What Determines the Household Expenditure on Engineering Education? Findings from Delhi, India Pradeep Kumar Choudhury

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1 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 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. pradeep.hcu@gmail.com 1

2 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 64 th round of NSS conducted in , 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 education 1. 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 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. 2

3 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 institutions 2. 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 appendix 3. 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 study 4. 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 considered 5. 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 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. 3

4 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 course 6. 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 study 7. 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 institutions 8. 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. 4

5 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 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 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, nonfee 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. 5

6 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, Other fees 6, Total 45, Non-fee Expenditure Drom/Housing 11, Food 11, Textbooks and other class materials 3, Transport 5, Others 6, Total 38, Additional Expenditure Improving English and computer 12, Cost of computer 8, Expenditure on internets and phones 7, Entrainment and other life expenses 12, Others 4, Total 46, Grand Total 1,31, 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. 6

7 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 nonfee 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 7

8 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 1,10,610 Private 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 Delhi 10. 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 ITrelated 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. 8

9 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 least 11. 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 1,32,900 Lower middle income families 46,070 39,030 43,930 1,29,030 Upper middle income families 42,190 45,350 51,800 1,39,340 Higher income families 38,430 35,030 58,890 1,32, 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). 9

10 Annual average expenditure of female students is slightly higher than that of male students 12. 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 64 th 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). 10

11 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. 11

12 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 12

13 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 52 nd 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 13

14 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). 14

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 + β 1 GENDER + β 2 SC + β 3 ST + β 4 OBC + β 5 HINDU + β 6 MUSLIM + β 7 SIKH + β 8 lnfamily_income + β 9 FATHOCP_PROFF + β 10 FATHOCP_BUSN + β 11 MOTHOCP_PROFF + β 12 MOTHOCP_BUSN + β 13 FATHER_SCHOOLING + β 14 MOTHER_SCHOOLING + β 15 SIBLING + β 16 NATIVE_PLACE + β 17 FAMOWN_HOUSE + β 18 SEC_LOCATION + β 19 SEC_MANGMT + β 20 SEC_MEDIUM + β 21 SEC_BOARD + β 22 SEC_MARKS + β 23 PART_TIME + β 24 MNGT_PVT + β 25 DEPT_IT + β 26 SCHOLARSHIP + β 27 EDU_LOAN + β 28 FURTHER_EDU1 + β 29 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 15

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 Caste Caste of the students (dummy variables) SC Scheduled Caste = 1, if the student belongs to Scheduled Castes ST Scheduled Tribe = 1, if the student belongs to Scheduled Tribes OBC Other Backward Class = 1, if the student belongs to Other Backward Classes GENERAL Unreserved category = 1, if the student belongs to non- Scheduled Castes, non-scheduled Tribes and non-other Backward Classes Religion Religion of the students (dummy variables) HINDU Hindu = 1, if the student is Hindu MUSLIM Muslim = 1, if the student is Muslim SIKH Sikh = 1, if the student is Sikh OTHERS Jain, Buddhist, Christian = 1, if the student is from other religion Household Characteristics lnfamily_income Annual income of the family Annual income of the family (in logarithimic form) Father s Occupation FATHOCP_PROFF Occupation of the father (dummy variables) Father s occupation is professional work = 1, if father occupation is professional work FATHOCP_BUSN Father s occupation is business = 1, if the father occupation is business 16

17 FATHOCP_OTHERS Father s occupation is others = 1, if father occupation is others (occupation other than professional work and business) Mother s Occupation MOTHOCP_PROFF Occupation of the mother (dummy variables) Mother s occupation is professional work = 1, if mother occupation is professional work MOTHOCP_BUSN Mother s occupation is business = 1, if mother occupation is business. MOTHOCP_OTHERS FATHER_SCHOOLING MOTHER_SCHOOLING SIBLING NATIVE_PLACE FAMOWN_HOUSE Mother s occupation is housewife and others Father s schooling in completed number of years Mother s schooling in completed number of years Total number of brothers and sisters in the family The state from where the student belongs (dummy variable) Whether the family own a house or not (dummy variable) = 1, if mother occupation is housewife and others The completed years of schooling of the father The completed years of schooling of the mother Total number of brothers and sisters in the family = 1, if the students belongs to Delhi or neighbouring states = 1, if the household owns a house Student s Academic Background SEC_LOCATION Location of the senior secondary school (dummy variable) = 1, if the students have studied from urban schools, i.e., if the students have studied from rural schools SEC_MANGMT SEC_MEDIUM SEC_BOARD Type of senior secondary school (dummy variable) Medium of instruction in the senior secondary school (dummy variable) Type of board of the senior secondary examination (dummy variable) = 1, if the students have studied from private schools, i.e., if the students have studied from government schools = 1, if the students have taught in English medium = 1, if the students have studied from the schools managed by central boards, i.e., from CBSE and ICSE boards, i.e., if the students have studied under state boards 17

18 SEC_MARKS PART_TIME Percentage of marks scored in the senior secondary examination Whether students have gone for parttime job during their course or not (dummy variable) Percentage of marks scored by the students in their senior secondary examination =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, i.e., if the students have enroled in government institutions DEPT_IT SCHOLARSHIP EDU_LOAN Further education FURTHER_EDU0 FURTHER_EDU1 FURTHER_EDU2 lnedu_cost Department of study of the student (dummy variable) Whether the students have received scholarship or not (dummy variable) Whether the students have availed educational loan from commercial banks or not (dummy variable) Level of further education the students plan to attain (dummy variable) If the students do not go for further education If the students wish to study up to master level If the students wish to study upto Ph.D. level Household expenditure on engineering education = 1, if the students have enroled in ITrelated departments, i.e., if the students have enroled in traditional departments =1, if the students have received scholarship =1, if the students have availed educational loan from commercial banks =0, otherwise = 1, if the students have no plan to study further = 1, if the students have planned to study up to master level = 1, if the students have planned to study up to doctorate level 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 18

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 SC -0.16* 0.10 ST OBC GENERAL Reference HINDU MUSLIM -0.34* 0.18 SIKH OTHERS Reference Household Factors lnfamily_income FATHOCP_PROFF FATHOCP_BUSN FATHOCP_OTHER Reference MOTHOCP_PROFF -0.17** 0.07 MOTHOCP_BUSN MOTHOCP_OTHER Reference FATHER_SCHOOLING MOTHER_SCHOOLING SIBLING NATIVE_PLACE FAMOWN_HOUSE Student s Academic Background SEC_LOCATION 0.09* 0.09 SEC_MANGMT SEC_MEDIUM SEC_BOARD SEC_MARKS Student s Current Education Status PART_TIME MGMT_PVT 0.54*** 0.07 DEPT_IT SCHOLARSHIP -0.12* 0.08 EDU_LOAN FURTHER_EDU1 0.31*** 0.07 FURTHER_EDU2 0.22**

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 20

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 significance 13. 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. 21