Revisiting the Gender Gap in Private Household Expenditure on Education in India: An Empirical Analysis

Abstract

The article presents empirical observations regarding the private household expenditure on male and female students incurred by Indian households at the disaggregated level of education. By using the data sets of two consecutive rounds of National Sample Survey Office (NSSO), that is, 64th and 71st, which were based on social consumption survey of health and education, the article explores the bias in household expenditure on education by the variable of gender. The result presents a different analysis when compared to the findings of earlier studies, in terms of persisting gender gap in expenditure on education at different levels of education like higher, technical or at diploma levels as compared to elementary level. The study finds that the biasness in expenditure decreases and, in some cases, even higher for female students for technical and diploma level of education. For the analysis of data, the statistical tool of percentage relative gap has been used.

Keywords

Gender gap private household expenditure education India

Introduction

Education is one of the most important variables for social advancement along with creating the equal opportunity for both the genders, thereby providing social mobility across the group. Concept of social mobility hinges upon the utilization of the capabilities which is fostered by educational system of any country (Piraino & Haveman, 2006). As the inclusive development ensures the provision of educational opportunity to everyone irrespective of class, gender and other socio-economic parameters, the expenditure bias on the line of gender has been a frame of reference for the literature since a quite long time. The Indian sub-continent has witnessed a much visible gap in private household expenditure in education for male and female students. However, the theoretical and empirical explanations have mostly centred on the social cause and effect rationale for which economic discrimination is considered as one of the outcomes.

The existence of a distinct gap in the allocation of household resources to its members has been documented in earlier studies (Bardhan, 1974). Deaton (1989) used household survey data for Cote d’Ivoire and Thailand to test for discrimination in the allocation of goods between boys and girls. Kingdon (2005) in her study reported significant gender bias in household educational expenditure across Indian states with the help of data from National Council of Applied Economic Research (NCAER). Chaudhuri and Roy (2006) suitably modified the specification of the Engel curve as proposed in Deaton (1989) and used data from the 1997 Living Standard Measurement Survey for Bihar and Uttar Pradesh to estimate an individual-level educational expenditure function. The presence of significant gender bias is established by their study in terms of expenses incurred by households in these two states. However, many of these studies are limited in terms of analysing the gender discrimination at disaggregated level, that is, within the state and across the states and also at different levels of education.

This article compared the private household expenditure at disaggregated level of general education of the two rounds of NSSO survey, that is, 64th round and 71st round. The rationale behind this empirical exercise has emerged out of the theoretical and empirical survey of literature which is mostly concentrated at the elementary and upper primary level of education. Inarguably, the importance of expenditure of education at elementary level can not be denied and therefore most of the government policies and initiatives are directed and implemented to ensure the 100 per cent enrolments at elementary level of education (Kumar et al., 2018). Both sides of the assessment based on the preference and pro-biasness towards male child has rendered a significant gap in private household expenditure on education supported by many studies. On the other hand, the data on general level of education disaggregated at various levels of higher education like technical, diploma or professional level has provided a different empirical result on expenditure for both the gender. We find no research at disaggregated level for which we mean gender gap in private educational expenditure on the basis of each level of education. While analysing the data set of 64th and 71st round of NSSO survey, the study has tried to capture the variations in PGR at higher levels of education and thereby analysing the empirical results which led to another level of discussion. A comparative line is drawn on the basis of two rounds of National Sample Survey (NSS) survey data on household expenditure on education which provides a change over a period of time in this pattern of household expenditure. The article is structured in five sections, whereas the next section is about theoretical and empirical review of the existing literature. Third section encompasses the used method and data for the study, further two consecutive sections discuss the results and other emerging questions from this study with implications of future research, respectively.

Theoretical and Empirical Review of Existing Literature

Earlier studies have mostly concentrated their analysis at elementary level for detecting the gender bias in incurring household expenditure by household. Some of the earlier studies have provided the theoretical explanation of this gender bias. Lancaster et al. (2008) utilized two different data sets: (i) the Survey of Living Conditions conducted in 1997–1998 in Bihar and Uttar Pradesh and (ii) the Household Consumption Expenditure Survey of the NSS in its 50th round (1993–1994), to detect gender bias in household spending on education. This study has adopted the Engel curve approach and applied a three-stage least squares estimation method and found the evidence of significant gender bias in educational expenditure in rural India. Himaz (2009) analysed data from the second round of the Young Lives Survey conducted in 2006 in Andhra Pradesh, India, and made an assessment of male child bias in household-level educational expenditure for children aged 5–19 years. The study pronounced the parents’ inclination towards the male child in terms of providing better quality of education; thus, more allocation of expenditure for male child. However, this confirms the general proposition prevailing in Indian society about the existing gender disparity between male and female child. Some of the studies have analysed that parental education plays a decisive role in the progress of children’s educational attainment which further exhibit gender bias in educational spending (Dreze & Kingdon, 2001; Holmes, 2003; Lillard & Willis, 1994; Maitra & Sharma, 2009). In Indian case, the study of Dreze and Kingdon (2001) noted that school participation amongst girls varies widely across different social groups, thereby indicating the social structure as a reason to foster gender bias in educational expenditure by the households. Tilak (2002) in his study pointed out that caste and religion are two significant variables which determine the level of educational expenditure in rural Indian households. The similar findings have been further supported and evidenced by the study of Saha (2013), whereby the gender disparity in household expenditure is more pronounced for rural areas than the urban ones.

Apart from the Indian case, some of the studies have been conducted for other South Asian countries which asserted that urban households have a prominent gender bias in terms of investment in education for their child. Such a kind of study was conducted by Aslam and Kingdon (2008) in Pakistan by analysing the intra-household allocation of educational expenditure. Using individual-level data from the household survey from Pakistan, their study observed gender biasness at two different levels. At initial level, biasness in decision to send or retain their child in school is reported, on the other hand, biasness is observed in the decision for allocation of expenditure if both male and female child has to be enrolled (contingent upon the prevailing socio-economic condition). At the progressive level (viz., secondary or at middle stage), the pro-male bias becomes more significant for both the conditions, that is, for enrolment and also the about the decision for the proportion of expenditure for both of them. However, at the primary level, the former channel of bias remained more applicable. Further, the gender difference in educational expenditure is observed within the household rather than across the household.

Even in Indian context, the same has been asserted by different studies at different point of time. For example, study by Chaudhuri and Roy (2006) tried to examine the gender gap in educational expenditure in two states of north India using the data from the Living-Standard Measurement Survey (1997). Their results showed that parents exhibit a gender bias while educating their children. The interesting finding of this study was size and extent of gender gap in educational expenditure differs across the age groups of children. The results, in general, are found to be more robust when information is used at the individual level rather than at the household level. At the societal level, the expenditure on education comes from two sources: institutional, that can include the expenditure of the state on education and private, which may consist of the household’s own expenditure on education (Tilak, 2002). The role of neither of these two components can be overemphasized, as they must complement each other to ensure the adequacy and the equity in education.

Theoretically arguing, some of the researches have analysed the gender bias in decision-making by household in terms of subjective and objective gender bias (Masterson, 2012). For example, in case of India, the gender bias is mostly observed and analysed at individual level. On the other hand, for example, in case of Paraguay, the gender biasness is more at the household level. Empirical studies tend to focus on subjective gender bias because of the availability of data, while the information necessary to assess objective gender bias (information about actual decision-making processes and power) is rarely available. This lack of data means that the presence of objective gender bias must usually be inferred. Therefore, it may be argued that the objective gender bias in Indian case is already assumed to be present, which most of the studies have pronounced empirically.

Interlinking with labour market discrimination against women, this theoretical aspect finds explanation in one of the studies by Kingdon (2005). Her study pointed that parental discrimination against the daughters and labour market discrimination are the most persistent explanations of the gender gap in education in developing countries including India. It has been further explained that educational marginalization in terms of gender gap is in fact dependent upon various factors which eventually affects the school participation rate of male and female child. It may include parental motivation, returns from employing the child to work (case of child labour), household resources and in many cases quality of school (the infrastructural facilities like toilet for girls, etc., can be included).

We basically find two basic streams of theories in literature which analyse the gender gap in private household expenditure in India. First is about the parental discrimination between male and female child at household and individual level which is rooted in sociological constructs of the Indian society. Secondly, labour market imperfections, which are reflected in terms of wages, working conditions, etc., and are different for male and female labour. Through this labour market dynamics, parents perceive education for their male and female child in terms of future returns.

Following the literature review of the existing empirical and theoretical aspects specifically in Indian context, it can be argued that most of the discussion falls within the ambit of aggregate level of education in terms of gender gap in private household expenditure. Moreover, the specification of educational level is centred upon elementary level and in rural and urban Indian households. To explore the interlinking of labour market dynamics with private expenditure on education requires analysis at disaggregated level, which is so far obscured in the literature.

This study attempts to address this gap with the help of an extracted hypothesis. Our major hypothesis is whether a persistent gender gap exists at different levels of education (higher and technical level) as it does at elementary level. To examine our hypothesis, we use expenditure on education at different levels in case of both male and female.

Data and Methods

Brief Description of Data

For this study, we used NSSO 64th (2007–2008) and 71th (2014) round data on private expenditure on education. The NSS Office, Ministry of Statistics and Programme Implementation, Government of India conducted national representative survey and collect the data for participation and private expenditure on education from 63,318 rural and 37,262 urban household covering 7,953 villages and 4,682 urban blocks in 2007–2008 (64th round) and 36,479 rural and 29,447 urban household covering 4,577 villages and 3,720 urban blocks in 2014 from all over the India except some part of Jammu and Kashmir, Nagaland and Andaman and Nicobar (Leh and Kargil districts of Jammu and Kashmir, interior villages of Nagaland situated beyond 5 km of a bus route and villages in Andaman and Nicobar Islands which remain unreachable during the year were not enclosed by the survey), respectively. NSSO generated two types of sample first is ‘Central Sample’ which is surveyed by directly NSSO person and another is ‘State Sample’ which is surveyed by state government who participate in survey. In this study, we are using central sample.

NSSO 64th and 71th both rounds have primarily focussed on participation in education and private expenditure for member of household aged between 5 and 29 years, who currently enrolled in any educational institution at any level and type of education etc. In addition, some socio-economic and demographic particulars, such as the religion, social group, household size and total monthly consumption expenditure of the household, and the age, sex, marital status and educational status of each member of the household, are also available. To prove our hypothesis, we basically use expenditure on education for deferent level of education in case of both male and female.

Following the tradition of NSSO, both the round once again used a multi-stage stratified random sampling design, treating 2001 and 2011 census village (for village) and urban blocks (for urban) as a first stage units (FSUs) for 64th and 71st rounds, respectively. Large villages and blocks are again sub-divided into a number of specified hamlet groups or sub-blocks. The ultimate stage units are the households for both the sectors. For the purpose of stratification, in general, each district of a state is split into two strata; first is rural stratum consisting of all rural areas of the district and second is urban stratum consisting of all the urban areas. From each rural sub-stratum, using the 2001 and 2011 population figures as size, four villages are selected with probability proportional to size with replacement for both rounds, respectively, whereas for urban areas, four FSUs were chosen from a sub-stratum with simple random sampling without replacement. Within each and every sub-stratum, samples are drawn in the form of two independent sub-samples for both the rural and urban sectors. Large FSU with more than certain level of population are divided into more than one Hamlet Groups in rural and Sub-Block in urban area.

There is some compatibility issue between 64th and 71st round; first is 64th has 1 year survey period, whereas 71st round was surveyed within 6 months. But, as per our understanding, these survey period will not create any problem for our analysis, because in both the rounds of NSSO use 1 year reference period to collect data for educational expenditure. Secondly, in 64th round, educational expenditure has been collected only for basic courses but in 74th round, it was extended for main two courses. But,71th round unit-level data shows almost (mare than 96 per cent) member of household engaged in only one courses, so this will also not an issue of comparability between these two rounds. To convert the educational expenditure from nominal to real term, we should use index of private final consumption expenditure. But, in this study, we have used percentage relative gap (PRG) to estimate gender gap in private educational expenditure, that is, ratio, so it does not require deflation.

Statistical Analysis

Present study uses PRG in average annual educational expenses, which is crude measure of gender gap in household expenditure on educational, defined as

PRG = \frac{{\bar{X}}_{E E M} - {\bar{X}}_{E E F}}{{\bar{X}}_{E E M}} \times 100.

Here, ${\bar{X}}_{E E M}$ is an average annual expenditure of male student and ${\bar{X}}_{E E F}$ for female student. A positive value of PRG is shows gender bias in favour of males, which means females are discriminated, whereas a negative value suggests the presence of males are discriminated in-household educational spending. But, PRG is unsophisticated measure of gender discrimination in-household educational expenditure. There could be various methodologies to estimate gender discrimination in private educational expenditure like Bliender–Oxaca, quintile regression modelling, etc. But, simple PRG is served the goal of this study, so in present study, we used only PRG to find gender gap in private educational expenditure.

Result and Discussion

Before analysing the major result of this study, it will be important to look into the trend of male and female literacy in India for a certain reference period. Figure 1 represents trend in male and female literacy in India by comparing the data from three consecutive rounds of NSSO viz., 2007–2008(64th), 2011–2012 (68th) and 2014 (73rd), respectively.

Figure 1.

Source: NSS report nos. 532, 566 and 575, respectively, for 64th, 68th and 73rd rounds.

The time period relates to 2007–2008 to 2014, whereby the gap between male and female seems to be equidistant. For females, the literacy rate grew from 2007–2008 to 2014 around 60 to 65 per cent. On the other hand, males have started with a base literacy rate of around 79 per cent and it further increased to the beyond 80 per cent. As per the Census data of 2011, the female literacy rate was worked out to be 64.6 per cent and for males, it was 80.9 per cent. The slopes of both the lines are more or less constant but there is change in the intercept for female literacy line and male literacy line at the Y-axis (literacy rate). It indicates that gender bias in terms of literacy has been persistent since earlier time periods and that too with a substantial gap. This gap has been persistent since then and continued till now, which is a matter of concern. The constant slope indicates that the growth rate has remained almost same during this period. This may imply about the presence of significant number of illiterate females even after the efforts made at different levels of education. The parameter of economic development in terms of narrowing the gap of gender disparity remains a distant task for policymakers.

Table 1 represents the average annual expenditure per student for any type of education for male and female at rural, urban level during the year 2007–2008 and 2014. The first observation is about the positive PRG across all the groups for both the time periods and for rural and urban setting as well. This confirms the observation of earlier studies that there is gender disparity in terms of per capita expenditure on education for male and female students in Indian households. The interesting observation is in urban areas where wider gap is observed as compared to the rural areas. In the rural area, the PRG has decreased from 3 points, while in urban areas it increased by 3 points. On the other hand, at all India level, the PRG has not shown much difference. The plausible reasons can be attributed to the fact that in rural areas, families send their children irrespective of gender to school because of the Mid-Day Meal scheme and Sarva Shiksha Abhiyaan like programmes from the government. In urban areas, families prefer to send their children in private schools which has larger burden on their expenditure and thereby they avoid sending their female child to these expensive private schools. But again, this does not confirm a generalized gender bias because level of education is not disaggregated in Table 2.

Table 2 moves a step further in explaining the gender gap in average private education expenditure per student for the year 2007–2008 to 2014 for rural and urban areas in terms of general education. It is observed that PRG for all sectors fell by 1 per cent. It can be inferred that the gender gap in private educational expenditure is almost constant for the level of general education.

Table 1.

Average Annual Expenditure (in ₹) Per Student

	Rural			Urban
	Male	Female	PRG	Male	Female	PRG
2007–2008	2,047	1,540	25	7,026	6,211	12
2014	6,362	4,952	22	19,263	16,290	15

Source: Estimated by authors from 64th and 71st rounds of NSSO unit-level data.

Note: Figure of this table incorporates all kinds of education together whether it is general, technical, diploma and all level of education (primary to above master’s degree).

Table 2.

Average Annual Expenditure (in ₹) Per Student Pursuing General Education by Sector

	Rural			Urban
	Male	Female	PRG	Male	Female	PRG
2007–2008	1,684	1,382	18	5,351	4,863	9
2014	4,854	4,042	17	13,426	12,323	8

Source: Estimated by authors from 64th and 71st rounds of NSSO unit-level data.

Note: Figure of this table incorporates only general education for all level (primary to above master’s degree).

In terms of average annual expenditure incurred by the household on technical education of male and female students, the PRG is found to be negative, as evident from Table 3. The value of PRG is negative for both the sectors, that is, urban and rural for the year 2014 as compared to the 2007–2008. This may have inference in terms of emerging labour market transformation in favour of females out of various factors. The characteristics of job market may have an influence over the preferences of parents for incurring expenditure on female student for technical level of education which have a comparatively higher probability for fetching jobs in labour market.

Table 4 represents average annual expenditure per student by any type of diploma education across the rural, urban settings for both the gender. In rural sector and at all India level, the PRG is negative which indicates there is no gender gap at diploma education level. But, the interesting observation is about the urban sector, whereby we find PRG is positive which shows gender gap in expenditure, although it decreased from the year 2007–2008 to 2014 by 13 points.

Table 5 depicts the average annual expenditure per student for general education by level of attendance, whereby it has been compared at all levels of education. At the graduate and post-graduate and above the PRG has been negative. The negative value of PRG is highest at the level of PG and above the interesting observation is about rural sector whereby the maximum negative value is recorded in PRG.

Table 3.

Average Annual Expenditure (in ₹) Per Student Pursuing Technical Education by Sector

	Rural			Urban
	Male	Female	PRG	Male	Female	PRG
2007–2008	28,453	23,760	16	35,630	33,714	5
2014	43,997	44,485	–1	67,779	69,392	–2

Source: Estimated by authors from 64th and 71st rounds of NSSO unit-level data.

Note: Figure of this table incorporates only technical education for all level (primary to above master’s degree).

Table 4.

Average Annual Expenditure (in ₹) Per Student Pursuing Any Type of Diploma Education by Sector

	Rural			Urban
	Male	Female	PRG	Male	Female	PRG
2007–2008	26,122	23,690	9	29,740	24,459	18
2014	45,478	46,162	–2	40,746	661	5

Source: Estimated by authors from 64th and 71st rounds of NSSO unit-level data.

Note: Figure of this table incorporates only diploma education for all level (primary to above master’s degree).

Table 5.

Average Annual Expenditure (in ₹) Per Student Pursuing General Education by Different Level

	2007–2008
	Rural			Urban
	Male	Female	PRG	Male	Female	PRG
Primary	897	741	17	3,764	3,458	8
Upper primary	1,434	1,289	10	4,587	3,893	15
Secondary	2,586	2,241	13	6,356	5,765	9
Higher secondary	4,283	4,051	5	9,369	8,002	15
Graduate	8,590	6,400	25	16,187	14,610	10
PG and above	15,260	13,562	11	25,260	23,316	8
	2014
	Rural			Urban
	Male	Female	PRG	Male	Female	PRG
Primary	3,061	2,512	18	10,604	9,489	11
Upper primary	3,603	2,813	22	11,864	10,940	8
Secondary	5,568	4,534	19	13,781	13,284	4
Higher secondary	9,820	8,012	18	21,681	18,442	15
Graduate	11,306	11,813	–4	17,480	16,161	8
PG and above	13,017	16,715	–28	19,090	16,565	13

Source: Estimated by authors from 64th and 71st rounds of NSSO unit-level data.

In terms of technical education, again the observation holds like earlier ones, in which higher the level of technical education lesser is the gender gap in education expenditure (see Table 6). For the diploma level, as shown in Table 7, the urban sector has noticed lesser gender disparity in comparison to rural ones.

Discussion

This empirical exercise has observed that private educational expenditure between both the gender has been almost constant over a period of time (here, it is 2007–2008 to 2014) for general education. On the other hand, it declined rapidly for the technical and diploma level of education. This sharp decline in gender gap has not been reflected in overall education expenditure for the field of diploma and technical education, because in 2007–2008 almost 97 per cent were enrolled in general education and in 2014, this figure was 95 per cent. Gender gap is observed to be minimum in case of higher, technical and diploma education. These results in terms of disaggregated level of education have different indications in terms of gender gap in private household expenditure on education. So far most of the studies as discussed in the earlier section of this article, have focused their observation at elementary level of education or at aggregate level for the rural and urban sector households. Some of the studies have compared the expenditure across the states for the combined level of education. However, in terms of higher level of education, the same observation and empiricization may not hold true.

In the present study, we observed that the gender gap in private household expenditure is narrowed and in some cases, like technical or diploma level it is in fact, reflects reverse trend, whereby more expenditure is incurred on female education. The possible explanations for this trend can be traced in different segments of economic and sociological constructs which is overlapped in Indian context. Prevalence of existing and comparatively higher gender gap at elementary level of education can be analysed through both economic and social factors as well.

Table 6.

Average Annual Expenditure (in ₹) Per Student Pursuing Technical Education by Different Level

	2007–2008
	Rural			Urban
	Male	Female	PRG	Male	Female	PRG
Up to graduate	34,132	21,343	37	4,1273	40,272	2
PG and above	36,813	27,161	26	43,550	52,925	–22
	2014
	Rural			Urban
	Male	Female	PRG	Male	Female	PRG
Up to graduate	59,979	62,201	–4	82,298	77,224	6
PG and above	50,067	56,526	–13	80,391	84,262	–5

Source: Estimated by authors from 64th and 71st rounds of NSSO unit-level data.

Table 7.

Average Annual Expenditure (in ₹) Per Student Pursuing Diploma by Different Level

	2007–2008
	Rural			Urban
	Male	Female	PRG	Male	Female	PRG
Below graduate	20,090	18,315	9	24,368	15,854	35
Graduate and above	33,907	40,941	–21	37,423	30,798	18
	2014
	Rural			Urban
	Male	Female	PRG	Male	Female	PRG
Below graduate	31,730	34,517	–9	37,382	36,388	3
Graduate and above	61,515	45,621	26	70,189	70,990	–1

Source: Estimated by authors from 64th and 71st rounds of NSSO unit-level data.

For example, household tends to spend more on private schooling and in many cases opt for private tutoring for their male child so that he can perform better in his educational advancement. The underlying factor may be rooted in the patriarchal set up for the promotion of male child rather than the female ones. On the other hand, the positive increment in female enrolment at elementary level of education may be attributed to government policies like Right to Education Act for free and compulsory education.

The economic explanation is intricately related to structural form of labour market which can not be ignored, specifically when we analyse the higher level of education. The decreasing gap in household expenditure on education at higher levels of education for both the gender emanates from the structural transformation in the labour market where the female labour force participation rate (LFPR) varies across the time. Some of the studies like Rangarajan et al. (2011) and Abraham (2013) argued that decline in domestication (for the urban areas remarkably, this rate had even declined after peaking in 1999–2000 from 49.7 to 31.2 per cent in 2009–2010), had been entirely compensated by increase in attending educational institutions. In this regard, the widely discussed theoretical proposition of U-shaped feminization hypothesis argues that subsequent stages of economic development have different repercussions for females’ labour market participation pattern. As per this hypothesis, women tend to withdraw from the labour force at initial level of economic development and after a minimum threshold point, their participation rates again start increasing thus forming a U-shaped curve. The reason for this U-shaped curve formation is explained on the basis of substitution and income effect on women’s choice between unpaid work at domestic level and paid work in labour market. At earlier stages of development, women mostly tend to devote their labour on subsistence activities like agricultural production as unpaid labour in the family. However, at advances level of development process, women shift for paid labour or wage labour jobs out of structural transformation in the economies and emergence of different sectors in labour market (Abraham, 2013; Durand, 1975; Goldin, 1995; Sinha, 1967). Here, the income effect is explained on the basis of rising white-collar jobs, expansion of higher and technical education amongst women which raises their real wage as well as decline in wage differential with males. This particular hypothesis is empirically tested and found to be true for many countries (at cross country level and individual level as well) which substantiates that women get more opportunity to higher education at later stages of development process of any country and this decreases the gender gap (Mammen & Paxson, 2000).

The sociological explanation is varied in itself regarding different contexts and levels of educational outcomes. Education is reported to enhance the efficiency and status production of women instead of equipping them with autonomy (Jeffery & Jeffery, 1994). In most of the developing countries, especially in Indian sub-continent, the marriage market dimension of educating the women reported to be quite evident (Horowitz, 1993; Sarukkai, 2015). Similarly, for other countries like Egypt, the attitudinal changes towards an educated bride have been reported to influence the educational pattern of women (Smock & Youssef, 1977). Women’s education is considered to act as a facilitator for their marriage promotions evidenced by earlier researches (Frankfurt, 1977). However, in Chinese context, it has been found that people were reluctant to look for a college-educated bride (Hooper, 1984). In Indian context, the factors of marriage market can be considered for explaining the increasing household expenditure on female education at higher level apart from the expectations form the absolute return from the labour market alone. It may be further argued that to present female in marriage market and to retain a social edge, it has become imperative to educate them at higher level. As Sarukkai (2015) argued that promoting females to opt for MBBS or other technical courses enable them to project in marriage market in a much lucrative manner. Thus, attending higher educational institutions is becoming the priority, compared to domestic activities or labour market participation amongst female children and young female adults.

However, it further requires more detailed and rigorous investigation and analysis to validate the claim that labour market has an indirect but profound impact upon the decision of household to incur expenditure on education irrespective of the gender. Also, the contextualization of gender inequality in terms of economic, cultural, political and historical interpretation requires further explanations.

Conclusions

Educational systems are argued to be influenced by vocational exigencies; therefore, the impact of structural transformation in the labour market bound to occur for male and female students at higher levels of education. In Indian context, women fare relatively well in higher education in terms of access; however, they are disadvantaged with respect to the outcomes of schooling. This has been an established fact by earlier researches that there exists gender disparity in terms of decision of household to allocate its resources to education on their male and female child across the rural and urban sectors. Nonetheless, there has been a substantial improvement in terms of enrolment at elementary level for both male and female child because of various incentives and flagship programmes run by the government in terms of free and compulsory education. These programmes and policies may have been instrumental in increasing the participation of female children in educational activities up to the secondary school level. However, this may not involve the expenditure from the household as it has been categorized as institutional expenditure or investment for increasing the overall literacy rates, thereby exists wider gap between private expenditure on male and female at higher levels of education.

This study observes that negative gender gap in private expenditure at higher level of education is due to structural transformation in labour market whereby more service sector and white collars jobs are being created. The white-collar job has generated attractive incentives and career opportunities for females. Further, most of the jobs in service sector demand sophisticated skills where females fare better than the males. Therefore, household has this incentive to invest in female education to reap this opportunity in labour market. This has further decreased the participation in domestic activities by females which has consequentially increased their participation in higher education programmes. But, still many contradictions remain to be researched further. For example, there are cases of dropout which is higher for females at all levels of education; this may inflate the calculation of value of PRG in case of household expenditure gap between male and female students. Also, the calculation of PRG is done with the help of average expenditure which may get influenced by extremes like higher and lower values. Further, the de-feminization of labour force has been a counter intuition to educate females for labour market. The process of de-feminization is marked by the trend of secular declination in female LFPR. This makes them available for their increased participation in education and other domestic activities. The evidence from earlier researches (Abraham, 2013) suggests that as compared to educational activities, women tend to engage more with domestic and allied activities. Therefore, the argument of higher returns from labour market for technical and professional education may not seem to be very substantiative in this case of private expenditure on education. This aspect requires further investigation.

In this study, the gap in private household expenditure on disaggregated level of education for male and female students indicate unequal pattern. Through this article, it has been argued that various factors are interlinked to explain the observation that gender gap has decreased (in some cases increased in favour of females) in terms of private expenditure on higher and technical education by household on both the sexes. The findings of the study confer that the concept of gender inequality with its extension to the logic of class reproduction has different analytical framework to the different level of educational system. The analogous conceptualization of gender inequality in lines of the class and gender does not hold well because of their respective differential relationship with educational system. The disadvantaged social position of females is the manifestation of lesser educational credentials, especially college degrees. This argument gets reflected in the fact that decreasing gender gap in private household expenditure at higher level might be an indication for the attainment of higher educational credentials for the purpose of job as well as for marriage market for women. But, because of higher return in labour market for technical and professional education, the same may not hold true as discussed throughout in this article.

Therefore, this study seeks for further research regarding the explanations of gender gap in higher education on the basis of separate aspects of class and gender along with the differentiation between processes and outcomes where women have gained parity with male counterparts and where she has not.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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