kohlin, G., 2006, Preferences for domestic fuel: Analysis with socio-economic factors
and rankings in Kolkata, India, Ecological Economics, 57(1), 107-121.
Heltberg, R., 2004, Fuel Switching: evidence from eight developing countries, Energy Economics, 26,
869-887.
Hendrickx, J., 2002, Review of regression models for categorical dependent variables using STATA
by Long and Freese, The Stata Journal, 2(1), 103-105.
IEA (International Energy Agency), 2002, World Energy Outlook 2002, (report available at http://www.worldenergyoutlook.org/media/weowebsite/2008-1994/weo2002_part1.pdf).
Kanagawa, M. and Nakata, T., 2008, Assessment of access to electricity and the socio-economic
impacts in rural areas of developing countries, Energy Policy, 36, 2016 – 2029.
Kemmler, A., 2007, Factors influencing household access to electricity in India, Energy for
Sustainable Development, 11(4), 13-20.
Khandker, S. R., Barnes, D. F. and Samad, H. A., (2010), Energy poverty in rural and urban India:
Are the energy poor also income poor?, The World Bank, Policy Research Working Paper (5463).
Lokniti Team, 2004, National Election Study 2004: An Introduction, Economic and Political Weekly,
39(51), 5373-5382.
Long, J. S. and J. Freese., 2001, Regression Models for Categorical Dependent Variables using
Stata, Stata Press, Texas.
18
Mishra, Vinod K., Retherford, Robert D. and Smith, K.R., 1999, Cooking with Biomass Fuels
Increases the Risk of Blindness, International Institute for Population Sciences, NFHS Bulletin,
Mumbai, (14).
MOSPI
–
Energy
Statistics,
2011,
Ministry
of
Statistics
and
Programme
Implementation, http://mospi.nic.in/Mospi_New/site/inner.aspx?status=3andmenu_id=171.
Modi, V., 2005, Improving electricity services in rural India, Center on Globalization and Sustainable
Development, The Earth Institute at Columbia University, Working Paper (30).
Neumark, D., 1988, Employers' discriminatory behavior and the estimation of wage
discrimination, Journal of Human Resources, University of Wisconsin Press, 23(3), 279-295.
Oaxaca, R., 1973, Male-female wage differentials in urban labor markets, International Economic
Review, University of Pennsylvania and Osaka, University Institute of Social and Economic Research
Association, 14(3), 693-709.
Oaxaca, Ronald L. and Ransom, Michael R., 1994, On discrimination and the decomposition of wage
differentials, Journal of Econometrics, Elsevier, 61(1), 5-21.
Oda, H., and Tsujita, Y., 2011, The determinants of rural electrification: The case of Bihar, India,
Energy Policy, 39(6), 3086 – 3095.
Pachauri S., 2004, An analysis of cross-sectional variations in total household energy requirements in
India using micro survey data, Energy Policy, 32(15), 1723 – 1735.
Pachauri, S. and Spreng, D., 2004, Energy use and energy access in relation to poverty, Economic
and Political Weekly, XXXIX(3), 17-23.
Pachauri, S., Mueller, A., Kemmler, A. and Spreng, D., 2004, On measuring energy poverty in Indian
households, World Development, 32 (12), 2083-2104.
Raha, D., Mahanta, P. and Clarke, M.L., 2014, The implementation of decentralized biogas plants in
Assam, NE India: The impact and effectiveness of the national biogas and manure management
programme, Energy Policy, 68, 80 – 91.
Rao, M.N. and Reddy, B.S., 2007, Variations in energy use by Indian households: An analysis of
micro level data, Energy, 32(2), 143-153.
Sen, Amartya K., 1976, Poverty: An Ordinal Approach to Measurement, Econometrica, Econometric
Society, 44(2), 219-231.
Smith, G., Pellissery, S., Rajan, S. and Dubuc, S., 2007, Indian Micro-Data Scoping Study,
Department of Social Policy and Intervention, University of Oxford.
Smith, K.R., 1993, Fuel combustion, air pollution exposure, and health: the situation in developing
countries, Annual Review of Energy and Environment, 18, 529-566.
Smith, K.R., 1994, Health, energy, and greenhouse-gas impacts of biomass combustion in household
stoves, Energy for Sustainable Development, 1(4), 23-29.
Smith, K.R., 2006, Rural Air Pollution: A Major But Often Ignored Development Concern, Testimony
at the Commission on Sustainable Development Session on Integrated Approaches to Addressing Air
Pollution, United Nations, New York.
Thorat, S., 2009, Dalits in India: Search for a Common Destiny, Sage Publications, New Delhi.
Tiwari, P., 2000, Architectural, demographic, and economic causes of electricity consumption in
Bombay, Journal of Policy Modeling, Elsevier, 22(1), 81-98.
19
UNDP and ESMAP, 2003, India: Access of the Poor to Clean Household Fuels, United Nations
Development Programme and the World Bank Energy Sector Management Assistance Programme.
UNDP, 2000. World Energy Assessment: Energy and the Challenge of Sustainability,, United
Nations Department of Economic and Social Affairs, World Energy Council. (Available at http://www.undp.org/content/undp/en/home/librarypage/environmentenergy/sustainable_energy/worl
d_energy_assessmentenergyandthechallengeofsustainability.html)
UNDP, 2006, LPG Rural Energy Challenge, Lessons Learned and Recommendations, UNDP
(Available at - http://www.cooking-for-life.org/uploads/Modules/Reports/lp-gas-rural-energychallenge-light.pdf).
UNICEF India, 2011, Cooking with cleaner fuels in India: a strategic analysis and assessment
(http://www.unicef.org/india/Policy_Brief_3-cooking_fuels_in_India.pdf).
Wooldridge, J., 2002, Econometric Analysis of Cross Section and Panel Data, Second Edition, MIT
Press.
World Bank, 2000, Energy Services for the World’s Poor: Energy and Development Report,
(Available at - http://www.worldbank.org/html/fpd/esmap/energy_report2000/).
World Energy Council, 2001, Pricing Energy in Developing Countries, World Energy Council,
London.
World Energy Outlook, 2007, International Energy Agency, China and India Insights,
(http://www.worldenergyoutlook.org/media/weowebsite/2008-1994/weo_2007.pdf).
Yun, M.S., 2004, Decomposing differences in the first moment, Economics Letters, 82, 275–280.
Zhang, J. and Smith, KR., 2007, Household air pollution from coal and biomass fuels in China:
measurements, health impacts, and interventions, Environ Health Perspect, 115, 848–855.
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