access to energy and impact on education for households

Indicus Analytics, An Economics Research Firm http://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 the affordability 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-constrained households. 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 also which 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 has been 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 dprobit model).

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 of fuels 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

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Background

India, being a country with large socio-economic and geographic diversity, has energy use 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 public ‘fair 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. Liquid Petroleum 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 is highly 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 not sufficiently 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 involved in 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, made into cakes, dried, and then used. Similarly firewood needs to be collected, broken into pieces 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 that there 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 child’s attendance in school, progress in school and young children’s health. The results showed that school attendance and collecting natural resource are not mutually exclusive and that natural resource 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 household and 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 compared to those from non-energy constrained households.

In the process of studying the relationship between energy and education, the differences in 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 any substitution 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 demographic segments – 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 habitations 2. 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 also expected 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 to energy-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 Children’s Welfare in Malawi: School Attendance, School Progress and Children’s 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 household

Broad 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 Care 4 13.9 14.3 14.0

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

Broad activity categories Bottom 20% of

households

Next 20%

Next 20%

Next 20%

Top-20% of

households

Females

Learning 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 & gender

Broad activity categories Not Energy Constrained

Energy Constrained

Total

FemalesLearning 4.4 3.5 4.2Social & Cultural Activities 1.9 1.7 1.9Personal Care Etc 14.0 14.4 14.1

MalesLearning 5.2 4.7 5.1Social & Cultural Activities 2.5 2.5 2.5Personal Care Etc 13.8 14.3 13.9

Data

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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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. 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. About 70 percent were rural households.

Apart from the data on age, gender, social group, and education level of individuals, the survey also collected information on the activities undertaken by individuals on the three types 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. The third 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 of individuals 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 towards learning 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 child in 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 urban habitations 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 to rural 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 in learning and related activities, after correcting for other important socio-economic and individual characteristics?”

Dependent VariableThe 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 different segments 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 time in any learning

related activities

Model 2: Determinants of a

male child spending time in

any learning related activities


female child spending time in



child from the bottom-most quintile, spending time in any

learning related activities

Age

Adult members in a household


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child spending time in any learning

related activities








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 (Energy constrained/non constrained)

Dummy for maximum level of education of women (age 18-60 yrs.) in a household

Per capita income of state where the household is located

Teacher pupil ratio of state in which household is 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 time in 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 and course-work; otherwise 0.

Again, there are four models, which use the same LHS variable for 5 different segments 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 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


child spending any time in doing

homework, studies and course review



doing homework, studies and course

review



doing homework, studies and course

review


child from bottom-most quintile,

spending time in doing homework,

studies and course review

Age

Adult members in a household

Number of children in a household

Dummy for economic status of household

Dummy for type of household (Energy constrained/non constrained)

Dummy for maximum level of education of women (age 18-60 yrs.) in a household

Per capita income of state 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 is dichotomous 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 dependent upon many economic, social and demographic characteristics and are studied accordingly. Different models have been used, based on the factors that could affect the performance of the dependent variables.

The particular method used is a variant of the probit model where the coefficients indicate 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 in probability 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 an infinitesimal change 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; this violates 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; this yields 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: Model 1 to 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. With increase 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 in learning 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 of children in a household, the time apportioned towards learning reduces by 1%. This follows the reasoning that more number of children is usually found in households 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 not income 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 the households 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 time devoted 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 the overall development of a country. Another variable that has a positive relationship with the child’s 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. Differences within 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 the ‘Others’6 category. Comparing these male ‘Others’ category children with

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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females highlights the gender difference. The differences are more pronounced 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 reduces when 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 for those belonging to backward sections and gender biases are prevalent which accentuates 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 as travel 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 quick and 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 otherwise spent 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 impact of 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 collecting wood and making dung-cakes than males. As a result constraints in energy might have a greater impact on a female child’s 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 that use 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


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have a strong impact among children of age 10 to 16 years in rural areas. It is found that with each year increase in age the likelihood of spending time in homework and coursework is likely to decrease by 5%. Also, the more the total number of children in the household, the less is the likelihood of the child spending time in studies.

The observation in case of variables capturing gender and social group impact is similar to what was observed in case of time spent in any learning related activity. Males from the social group ‘Scheduled Tribe’ and females form the social group ‘Scheduled Tribe’ and ‘Others’ are less likely to be spending time doing homework, course work etc. in comparison to males from the social group ‘Others’. The impact is greater for females (reflecting the gender biases existing) as compared to males. Females belonging to ‘Scheduled Tribe’ are 31% less likely in spending time doing homework and course work when compared to males from ‘Others’. The social group biases existing get highlighted when Models 6 and 7 are observed. In comparison to a female child belonging to the social group ‘Others’ a female child from ‘Scheduled Tribe’ is 23% less likely in spending time doing homework, course work etc. A bias of similar nature is observed when the universe considered is only male children (Model 6). In case of Model 8, where the universe considered are individuals belonging to households falling in the bottom most quintile, an impact of a similar nature is observed, thus further adding to the gender and socio-economic biases existing in various parts of the economy.

Again the highest level of education of women in a household is found to have a strong impact in the likelihood of spending time in doing homework, studies and course work.

The economic status of a household also plays a significant role in the probability of a child devoting time in studies and homework. With increase in the economic status of the household the likelihood of a child spending time in studies improves significantly.

b. Impact of energy use on homework, coursework and studies (Without considering the time involved in traveling related to learning activities, attending school, non-formal education, training etc.)

One of the important components of learning is the time devoted in studies, homework and course review. Many of the other activities such as traveling related to learning, attending school etc., involve a fixed amount of time. For instance, if a child goes to school then the travel time involved in going and coming back from school is indispensable. The time spent in these activities thus being more or less constant, it cannot be substituted for by any other household related activities. Thus if a child chooses to go to school, then this fixed component of learning (spending time in school, and the travel time to school etc.) might have to be substituted for the time devoted for collection of energy


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sources. This however, might not hold in case of the time that is spent doing 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 examined in Model 5 to Model 8.

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

Female children belonging to non-constrained households are 7% more likely to spend 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 that energy 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 of the 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 poorest households, 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 a household has a significant impact on children’s 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 to traditional 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 are sent 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 child’s 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 VariablesImpact on the

dependent 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 for energy constrained)Dummy for energy non-constrained 0.073

(2.40)**Highest education level of women in the household (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 Group ‘Others’)Interaction term - Males from SC 0.029

(0.47)Interaction term - Males from ST -0.181


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(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 7090Robust z statistics in parenthesesSignificant at 10%; ** Significant at 5%; *** Significant at 1%


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Model 2: Determinants of a male 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 VariablesImpact on the dependent

variableAge -0.060

(5.91)***Adult members in a household 0.002


(0.25)Quintiles (Reference: Lowest Quintile)Lower middle quintile 0.053

(1.69)*Middle quintile -0.005



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

(1.04)Highest education level of women in the household (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



(1.52)Per capita gross state domestic product 0.000


(2.19)**Interaction terms- Social Group and Gender (Reference: Males from Social Group ‘Others’)Interaction term - Males from SC 0.023


(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



(2.19)**Quintiles (Reference: Lowest Quintile)Lower 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 in the 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 and Gender (Reference: Females from Social Group ‘Others’)Interaction term - Females from SCs 0.032



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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(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



(1.89)*Interaction term - Females from STs -0.374



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

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




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







(9.57)***Primary 0.202

(8.06)***Middle 0.296

(8.54)***Secondary 0.256












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

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

Explanatory VariablesImpact on









(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



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






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Indicus Analytics, An Economics Research Firm http://indicus.net/Model 7: Determinants of a female child spending time in doing homework,

studies and course review






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





(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





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




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Indicus Analytics, An Economics Research Firm http://indicus.net/Model 8: Determinants of a child (age group 10-16 years) from bottom-most

quintile, spending time in doing homework, studies and course review


Explanatory VariablesImpact on




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



(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









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Indicus Analytics, An Economics Research Firm http://indicus.net/Table A1: Regression to show that collinearities between being a member of

a scheduled caste instead of tribe and being in a higher income quintile

(Comparing the regression results with and without highest quintile)Dependent variable=1, if the child spends time learning; otherwise 0

Explanatory Variables

Impact on the dependent variable-

Highest quintile dropped

Impact on the dependent 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: Highest Quintile)

(Reference: Lowest Quintile)

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 of women in the household (Reference: Illiterates)Literate through attending NFEC/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 1000 students

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

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

-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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Indicus Analytics, An Economics Research Firm http://indicus.net/Table A2: Regression to show the impact of presence of eldest daughters in

a household

(Examining the impact of eldest daughters in a household)Dependent variable=1, if the child spends time learning; otherwise 0




(0.24)Eldest daughter in a household -0.029






(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











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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 OthersMales 0.029 -0.181 0Females -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 0.1Thus 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 OthersMales 0.01 -0.179 0Females -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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Indicus Analytics, An Economics Research Firm http://indicus.net/Table A4: Difference between observed P and predicted P (an alternative to

R^2)

Model Number Observed P Predicted P Difference

1 0.599 0.615 0.0152 0.639 0.655 0.0163 0.553 0.565 0.0114 0.542 0.547 0.0045 0.502 0.500 -0.0026 0.535 0.537 0.0027 0.463 0.454 -0.0088 0.434 0.425 -0.010


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access to energy and impact on education for households

Business