Access to Energy and Impact on Education

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Indicus Analytics, An Economics Research Firmhttp://indicus.net/

Households Access to Energy and Impact on Education

Indicus AnalyticsNovember 2007

Abstract

Energy use patterns for domestic purposes vary considerably across households in India.

While modern domestic fuels are becoming popular, the majority still relies on traditional

sources of energy. This is especially true for households in rural areas for whom theaffordability and accessibility to modern fuels is still a major problem. Inaccessibility to

modern sources of fuel by a household has a direct bearing on the time spent on learning

by children in these households.

Children belonging to energy-constrained households are likely to be spending lesser (7%

less likely) time learning as opposed to those not belonging to energy-constrainedhouseholds. The reason for this being the fact that energy constraints leads to individuals

budgeting their time collecting fuel, which leaves them with lesser time for learning and

related activities. However, once a child is enrolled in school energy constraint of the

household alone does not impact the time-spent learning. There are other factors alsowhich plays an important role in determining the time apportioned for learning. These are

gender, social group, economic status of households, and education level of women in a

household, etc.

This paper examines the impact of these variables on time spent in learning with specific

reference to energy constraint and non-constraint of a household. A probit model hasbeen used for the same, where the coefficients indicate a change in probability for an

infinitesimal change in the continuous explanatory variable (It is sometimes also referred

to as dprobitmodel).

The analysis which uses data from the Time Use Survey conducted in 1998-99, reveals

that children belonging to energy constrained households budget their time in order to

allow for collection of various sources of fuel. Lack of accessibility to modern sources offuels thus has a significantly negative impact on the likelihood of their spending time on

education and learning related activities. This impact is greater in case of girls, the

probable reason being the lesser importance accorded to education of girls in India,especially in rural areas. On an average, a female child in the age group 10 to 16 years

spend 0.44 hours in a day collecting fuel, firewood etc. compared to boys in the same age

group, who spend 0.15 hours.

Indicus Analytics for Stanford Energy Research Institute 1
http://indicus.net/http://indicus.net/


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Background

India, being a country with large socio-economic and geographic diversity, has energyuse patterns that differ considerably across households. While modern domestic fuels are

becoming popular, the majority still relies on traditional sources of energy. One of the

most commonly used modern fuels for cooking in India is kerosene, available at publicfair price or ration shops as well as in private stores. However, the credibility of the

public distribution system of the fair price shops has come under criticism especially with

respect to inadequate coverage, adulteration, and lack of regular availability. LiquidPetroleum Gas or LPG and kerosene are the other major modern domestic fuels. Their

use is quite widespread in the large urban areas and is also growing steadily in the small

towns and villages. However, accessibility and affordability of the modern sources of

fuels are still major problems for the bulk of rural India. Consequently, their use ishighly limited in rural areas, which account for 70 percent of the total population in India.

Infrastructure bottlenecks and low incomes have only added to the limited spread of the

modern fuels usage in India.

Many poor and rich households located in the hinterlands are unable to access these

sources of energy for a variety of reasons. First, the distribution network is notsufficiently widespread. Second, private markets have not developed due to lack of

infrastructure and connectivity with economic centers. Third, regular supplies of the

major forms of energy are often just not available. Fourth, these are more expensive than

other energy sources that are locally available and traditionally used (firewood, dung,etc.).

Using the traditional sources of energy involves time, both in the form of time involvedin traveling to the place from where fuel has to be collected and also the time involved in

converting the source to a usable form. Dung for instance needs to be collected, madeinto cakes, dried, and then used. Similarly firewood needs to be collected, broken intopieces of right sizes, dried, and then used.

It is also well known that it is usually the women and children who devote more time in

collecting these energy sources. The daily collection of energy sources, we argue,contributes significantly to the work/activity schedule of those involved. For instance in

the case of children, the budgeted time for learning is likely to be reduced on account of

the time spent in collection of the energy sources. This reasoning arises from the fact thatthere is a substitution i.e. lesser time for studies and more time in collection of the

sources of energy. Thus the energy- education link.

This Study

This study aims at finding the relationship between households access to energy and

education of children. It can be conjectured that households that are energy constrained

may have to devote more time in finding alternate sources of fuel, thereby impacting the



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time-budget of household members. This, in turn, may affect the time spent on education

and learning.

Nankhuni (2004)1 investigated the impact of natural resource scarcity on child quality in

Malawi. In his paper, child quality was measured by childs attendance in school,

progress in school and young childrens health. The results showed that schoolattendance and collecting natural resource are not mutually exclusive and that naturalresource scarcity impacts school progress.

For the purpose of our study, households have been classified into two categories, viz,energy constrained and non-energy constrained households. Energy constrained

households are taken to be those households which are involved in making dung cakes or

wood cutting, chopping and stocking firewood or collection of fuel/fuel wood/twigs.Households that were not involved in any of the activities mentioned above have been

classified as non-energy constrained.

We observe a significant relationship between type of energy/fuel used by a householdand the likelihood of spending time in learning. Even after correcting for various socio-

economic and individual characteristics, children belonging to energy constrained

households were less likely to be involved in education and related activities as comparedto those from non-energy constrained households.

In the process of studying the relationship between energy and education, the differencesin the time use pattern across different demographic segments have also been examined.

These include those across gender, age groups, and economic class (quintiles, rural/urban

etc) to which the household belongs. This is done in order to see if there is anysubstitution in terms of the time one utilizes for energy sources collection and the time

one utilizes in doing other things, and how this differs across different demographicsegments across gender, occupations, ages, and economic classes.

Table 1 shows the average time spent on different activities in a day by children in the

age group 10-16 years in rural habitations2. It is observed that those belonging to energy-

constrained households spend lesser time learning, compared to those belonging to non-constrained households. The difference in time use patterns across gender is presented in

Table 2. It is found that female children spend less time learning as opposed to boys of

the same age group and same quintile. The gender differences observed are alsoexpected to exist across type of households. Table 3 brings out this difference. Where on

one hand females belonging to non-constrained households spend 4.4 hours in a day

learning, boys spend 5.2 hours. This time is less for both boys and girls belonging toenergy-constrained households, with girls again devoting lesser time learning than boys.

Irrespective of the type of households, female children on an average spend less time

learning in comparison to male children.

1 Nankhuni, Flora, 2004. Environmental Degradation, Resource Scarcity and Childrens Welfare in

Malawi: School Attendance, School Progress and Childrens Health, Pennsylvania State University, The

Graduate School, College of Agricultural Sciences.2 Only three activities have been included in the table as these are the major activities undertaken in a day

by a child in the age group 10-16 years.



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Table 1: Energy-Education Link: Average time spent (hrs) by children of age 10-16

years across type of householdBroad activity categories Not Energy

Constrained

Energy

Constrained

Total

Learning 4.8 4.1 4.7

Social and Cultural Activities3 2.2 2.2 2.2

Personal Care4 13.9 14.3 14.0

Table 2: Average time spent (hrs) by children of age 10-16 years across economic

status of householdsBroad activity categories Bottom

20% of

households

Next 20% Next 20% Next 20%Top-20% of

households

FemalesLearning 3.6 4.1 4.1 4.6 5.2

Social and Cultural Activities 1.8 1.6 1.9 2.1 2.2

Personal Care 14.5 14.2 14.0 13.8 13.4

Males

Learning 4.7 5.1 5.1 5.1 6.0

Social & Cultural Activities 2.2 2.3 2.4 2.9 2.9

Personal Care 14.2 14.0 13.8 13.8 13.5

Table 3: Average time spent (hrs) by children of age 10-16 years type of household

& genderBroad activity categories Not Energy

Constrained

Energy

Constrained

Total

Females


Social & Cultural Activities 1.9 1.7 1.9

Personal Care Etc 14.0 14.4 14.1

Males


Social & Cultural Activities 2.5 2.5 2.5

Personal Care Etc 13.8 14.3 13.9

Data

The dataset used for the analysis is from the Time Use Survey, conducted by the

Central Statistical Organization, Government of India from July 1998 to June 1999.

3 Participation in wedding, music functions, religious activities, socializing, sports, reading, and

entertainment, travel related to social & cultural activities etc.4 Eating, drinking, sleep, personal hygiene, health care, talking, gossiping, resting, travel related to personal

care etc.



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18,591 households were surveyed, covering approximately 110 thousand individuals

from six states and the sample consists of the east, west, and north, south, central and

northeastern regions in India.5 The sample is representative at the all-India level. About70 percent were rural households.

Apart from the data on age, gender, social group, and education level of individuals, thesurvey also collected information on the activities undertaken by individuals on the threetypes of days- normal day, weekly variant and abnormal day. In a week an individual

usually follows a routine, captured by normal day activities. However this routine might

not be followed on weekends. These activities were captured under weekly variant. Thethird type of day is abnormal day, which was used to capture the activities of days

affected by some unforeseen events, festivals, holidays etc. Normal day constitutes 93

percent of all the days covered in the survey. We only study the normal day activities ofindividuals in this paper. The survey also furnishes information on the amount of time

spent in different activities. The activities that the individuals were involved in were

divided into sixteen broad categories of which learning was one.

The present study takes into account the normal day activities of children in the age

group 10-16 years in rural areas. This age group could be considered as the prime

learning stage of children and ideally most of their time should be devoted towardslearning or activities related to learning. At the same time, these children are also able to

cooperate in the household activities depending on the priority accorded to their

education, as well as the prevalent circumstances of the household. Moreover for a childin a rural household, it is more likely that there is a trade-off between the time devoted

for learning and household chores. There is also a greater prevalence of school dropout

rates in this age group.

Further, the analysis has been restricted to only rural habitations as majority of the urbanhabitations are likely to be equipped with modern fuels. Thus the energy-education link

would be best captured by studying the patterns of time usage of children belonging torural households.

5 The six states were, Orissa, Gujarat, Tamil Nadu, Haryana, Madhya Pradesh and Meghalaya.



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Model

Using econometric modeling we study the following two issues:

Issue 1:How do constraints of energy in a household impact the likelihood of spending time inlearning and related activities, after correcting for other important socio-economic and

individual characteristics?

Dependent Variable

The dependent variable takes the value 1, if the child spends any time in learning and

related activities; otherwise 0. These activities include the following:

General education in school and other educational institutions attendance.

Studies, homework and course review related to general education

Additional study, non-formal education under education programmes.

Non-formal education by children

Work related training

Training under government programmes

Other training/education

Learning not elsewhere classified

Travel related to learning.

There are four models which uses the same LHS variable mentioned above for 4 differentsegments of population. They are (i) all children of age 10 to 16 years of age (ii) 10 to 16

years old males (iii) 10 to 16 years old females (iv) 10 to 16 years old children from the

bottom-most 20% of the households based on economic status.

These four models with the explanatory variables are described below in Table 4.

Table 4: Model 1, 2, 3, and 4

Explanatory Variables Model 1:

Determinants of a

child spending timein any learning

related activities

Model 2:

Determinants of a

male childspending time in

any learning

related activities

Model 3:

Determinants of a

female childspending time in

any learning related

activities

Model 4:

Determinants of a

child from thebottom-most quintile,

spending time in any

learning related

activities

Age

Adult members in a

household



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Explanatory Variables Model 1:

Determinants of a

child spending time

in any learning

related activities

Model 2:

Determinants of a

male child

spending time in

any learning

related activities

Model 3:

Determinants of a

female child

spending time in

any learning related

activities

Model 4:

Determinants of a

child from the

bottom-most quintile,

spending time in any

learning related

activities

Number of children in a

household

Dummy for economic

status of household

Dummy for type of

household (Energyconstrained/non

constrained)

Dummy for maximum

level of education ofwomen (age 18-60 yrs.)

in a household

Per capita income of

state where the

household is located

Teacher pupil ratio of

state in which householdis located

Interaction terms

between gender and

social group of a child

Issue 2:How do constraints of energy in a household impact the likelihood of spending any timein homework, studies and course-work after correcting for other important socio-

economic and individual characteristics?

The dependent variable for this model is the time spent in doing homework, studies and

course-work, which is one of the components of the broad activity learning.

Dependent VariableThe dependent variable is 1, if the child spends any time in homework, studies andcourse-work; otherwise 0.

Again, there are four models, which use the same LHS variable for 5 different segmentsof population. They are (i) All children of age 10 to 16 years of age (ii) 10 to 16 years old

males (iii) 10 to 16 years old females (iv) 10 to 16 years old children bottom-most 20%

of the households based on economic status



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These four models with the explanatory variables are described below in Table 5.

Table 5: Model 5, 6, 7 and 8

Explanatory Variables Model 5:Determinants of a

child spending any

time in doing

homework, studies

and course review

Model 6:Determinants of a

male child spending

time in doing

homework, studies

and course review


female child

spending time in

doing homework,

studies and course

review


child from bottom-

most quintile,

spending time in

doing homework,

studies and course

review

Age

Adult members in ahousehold

Number of children in a

household

Dummy for economic

status of household

Dummy for type of

household (Energy

constrained/non

constrained)

Dummy for maximumlevel of education of

women (age 18-60 yrs.)

in a household

Per capita income ofstate where the

household is located

Teacher pupil ratio of

state in which household

is located



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Methodology

The study estimates maximum likelihood probit models, where the dependent variable isdichotomous having values 1 (when the condition is met) and 0 (when the condition is

not met). These characteristics of the dependent variable are considered to be dependentupon many economic, social and demographic characteristics and are studied

accordingly. Different models have been used, based on the factors that could affect theperformance of the dependent variables.

The particular method used is a variant of the probit model where the coefficientsindicate a change in probability for an infinitesimal change in the continuous explanatory

variable (It is sometimes also referred to as dprobit model). In the case of dummy

variables, the coefficient from the dprobit technique indicates the discrete change inprobability of dependent variable resulting from a discrete change in the independent

variable from value 0 to 1. In the case of continuous independent variables (such as per

capita GDP) it indicates the change in the likelihood of the dependent variable for aninfinitesimalchange in the independent variable.

Some variables used may be at the state level, such as Per Capita State GDP and Teacher

Pupil Ratio. The values are the same across many observations for a particular state; thisviolates the independence of observations assumption. If left uncorrected, this would

lead to an underestimation of the standard errors. The cluster command in StataTM is

therefore used to identify the repetition of an observation at the state or village level; thisyields values of the variance-covariance matrix of the estimators (VCE) and standard

errors that are corrected for.



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Results

A. Determinants of a child spending time in any learning related activities: Model1to Model 4 (Refer to Model 1 to Model 4 in the Appendix)

a. Impact of socio-economic and household characteristics

The age of the child is found to have a very significant impact on the likelihood of

a child being involved in any kind of learning and related activities. Withincrease in each year of age the likelihood of spending any time in learning

decreases by 7%. The reason being that older children might be helping in

household chores, which in turn would leave them with lesser time for learning

and sometimes it might lead to the decision of whether to spend any time at all inlearning or not.

Presence of greater number of children in a household is also found to affect the

time spent in learning significantly. With each unit increase in the number ofchildren in a household, the time apportioned towards learning reduces by 1%.

This follows the reasoning that more number of children is usually found inhouseholds where children are seen more as a means of contributing to the

household income- thus naturally affecting their time spent learning. An

alternative to this could be their contribution towards household chores if notincome earning activities, more so in case of female children.

Economic status is found to have a significant impact on education only when the

households belong to either lower quintiles or the top-most quintile. For thehouseholds in the middle quintile the impact is not at all significant.

There is also a positive bearing of educated women in a household on the timedevoted for education. The more the highest level of education of women in a

household, the more the importance accorded to education in that household. This

point could be used to highlight the importance of education of women for theoverall development of a country. Another variable that has a positive

relationship with the childs likelihood of learning is the teacher-pupil ratio of the

state, which is also one measure of the quality of education being imparted.

The model also captures both gender as well as social group differences by

generation of interactive terms. These differences do exist in various parts of

India- with the difference being more pronounced in rural India. Differenceswithin social groups gets highlighted when one observes that a male child

belonging to the social group Scheduled Tribe is 18% less likely to spend time

in any learning related activities as opposed to a male child belonging to theOthers6 category. Comparing these male Others category children with

females highlights the gender difference. The differences are more pronounced

6 The social group Others refer to the children who are neither Scheduled Caste nor Scheduled Tribe.

They are essentially children belonging to the General category.



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when compared with female child belonging to the social group Scheduled

Tribe. A female child in this social group is 32% less likely to spend time

learning as opposed to males in the social group Others. This difference reduceswhen compared with females in the Others category. The likelihood of these

children spending time in learning related activities, as opposed to male from

Others category is 11% less. Thus these results show that more biases exist forthose belonging to backward sections and gender biases are prevalent whichaccentuates in case of girls belonging to deprived sections.

b. Impact of energy use on education

The results reveal that the type of energy used by a household has a significant

impact on the likelihood of spending time in learning and related activities such astravel to the place of study, doing course work etc. The model uses dummies for

energy constrained and non-constrained households. A child belonging to a non-

constrained household is found to be 7% more likely to be involved in learning as

compared to one from an energy-constrained household. Lack of access to quickand efficient sources of energy such as LPG etc. could force parents/elders of a

household to send their children for collecting other traditional sources of energy.

This in turn, might adversely impact the time the child would have otherwisespent in learning.

In case of female children the impact of the type of energy use is even stronger.A female child from a non-energy constrained household is 12% more likely to be

engaged in any kind of learning and related activities than that from an energy-

constrained household. However, the results do not show any significant impactof energy use in learning in case of male children. This might be due to the

general practice is rural areas that females tend to be more involved in collectingwood and making dung-cakes than males. As a result constraints in energy mighthave a greater impact on a female childs education than that of a male child.

For children from the poorest 20% of households in rural areas, energy use has a

strong impact on education. In the poorest quintile children from households thatuse traditional fuels for energy such as wood, dung-cakes etc. are 8% less likely to

be involved in learning as compared to those from non-energy constrained

households.

B. Determinants of a child spending time in homework, studies and course-work:Model 5 to Mode 8 (Refer to Model 5 to Model 8 in Appendix for results)

a. Impact of socio-economic and household characteristics

As observed in case of overall learning activities, when we look at the likelihood

of spending time in homework and studies, the age of the child is still found to

have a strong impact among children of age 10 to 16 years in rural areas. It is



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homework, studies and course review. This component of learning, the non-

mandatory part, could vary depending on the importance accorded to education.

Therefore the impact of energy use on studies and homework has been examinedin Model 5 to Model 8.

The results obtained support this line of reasoning, where the impact of the typeof energy use by a household is not found to have any significant impact on thelikelihood of spending time in doing homework, studies and course review.

Female children belonging to non-constrained households are 7% more likely tospend time learning as compared to female children in energy-constrained

households. However, the results do not show any impact of energy use on the

time devoted by male children for homework and course-work. This suggests thatenergy constrained households might have more of their female members being

engaged in collection of sources of energy, thus impacting the time they could

have devoted towards learning.

Unlike the impact on overall learning, the economic status of a household does

not affect the likelihood of the time spent in homework, studies and course

review.



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Conclusion

Using data from the Time Use Survey conducted during 1998-99, the potential impact ofthe type of energy use on likelihood of spending time in learning has been explored in

this paper. The universe considered for the econometric analysis was children of age

group 10 to 16 years from rural habitations.

This paper also touches upon the gender differentials in terms of likelihood of spending

time learning as well as time spent doing homework and other course related study.Females belonging to energy-constrained households are likely to be much less involved

in learning. This does not hold in the case of boys.

Quintile effects explored in the analysis show that for those belonging to pooresthouseholds, the impact of energy on education is positive and significant. This impact is

not seen when we consider the probability of time spent in doing homework and course-

work.

This paper more importantly examines the energy usage patterns of households and the

resultant impact on learning. The results show that the type of energy used by ahousehold has a significant impact on childrens learning. It is observed that the

probability of a child spending time learning is much higher if she belongs to non-energy

constrained household. However, this impact is not found to be significant when the time

spent in studies, homework and course review was considered separately. As opposed totraditional fuels, households using modern fuels (classified as non-energy constrained

households) see a greater importance being accorded to the education of their children

and this impact is restricted to the decision of whether the children of that household aresent to school. However, once in school, the impact on time spent in doing homework

and other course related studies does not get impacted by energy constraints.

Thus it can be concluded that energy constraints adversely affect a childs involvement in

learning and related activities. This impact would be greater in rural areas as there is a

greater prevalence of such constraints in rural areas. Proper policy implementation could

help reduce this effect, if not eliminate it totally.



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APPENDIX

Model 1: Determinants of a child (age group 10-16 years) spending time in

any learning related activities

Dependent variable=1, if the child spends time learning; otherwise 0

Explanatory Variables

Impact on thedependent

variableAge -0.069

(5.96)***Adult members in a household -0.001

(0.05)Children in a household -0.010

(1.67)*Quintiles (Reference: Lowest Quintile)Lower middle quintile 0.058

(2.82)***

Middle quintile 0.024(1.28)

Upper middle quintile 0.039(1.36)

Topmost quintile 0.057(1.93)*

Household Type (Reference: Dummy forenergy constrained)Dummy for energy non-constrained 0.073

(2.40)**Highest education level of women in thehousehold (Reference: Illiterates)Lit. through attending NFEC/AEC,TLC, others 0.097

(2.83)***Below Primary 0.171

(7.43)***Primary 0.199

(10.30)***Middle 0.265

(7.21)***Secondary 0.278

(5.55)***

Higher Secondary 0.342(5.82)***

Graduate & Above 0.243(2.82)***

Per capita gross state domestic product 0.000(0.28)

Teacher Pupil Ratio per 1000 students 0.009(4.08)***

Interaction terms- Social Group and Gender(Reference: Males from Social GroupOthers)Interaction term - Males from SC 0.029

(0.47)



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Interaction term - Males from ST -0.181(5.13)***

Interaction term - Females from SCs -0.086(1.34)

Interaction term - Females from STs -0.318(4.66)***

Interaction term - Females from Others -0.107(6.42)***

Observations 7090

Robust z statistics in parentheses

Significant at 10%; ** Significant at 5%; *** Significant at 1%



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Model 2: Determinants of a male child (age group 10-16 years) spendingtime in any learning related activities

Dependent Variable=1, if the child spends time learning; otherwise 0


Impact on the dependent

variableAge -0.060(5.91)***

Adult members in a household 0.002(0.14)

Children in a household -0.002(0.25)

Quintiles (Reference: Lowest Quintile)Lower middle quintile 0.053

(1.69)*

Middle quintile -0.005(0.18)


Topmost quintile 0.042(1.64)

Household Type (Reference: Dummy forenergy constrained)Dummy for energy non-constrained 0.029

(1.04)Highest education level of women in thehousehold (Reference: Illiterates)Lit. through attending NFEC/AEC,TLC, others 0.001

(0.01)Below Primary 0.122

(11.95)***Primary 0.164

(11.25)***

Middle 0.202(5.45)***

Secondary 0.217(3.70)***


Graduate & Above 0.183(1.52)


Teacher Pupil Ratio per 1000 students 0.007(2.19)**

Interaction terms- Social Group andGender (Reference: Males from Social

Group Others)Interaction term - Males from SC 0.023

(0.36)Interaction term - Males from ST -0.186

(4.78)***

Observations 3825, Robust z statistics in parentheses, * Significant at 10%; ** Significant at 5%;

*** Significant at 1%Model 3: Determinants of a female child (age group 10-16 years) spending

time in any learning related activities



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Explanatory Variables Impact on the dependent variableAge -0.080

(6.02)***

Adult members in a household -0.002

(0.13)Children in a household -0.019(2.19)**

Quintiles (Reference: LowestQuintile)Lower middle quintile 0.065

(2.58)***Middle quintile 0.065

(3.55)***Upper middle quintile 0.057

(1.78)*Topmost quintile 0.077

(2.09)**Household Type (Reference: Dummy

for energy constrained)Dummy for energy non-constrained 0.122(2.80)***

Highest education level of women inthe household (Reference: Illiterates)Lit. through attending NFEC/AEC,TLC,others

0.222(5.28)***

Below Primary 0.232(4.57)***

Primary 0.246(5.64)***

Middle 0.339(8.01)***

Secondary 0.346

(7.83)***Higher Secondary 0.390(5.97)***

Graduate & Above 0.310

(4.19)***Per capita gross state domestic product 0.000

(0.61)Teacher Pupil Ratio per 1000 students 0.012

(6.33)***Interaction terms- Social Group andGender (Reference: Females fromSocial Group Others)Interaction term - Females from SCs 0.032

(0.69)



*** Significant at 1%Model 4: Determinants of a child (age group 10-16 years) from bottom-most

quintile, spending time in any learning related activities


Explanatory Variables Impact on



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dependent variableAge -0.065

(5.35)***Adult members in a household 0.010


(2.56)**Household Type (Reference: Dummy for energyconstrained)Dummy for energy non-constrained 0.081

(2.16)**Highest education level of women in the household(Reference: Illiterates)Lit. through attending NFEC/AEC,TLC, others 0.034


(3.52)***Primary 0.236

(13.37)***Middle 0.235

(3.59)***Secondary 0.214

(1.44)Higher Secondary 0.332

(1.69)*Graduate & Above 0.296

(1.84)*Per capita gross state domestic product 0.000


(2.09)**Interaction terms- Social Group and Gender(Reference: Males from Social Group Others)Interaction term - Males from SC 0.022

(0.48)


Interaction term - Females from SCs -0.142(1.89)*




*** Significant at 1%

Model 5: Determinants of a child (age group 10-16 years) spending anytime in doing homework, studies and course review

Dependent Variable=1, if the child spends time in homework, studies and coursereview; otherwise 0.

Explanatory VariablesImpact on the




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(2.81)***Quintiles (Reference: Lowest Quintile)Lower middle quintile 0.066



(1.51)Topmost quintile 0.069

(1.34)Household Type (Reference: Dummy for energyconstrained)Dummy for energy non-constrained 0.035

(1.48)Highest education level of women in the household(Reference: Illiterates)Lit. through attending NFEC/AEC,TLC, others 0.092


(9.57)***

Primary 0.202(8.06)***

Middle 0.296(8.54)***

Secondary 0.256(6.33)***



Per capita gross state domestic product 0.000(0.09)Teacher Pupil Ratio per 1000 students 0.011

(9.53)***Interaction terms- Social Group and Gender(Reference: Males from Social Group Others)Interaction term - Males from SC 0.010


(4.52)***Interaction term - Females from SCs -0.059

(1.31)Interaction term - Females from STs -0.306

(3.95)***


Observations 7090, Robust z statistics in parentheses, * Significant at 10%; ** Significant at 5%;*** Significant at 1%



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Model 6: Determinants of a male child (age group 10-16 years) spendingtime in doing homework, studies and course review

Dependent Variable=1, if the child spends time in homework, studies and coursereview; otherwise 0

Explanatory VariablesImpact on




(1.58)Quintiles (Reference: Lowest Quintile)Lower middle quintile 0.056



(1.51)Topmost quintile 0.090

(2.39)**Household Type (Reference: Dummy for energyconstrained)Dummy for energy non-constrained 0.008

(0.37)Highest education level of women in the household(Reference: Illiterates)Lit. through attending NFEC/AEC,TLC, others -0.019


(7.22)***Primary 0.167

(5.35)***Middle 0.226

(6.60)***Secondary 0.154

(2.59)***Higher Secondary 0.264

(3.25)***Graduate & Above 0.206

(1.67)*Per capita gross state domestic product -0.000


(4.40)***Interaction terms- Social Group and Gender

(Reference: Males from Social Group Others)Interaction term - Males from SC 0.005


(4.33)***





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Model 7: Determinants of a female child spending time in doing homework,studies and course review




variableAge -0.063



(2.23)**Quintiles (Reference: Lowest Quintile)Lower middle quintile 0.075


(2.59)***


Topmost quintile 0.040(0.57)

Household Type (Reference: Dummy for energyconstrained)Dummy for energy non-constrained 0.067

(1.76)*Highest education level of women in the household(Reference: Illiterates)Lit. through attending NFEC/AEC,TLC, others 0.233


(4.80)***

Primary 0.252(4.81)***

Middle 0.379(8.09)***

Secondary 0.362(13.37)***





Interaction terms- Social Group and Gender(Reference: Females from Social Group Others)Interaction term - Females from SCs 0.036

(1.16)Interaction term - Females from STs -0.230

(3.31)***





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Model 8: Determinants of a child (age group 10-16 years) from bottom-mostquintile, spending time in doing homework, studies and course review


Explanatory VariablesImpact on


(3.66)***Adult members in a household 0.001


(4.86)***Household Type (Reference: Dummy for energyconstrained)Dummy for energy non-constrained 0.052

(1.41)Highest education level of women in the household(Reference: Illiterates)

Lit. through attending NFEC/AEC,TLC, others 0.041(0.59)

Below Primary 0.179(3.15)***

Primary 0.264(21.58)***

Middle 0.258(4.73)***

Secondary 0.256(2.23)**

Higher Secondary 0.033(0.30)

Graduate & Above 0.332(1.74)*

Per capita gross state domestic product -0.000(0.76)


Interaction terms- Social Group and Gender(Reference: Males from Social Group Others)Interaction term - Males from SC 0.032


(9.96)***Interaction term - Females from SCs -0.052

(1.16)







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Table A1: Regression to show that collinearities between being a memberof a scheduled caste instead of tribe and being in a higher income quintile

(Comparing the regression results with and without highest quintile)



Impact on thedependent variable-

Highest quintiledropped

Impact on thedependent variable-With highest quintile

Age -0.069 -0.069(5.96)*** (5.96)***

Adult members in a household -0.001 -0.001(0.05) (0.05)

Children in a household -0.010 -0.010(1.67)* (1.67)*

Quintiles (Reference: HighestQuintile)

(Reference: LowestQuintile)

Bottom most quintile -0.058(1.93)*

Lower middle quintile 0.002 0.058(0.03) (2.82)***

Middle quintile -0.034 0.024(0.75) (1.28)

Upper middle quintile -0.018 0.039(1.01) (1.36)

Topmost quintile 0.057(1.93)*

Household Type (Reference:Dummy for energy constrained)Dummy for energy non-constrained 0.073 0.073

(2.40)** (2.40)**Highest education level ofwomen in the household

(Reference: Illiterates)Literate through attendingNFEC/AEC, TLC, others

0.097 0.097(2.83)*** (2.83)***

Below Primary 0.171 0.171(7.43)*** (7.43)***

Primary 0.199 0.199(10.30)*** (10.30)***

Middle 0.265 0.265(7.21)*** (7.21)***

Secondary 0.278 0.278(5.55)*** (5.55)***

Higher Secondary 0.342 0.342(5.82)*** (5.82)***

Graduate & Above 0.243 0.243

(2.82)*** (2.82)***Per capita GSDP 0.000 0.000

(0.28) (0.28)Teacher Pupil Ratio per 1000students

0.009 0.009(4.08)*** (4.08)***

Interaction terms- Social Groupand Gender (Reference: Malesfrom Social Group Others)Interaction term - Males from SC 0.029 0.029

(0.47) (0.47)



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Interaction term - Males from ST -0.181 -0.181(5.13)*** (5.13)***

Interaction term - Females from SCs -0.086 -0.086(1.34) (1.34)

Interaction term - Females from STs -0.318 -0.318(4.66)*** (4.66)***

Interaction term - Females fromOthers

-0.107 -0.107(6.42)*** (6.42)***

Observations 7090, Robust z statistics in parentheses, * Significant at 10%; ** Significant at 5%; ***

Significant at 1%



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Table A2: Regression to show the impact of presence of eldest daughters ina household

(Examining the impact of eldest daughters in a household)




variableAge -0.066


(0.24)Eldest daughter in a household -0.029

(1.13)Quintiles (Reference: Lowest Quintile)Lower middle quintile 0.057


(1.47)


Topmost quintile 0.063(2.04)**

Household Type (Reference: Dummy for energyconstrained)Dummy for energy non-constrained 0.074

(2.41)**

Highest education level of women in the household(Reference: Illiterates)Lit. through attending NFEC/AEC, TLC, others 0.096


(7.51)***

Primary 0.199(10.22)***

Middle 0.263(7.09)***

Secondary 0.278

(5.38)***Higher Secondary 0.343

(6.02)***Graduate & Above 0.242

(2.82)***Per capita gross state domestic product 0.000


(3.68)***Interaction terms- Social Group and Gender (Reference:Males from Social Group Others)

Interaction term - Males from SC 0.029(0.46)


Interaction term - Females from SCs -0.065(1.09)




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Observations 7090, Robust z statistics in parentheses, * Significant at 10%; ** Significant at 5%; ***Significant at 1%



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Table A3: Result showing differences between tribes, castes and others &males and females

A3(a): Regression result showing the impact of interaction terms of Model 1Categories SC ST

Others

Males 0.029-

0.181 0

Females-

0.086-

0.318-

0.107

In case of males/females the difference between SC & ST is 0.2In case of SC/ST the difference between males & females is 0.1

Thus the difference between SC & ST is greater than males & females.

A3(a): Regression result showing the impact of interaction terms of Model 5Categories SC ST

Others

Males 0.01-

0.179 0

Females

-

0.059

-

0.306

-

0.087

In case of males/females the difference between SC & ST is 0.2In case of SC/ST the difference between males & females is 0.1Thus the difference between SC & ST is greater than males & females.



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Table A4: Difference between observed P and predicted P (an alternative toR^2)

ModelNumber

Observed P

Predicted P

Difference

1 0.599 0.615 0.015

2 0.639 0.655 0.0163 0.553 0.565 0.011

4 0.542 0.547 0.004

5 0.502 0.500 -0.002

6 0.535 0.537 0.002

7 0.463 0.454 -0.008

8 0.434 0.425 -0.010

Date post:	30-May-2018
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Access to Energy and Impact on Education

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