What Determines the Household Expenditure on Engineering...
Transcript of What Determines the Household Expenditure on Engineering...
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What Determines the Household Expenditure on Engineering Education? Findings from
Delhi, India
Pradeep Kumar Choudhury
Abstract
The paper examines the patterns and determinants of household expenditure on engineering
education in Delhi using the data collected from a student survey from the final year students
pursuing B.Tech (both traditional and IT related courses) in various engineering colleges (both
government and private un-aided) in Delhi in the academic year 2008-09. The household
expenditure here refers to the expenditure made by the households on tuition fees, other fees,
expenditure on dormitory/housing, food, textbooks, transport etc. Besides these the data is also
collected on additional expenditure made by the engineering students on learning English and
computer, purchasing cost of computer and cell phones, telephone or cell phone fees, internet
fees, entertainment expenses and other necessary life expenses. First, the pattern of household
expenditure by different socio-economic and institutional characteristics of the students is
analysed. In addition to this, the pattern of financial assistance received by the students in the
form of scholarships, tuition waiver, room or board allowances and work study opportunities
provided is also discussed. Second, using OLS technique, an attempt has been made to analyse
the determinants of household expenditure. The paper finds that households have spent a
significant portion of their annual income per children to provide a B.Tech degree. Further, the
larger household expenditure on engineering education in Delhi is not only because of high
tuition and other fees charged by the institutions but also due to higher expenditure incurred on
non-fee and additional heads of expenditure. Hence, the pattern of household expenditure on
engineering education does not confirm the general perception that a substantial portion of the
household expenditure goes towards fees.
Keywords: Household Expenditure; Engineering Education; India; Traditional and IT-related Engineering Courses; Type of Engineering Institutions
The present paper is a part of my ongoing doctoral thesis work titled as “An Economic Analysis of Demand for Higher Education in India: A study of Engineering Education in Delhi” at National University of Educational Planning and Administration (NUEPA), New Delhi, India. Assistant Professor, Institute for Studies in Industrial Development (ISID), 4 Institutional Area, Phase II, Vasant Kunj, New Delhi, India. E-mail: [email protected]
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1. Introduction
While there is availability of a good and reasonably reliable database on public expenditure
on education in India, information on household expenditure on education is limited. There is
hardly any attempt [except a few National Sample Survey (NSS) rounds] to collect the household
expenditure data on education on a regular basis. But it is increasingly realised that ignoring the
importance of household investment on education proves too costly for educational planning. It
is argued that higher education sector in India is a quasi-merit good and the students attending
higher and professional education need to pay a substantial part of the expenditure from the
private source (Tilak 1983, 2008).
According to 64th round of NSS conducted in 2007-08, the annual per capita household
expenditure on technical education was reported to be R42,637 in Delhi which is about four times
higher than that of general higher education1. Fees accounts for 80 per cent (42 per cent on
tuition fees and 38 per cent on examination and other fees) of the total household expenditure on
technical education and the rest 20 per cent goes for books and stationery, uniform, transport,
private coaching, and other related items. On the other hand, the share between total fees and
other items of household expenditure was stated to be in the ratio of 58:42 in general higher
education. Present study did not use the NSS data, as it does not give the household expenditure
data on engineering education clearly. Further, the data collected in the NUEPA survey (used in
the present study) is a part of the bigger survey. Thus, an attempt is made to analyse the pattern
and determinants of household expenditure on engineering education in Delhi using the data
collected through NUEPA survey.
The data was collected in 2009-10 by the National University of Educational Planning and
Administration (NUEPA), New Delhi in the context of a larger research project titled “Potential
Economic and Social Impact of Rapid Expansion of Higher Education in the World’s Largest
Developing Economies.” This international comparative study was conducted in collaboration
with Stanford University, United States of America (USA) covering Brazil, Russia, India and
China, and the study on India was conducted by Professor Jandhyala B. G. Tilak at NUEPA. The
survey provides both quantitative and qualitative information on the status of engineering
education in four major states of India, namely Delhi, Maharashtra, Karnataka and Tamil Nadu.
However, the present study is based on the data collected from Delhi only.
1 Household expenditure on technical education reported here is the inclusion of engineering education, as the NSS round had not collected the household expenditure on engineering education separately.
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The survey covers all the then existing 15 graduation level engineering institutions in Union
Territory (UT) of Delhi in the survey; however, data was collected from 11 institutions2.
Information like year of establishment, management type, intake in different
departments/branches of study of all the 15 institutions are given in Table A3.1 in appendix3.
Courses offered at undergraduate level of engineering are classified here into two categories:
(a) Traditional departments of study: standard fields like mechanical engineering, civil
engineering, electrical engineering which have been the standard departments in
engineering institutions for a long period; and
(b) Information Technology (IT) related departments, also called modern departments:
computer science and engineering, electronics and communication engineering,
information technology etc.
From each selected institution, at least one traditional and all the available IT-related
courses were considered for the study4. The traditional courses were selected in the order of
electrical or mechanical or civil engineering, as per availability in respective colleges. In a sense,
the first attempt was to include the students from electrical engineering in the survey and if this
particular course is not offered in the institution or the survey could not cover them for any other
reason, students from mechanical engineering were selected. Similarly, if the survey could not
include the students of both electrical and mechanical engineering departments, the students of
civil engineering department were considered5.
Similarly, as in the context of survey in other countries, only the students in the fourth-year
of studies (seventh semester) were considered as the sample for the study. This is with the
common understanding that they have completed three years and hence, assumed to be matured
2 The survey in Delhi did not cover the four remaining engineering institutions because: (a) two private colleges, namely Amity School of Engineering and Technology; and Northern India Engineering College did not permit to conduct the survey; and (b) two colleges, namely National Power Training Institute and Delhi Institute of Tool Engineering do not have any traditional and/or IT-related departments of study, as these institutions offer courses only in power engineering and tool engineering respectively. 3 As per AICTE lists there were 18 degree level engineering colleges in 2009-10 in Delhi, which includes three colleges from National Capital Region (NCR).
4 The study has mostly covered Mechanical Engineering, Civil Engineering, and Electrical Engineering under ‘traditional’ departments. Under ‘modern’ departments Computer Science and Engineering, Electronics and Communication Engineering, and Information Technology have been covered in the study. Other branches of engineering like chemical engineering, Environmental Engineering, Power Engineering, Production and Industrial Engineering etc. have not been included in the study, as they do not exist in most institutions. 5 This is the same pattern adopted in the larger international study. In the engineering institutions of other countries electrical engineering is taken as the first preference, mechanical engineering as second and civil engineering as third preference. Thus, the same pattern was followed in Delhi for data collection.
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to answer the questions asked in the survey more consistently. In the same token, the information
related to the labour market aspects can be answered by graduates in the fourth-year studies, as in
majority of the colleges the campus recruitment among students takes place when they are in
third/fourth year of their course6. Thus, selection of students in the fourth-year of studies and
different departments (traditional and IT-related) was purposive for the NUEPA survey. All the
students studying in fourth-year in all selected departments of the institutions were considered as
sample for the study7. The institutions covered in Delhi include one Indian Institute of
Technology (IIT), i.e., IIT Delhi and one central university namely, Jamia Millia Islamia; three
state government institutions; and six private institutions.
The NUEPA survey had collected data from students in fourth-year of studies of selected
departments in eleven engineering institutions in Delhi. The total number of students surveyed
was 1,178 out of which 15 per cent were from central government institutions, 26 per cent from
state government institutions and 59 per cent were from private institutions8.
Household expenditure on engineering education in Delhi includes the expenditure made by
the students on tuition fees, other fees (library fees, examination fees, fees on games and sports),
dormitory or housing, food, transport, textbooks and other class materials, improving English,
cost of computers, internets, phones, entertainment and other necessay life expenses. These
expences are categorised into three major heads namely, (a) expenditure on fees which includes
tuition fees, library fees, examination fees, fees on games and sports; (b) non-fee expenditures
like dormitory or housing, food, transport, textbooks and other class materials; and (c)
‘additional’ expendture on improving English, cost of computers, expenditure on internets and
phones, entertainment and other necessay life expenses. The additional expenditure is the
spending of students in addition to fees and non-fee expenditure. However, the total household
expenditure includes all the three components namely fees, non-fee expenditure and additional
6 One of the contributions of the present study is to analyse the labour market aspects in engineering education, which is discussed in Chapter 7. 7 Some students were absent at the time of data collection and some who were present did not wish to be included in the survey. The absentees and who do not wish to participate in the survey together constitute 1 to 4 per cent of total enrolment in different engineering institutions.
8 Students share of two central government institutions to total sample was small. Thus, both central and state government institutions were aggregated as government institutions in the analysis.
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expenditure. In addition to these components, the survey has also collected the extent of
expenditure of students on pre-admission coaching, which is discussed in appendix 5.19.
Expenditure on fees and non-fee items are almost essential in nature and hence all students
must spend on these during their course. On the other hand, additional expenditure is elastic to
income/needs of the students. In some cases students can avoid or may spend more or less on the
additional items. For example, the expenditure on computer depends on the students, as it is
available in the institution. Some students manage in the institution and some other purchase it.
However, it is important to note here that all the three components of household expenditure are
important and also related with the educational process of the students.
Pattern of household expenditure by different socio-economic and institutional
characteristics of the students is analysed in section 2. With the help of OLS technique, an
attempt has been made to analyse the determinants of household expenditure in section 3.
Summary of major findings are discussed in section 4.
2. Household Expenditure on Engineering Education
On an average, a household in Delhi found to be spending around R1,31,000 annually per
student on undergraduate level of engineering education. Expenditure incurred by the students
comes from three sources, such as: (a) income of the household; (b) financial assistance; and (c)
educational loans.
Annual average fees paid by the students is R46,000 which constitutes 35 per cent of the
total family cost of engineering education. Share of tuition fees to total fees is nearly 85 per cent
and the rest 15 per cent goes towards library fees, examination fees, fees on games and sports.
Large share of tuition fees to total fees is mainly because of charging substantially higher amount
of tuition fees by the institutions than other fees, though there exists some inter-institutional
differences in the proportion of tuition fees and other fees to total fees.
9 This is not a component of the three types of expenditures incurred on engineering education namely fees, non-fee expenditure and additional expenditure; as the expenditure on coaching is incurred pretty before the enrolment. Though it is not considered as a part of the household expenditure in our analysis, it can be a part of the expenditure on engineering education and analysed separately in appendix. Further, only 20 per cent of students have reported the expenditure data on coaching, even though 45 per cent students have gone for the same. Hence, due to the limited data, this is not included in the analysis of total household expenditure.
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Table 1: Annual Per Student Household Expenditure on Engineering Education
Items of expenditure Per student Household
Expenditure
Percentage of total
Percentage of annual family
income Fees Tuition fees 39,040 30 10 Other fees 6,850 5 2 Total 45,890 35 12 Non-fee Expenditure Drom/Housing 11,980 9 3 Food 11,840 9 3 Textbooks and other class materials 3,880 3 1 Transport 5,880 4 1 Others 6,310 5 2 Total 38,890 30 10 Additional Expenditure Improving English and computer 12,380 9 3 Cost of computer 8,960 7 2 Expenditure on internets and phones 7,860 6 2 Entrainment and other life expenses 12,240 9 3 Others 4,850 4 1 Total 46,290 35 11 Grand Total 1,31,070 100 33
Source: Compiled by the research scholar based upon NUEPA survey data.
Annual average non-fee expenditure incurred by the students is R39,000. This constitutes
nearly 29 per cent of the total expenditure (see column 3 of Table 1). Major portion (61 per cent)
of non-fee expenditure goes towards dormitory and food while the rest 39 per cent is spend on
textbooks and other class materials, transport, and other related expenses. Higher expenditure on
dormitory and food items may be due to the lack of hostel facilities in some of the institutions in
Delhi. Out of 11 institutions covered in the study, hostel facility exists in seven institutions (four
government and three private). Further, hostel facilities provided by three private institutions are
having significantly less number of seats as compared to their intakes. As a result, some students
are staying in rented house and spending more money on dormitory and housing. Further,
staying away from the campus add their spending on transport. Students are spending 15 per cent
of the non-fee expenditure and six per cent of the annual average household expenditure on
transport.
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Annual household expenditure per student on additional items is R46,000 which constitutes
35 per cent of the annual average household expenditure on engineering education. Per head
annual spending is R12,000 on improving English and computer. This may be due to the
students’ own interest to improve their English and computer knowledge, which helps them to
perform better in their course and in job interviews after completion of their course.
Additional expenditure accounts for the highest share of household expenditure followed by
fees and non-fee expenditure. Share of expenditure on additional items to total family cost per
student is 35.3 per cent, whereas it is 35.1 per cent on fees and 29.6 per cent on non-fee items. It
is pertinent to note here that the larger household expenditure is not only because of high fees
charged by the institutions but also due to the higher expenditure incurred by the students on non-
fee and additional heads.
The share of annual per capita expenditure to annual average family income is 34 per cent,
as shown in Table 1. This reveals that households spend a significant portion of their annual
income for engineering education of their children. Total fees account for 12 per cent of annual
average family income, the share being 10 per cent for tuition fees and 1.7 per cent for other fees.
Expenditure on non-fee heads as a percentage of annual income of the family is 10 per cent, out
of which the expenditure on dormitory and food items constitute 6 per cent. Similarly, the share
of additional expenditure to annual average family income is 12 per cent. Thus, the share of
expenditure on non-fee items to annual family income is more or less same as the share of
additional items. However, it is important to note here that students do not spend on engineering
education only from their family income; they also get educational loan and financial assistance
to support their study.
Per student annual household expenditure in private institutions is reported to be
significantly higher than the students studying in government institutions. It is R1,08,000 in
government institution and R1,50,000 in private institutions. However, there is not much
difference in the expenditure between state government and central government institutions (see,
column 5 of Table 2).
There exists a large difference in per student expenditure on fees between government and
private institutions. Students of government institutions are found to be spending R25,000 on
fees, whereas it is R59,000 in private institutions. The difference in fees is largely due to tuition
fees, as students from private institutions spend 2.5 times higher tuition fees than the students of
government institutions. Further, fees in Indian Institute of Technology, Delhi and Jamia Millia
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Islamia are much less than state government institutions, such as Delhi College of Engineering,
Netaji Subash Institute of Technology and Ambedkar Institute of Technology.
Table 2: Annual Per Capita Household Expenditure on Engineering Education by Type of
Institution
Type of Institution Fees Non-fee items Additional items Total Government 25,290 33,570 49,370 1,08,230 a) Central Government 22,480 29,680 52,990 1,05,150 b) State Government 27,310 35,220 48,080 1,10,610Private 59,140 46,810 44,470 1,50,420
Per student expenditure on non-fee items such as dormitory or housing, food, transport,
textbooks and other class materials is higher for the students of private institutions (R47,000)
than government institutions (R34,000). This difference is mainly due to the higher expenditure
on dormitory or housing by the students of private institutions than government institutions.
Students from private institutions found to be spending twice more on dormitory than the
students of government institutions. This may be due to the non-availability of hostel facilities in
majority of the private institutions in Delhi10. Average annual per capita expenditure on transport
is R7,000 for the students enroled in private institutions, whereas it is R4,000 for the students of
government institutions. On the whole, per student household expenditure on fees and non-fee
items is higher for the students enroled in private than government institutions.
As per the annual per student additional expenditure is concerned, students of government
institutions spend higher than private institutions. It is R49,000 for the students enroled in
government institutions and R44,000 for private institutions. The lower level of per head
additional expenditure by the students of private institutions may be due to the fact that they
spend more on fees and non-fee items, which are compulsory in nature and hence, not able to
spend more on additional items.
There is not much of a difference in per student expenditure between traditional and IT-
related courses. Students enroled in traditional courses are found to be spending R7000 extra
than the students of IT-related courses. The per head expenditure on additional items like
improving English, cost of computers, expenditure on internets, phones, entertainment and other
necessay life expenses are more or less same in both the departments. On the other hand,
10 Out of six private institutions covered in the study only three are having hostel facilities, while three out of five government institutions covered in the study have hostels. Obviously, the students enroled in private institutions reside in rented houses and spending a large sum of money on the same.
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students in traditional departments spending slightly more both in fees and non-fee items than the
students enroled in IT-related courses (Table 3).
Table 3: Annual Average Household Expenditure on Engineering Education by Department
of Study Department Fees Non-fee items Additional items Total Traditional 47,130 43,610 46,810 1,37,550 IT-related 45,490 38,720 46,310 1,30,520
The annual per capita expenditure incurred by the students is expected to be positively
related with the annual family income. However, the present study shows a mixed response.
Students of upper middle income group spend the highest, while the students of lower middle
income groups are found to be spending least11. Households belonging to bottom and top income
groups spend nearly same amount of money.
Surprisingly, the annual average fees paid by the students from different income groups
vary negatively with the annual family income, i.e., higher the annual income of the family, lower
is the amount of fees paid by the students. This may be due to the fact that the rich households
have managed to send their children to government institutions where they have to spend less on
fees compared to private institutions. Similarly, students from higher income groups are found to
be spending R35,000 per annum on non-fee items such as dormitory or housing, food, textbooks
and other class materials, and transport, whereas students belonging to lower income groups
spend R44,000. Per student expenditure on additional items like improving English, cost of
computers, expenditure on internet, phone, entertainment and other necessay life expenses is
positively related with the annual income of the family. It ranges from R40,000 for lower income
households to R59,000 for higher income households (Table 4).
Table 4: Annual Per Head Household Expenditure on Engineering Education by Annual
Income of the Family
Annual Family Income Fees Non-fee items Additional items Total Lower income families 49,570 43,620 39,710 1,32,900Lower middle income families 46,070 39,030 43,930 1,29,030Upper middle income families 42,190 45,350 51,800 1,39,340Higher income families 38,430 35,030 58,890 1,32,350
11 The annual income of the households is classified as lower income groups (annual family income of less than R1 lakh), lower middle income groups (annual family income of R1 to 5 lakh), upper middle income groups (annual family income of R5 to10 lakh) and higher income groups (annual family income of more than R10 lakh).
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Annual average expenditure of female students is slightly higher than that of male
students12. Similarly, the annual average amount of fees incurred by the female students is higher
than the male students (R45,000 against of R51,000). One of the reasons seems to be that the
proportionate share of SCs and STs is higher among males than females. Out of total male
students, 13 per cent belong to SCs and STs as against of nine per cent among females. As large
share of female students belonging to general category and may not have got any subsidy on fees,
the average fees becomes higher for them. One more similar kind of a reason seems to be that 75
per cent of total female students have taken admission in private institutions (as against of 50 per
cent among males) where they have to pay higher fees than in government institutions. Female
students spend less money on non-fee items than male students. This could be due to the less
expenditure of girl students on dormitory, food and transport than boys, as they get hostel
facilities on priority basis in some institutions.
Table 5: Annual Per Student Household Expenditure on Engineering Education by Gender
Gender Fees Non-fee items Additional items Total Male 45,050 40,020 46,700 1,31,770 Female 50,690 38,640 41,920 1,31,250
Lower and lower middle income households are found to be spending comparatively more
on male than female students. On the other hand, households belonging to upper middle and
higher income groups spend more on females than males. This is mainly due to the mindset of
the poor households to invest less on education (specifically higher education) of the girls as the
parents did not expect to get return from this investment. It is observed that the poor households
would prefer to go for educational loans or even in some special cases sell their land and other
assets for the education of their sons, while they hesitate to do these for their daughters. On the
other hand, the households belonging to higher income groups might not discriminate much in
investment on education of their children by gender. However, this needs to be probed further.
Annual per capita expenditure also varies across the social categories. Students belonging
to STs spend R1,01,000, SCs R1,18,000, OBCs R1,26,000 and the students belonging to general
category are found to be spending R1,34,000. Hence, as expected, the annual family cost per
student is the lowest for the students belonging to STs and the highest for the students belonging
12 Similar trend is also observed in the estimation of average annual household expenditure on technical/professional education in Delhi in 64th round of NSS (female students enroled in technical or professional education in Delhi spend R43,000 per annum, whereas males are found to be spending R42,000).
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to general category. Similar trend is also found in the payment of total fees per annum by the
students from different castes, i.e., students from general category incurred the highest
expenditure on fees (R46,700) and ST students spend the lowest, i.e., R25,500 (Table 6). This is
mainly due to the subsidisation of tuition and other fees for the students belonging to SCs and
STs (mainly in government institutions).
Table 6: Annual Average Household Expenditure on Engineering Education by Caste Caste Fees Non-fee items Additional items Total SC 42,000 42,270 34,560 1,18,830 ST 25,470 34,160 41,050 1,00,670 OBC 43,400 48,190 34,720 1,26,310 General 46,710 39,020 48,100 1,33,830
Students belonging to general category are found to be spending less than SCs and OBCs
and more than STs on non-fee heads. Large shares of students from general category do not
spend on dormitory or housing and food as majority of them might have living in their own
houses. The low level of per head annual non-fee expenditure by the students belonging to STs
may be due to the fact that approximately 97 per cent of them have enroled in government
institutions where they got the hostel facilities at a subsidised rate.
Students belonging to general category are found to be spending the highest amount
(R48,000) per annum on additional items like improving English, meeting the cost of computers,
expenditure on internet, phone, entertainment and other necessay life expenses than the students
belonging to other social categories such as SCs, STs and OBCs. Further, ST students spend
more on additional items per annum than the students belonging to SCs and OBCs.
3. Determinants of Household Expenditure
Given the importance of household expenditure on education, several studies have
discussed quite a few important dimensions of it in Indian context (e.g., Kothari 1966;
Panchamukhi 1990; and Tilak 2002).
Earlier studies
The amount of expenditure incurred by the households on education may differ
significantly with their socio-economic settings, importance assigned towards education by the
households and with many other supply side factors like the type of institution, type of discipline
or course etc.
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Several Studies (Tan Jee-Pang 1985; Panchamukhi 1990; Hashimoto and Heath 1995;
Kanellopoulos and Psacharopoulos 1997; Psacharopoulos et al 1997; Acevedo and Salinas 2000;
Psacharopoulos and Mattson 2000; Tilak 2000, 2002; Chaudhuri and Roy 2006; Tansel and
Bircan 2006; Dang 2007; Shafiq 2011) have found that gender is an important factor in the
determination of household expenditure on education. It is generally believed that investment on
education of the girls by the households is not taken at par with the boys in many developing
countries including India. In a sense, households spend more on the education of male students
than that of females. Preference for households to invest in the education of boys than that of the
girls is widely prevalent and such difference widens further with the increase in the level of
education. The return on the investment made by the households on girls’ education does not
come to the parents; rather it goes to the in-laws families after marriage. In addition to this,
investment of households on girls’ education may work like ‘negative dowry’ in Indian society as
the higher educated girls need better educated groom for their marriage who, in turn, expect more
dowries. Though this is a country-wide phenomenon, it is more stretched in rural areas and
traditionally orthodox families. Alternatively, to some extend it is also recognised that
investment on girls’ education works as a substitute to dowry as some grooms willingly marry
higher educated girls with less or even without any dowry, in an expectation that they could
easily earn for the family in the future.
In the literature we found three patterns of gender discrimination in the household
expenditure on education: (a) households spend more on male students than on female students;
(b) households have no gender preference in the investment in education; and (c) households
spend more on females than on males. But in practice the focus has been on reducing investment
on education of the female children by the households. The study of Chaudhuri and Roy (2006)
in Uttar Pradesh and Bihar based on Living-Standard Measurement Survey (1997) shows that
parents exhibit a gender bias while educating their children, as they spend more on sons than on
daughters both in school and higher education. The study by Tilak (2002) based on the data from
a household survey in 16 major states conducted by the National Council of Applied Economic
Research (NCEAR) in 1994 showed that households have spent more on male students’
schooling than on female students. However, some studies have also shown that there is no
evidence of gender discrimination in the household expenditure in both school and higher
education (see Tilak 2000; Dang 2007). On the other hand, the study by Panchamukhi (1990) on
private expenditure on education with the help of a household survey conducted in three states of
India namely Maharashtra, Karnataka and Rajasthan found that estimated expenditure per pupil is
higher for girls’ education than that of the boys. Similarly, Shafiq (2011) found that households
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in urban Bangladesh were less likely to spend on education of the boys than of the girls, holding
all else constant.
Second, positive relationship between the amount of household expenditure and annual
income of the family has been found in many studies (e.g., Tan Jee-Pang 1985; Hashimoto and
Heath 1995; Psacharopoulos et al 1997; Acevedo and Salinas 2000; Psacharopoulos and Mattson
2000; Tilak 2002; Tansel and Bircan 2006; and Shafiq 2011). Rich households spend more on
the education of their children than the lower income and poor households. Tilak (2000) using
52nd round survey data of NSS found that average household expenditure of the top income group
on school education is six times higher than that of the expenditure of the bottom income group.
Many studies have also used the annual aggregate expenditure of the households as proxy for
annual income of the family. The study by Kanellopoulos and Psacharopoulos (1997) in Greece
revealed that the probability of spending on education increases along the household’s
expenditure level. More clearly, households belonging to bottom 20 per cent of expenditure
distribution spend six per cent of their annual income on education, whereas it is 56 per cent for
the households belonging to upper 20 per cent of the expenditure distribution.
Besides analysing the relationship between household income and their expenditure on
education, some studies have measured the income elasticity of expenditure on education, i.e.,
change in the expenditure on education by the households with one unit change in their income.
As expected, all the studies reviewed here have shown a positive elasticity coefficient which
suggests that household expenditure on education is positively influenced by total income of the
households (e.g., Tan Jee-Pang 1985; Tilak 2000, 2002; Tansel and Bircan 2006; and Hashimoto
and Heath 1995). Positive value of the elasticity coefficient may be of: (a) less than unity; or (b)
more than unity. The elasticity coefficient value of less than one (say for example, 0.9) tells that
one per cent increase in income brings 0.9 per cent increase in household expenditure on
education, which suggests that education is a necessary item in the household budget. Elasticity
coefficient value of more than unity (say 1.5) suggests that one per cent increase in total
household income brings 1.5 per cent increase in household expenditure on education, in which
case education is treated as a luxury item of the households budget.
Third, large households spend greater portion of their total income on the necessary items
(food, shelter, clothing and other related items), leaving less resource for education. Hence, the
per student expenditure made by the households on education and the size of the family are
negatively related as established in a number of studies both in India and international context
(e.g., McMahon 1974; Psacharopoulos and Mattson 2000; Tilak 2000, 2002; and Tansel and
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Bircan 2006). On the other hand, the study of Shafiq (2011) in urban Bangladesh shows that the
presence of other children in the family does not affect the decision of the households on
spending in education.
Four, the education of the parents or head of the households has a positive effect on
household expenditure on education. Educated parents are more aware of the benefits of
education for their children and accordingly spend more on it, which is established in quite a
number of studies both in India and abroad (e.g., Tan Jee-Pang 1985; Psacharopoulos et al 1997;
Kanellopoulos and Psacharopoulos 1997; Tilak 2002; Chaudhuri and Roy 2006; and Dang 2007).
Psacharopoulos and Mattson (2000) reported that an increase in the years of schooling of the
head of household by one year increased expenditure of the household in primary education of
their children by eight per cent in Bolivia. The available research evidences show that the
educational level of the mother is having a larger effect on household expenditure on education of
their children than father’s level of education (e.g., Tansel and Bircan 2006; Shafiq 2011).
Hence, a positive relationship is established between years of schooling or levels of education of
the parents and household expenditure on education.
Five, household expenditure is also determined by government expenditure. Education
needs investment both form individual and social domains (Majumdar 1983). This is such an
area where the interaction between government and households is particularly important. The
investments from public and private sources in education are of high significance not only
because of their magnitudes, but also because of the nature and characteristics associated with
those investments. The two components of investment in education (public and
private/household expenditure) are so inter-related and inter-dependent that in the absence of
either of them, it is likely to result in under-allocation of resources for education. The public and
private investments on education may substitute or complement each other. The substitutive
principle reveals a negative relationship between these two, whereas the complementarity
principle establishes a positive relationship. One line of argument says that more investment by
the government on education demand less resource from the households for the same
(substitutive role), whereas other line of argument gives an idea that the large investment by the
government on education will influence the households to spend more to get better quality
education (complementarity principle). However, most of the studies both in India and
international contexts have established a complementary relationship between these two (e.g.,
Tilak 1991, 2000, 2002; Mehrotra and Delaminica 1998).
15
Tilak (1991) using National Accounts Statistics (NAS) data showed that both household
and government expenditure were positively influenced by each other, i.e., higher the government
expenditure, higher would be the expenditure of household on education and vice-versa (Tilak
1991). Similarly, the study of Mehrotra and Delaminica (1998) in five low income countries
(Burkina Faso, Bhutan, Myanmar, Uganda and Viet Nam) shows that the countries where the
government invest less in primary education, there is a heavy incidence of costs on parents and
vice-versa.
Perhaps there may be some other factors discussed in the literature that determines the
household expenditure on education. But the five important factors repeatedly discussed in most
of the literature reviewed on this aspect and discussed here are: gender, family income, family
size, parents’ education and level of public expenditure on education.
Determinants of Household Expenditure on Engineering Education (Present Study)
In the present section an attempt is made to analyse the factors determining annual
household expenditure on engineering education using OLS technique. The equation used for the
estimation is as follows:
lnEDU_COST = α + β1 GENDER + β2 SC + β3 ST + β4 OBC + β5 HINDU + β6 MUSLIM + β7
SIKH + β8 lnFAMILY_INCOME
+ β9 FATHOCP_PROFF + β10 FATHOCP_BUSN
+ β11 MOTHOCP_PROFF + β12 MOTHOCP_BUSN
+ β13 FATHER_SCHOOLING + β14 MOTHER_SCHOOLING
+ β15 SIBLING + β16 NATIVE_PLACE + β17 FAMOWN_HOUSE
+ β18 SEC_LOCATION + β19 SEC_MANGMT
+ β20 SEC_MEDIUM + β21 SEC_BOARD + β22 SEC_MARKS
+ β23 PART_TIME + β24 MNGT_PVT + β25 DEPT_IT
+ β26 SCHOLARSHIP + β27 EDU_LOAN + β28 FURTHER_EDU1 + β29
FURTHER_EDU2 + ε (Eqn. 5.1)
where,
lnEDU_COST = per student annual household expenditure on engineering education
α = constant
βi = respective coefficient of the explanatory variables
ε = error term
16
The notation and definition of the explanatory variables used in estimating the determinants
of household expenditure on engineering education are presented in Table 7.
Table 7: Notation and Definition of the Explanatory Variables used in the Estimation of Different Econometric Models
Notation of the variable Name of the variable Definition of the variable Individual Characteristics
GENDER Sex of the students (dummy variable)
= 1, if the student is male = 0, otherwise
Caste Caste of the students (dummy variables)
SC Scheduled Caste = 1, if the student belongs to Scheduled Castes = 0, otherwise
ST Scheduled Tribe = 1, if the student belongs to Scheduled Tribes = 0, otherwise
OBC Other Backward Class = 1, if the student belongs to Other Backward Classes
= 0, otherwise GENERAL Unreserved category = 1, if the student belongs to non-
Scheduled Castes, non-Scheduled Tribes and non-Other Backward Classes
= 0, otherwise Religion Religion of the students (dummy
variables)
HINDU Hindu = 1, if the student is Hindu = 0, otherwise
MUSLIM Muslim = 1, if the student is Muslim = 0, otherwise
SIKH Sikh = 1, if the student is Sikh = 0, otherwise
OTHERS Jain, Buddhist, Christian = 1, if the student is from other religion = 0, otherwise
Household Characteristics
lnFAMILY_INCOME Annual income of the family Annual income of the family (in logarithimic form)
Father’s Occupation Occupation of the father (dummy variables)
FATHOCP_PROFF Father’s occupation is professional work
= 1, if father occupation is professional work = 0, otherwise
FATHOCP_BUSN Father’s occupation is business = 1, if the father occupation is business = 0, otherwise
17
FATHOCP_OTHERS Father’s occupation is ‘others’ = 1, if father occupation is others (occupation other than professional work and business)
= 0, otherwise
Mother’s Occupation Occupation of the mother (dummy variables)
MOTHOCP_PROFF Mother’s occupation is professional work
= 1, if mother occupation is professional work = 0, otherwise
MOTHOCP_BUSN Mother’s occupation is business = 1, if mother occupation is business = 0, otherwise.
MOTHOCP_OTHERS Mother’s occupation is housewife and others
= 1, if mother occupation is housewife and others
= 0, otherwise
FATHER_SCHOOLING Father’s schooling in completed number of years
The completed years of schooling of the father
MOTHER_SCHOOLING Mother’s schooling in completed number of years
The completed years of schooling of the mother
SIBLING Total number of brothers and sisters in the family
Total number of brothers and sisters in the family
NATIVE_PLACE The state from where the student belongs (dummy variable)
= 1, if the students belongs to Delhi or neighbouring states
= 0, otherwise
FAMOWN_HOUSE Whether the family own a house or not (dummy variable)
= 1, if the household owns a house = 0, otherwise
Student’s Academic Background
SEC_LOCATION Location of the senior secondary school (dummy variable)
= 1, if the students have studied from urban schools
= 0, otherwise, i.e., if the students have studied from rural schools
SEC_MANGMT Type of senior secondary school
(dummy variable) = 1, if the students have studied from
private schools = 0, otherwise, i.e., if the students have
studied from government schools
SEC_MEDIUM Medium of instruction in the senior secondary school (dummy variable)
= 1, if the students have taught in English medium
SEC_BOARD Type of board of the senior secondary examination (dummy variable)
= 1, if the students have studied from the schools managed by central boards, i.e., from CBSE and ICSE boards
= 0, otherwise, i.e., if the students have studied under state boards
18
SEC_MARKS Percentage of marks scored in the senior secondary examination
Percentage of marks scored by the students in their senior secondary examination
PART_TIME Whether students have gone for part-time job during their course or not (dummy variable)
=1, if the students have gone for part-time job during their course
=0, otherwise
Student’s Current Education Status MNGT_PVT Type of institution (dummy variable) = 1, if the students have enroled in private
institutions = 0, otherwise, i.e., if the students have
enroled in government institutions
DEPT_IT Department of study of the student (dummy variable)
= 1, if the students have enroled in IT-related departments
= 0, otherwise, i.e., if the students have enroled in traditional departments
SCHOLARSHIP Whether the students have received
scholarship or not (dummy variable) =1, if the students have received scholarship = 0, otherwise
EDU_LOAN Whether the students have availed educational loan from commercial banks or not (dummy variable)
=1, if the students have availed educational loan from commercial banks
=0, otherwise
Further education Level of further education the students plan to attain (dummy variable)
FURTHER_EDU0 If the students do not go for further education
= 1, if the students have no plan to study further
= 0, otherwise
FURTHER_EDU1 If the students wish to study up to master level
= 1, if the students have planned to study up to master level
= 0, otherwise
FURTHER_EDU2 If the students wish to study upto Ph.D. level
= 1, if the students have planned to study up to doctorate level
= 0, otherwise
lnEDU_COST Household expenditure on engineering education
Annual household expenditure on engineering education (in logarithmic form)
Results of the OLS reported in Table 8 show that among the dummy variables included
under individual characteristics, only SC and MUSLIM were statistically significant. As
expected, students belonging to SCs spend less than the students belonging to unreserved
category (GENERAL). This is because around 90 per cent of students belonging to SC had come
19
from lower and lower middle income households (with the annual income of less than R5 lakh)
and hence, they were not able to spend more. Similarly the students belonging to Muslim
religion are found to be spending less by 34 per cent than the students belonging to other
religions like Christians, Buddhists and Jains (OTHERS).
Table 8: OLS Estimate of the Determinants of Household Expenditure on Engineering Education
Variable Coefficient Standard Error
Individual Characteristics GENDER 0.05 0.08 SC -0.16* 0.10 ST 0.08 0.14 OBC 0.11 0.12 GENERAL Reference HINDU -0.01 0.11 MUSLIM -0.34* 0.18 SIKH -0.18 0.16 OTHERS Reference Household Factors lnFAMILY_INCOME 0.01 0.04 FATHOCP_PROFF -0.05 0.09 FATHOCP_BUSN -0.09 0.10 FATHOCP_OTHER Reference MOTHOCP_PROFF -0.17** 0.07 MOTHOCP_BUSN -0.19 0.14 MOTHOCP_OTHER Reference FATHER_SCHOOLING -0.01 0.02 MOTHER_SCHOOLING -0.01 0.01 SIBLING 0.00 0.03 NATIVE_PLACE -0.02 0.07 FAMOWN_HOUSE 0.12 0.10 Student’s Academic Background SEC_LOCATION 0.09* 0.09 SEC_MANGMT 0.07 0.07 SEC_MEDIUM 0.10 0.10 SEC_BOARD 0.12 0.12 SEC_MARKS 0.00 0.00 Student’s Current Education Status PART_TIME -0.01 0.07 MGMT_PVT 0.54*** 0.07 DEPT_IT 0.01 0.06 SCHOLARSHIP -0.12* 0.08 EDU_LOAN 0.07 0.06 FURTHER_EDU1 0.31*** 0.07 FURTHER_EDU2 0.22** 0.10
20
FURTHER_EDU0 Reference Constant 3.45*** 0.56 R Square 0.18 Adjusted R Square 0.14 F -Value 5.38*** Number of Observations 751 Note: ***significant at 1 per cent level of significance; **significant at 5 per cent level of
significance; * significant at 10 per cent level of significance.
Among the household factors, only MOTHOCP_PROF variable has been found to be
statistically significant in the determination of expenditure. The coefficient reported for this
suggests that annual per student expenditure is less for the student whose mother was a
professional worker than the student whose mother was engaged in other occupations, i.e.,
occupations other than professional work and business (MOTHOCP_OTHER). Majority of
students whose mothers were professional workers had enroled in government institutions where
they had to spend less than the students enroled in private institutions. The annual per head
expenditure on government institutions is R1,08,000, whereas it is R1,50,000 for the students
enroled in private institutions. Surprisingly, the annual income of the family is statistically not
significant in the determination of household expenditure, though it is positively related. The
evidence does not support the hypothesis that the level of household expenditure on engineering
education is significantly influenced by economic background of the parents.
Students’ academic background (senior secondary level of education) seems to have had an
impact on their level of expenditure. It is widely felt that the students who complete their senior
secondary schooling from quality schools train themselves in many aspects in advance and hence
they may spend less in aggregate level and particularly on additional items like improving
English, computer and other such academic requirements. However, among the variables
included under student’s academic background the coefficient in respect of only
SEC_LOCATION was found to be statistically significant. It is evident that the annual per
student expenditure is higher for the students who have completed their senior secondary school
from urban region than rural region. This is mainly due to the fact that proportionately more
share of students who completed their senior secondary schooling from urban region had come
from rich households who are capable to spend more. In addition to this, approximately 60 per
cent of the students who have completed their senior secondary schooling form urban region have
enroled themselves in private institutions where they have to spend more than the students
enroled in government institutions. The coefficients of other academic background variables like
21
SEC_MANGMT, SEC_MEDIUM, SEC_BOARD and SEC_MARKS were statistically not
significant.
Regression results reported in Table 8 show a positive relationship between MGMT_PVT
and annual household expenditure. More clearly, students from private institutions are found to
be spending 54 per cent more than students studying in government institutions and the result was
statically significant at one per cent level of significance13. The pattern of expenditure discussed
before also shows that the students enroled in private institutions found to be spending
substantially higher than the students of government institutions.
Theoretically, the availability of scholarship may increase or minimise the household
expenditure of students. If the amount of scholarship is spend in addition to their household
expenditure then it will increase the spending. On the other hand, if it substitutes the expenditure,
student’s spending will decrease. Hence, the effect of receiving scholarship may affect the
expenditure either positively or negatively. The present analysis shows that SCHOLARSHIP is
negatively related with the annual per capita expenditure. Students availing scholarship are found
to be spending 12 per cent less per annum than the students who are not getting scholarship.
Hence, in this case the scholarship received by the students substitute the level of household
expenditure on engineering education.
It appears that the students intending to go for higher studies (master or Ph.D. level) after
graduation need to spend more than the students who are not willing to study further. This may
be because the students wishing to go for higher studies spend some extra money on different
academic activities such as improving English and computer knowledge, besides the formal
training they get from the institutions. Present analysis reveals the same. Annual per student
expenditure is higher for the students who have planned to go for further education
(FURTHER_EDU1 and FURTHER_EDU2) compared to the students who are willing to study
upto graduation level (FURTHER_EDU0). Students intending to study upto master and Ph.D.
level are found to be spending 31 per cent and 22 per cent more, respectively, than the graduates
who are not going for further study.
13 The study of McMohon (1974) has found that income elasticites of expenditure on higher education are much higher in public institutions than private institutions in the context of United States.
22
4. Summary and Conclusions
The present analysis highlights the pattern and determinants of household expenditure by
estimating household expenditure function. Some of the findings of the study on pattern and
determinants of household expenditure on engineering education support the basic arguments of
the established theories, and a few contrasts to the ideas and some studies also provide new
dimensions. Major findings are as follows:
Per student household expenditure on engineering education is about R1,31,000, which
constitutes 34 per cent of the annual average income of the family. Out of the total
household expenditure, 35 per cent goes towards fees, 30 per cent on non-fee items
(dormitory, food, transport, textbooks and other class materials) and rest are on additional
items like improving English, purchasing of computers, internets, phones, entertainment
and other necessay life expenses.
Annual average household expenditure is more or less same for male and female students.
However, the annual per capita fees paid by the female students is higher than the male
students. Similarly, the annual per head expenditure varies widely across the social
categories; lowest is found to be spend by the students belonging to STs and the highest
by general category students.
Students enroled in government institutions are found to be spending much less than the
students of private institutions, which is mainly due to the difference in their fees level.
Students from private institutions pay 2.5 times higher tuition fees than the students of
government institutions. On the other hand, there is no significant difference on the
annual average household expenditure incurred by the students on non-fee items and
additional items between government and private institutions. Similarly, there is not
much of a difference in per student household expenditure between traditional and IT-
related courses.
Annual per capita expenditure is highest for upper middle income groups and lowest for
lower middle income groups. Households belonging to lower and higher income groups
spend an equal amount of money per student. Furthermore, the lower and lower middle
income households spend comparatively more on male students than females. On the
other hand, households belonging to upper middle and higher income groups spend more
on girls than boys.
23
Only about 13 per cent of students have received financial assistance in the form of
scholarship, tuition waivers, room/board allowance and work study opportunity provided.
More students enroled in government institutions have received financial assistance than
the students enroled in private institutions. The annual average amount of financial
assistance received by students is R16,000 which covers only 12 per cent of the annual
average household expenditure.
The results of the regression analysis on determinants of expenditure reveal that the caste,
religion, mother’s occupation, type of institution the students have enroled, whether the
students have received scholarship or not and whether students have planned to go for
further studies or not are statistically significant; and more importantly, most evident
expected signs. But, some important factors like gender, annual family income, number
of siblings in the family that are expected to have impact on the household expenditure,
were statistically not significant, though most of them had expected signs.
As one expects, type of institution has a strong significant effect on the expenditure, as
revealed from the OLS estimation. Students enroled in private institutions necessitate
higher levels of expenditure than the students enroled in government institutions. This is
one of the most important variables in terms of the size of the coefficient and also the
standard error.
The second most important factor influencing the expenditure is the students’ decision to
go for further studies (master and Ph.D. level) or not. More clearly, the expenditure is
higher for a student wishing to go for further studies than a student who is not willing to
do so after completion of graduation.
Among the individual characteristics, the caste and religion of the students are two
statistically significant factors influencing the household expenditure. More specifically,
students belonging to SCs are found to be spending less than the students belonging to
general category. Similar is the case of Muslim students than that of other religions
(Christians, Buddhists and Jains).
Among the household factors, only occupation of the mother is statistically significant in
the determination of household expenditure. Students whose mothers are engaged in
professional and technical works are found to be spending less compared to the students
whose mothers are engaged in other non-technical works. This is may be due to the fact
that majority of the students whose mothers were professional and technical workers had
24
enroled their wards in government institutions where they had to spend less than the
private institutions. But, father’s occupation is statistically not significant.
Two major policy implications of the present analysis are as follows:
Households spend a large portion of their income (34 per cent) on engineering education of
their ward and more so if the students have enroled in private institutions. Huge financial burden
on households (particularly poor households) works as an obstacle to access engineering
education by their wards, even if they might have a strong desire and intellectual potential to
pursue it. Thus, reducing financial burden of households (particularly focusing on lower income
households) to provide wider access to engineering education may be necessary. This is possible
mainly by allocating more public fund towards engineering education. However, this needs to be
examined in more detail.
25
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