Abstract
Using microdata from a field survey of children in rural Andhra Pradesh, India, we estimate econometric models which aim to identify the key explanatory factors in the decision on schooling. The approach adopted is to focus on the effects of sibling competition within the household, by paying close attention to the number, age and gender of a child's siblings, while also taking account of the characteristics of the household and community. Our findings suggest that the schooling decision depends as much on the child's characteristics and position within the household, as on the circumstances in which the child lives.
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Notes
Bhalotra and Heady (2004) consider an inter-generational flow of wealth and human capital accumulation through education and work experiences to take account of the dynamic nature of household decision-making. See also Becker and Tomes (1976), Behrman et al. (1982) and Behrman (1988a,b) for household behaviour on intra-household resource allocation (e.g. altruistic, inequality aversion and reinforcing the difference).
Brides are normally matched with grooms whose educational levels are equal to or higher than theirs. Since grooms with higher educational level are likely to have better-paid jobs, they tend to command higher amounts of dowry. Parents who find it hard to raise money for dowry are reluctant to educate girls. See Debi (2001).
A related theory is that of ‘effective literacy’ which identifies the positive externalities that arise when only some household members are educated (Basu and Foster 1998).
Although this applies to salaried workers whose incomes increase with tenure, it may not apply to waged labourers.
They also present an interesting finding that the effect of birth order on the schooling decision is observed among low-income households, but it decreases among middle- to higher-income households. They attribute this to credit constraints and socio-cultural context as well as to resource constraints (i.e. poverty).
To focus, as we do, on the 5 to 14 age group is consistent with the Constitution of India which stipulates that employment of children below the age of 14 years in hazardous occupations is prohibited and seeks to provide free, compulsory, education for all children up to the age of 14.
Grootaert (1998) states that multi-purpose household surveys, whose original purposes are unrelated to child schooling but which nevertheless contain some relevant information on children, are often the best source of data due to the scarcity of data on children's schooling and work.
It would possible to use a bivariate probit model, as Canagarajah and Coulombe (1997) and Pal (2004) do, to analyse the schooling and work decision simultaneously. However, since in our data set, the numbers of children who both work and attend school and who do neither are very small, the work versus schooling decision is, for practical purposes, a dichotomy, and it is for this reason that we use the simple probit model. For information, when estimating a bivariate probit model for our model 4, the correlation (rho) is estimated as very close to −1.0, confirming the dichotomous nature of the decision and the appropriateness of the univariate approach taken.
Variable set I might be expected to include factors representing a child's innate ability, such as health condition. However, such variables are not included in this study due to non-availability of data.
It is known that school variables have effects on schooling, but these were not used in the analysis due to the lack of variations in the schooling conditions in the sample: All villages have primary schools; the school facilities, such as toilets and drinking water, were mostly not available; the pupil–teacher ratios are not very different; and so on.
Due to missing values, the sample size varies according to the model estimated.
The negative coefficient of the dummy variable sex should be interpreted in conjunction with that of the variable age × sex. The former simply represents the gender difference at age zero. It must also be noted that a quadratic term in age was included during the specification search, but was found not to be significant.
One question we considered was whether it was appropriate for the last-born in a large household to be assigned a higher ‘score’ than the last-born in a small household, as is necessarily the case if birth order itself is used. Using the log of birth order instead is a way of reducing any distortion arising from this. This was tried but did not improve the model according to the AIC.
This statement is also supported by an unreported model in which an interaction term combining the first-born dummy with gender was introduced and found not to be significant.
During the specification search, a dummy variable which distinguishes single-sex from mixed-sex sib-constellations was included, since previous studies have found (see Section 2) that girls with only sisters may be advantaged relative to girls with brothers and sisters. However, this dummy did not show significance.
For example, the schooling probabilities for only-children are the same for a 14-year-old boy and an 8-year-old boy (0.59), while the probabilities are 0.21 for a 14-year-old girl and 0.66 for an 8-year-old girl.
In an attempt to circumvent the endogeneity problems, an instrumental variable estimator was applied due to Newey (1987). The results obtained confirm the results concerning the effects of siblings' schooling status mentioned in the text. Models 4 and this unreported model were also regressed separately for boys and girls. The results suggest that sibling competition takes place first between boys and girls and then within brothers and within sisters. Boys are likely to attend school when they have elder sisters but do not have elder or younger brothers, in particular, school-going brothers. Similarly, girls are likely to attend school when they have elder sisters but do not have younger brothers. Furthermore, they are benefited from having either elder or younger sister who is working. These unreported results are available from the authors upon request.
See Pearson (1998) who reports that daughters work to finance sons' education.
The ‘negative’ teaching effect can be incorporated in the resource dilution effect in this paper, since the issue here relates more to the decision on schooling rather than to a child's performance at school.
Some theoretical models (e.g. Becker 1991) imply that the number of children is an endogenous variable, while some empirical work on schooling decision (e.g. Canagarajah and Coulombe 1997; Morduch 2000) implicitly assumes exogeneity of this variable in the estimation. We follow the latter. Since children do not attend school for the first few years of their lives, it is reasonable to assume that the decision on the number of children is made prior to the schooling decision pertaining to a particular child. Hence, the number of children is pre-determined and can therefore be treated as exogenous (Gujarati 2003).
We have adopted the variable, income group, which divides the sample households by quintiles, rather than the actual amount of income, due to the difficulties involved in the collection of accurate data on income.
See Table A.1 in Appendix for the definition of asset index.
Model 1 uses the variables, educational level and age separately for each parent. In the other models, a single variable is used for each factor for two reasons: these two variables are highly correlated between fathers and mothers; and due to the fact that 35 children are from female-headed households, the use of separate variables entails missing observations and therefore a lower sample size.
The younger the generation, the more likely people are educated.
See Ota (2002) for the supporting evidence.
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Acknowledgements
We are very grateful to the managing editor and two anonymous referees for their useful comments on a previous draft. We are also grateful to Professor Ashok Parikh, Dr. Kunal Sen and Dr. Vegard Iversen for their thoughtful advice.
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Appendix
Appendix
Table A.1. Definitions, means and standard deviations of variables
Name of variable | Definition | Mean | SD |
|---|---|---|---|
Sex | 1: boy, 0: girl | 0.45 | 0.50 |
Age | Age of a child in years | 9.84 | 2.67 |
Sex × age | Interaction term of sex and age | 4.32 | 5.16 |
Birth order | Birth order of a child: 1, 2, 3... from the eldest (including children of all ages in a household) | 2.92 | 1.47 |
First-born | 1: first-born child, 0: otherwise | 0.15 | 0.35 |
Mid-born | 1: mid-born child, 0: otherwise | 0.52 | 0.50 |
Last-born | 1: last-born child, 0: otherwise | 0.31 | 0.46 |
Only-child | 1: only-child, 0: otherwise | 0.03 | 0.16 |
No. of elder brothers | Number of elder brothers aged between 5 and 14 | 0.75 | 0.85 |
No. of elder sisters | Number of elder sisters aged between 5 and 14 | 1.17 | 1.24 |
No. of younger brothers | Number of younger brothers aged between 5 and 14 | 0.47 | 0.60 |
No. of younger sisters | Number of younger sisters aged between 5 and 14 | 0.55 | 0.77 |
Household size | Number of household members | 6.45 | 2.06 |
Household size squared | Square of number of household members | 45.78 | 34.12 |
No. of children (5–14) | Number of children aged between 5 and 14 | 3.06 | 0.94 |
No. of children (5–14) squared | Square of number of children aged between 5 and 14 | 10.22 | 5.72 |
No. of working adults | Number of working adults in a household | 2.39 | 1.03 |
No. of working adults squared | Square of number of working adults in a household | 6.78 | 6.18 |
Infants | 1: if infant(s) (aged 0–4) is present, 0: otherwise | 0.21 | 0.41 |
Infants and the elderly | 1: if infant(s) and non-working grandparent(s) are present, 0: otherwise | 0.08 | 0.27 |
Income group | Quintile income groups (1: the poorest,..., 5: the wealthiest) | 3.00 | 1.16 |
Asset index | Asset index: sum of point scores given to assets (TV: 4, radio: 1, casette: 1, bike: 1, fun: 1, camera: 1) | 1.07 | 1.80 |
Land-holding | 1: if a household owns land, 0: otherwise | 0.49 | 0.50 |
Livestock | 1: if a household owns livestock, 0: otherwise | 0.57 | 0.50 |
Room | Number of rooms of a house in which a child lives | 1.89 | 0.97 |
Education of father | Completed years of schooling for a father | 0.48 | 1.47 |
Education of mother | Completed years of schooling for a mother | 0.17 | 1.04 |
Education of household | 1: if at least one household member is educated, 0: otherwise | 0.33 | 0.47 |
Age of father | Age of father | 40.95 | 7.74 |
Age of mother | Age of mother | 33.24 | 5.69 |
Age of household head | Age of head | 40.31 | 8.06 |
SC | 1: if a household belongs to scheduled caste, 0: otherwise | 0.42 | 0.49 |
ST | 1: if a household belongs to scheduled tribe, 0: otherwise | 0.17 | 0.38 |
BC | 1: if a household belongs to backward castes, 0: otherwise | 0.32 | 0.47 |
OC | 1: if a household belongs to other castes, 0: otherwise | 0.05 | 0.22 |
Hindu | 1: if a household's religion is Hindu, 0: otherwise | 0.82 | 0.38 |
Muslim | 1: if a household's religion is Muslim, 0: otherwise | 0.04 | 0.20 |
Christian | 1: if a household's religion is Christianity, 0: otherwise | 0.13 | 0.34 |
Uyyalawada | 1: if a child lives in Uyyalawada, 0: otherwise | 0.21 | 0.41 |
Dhobipet | 1: if a child lives in Dhobipet, 0: otherwise | 0.17 | 0.38 |
Dandu | 1: if a child lives in Dandu, 0: otherwise | 0.19 | 0.39 |
Amdapur | 1: if a child lives in Amdapur, 0: otherwise | 0.09 | 0.28 |
Palapadu | 1: if a child lives in Palapadu, 0: otherwise | 0.13 | 0.34 |
Rajbolaram | 1: if a child lives in Rajbolaram, 0: otherwise | 0.21 | 0.41 |
The numbers of observations are 232 for ‘Education of father’ and ‘Age of father’, 254 for ‘Room’, 265 for ‘Education of mother’ and ‘Age of mother’ and 267 for all of the variables.
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Ota, M., Moffatt, P.G. The within-household schooling decision: a study of children in rural Andhra Pradesh. J Popul Econ 20, 223–239 (2007). https://doi.org/10.1007/s00148-005-0033-z
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DOI: https://doi.org/10.1007/s00148-005-0033-z