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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: pradeep.hcu@gmail.com

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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

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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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