is the crucial component of socio-economic background that impacts post-school .... grades 11 to 12. ...... Notes on social capital and economic performance.
Determinants of Post-School Choice in India - A Discriminant Analysis Autar S. Dhesi Abstract Based on a sample of 1018 respondents in secondary schools, discriminant analysis identifies five variables such as father’s expectations of student’s performance, mother’s interest in education, mother’s commitment to education, expected return, and achievement which discriminate significantly between those with postschool education plans and those intending to enter the labour market. The first three variables approximate social capital in the family. it is clear that social capital is the crucial component of socio-economic background that impacts post-school choice. influence of the other components of background-financial capital and human capital— may operate indirectly through expected return and achievement. The significant impact of parental behaviour on post-school decision draws attention to the role that a non-traditional, largely untapped resource can play in broadening the social base of participation in higher education. The basis of investment in the human capital model of education is expected return. The model assumes efficient markets (zero transaction costs) and equilibrium conditions. It assumes rational, self-interested behaviour, least affected by the social relations and norms. With the assumption of rational behaviour, no equilibrium can answer in which individuals fail to maximize their preferences. The individual is assumed to have all the necessary information and capacity to evaluate it. With the prime focus on optimum equilibrium solutions, the noneconomic environment in which an individual makes choices is ignored. The institutions, formal as well as culturally embedded informal, have no independent role in economic analysis (North, 1990). The details of human action and interaction including the social or cultural as well as individual aspects of the phenomenon are ignored. So are implications of incomplete information and the
cmp1exity of the environment as well as an individual’s perceptions of it. If choices are simple, regular and repetitive with substantial and rapid feedback, this model of substantive rationality will suffice. However, in non- repetitive and unique situations with uncertain outcomes where information is incomplete, and individuals have limited computational ability, this will be inadequate (North, 1990). Education decision at the secondary school stage can be realistically characterized as unique, complex and non-repetitive where information is incomplete and outcomes are uncertain. In such a situation, institutional structure and subjective processing of the limited information are two important determinants of choice. Institutions structure and order the environment. In other words, they define and limit the set of choices. The incentive structure, consequent upon institutional structure, and the subjective interpretation of the environment are important factors that influence an individual’s choice (North, 1990). In any case, each individual is socially situated. She/he inherits some social capital, that is, values, norms, trusts and social networks. Its role in the creation of human capital has been given some recognition in recent years (Coleman, 1988; Lowy, 1998). In this view, economic analysis with focus on the depersonalized agent making the best of the available opportunities is far removed from reality. A realistic approach should be to consider the individual as socially situated. Her/his placement in the social structure is an important determinant of her/his access to various resources and her/his ability to work on them. Her/his social situation is likely to condition her/his perception of benefits and costs and choices. In early childhood, parental attitudes and behaviour reflecting their worldview is an important factor in an individual’s development. The norms of an individual’s social groups would determine whether she/he is able to attain her/his
full potential or not. If these norms are not conducive to the pursuit of intellectual activities, the individual’s intellectual development would be thwarted because of her/his concern for status and conformity (Dhesi, 2000b). Further, imperfect capital markets for educational investment necessitate dependence on personal ties for finance (Loury, 1998). Individual actions are thus shaped, directed and constrained by the context Therefore, individualistic theory should “allow for the fact that a lot of economically relevant behaviour is socially determined” (Solow, 1999). In view of the above comments, it would be appropriate to modify the investment in the human capital model to take into account the role of formal institutions and culturally embedded informal institutions (social capital) in the formation of demand for schooling. In the modified analytical framework, the individual decision is subject to institutional constraints - both formal and informal, in addition to traditional constraints (North, 1990)1. In the extensive literature on the determination of educational aspirations across different disciplines, several approaches have been used to classify factors influencing post-school choice (Psacharopoulos & Soumelis, 1979). The main objective of this paper is to explore which individual and institutional factors among many are useful for discriminating between those with aspirations for higher education places, and those who desire to enter the labour market/vocational training. The study should help in understanding the formation of social demand for higher education, thus improving the quality of policy responses. India’s education system is quite large. As per the sixth All India Education Survey, 98 million children were enrolled in 575,000 lower primary schools in 1993, another 34 million in 161,000 upper primary schools, and 22 million in 90,000 secondary schools. Roughly 5 million students were enrolled in over 8,000 institutions at the tertiary level (World Bank, 1997).
Education is primarily the responsibility of states, and central government’s effective role is catalytic, facilitating, coordinating and complementary. In many states this responsibility is not adequately fulfilled and school education is underfunded. Broadly, schools are of three types: fully government, government—aided private schools, and fully private. Government and government aided private schools are publically financed. The structure of public schools varies across states, although in most it follows a similar pattern. Elementary education covers grades 1 to 8, with upper primary schools beginning at grade 5 in some states and grade 6 in others. Lower secondary covers grades 9 to 10, and upper secondary covers grades 11 to 12. Schooling is nominally compulsory in 14 major states and union territories. However, the target of 100 per cent enrollment of children in age 6-10 is yet to be achieved. The medium of study in government schools in often the official language of the state. There are fourteen major recognized languages in the country. In private schools, the medium of study is English. In 1987, about 40 per cent of urban primary schools and more than half of all secondary schools were private (World Bank, 1997). Their share has shown an increasing trend over time and varies across states with relatively developed states having higher proportions. The private schools provide a first-class education to those who can afford it. The quality of education in government schools is often inadequate, as reflected in low levels of learning achievement and high drop out rates. Children who have reached the final year of lower primary school often have mastered less than half the curriculum taught the year before. About 40% of those who enroll drop out before completing the primary cycle. The rural areas are mainly served by government schools. These schools often suffer from acute shortage of qualified teachers and poor infrastructure that adversely affects the learning environment.
The study of transition from secondary to higher education, or to the labour market; has gained importance in India as the number of students going in for secondary education has been increasing over time. Secondary schooling is no longer a privilege of the elite. The percentage of population ranging in the age from 14 to 17 years enrolled in schools increased from 10.6% in 1961 to 24% in 1990, the latest year for which information is available. In 1996, 62% of 1,177,164 students who appeared in the 12th grade examination in Punjab passed it and became eligible for admissions to higher education. Of the eligible students, 67% were able to get places in higher education, 12% got into some sort of training. The remaining 21% were available in the labour market for employment.2 In section II, we briefly discuss the source of data and methodology. In section if, explanatory variables are given. Empirical findings are given in section IV, followed by conclusions and policy implications. II. DATA & METHODOLOGY The data come from a research project, “A Quantitative Analysis of Demand for Higher Education”, funded by the Indian Council of Social Science Research. The primary data were collected through field survey from 1050 students of secondary classes and partially from their parents. The size of sample was predetermined keeping in view time and financial constraints. The final sample used in analysis contains 1018 observations as a result of missing information on some key variables in the remaining schedules. The multistage stratified random sampling procedure was used to select the sample individuals from Punjab (including Chandigarh).3 For the first stage, Punjab was divided into four broad homogenous regions on the basis of socio-economic development. Punjab being a border state, due consideration was given to the special educational problems of the border districts. From each region, one district
was selected randomly. In the next stage, colleges (offering pre-university classes) and secondary schools were enumerated in each district. Since districts vary not only in size but also with respect to development of educational infrastructure, different fractions were used to select colleges and schools from rural, semi-urban, and urban areas in each district. Similarly, due consideration was given to represent each type of institution in the sample. The number of students selected from each district were in proportion to its share in the total number of students in the four districts, the students selected randomly from each institution were in proportion to its share in the subsample for the district. The multistage stratification procedure ensured adequate representation of institutions and pupils in the state. The investigators personally administered the survey schedule to students and their parents to collect the relevant information. Multiple discriminant analysis is used to test the following hypotheses. 1.
Socio-economic background significantly influences post-school education decision and its crucial component is social capital.
2.
Educational achievement is significantly associated with post- school plan.
3.
Expected return to investment in education is an important determinant of post-school decision. Discriminant analysis is useful in situations in which one wants to build a
predictive model of group membership based on observed characteristics of each individual. One begins with subjects/individuals in two or more known groups like a one-way multivariate analysis of variance. Then the discriminant procedure is applied to identify linear combinations of the predictor variables or discriminant functions that provide the best discrimination between groups. The number of functions equals the number of groups minus one. The procedure estimates the coefficients of functions which can be applied to new cases with values for the
predictor variables but unknown group membership. The discriminant analysis is also applied to identify which variables among many are most useful for discriminating among groups. There are several methods for building a model in a stepwise manner. We use Wilk’s Lambda method for the purpose. Wilk’s Lambda provides information regarding differences among groups. It is the ratio of the within-groups sum of squares to the total sum of squares. Its values range from 0 to 1.0. Small values indicate strong group differences. In each step, stepwise selection begins by identifying the variance for which the group means are most different and proceeds by adding the next best variable step by step. For each candidate variable, an F-statistic that measures the change in Wilk’s Lambda is computed. The variable with the largest F enters the model. The method also checks variables already included and removes a variable if its F-to-remove value is too small. III. DISCRIMINATING VARIABLES III A Expected Returns The human capital theory regards education as an investment where current costs are compared with future gains. The individual chooses to invest so as to maximize the present value of life-earnings (Willis, 1986). The underlying notion is that additional education is an investment of current time and money for future monetary compensation (Freeman, 1986). The monetary compensation equivalent to a rate of return is the crux of the human capital theoiy.4 The calculation of rate of return requires data on (1) the net life- income stream with post-school education; (2) the life-income stream without post-school education. In addition, it requires some method of adjustment for the fact that one rupee today is worth more than promise of one rupee adjusted for inflation at some future date. Further, the fact the time of incomes in the two streams may be
different also has to be taken into account. Let Yt and Xt denote income streams for individuals with higher education and with secondary education respectively. Adjusting Yt for direct costs (Dt) during the period of higher education in any year t, the net earnings of those with higher education are Yt-Dt. Let the subscript t refer to the number of years at which an income accrues, with t=0, the year of secondary school completion and t=n, the retirement year. Let r be the discount rate. The present value of the higher education stream and of the secondary school stream from year t0 are found by summation:
At some discount rate, Y*0 and X*0 will be equal; this is the internal rate of return (see Bowman, 1981 for details). This simple model has been extended to deal with on-the-job training uncertainty of future earnings, and employment prospects etc. (Bowman, 1981, Knight & Sabot, 1990; Kodde & Ritzen, 1988). A large number of studies have been conducted to estimate rates of return both in developed countries (DCs) and less developed countries (LDCs). These studies have been based on two types of data-cross tabulation of earnings against age-education categories of the population census, and longitudinal data on schooling and earnings careers of individuals. Each has advantages and disadvantages. The two conventional methods of estimating rates of return are: elaborate method, and earnings function method. The first method is based on comparison of education costs and differences in earnings by age and education to
reflect differences in labour productivity. The second method is based on the estimation of earnings functions. The underlying assumption is that a substantial proportion of the estimated rate of the return can be attributed to the effect of education.5 However, it is difficult to separate the effect of education on earnings from the influence of other factors that affect earnings. In particular, observers have been concerned with the consequences of typically unobserved factors (ability, motivation, the rate of time preference etc.) that affect both higher education and wage outcomes. It is referred to as the endogeneity problem (Blundell, et al., 2000). The answer has been to use variables such as academic achievement, parental education, and family income etc. that can proxy the unobserved factors. Another major concern of observers has been with the functioning of the labour market. The conventional approach to estimation of rates of return assumes that the wage structure accurately reflects the difference in productivity between individuals with more education and those with less education. Further in most LDCs, government actively intervenes in the labour market which has the potential for creating labor market distortions and biasing estimates of rates of return (Knight & Sabot, 1990, p. 48). The empirical findings, however, suggest that returns to education are quite high after accounting satisfactorily for endogeneity and other problems (Harmon & Walker, 2000). There is an extensive literature examining individual post-school choice (see McVicar & Rice, 2001). The expected rate of return is identified among other factors as its main determinant. Most of the studies, however, are based on expost analysis of education choice. Findings in them lend support to the rate of return or some proxy for it as an important determinant of education choice. There are also a few studies which estimate ex-ante or expected rates of return to post- school education.6 Findings in them suggest that students have a fairly
realistic view of returns to (or benefits of) higher education (Williams & Gordon, 1981; Bowman 1981; Kodde & Ritzen, 1988; Wolter, 2000; Dhesi, 2001a; 2002). Some of the proxies used for rate of return to post-school education are: proportion of work force in the white collar occupation (Whitefield & Wilson 1991), ratio of the average earnings of those employed in managerial, professional, and related occupations to the average earnings of manual workers (McVicar & Rice, 2001), measures of relative earnings of qualified and unskilled employees at different stages of life cycle (Pissarides, 1981), expected earnings with and without higher education (Kodde & Ritzen, 1988). Ignoring direct costs, Psacharopoulos (1979), and Bowman (1981) use ratio of difference between expected earnings with post-school education and expected earnings foregone during course of study. Empirical findings suggest that rate of return has significant impact on education choice. In sum, the main determinants of expected returns are expected earnings with and without higher education, as well as direct costs. These determinants are in turn influenced by labour market and education policies. However, due to capital market imperfections for financing personal investments in human capital, access to finance for schooling depends on a family’s economic situation. In a reasonable approximation, interest costs may be expected to rise exponentially with funds needed from outside sources to finance education (Bowman, 1981). Therefore, we expect a systematic association between family’s economic position on the one hand and expected return on the other. On the benefit side, labour market distortions and subsidization of higher education in India contribute to making higher education attractive.7 In the context of largely imperfect political and economic markets, the elite has been concerned with appropriating maximum gains from public spending by manipulating these markets. As a result, higher education has been favoured in
public spending; and there is a structural bias in its favour (Tan & Mingat, 1992; Dhesi, 1998). Private financing of higher education is very limited. Fees for higher education constitute on average about 5% of unit operating cost and about 1.5% of total costs (Tan & Mingat, 1992; Rao & Mundle, 1992). The subsidy to students increases with the level of education. In general, the more expensive the course, the higher is the subsidy level. The social costs increase more rapidly than private costs with the level of education. A second degree yields higher rates of return than the first As result, private returns to education are higher than social returns at all levels (Psacharopoulos, 1994). The incentive structure, consequent upon institutional structure, for employment of persons with higher education, has significant influence on social demand for higher education. The higher the qualification, better is the chance of attaining a higher salary and status (Dhesi, 2000a). There is emphasis on formal qualifications rather than competency in the relevant skills in the public sector which is the main employer of the educated in India. The share of the modem (organized) sector, characterized by security of employment and regulated employment conditions, in total employment is about 8.5%. However, within the modern sector the public sector dominates. Its share increased from 68.38% in 1981 to 70.38% in 1995. Wage and wage differentials in this sector are largely the outcome of administrative decisions. The link between wage and productivity is weak. Other things being equal, the probability of becoming unemployed is considerably lower in the public sector than in the private sector. Therefore, one may expect the educated youth to prefer the public sector for employment (Dhesi, 2000a). Generally they prefer to remain unemployed for some time than accept work outside the public sector.
Non-competitive labour markets and subsidization of higher education create a situation of excess demand for higher education amidst increasing educated unemployment (Dhesi, 1998; 2001b). A high rate of unemployment by itself; however, does not push students to higher education. They opt for it in order to improve their employment prospects. Empirical evidence suggests a negative relationship between incidence of unemployment and years of schooling (Whitefield & Wilson, 1991). Additional education increases the probability of getting employment. In any case, unemployed individuals have to bear negligible, if any, indirect costs (earnings foregone) of higher education. Therefore, in a situation of high unemployment, highly subsidized higher education becomes an attractive alternative. The expected earnings after completion of higher education would seem to be a reasonable indicator of benefit from this alternative. Students were asked to indicate their expected peak earnings with and without higher education. Using this information, we constructed a rough indicator / proxy for expected rate of return R = Yhe -Ylm, / Ylm x 4 where Ylm and Yhe, are expected career peak monthly incomes without and with higher education respectively. In the denominator, Ylm, is multiplied by 4 average number of years spent in higher education. The direct outlays on education (tuition fees etc.) are ignored.8 III B. Ability There is a higher probability of able students opting for higher education than others for two reasons. First, they can transform various resources into human capital more efficiently than the less able. Second, the more able students can earn more than the less able at a given level of schooling. Thus ability differences result in differences in net investment costs and hence in expected returns (Willis & Rosen, 1979). Therefore, it is rational for the able to invest more in education.
The often used indicators of ability are IQ scores and examination grades. However, early childhood IQ scores, closely related to later year scores, are largely determined by home investments and the inherent genetic ability (Leibowitz, 1974). In the absence of IQ scores, we have used three quasi-measures of ability available to us. Two of them are based on self-ranking by a student in relation to his classmates : self-assessment of ability (rank in the class) and self-assessment of achievement (score-based rank in the class). The third measure is based on average examination grades or scores in the secondary school9. III C. Socio-economic Background The variables related to socio-economic background have been found important in explaining differences in educational participation in empirical studies. Family background conditions both realities an individual may anticipate and her/his perceptions of life-opportunities. In early childhood, parental socioeconomic characteristics, attitudes and behaviour are important determinants of a child’s intellectual development (Murnane, 1981). Ordinarily, it has been treated as a single entity. However, it may be useful analytically to separate socioeconomic background into three different components; financial capital, human capital and social capital (Coleman, 1988). However, these different forms of capital interact with each other. Financial capital is approximately measured by the family’s wealth arid income. It affects an individual’s educational achievement in a number of ways. For instance, it provides physical resources that can facilitate achievement, a fixed place in the home for studying, and materials to aid learning. In any case, financial capital provides the economic foundation for family welfare. Lower levels of financial capital often cause parental distress that may hamper constructive parent-child interaction.
Human capital is approximately measured by parents’ education and provides the potential for a cognitive environment for the child that aids learning. Education of parents is one of the important determinants of achievement The better the parents’ education, the more valuable the “inputs” they contribute directly to enhance their children’s education. Also for any given level of income, better educated parents may be prepared to make a greater economic sacrifice to help children in their intellectual development. In general, quantity and quality of resources invested in children are positively related to parental attributes (Leibowitz, 1974). The concept of social capital parallels other concepts of capital. It represents resources just as other forms of capital do. Yet, social capital, which inheres in social relationships, is relatively less tangible compared to other forms of capita]. The components of social capital are many and varied, corresponding to different types of social relationships (Dasgupta, 1999). The unit of analysis can be individual, household, community or even nation. However, a broadly acceptable, comprehensive definition of social capital is still eluding social scientists. It has meant different things to different people.10 The concept of social capital is still vague and difficult to operationalize. Only proxy measures for some aspects of social capital relevant to a given study have been used.11 Some observers have questioned the use of the term capital in this case as the term capital is commonly associated with tangible, describable fungible objects whose accumulation and value can be estimated (Arrow, 1999; Solow, 1999).12 Dasgupta (1999) also suggests that social capital should not be regarded on a par with
physical,
human,
and
other
forms
of
capital13
However,
inadequate/incomplete measures of social capital should not deter us from recognizing the usefulness in ascertaining the influence of social factors on economic behaviour. A general consensus is emerging that an individual’s
behaviour is influenced by her/his social situation. While introducing the concept of social capital, researchers tend to focus attention on the positive consequences of sociability and ignore the less attractive features (Portes, 1998). In reality, social capital has both positive and negative consequences. In any case, there is always a danger of making a tautological statement if care is not taken to distinguish between the sources and the consequences of social capital. To possess social capital, a person must be related to others, and those others are the source of his or her advantage. The two other sources are bounded solidarity and enforceable trust The three basic functions of social capita] are: (a) source of social control which helps parents to maintain discipline, (b) source of family support; (c) source of network-mediated benefits beyond the immediate family (Portes, 1998). However, different functions of social capital may clash with each other. For example, social capital in the form of social control by family/community may clash with social capital in the form of network-mediated benefits. Just as sources and functions of social capital are plural, so are its consequences. One of the empirical applications of the concept of social capital is as a determinant of children’s educational performance.14 The family social capital, which inheres in relations between parents and children, can contribute directly to children’s educational achievement and indirectly by synergising the impact of human and financial capital. Even when human capital of parents is low, social capital available for children’s education can be high if parents devote a lot of effort and time to the purpose. For example, success in school among poor children has been found to be related to deliberate efforts on the part of parents to inculcate discipline and good study habits (Clark, 1982).15 However, human capital in the family has hardly any relevance to their children’s education if social capital in the family is weak.16
Examination of literature on child development and analysis of status attainment should help understand some of the processes through which social capital influences educational aspirations. The general views on child development as well as notions of investment in social capital emphasize long term patterns of influence. Multiple sources of influence on children that have been extensively examined are parental characteristics, family size17 and structure, parental practices and cultural environment (Parcel & Menaghan, 1994). Research on child development clearly indicates that parenting practices — general supervision and monitoring of school work, interaction, frequent communication, affection, and educational aspirations - are associated with positive attitudes towards school and educational performance (Astone & Mclanahan, 1999). Status discriminent research suggests that high educational aspirations of parents are associated with high aspirations in children (Sewell & Shah, 1968). However, it is important that parents do not just hold high aspirations but also transmit their aspirations to children. The critical factor is quality and amount of time spent with children. Parental warmth makes children receptive to parents’ overtures. Studies show a positive association between parental affection and a child’s attitudes toward school (for references see, Parcel & Menaghan, 1999). If children do not feel close to their parents, or if parents are not available to supervise them, parental influence may be seriously undermined. Parental intellectual and other resources contribute to forms of social capital that facilitate child outcomes. However, social capital is relatively greater in two parent families, those with fewer children, and those whose parents have high aspirations for children. These conditions foster parental attention, more time spent with children, and the emergence of achievement orientation among children. Parental values and behaviour patterns are involved in the socialization process of children, not only through mechanisms such as children’s observation and imitation, but also through the norms and expectations that parent-child
interaction will convey (Parcel & Menaghan, 1994). The social capita] that encourages children’s identification with parental goals and values is likely to influence children’s aspirations (Coleman, 1988). The concept of social capital is valuable as it analytical framework for focusing attention on non-monetaly resources that influence behaviour, and it can be considered an important determinant of educational achievement and post-school choice. However, social capital does not exist solely within the family. It may also inheres in social relations outside the family in the community. As discussed earlier, individual behaviour is influenced by norms, dominant values, prevalent in the community. The social norms of a community that give importance to learning would be conducive to the educational attainment of youth. The opposite would be the case if social norms discourage learning. Since it is difficult to measure this type of social capital, researchers have tried to catch its effect by using geographic location. The rationale of using a geographic location variable is that differences are likely to exist in the social norms of rural, semi-urban and urban communities with regard to education (Dhesi, 2000b). However, social capital in the community also operates through school. Its influence depends upon the type of community in which school is embedded. It has also been suggested that students in certain types of schools may benefit from non-monetary resources and enjoy a learning advantage (Morgan & Sérensen, 1999). However, type of school can also be used as a proxy for influence of classroom composition and peer group on educational aspirations and achievement. The presence in the classroom of a comparatively large proportion of students with highly educated parents generally raises aspirations for education even after the introduction of controls for an individual’s background or for her/his preferences and expectations (Bowman, 1981). Therefore, school-related variables should be used to catch various effects associated with school.
Schools can be differentiated broadly as norm-enforcing schools and horizon-expanding schools according to the social context in which they operate (Morgan & Sérensen, 1999). The two types of school depend upon different forms of social capital. The norm-enforcing schools rely on the capacity of closed networks to enforce social norms whereas horizon-expanding schools exploit a different type of social capital - information about opportunities in extended social networks of parents and other adults. The choice of school represents the influence of a complex set of socioeconomic variables. The choice of school represents the influence of a complex set of socio-economic variables. The characteristics of parents and those of schools have been found to be closely related (Hanushek, 1986). Usually better educated and financially well-off parents enjoying higher social status send their children to the English medium private schools. In addition, in urban areas a few Hindi medium private schools cater to the articulate lower middle class strata. The government Punjabi medium schools mainly cater to lower strata. Consequently, school populations have modally different attitudes towards educational aspirations. The English medium schools typically emulate Christian Missionary schools even when they are run by other religious denominations. Focus in these schools is on discipline, and devotion to learning buttressed by religious orientation. Being located mainly in urban areas, networks of parents surrounding them are likely to be characterized by Burt’s (1992) structural holes permitting heterogeneous flows of information.18 Availability of information about a wide range of opportunities is likely to motivate students to increase effort for learning. In contrast, the schools embedded in closed networks can help maintain the value consistency of a functional community. It has been observed that communities tend to be more cohesive in rural areas than in urban areas. A village
community is likely to maintain the value consistency of a largely closed functional community (Dhesi, 1996b; 1998). However, such communities do not always construct and maintain norms that promote learning. The community norms may even subvert students’ efforts in school by demanding community devotion. Excessive community or familistic orientation may have a negative impact on educational performance. Socio-economic background variables, other than those approximating human capital and social capital, are father’s occupation, monthly income, wealth status, type of household, family size, quality of living (own room). Human capital is approximated by father’s education and mother’s education. Social capital in the family is approximated by father’s expectation of the student’s performance, father’s interest in education, his commitment to education, mother’s expectation of the student’s performance, mother’s interest in education and her commitment to education. There is an area of origin variable approximating social capital in the community. The two school related variables are the type of primary school attended and the medium of study in the secondary school. The descriptions of the other variables used in analysis are given in Table 1 (pp. 51-54). 1V. EMPIRICAL RESULTS Table 2 (pp. 55-56) summarizes results of the discriminant analysis. Five variables such as father’s expectation of student’s performance (0.360), mother’s interest in education (0.223), mother’s commitment to education (0.480), expected return (0.347), and achievement (0.163) approximated by grade at the time of graduating from primary school are found to be significant based on Wilk’s Lambda. The rempining variables have not entered the analysis. The values of standardized canonical coefficients are given in parentheses. When variables are measured in different units, the magnitude of an unstandardized coefficient provides little indication of the contribution of the variable to overall
discrimination. The magnitude of a standardized coefficient provides indication of the relative contribution of the variable to the overall discrimination. Mother’s commitment to education (0.480), appears to have greatest impact, followed by father’s expectation of student’s performance (0.360), expected return (0.347), mother’s interest in education (0.223), and achievement (0.163). The information on group means of five variables simultaneously or group centroids is also provided. The centroid for group 1 is 0.210 and for group 2 it is 0.865. Wilk’s Lambda, also called U statistic, is used to test equality of group centroids. It is the proportion of the total variation in the discriminant scores not explained by difference among groups. As a test of its size, Lambda is transformed to a variable with an approximate chi-square distribution. The chi-square is significant at a=0.000, suggesting a significant difference between the two group means or centroids. The classification function coefficients are also given in table 2. Each column contains estimates of coefficients for one group. The functions are used to assign or c1assi1’ cases into groups. A case is predicted as being a member of the group in which the value of classification is the largest. Thus, for each case for each group, each coefficient is multiplied by the value of the corresponding variable. Summing products for all the variables and adding the constant, a Z score is obtained. Based on Z scores, classification results indicate the predictive
accuracy of the discriminant model. The percent of grouped cases correctly classified is 74.6% which suggests a reasonable level of validity of the discriminant function derived. The first three variables — father’s expectation of students performance (0.360), mother’s interest in education (0.223) and mother’s commitment to education (0.480) — approximate social capital in the family. Social capital is found to be the most crucial component of socio-economic background that has an impact on post-school plans. The variables related to other components of socioeconomic background have not been found significant. However, their influence on post-school plans operates indirectly through expected return and achievement. As discussed in section III. A, a family’s economic position is an important determinant of cost of financing education. Interest cost is expected to rise with funds needed from outside source to finance private investment in education. Therefore, we may expect socioeconomic background to impact expected rate of return. Just as in the human capital model of education choice, expected return is an important determinant of education choice in its extended version. Our result is in line with findings of earlier studies (see section III A) in which expected rate of return is significantly associated with education choice. However, all three components of socio-economic background- human capital, financial capital, and social capital - are found significantly associated with achievement in empirical studies which in turn influences post-school plans (Dhesi, 2000c). Since early age achievement, a proxy for earlier family and school inputs, is found significantly associated with achievement at a later stage in empirical studies, it is not surprising to find the former variable entering the discriminant function at Lambda test.
However, we have to keep in mind a limitation of our data in computing expected rate of return. As we could not include the direct private cost of higher education in computation of rate of return, there is likely to be an upward bias in its estimate. Limitations of our model notwithstanding, we find some empirical support to our hypotheses, that is, socio-economic background is significantly associated with post-school education decision. And its crucial component is social capital whose role in formation of demand for schooling has been largely overlooked in empirical studies. Data also support our contention that educational achievement is significantly associated with post-school plans. Similarly, our hypothesis that expected return is an important determinant of post-school decision finds empirical support. CONCLUSION Socio-economic background is undoubtedly a significant determinant of achievement. But our findings suggest that it is useful to distinctly elaborate socioeconomic background into its different components to bring out the importance of social capital in influencing educational achievement and post-school education decision. It is the least investigated determinant of formation of demand for schooling. However, social capita] embodied in social relations may not only enhance the effectiveness of human capital but also compensate for its low level in influencing achievement and hence demand. Our findings underline the importance of including variables related to social capital in the family, such as parental expectation of student’s achievement, and their commitment and interest in education, in the analysis of demand for schooling. The study also suggests that early achievement, a proxy for family and school inputs, is an important determinant of post-school education decision. Policy should, therefore, aim at improving quality of education in primary schools by providing better staff
and facilities. Poor children may be given stipends to compensate them for lack of adequate material support at home. The significant impact of parental attitude on achievement and post- school decision has an important policy implication. There is a need to educate parents as to the profound effects their attitudes have on student’s achievement and their demand for schooling. This is a nontraditional resource that can contribute to broaden the social base of participation in higher education. Finally, possible upward bias in its estimate not-withstanding, the expected rate of return to investment in higher education is significantly associated with post-school choice. The policy implication is that to influence demand for higher education, the gap between private benefits and costs should be monitored closely. The low cost recovery in India exaggerates private valuation of higher education and creates excess demand. The enhancement of cost recovery should not only generate additional resources for quality improvement but also moderate demand. However, to ensure a broad base of participation in higher education, cost-recovery should be linked to economic status. NOTES 1.
For further discussion and references see Dhesi (1 996a).
2.
Data were supplied by Department of Education, Government of Punjab.
3.
As discussed in Section I, education is essentially the responsibility of states which vary in level of development as well as in efficiency and equity of schooling. The medium of study in public schools is often the official language of the state. It is Punjabi in the case of Punjab. The neighbouring states are mainly Hindi speaking. Given constraints of resources and time, the area of sample was therefore confined to Punjab.
4.
Non-economic benefits of higher education are ignored. For discussion on them, see Dhesi (2001b).
5.
An implicit assumption in virtually all estimates of rates of return to schooling is that rates of return to schooling and to investment after schooling (typically learning on job with reduced earnings) are the same. Further, effect of growth on earnings is assumed to be negligible.
6.
There is an advantage is using data on individual expectations with respect to alternative post-school choices. The results are controlled for persona] attributes that bias results when we compare sets of people who have already exercised their choices (Bowman, 1981; Kodde & Ritzen, 1988).
7.
For further discussion see Dhesi (2000a).
8.
One may use a proxy for expected rate of return based on expected earnings at the beginning, fifth year or mature year of career. However, empirical evidence suggests that education is a poor indicator of initial earnings (Bowman, 1981). The analysis of our survey data at the aggregate level also supports this. There is a high degree of independence between expected first year earnings and peak earnings. Association between level of peak earnings and the proportion of the difference between peak earnings and those in the first year accounted for by the early gains (up to fifth year) is negative. This suggests that only expectations of sustained growth in earnings lead to the highest peak earnings levels (Dhesi, 1998).
9.
In the absence of adequate measures of ability and educational achievement, it is possible that the observed influence of parental interest and expectations may get pronounced.
10.
Social capital has been identified with such features of social organization as trust by some observers. Others consider it as social networks or as a set of
behavioural norms. For some, all of them together constitute social capital (Dasgupta & Serageldin, 1999). 11.
Such attempts are needed as they may be the necessarily building blocks for resolving conceptual and measurement issues.
12.
Arrow (1999) recognizes the economic value of networks to the concerned individuals. But he holds the view that much of the reward is intrinsic. Similarly, Solow (1999) also recognizes the importance and complexity of ideas underlying the concept of social capita]. However, he would prefer the term “behaviour patterns” to social capita]. According to him, the task of unraveling the processes of interaction between society and economy is difficult, and requires serious attention.
13.
An alternative view is that in some respects, social capital is similar to knowledge and skills which are widely considered as forms of capital. So there can be inter-relationships (Dasgupta, 1999).
14.
Woolcock and Narayan (2000) have identified nine primary fields where current research in social capital is concentrated. These are: families and youth; behaviour; schooling and education; community life; work and organization; democracy and governance; collective action; public health and environment; crime and violence; and economic development
15.
There is a long history of research studies which identi1’ parental attitudes and encouragement as predictors of educational attainment and participation, dating back to the late fifties and early sixties. The Plowden studies in England are the early important ones among them. Among the issues raised by earlier studies are the problems of causality: do parents take interest in children’s education because their children are doing well in education and have high aspirations? With focus on sources of social capital
and mechanisms of its influence on behaviour in our study, direction of causality becomes, at least, discernible. 16.
The success of Asian Americans in education has drawn a lot of attention in recent years. The family social capital is assumed to make a significant contribution to Asian children’s educational performance. Coleman (1988) approvingly cites the practice of Asian mothers who not only stay home but often purchase second copies of school books to help their offspring with their homework.
17.
The results of studies suggest that family size is negatively associated with educational attainment (Shavit & Pierce, 1991). The negative effect of family size on educational attainment persists after the socio-economic characteristics of family are statistically controlled. The explanation given is that large families spread their resources — economic, cultural, and affective — more thinly than small families.
18.
Burt (1992) argues that the relative paucity of ties rather than their density is the source of capital. Information is the form of social capital available through the extended networks.
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