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The Indian Education Setup And Labor Market Economics Essay

This paper estimates the economic returns to education in India. Education is a form of investment in human capital and is critical to both economic growth and development. Higher levels of educational attainment typically lead to greater labor participation and higher labor wages. This is primarily because individuals with more education take up a more competitive position in the labor market. Also, such individuals need to recover their investment in human capital which is by far greater than that of the labors in the lower rung of the market.

The human capital investment theory states that an individual would not mind attending school only if there is a fair chance of the present value of the expected benefits from schooling exceeding that of the expected expenditure (Becker, 1993). The expected benefits play a vital role in determining the demand for schooling or education. However, the benefits depend upon the quality and quantity of an individual’s willingness to work. This, in turn depends upon the human capital, skills and training acquired during various levels of schooling. The education-wage relationship can thus be used to measure the returns to schooling. Estimating returns to education is important for the purposes of resource allocation at both household and national levels. But estimating these returns often suffers from the problem of omitted variables bias. One of the key omitted variables is individual ability which is correlated with the individual's educational attainment. This can potentially lead to underestimation of returns to education. In order to overcome this problem, I make use of a newly available data set-the India Human Development Survey, 2005. The paper estimates the returns to education after controlling for individual ability, age, geography and demography.

The Indian economy is characterized by low literacy rates, large gender inequality in enrolment, educational attainment and labor market participation. There is a commonly held belief that there is a surplus of education and schooling in the economy but the subsequent yield of the labor force is comparatively low. Questions have thus been raised about the efficacy of the Indian educational setup and whether investing in education yield the desired returns. This paper looks at such issues and tries to answer such questions quantitatively.

In the past, attempts have made to estimate monetary returns to education in India using small sample surveys (Malathy 1983; Tilak 1987; Divakaran, 1996; Dutta 2006). Duraisamy (1993, 1995, 2000) estimates the returns to education and technical education using national level degree holders and technical personnel survey data. Majority of work focuses on the Mincerian wage equation to estimate the returns to schooling using the National Sample Survey (NSS) data where number of years of schooling is not recorded. One of the drawbacks of using NSS data is that it limits the estimation to level of educational attainment because years of schooling is not recorded. This paper estimates the returns to education in India using individual-level data from India Human Development Survey (IHDS), 2005. IHDS contains individual level measure of wages, years of schooling, demographic characteristics, and other household level information. Additionally, IHDS is the only data that has information on individual's aptitude test scores for India.

This study focuses on the economic or monetary returns to education. The variation of returns for gender and demographic locations helps in understanding the performance of labor market and the disparity in returns to education based on gender and thus, in guiding the educational investment policies. Overall, this study helps in understanding the role of education in the well being of households in India.

This remainder of this thesis is organized as follows............................

Literature Review:

The importance of schooling and its role in enhancing employment opportunities and earnings has been thoroughly researched and documented by economists. However, the economic benefits of primary education might not be consistent for all levels and types of education. The return to fifteen years of schooling is likely to be different from the returns to five years of schooling. Likewise, a post graduate will probably have better returns than an under graduate. The Mincerian wage regression typifies the link between observed wages, years of education and labor market experience including training and know-how. The wage equation is based on the human capital theory (Mincer, 1958, 1974) and it exhibits a positive relationship between wages and education. Workers with higher level of human capital attained through skill, training, education and experience generally receive a higher wage premium than the lesser educated or trained workers. But, the Mincer wage equation is only expressive and it is not informative about the optimal quantity of schooling. It is usually developed under the assumption of an exogenously determined rate of human capital growth. This IHDS data set, however, gives information not only about the years of schooling but also about individual exam scores and test performance. The estimates from this paper thus add on to previous studies for educational returns in India.

Questions regarding the profitability of investing in education in India have been looked at and answered to a satisfying level by studies conducted in the past. Blaug, Layard, and Woodhall (1969) convincingly show that investing in education is profitable by looking at national level estimates of private rate of return to education in the labor market made for urban India in 1960. Their estimate of the private returns to education varies from 9% to 17% across different levels of education. An interesting finding from this study was that returns to most of the educational levels were higher than that expected by the Government of India which estimated returns of 12% from investment in physical capital (industry). A wide-ranging summary of this study and many other studies on monetary returns to education in the labor market in India has been looked at by Psacharopoulos and Hinchliffe (1973) and Heyneman (1980). These studies however have their drawbacks. In spite of having made significant contributions to the literature on the returns to schooling in India, the estimates are based only on an urban sample and are now passé. Since then, several authors (Malathy, 1983; Tilak, 1987; Divakaran, 1996; Kingdon, 1997) have attempted to estimate the returns to education using different sample surveys and different control variables. A more recent study by Duraisamy and Duraisamy (1993, 1995) estimated the returns to higher education in India and also included returns to scientific and technical education using the national level Degree Holders and Technical Personnel survey data of 1981. The estimates in these studies give a better understanding of the returns to education and agree with the facts and figures observed for other developing countries (Psacharopoulos, 1994). But, like the previous studies, they have their shortcomings. A major limitation in these works is that individuals with higher education make up only a small fraction of the Indian labor force and hence, they do not give a complete picture of the educational yield in the labor market. In addition, it is almost impossible to distinguish between any time trends from these returns because the studies are not comparable.

India has been a developing country since its independence in 1947. One can look at studies involving other developing countries and compare it with the Indian labor market. For economists working on returns to education, the study of developing countries puts forth policy questions of fundamental importance and a rich set of experiences to examine. Policy questions and decisions branch from the prospective role of schooling in improving the wellbeing of the people residing in developing countries. Macroeconomists including Lucas,

1988; Barro, 1991; Mankiw, Romer and Weil, 1992 have stressed on the role of education on the economic growth of individuals. Questions have also been raised about the fundamental relationship between education and economic and monetary growth. Microeconomists have used Ordinary Least Squares (OLS) regressions using instrumental variable techniques (Psacharopoulos, 1985 and 1994 and Duflo, 2001) to estimate the social and private rates of returns to education. Their studies indicate a high return to education in developing countries. Apart from the monetary gains, education has also lent a helping hand in improving health and in controlling population (Schultz, 1997 and 2002; Strauss and Thomas, 1995). It also has enabled major improvements in bettering agricultural techniques in developing countries (Foster and Rosenzweig, 1996). Studies by Beheman (1999), Huffman (2001), Glewwe (2002) and Madheswaran & Attewll, (2007) review microeconomic theories involving the impact of schooling and other factors in developing countries like India.

Past studies of returns to education in India have been carried out using the National Sample Survey Data (NSS). One such study was conducted by Duraisamy (2000) which estimates the work participation in India for adult men and women. Another study by Dutta (2006) finds that regular workers earn three-quarters of the total income in the labor market even though they make up only one half of all wage workers. Both the above studies sum up the estimates for returns to schooling in India. Duraisamy (2000) finds that wage returns increase with the level of schooling up to the secondary level. Men receive 6.4, 15.7, 8.9 percent returns on middle, secondary and higher secondary levels, compared to 10.3, 33.7 and 11.8 percent returns to women. Dutta (2006) estimates the rates of return of 3.3, 2.4 and 5.3 percent for primary, middle and secondary levels respectively. However, these estimates can be biased because they use level of education as a replacement for years of schooling. There is no control for an individual's ability through test scores and aptitude. In addition, there is no account of an individual repeating or failing a grade during his or her education. The IHDS data set not only has years of schooling but it also has information on individual test scores and whether or not an individual repeated a grade to get to their current educational level. The repetition of a grade represents the student's inability to perform well in class or lack of motivation. Thus, grade repetition is important information provided by the IHDS data set that can be used as an alternative for controlling ability.

Estimating returns to education using India as a locale is important for various reasons. Apart from the obvious increase in economic returns in the form of higher wages in the labor market, education offers other benefits in the Indian milieu. One such benefit of education is its positive effect on efficiency in self-employed occupations or businesses such as self owned enterprises and farming (Jamison and Lau, 1982). A study by Duraisamy (1992) shows that farmers’ education (4 years and above) increases the gross value of farm output by 4% in India and also found that well educated farmers are technically better off and generate more revenue as compared to their lesser educated counterparts. Schooling also has certain consumption benefits such as child care, better general awareness and health and controlled family size (Schultz, 1988 and McMahon, 1995). The IHDS household data however is inadequate for such a comprehensive and in-depth study. This paper focuses on returns for education in the wage or employability sector for which data available are much precise.

Structure of the Indian Education Setup and Labor Market:

The school education system in India comprises of primary, middle and secondary levels of education and varies in terms of quality and language of instruction across different regions and states. Most of the states follow 5 years of primary, 3 years of middle and 2 years each of secondary and higher secondary levels. The first level degree in non-technical subjects usually requires about 3 years while the technical degree courses span 4 years. In the public schools, the lessons are taught mostly in the regional languages and English is learned as a second language, whereas in the private schools most of the subjects are taught in English. The system of higher education is however more or less uniform across the country and taught mostly in English.

The work participation rates of the population and the distribution of workers over various sections in the economy provide an overview of the structure of the Indian labor markets. The labor force in India comprises of salaried, casual, self-employed and unpaid individuals. In case of India, one can get reliable wage measures for salaried and casual laborers only. The distribution of labor participation varies over different sectors within an economy. This distribution gives a fair idea about the structure of the labor markets in India. The Indian labor market consisted of 430 million workers in 2004-05, growing 2% annually, with a stable worker-population ratio of 40%. The labor market in India is broadly classified into 3 sectors - the rural sector, which makes up over 60% of the labor force, the organized sector constituting 8% of the workforce and the rest make up the urban informal sector.

Empirical Framework and Methodology:

The returns to different levels of schooling can be estimated using two different methods - the elaborate method and the extended earnings function method (Psacharopoulos, 1994). The elaborate method is a very thorough process and requires data involving the cost of education at different levels to estimate the internal rate of return to education. Such data are almost impossible to find and hence, the earnings function method is the most popular technique used to estimate rates of return to education. There are certain assumptions made while using the earnings function. First, it is assumed that the money spent in the process of schooling and educational attainment, like tuition fees, expenditure on stationary and books and other necessities, is insignificant (Dougherty & Jimenez, 1991). The second assumption is that the cost incurred while studying is considered to be income or earnings lost (Psacharopoulos, 1994). This assumption may lead to the underestimation of returns to schooling for primary level of schooling as children in primary school rarely have any sort of income or earnings. The third assumption is that there is no credit market constraint, that is, funds are available to all individuals to invest in their human capital at the same interest rate (Schultz, 1988). The fourth and final assumption of the earnings function is that the earnings profiles are isomorphic, i.e., the slope of the earnings function is the same for all levels of education and there is variation only in the intercept.

This thesis runs an Ordinary Least Squares regression to estimate the returns for years of schooling on wages for individuals in the labor market. The relationship between years of schooling and wage of an individual could be approximated as:

yi = α + β(Y ears of Schooling)i + ϒ(X)i + ei (1)

where,

yi is the log wages of individual i,

(Years of Schooling)i is a measure of years of schooling,

(X)i is a set of demographic controls (e.g., age, gender and location)

and ei is the error term

The coefficient β is the most important as it gives the returns to education. The values of β are tabulated in the subsequent sections of this paper.

The above equation has been stated in a very basic form, although the study looks into several different factors which affect the returns to education in India. Some of the parameters considered for the regression include geographic location, economic conditions and educational environment and competitiveness in an area. Hence, it is essential to look into local conditions and places of residence of individuals. Places of residence have been broadly classified as rural and urban for this study. The regression is run to add location based controls to give returns to education based on geographic locations namely urban residence and rural residence and their difference is looked at. The other significant source of bias is the ability bias. It is a very commonly known fact that certain individuals with higher-ability are more likely to achieve higher years of schooling as compared to individuals with lower ability. The IHDS data allows addressing the ability bias in two different ways - the individual's performance in the Secondary School Leaving Certificate (SSLC) examination and the educational qualifications of the family members. Students in India must pass a written examination developed by the board of education to be awarded a SSLC. This exam is usually taken at the end of 10th grade and the passing categories are Class I, II and III which are in order of highest to lowest level of distinction. Performance in the SSLC exam is a reasonably good indicator of an individual's ability and this can be used to control for aptitude test scores to deal with the ability bias when estimating the returns to schooling. The educational level of the family members is a pointer towards the importance of education for an individual and may result in varying performances of individuals. The regression also controls for effects of age.

After controlling for the above factors, the estimating regression equation can be transformed as follows.

yi = αs + β(Years of Schooling)i + λ(Urban)i + δ(Ability)i + πXi + ei (2)

where,

αs is a constant representing the state fixed effects

(Years Schooling)i is years of schooling completed

(Urban)i indicates an individual's place of residence - urban or rural

and (Ability)i captures ability based on the SSLC exam performance and family's education

The average rate of returns per year of schooling can then be calculated from the estimates of the regression for the different levels. For estimates with n different levels, the returns can be calculated for the kth level as follows

rk = (βk - βk−1)/Yk (EQ no)

where,

βj and βj−1 are estimates for returns for the kth and (k-1)th level

and Yk is the number of years of schooling at the kth level.

The forthcoming section gives an overview about the India Health Development Survey data which has been used for this study.

Data

Data Sources

This paper uses the India Human Development Survey (2004-05) which is a part of a collaborative research program between researchers from the National Council of Applied Economic Research (NCAER), New Delhi and University of Maryland (Desai, Reeve and NCAER 2009). It is a nationally representative survey covering 41,554 households across 1503 villages and 971 urban locations in India. The survey covered all the states and union territories of India except Andaman and Nicobar and Lakshadweep, two union territories which together account for less than .05% of India's population.

The prime purpose of this extensive survey is to document changes in daily lives of Indian households in a society which has constantly been undergoing changes in terms of lifestyle, health habits, political awareness and education system among many others. The data were obtained by conducting extensive interviews in each of the households. The interviews were organized into two parts and covered questions about health, education, employment, economic status, marriage, gender relations, and social capital. Children aged 8-11 completed short reading, writing and arithmetic tests. Village, school, and medical facility interviews were also conducted and are available for viewing. The survey began in November 2004 and was completed by October 2005. Funding for the 2004-05 survey was provided by the National Institutes of Health, grants R01HD041455 and R01HD046166. The survey provides insight into the lifestyle of the general population in India. The survey is based on the NCAER's previous work in the 1990's and it provides a very detailed data base which is available free of charge for researchers, economists and policy makers all over the world, influencing political and economic decisions. The IHDS data are being used by about 2,000 users worldwide.

I decided to use the IHDS data set instead of the commonly used National Sample Survey (NSS) as it puts forth a lot more questions than what NSS does. The IHDS data set provides information on the number of years of schooling and also provides aptitude and test scores for the Secondary School Leaving Certificate (SSLC) exam. The data also contains details about public and private level schooling and, which were relatively unknown before this survey was carried out. The IHDS data helps in generating a robust model and is a very good indicator of the economic returns to education in India.

Outcome Variables

Earnings or Wages

This paper looks into the returns to education. People in India generally work in between the ages of 18, the age after finishing school, and 65, the age of retirement. The data used has thus been restricted to the individuals aged between 18 and 65. Earnings of an individual are not only one's wages or salary. It also includes income from a self owned-business, assets held and property. For simplicity, the analysis in this paper focuses on the section of the labor market in which individuals work for a wage or salary.

Control Variables

Control for Educational Attainment

The education system in India is broadly classified into 5 categories - primary school, if years of school attended are 5, middle school if years of schooling are 8, secondary if school in attended for 10 years, higher secondary if years of schooling are 12 and graduate degree if school is attended for 15 years or more. In addition, individuals who haven't attended any schooling at all have not been considered in this study. The table below summarizes the different levels of schooling in India based on accomplishment of years of schooling.

Educational Achievement

Years of Schooling

No Schooling

0

Primary

5

Middle

8

Secondary

10

Higher Secondary

12

Graduate

15

Table 1 Educational Achievement and Years of Schooling

Note: Individuals with no schooling or 0 years of schooling have been omitted from the analysis

Control for ability

There IHDS data allows for controlling the regression based on an individual's ability bias. The board of education in India is responsible for the educational setup. Students in India must take and clear a centrally conducted examination by the board in order to receive a Secondary School Leaving Certificate (SSLC). Only then can a student pursue a graduate degree and focus on a specific field of study. The SSLC exam is generally taken at the end of 10th grade. The candidates who pass the exam are segregated into three broad categories - Division I, II and III based on their exam scores. An aggregate score of 60% and above is considered Division I, scores ranging from 50% to 59.99% is considered Division II, and scores ranging from 33% to 49.99% is considered Division III. Students who fail to obtain the minimum of 33% fail the exam and have to appear for a re-examination. The table summarizes the division a student achieves based on the exam scores in the SSLC examination.

Division

Examination Scores

I

60% and above

II

50% to 59.99%

III

33% to 49.99%

Table 2 SSLC Divisions based on Examination Scores

Control for age:

The sample of IHDS data is restricted to the age group of 18 to 65. This is because generally in India a person starts working and earning wages after completion of at least the higher secondary schooling which, if the candidate hasn't repeated a grade, is completed at or after 18 years of age. There are instances of people working before this age too, but such cases are generally individuals who do not have a proper pay structure and are few and far in between. The upper limit is set to 65 years because most of the people working with firms or companies retire at the age of 65. People do earn after this age too mostly through earnings from businesses, real estate and as freelance consultants. This paper does not look at returns of education for such earning methods and only concentrates on returns to education on wages or salary. The sample is separated into four different age groups - 18 to 25, 26 to 40, 41 to 50 and 51 to 65. The sample has been separated into different age groups because the quality of education varies over a period of time and hence individuals in different age groups of the sample may have gone through different quality of schooling.

Location effects - Rural and Urban residence:

The infrastructure and quality of education differs noticeably in rural and urban sectors. (Duraisamy, 2002) Generally, schools in the urban area are equipped with better facilities and the educational techniques are much more contemporary in nature as compared to those available in the rural area or in villages. To account for this difference in quality of education, the IHDS sample has been divided according to an individual's place of residence. An assumption made is that a person attends schooling in the same sector of his or her residence. This paper looks at the difference in returns to schooling in urban and in a rural sector. The urban-rural difference in returns will also reflect the prevailing labor market situation in these areas.

Results and Discussions:

The results of the estimating Equation 2 is tabulated in Table 1. The first column presents the difference in points of log hourly wages by educational attainment. The subsequent columns add additional sets of controls which have been explained in the earlier sections. The estimates obtained in Table 1 are then used to calculate the percentage returns with each increasing year in the level of education. Compared to the returns of each increasing year in educational attainment of 5.89% for those who have completed primary education, individuals with middle, secondary, higher secondary and graduation educational levels earn returns of 6.13%, 9.33%, 7.82% and 12.67% respectively. The differences observed in these values are quite steep but, these estimates probably overestimate the returns of schooling due to many different factors which are known to be correlated with earnings. Such factors include age, location of residence and ability of the individual. Column 2 takes into account, the effects of age on the returns to education and by looking at the estimates one can be clearly observe that the estimated coefficients for educational attainment have reduced considerably. In column 3, the regression is controlled for location by adding a complete set of residence dummies. In column 4, dummies for SSLC examination performance are added to control for ability bias. This further reduces the magnitude of the estimated coefficients. As expected, the coefficients successively decrease for decrease in the exam scores. The magnitude of coefficients for Division I is found to be higher than that of the other divisions. Finally, in column 6, the full set of family's education dummies is added. For this study, the family education corresponds to the highest level of education in a household. As expected the magnitude of the coefficients decreases for Division II and Division III as compared to those who scored in the Division I category.

The coefficients for returns to schooling are tabulated for males and females separately in Table 2 and Table 3 respectively. The obtained estimates are close to what was expected. From the estimates, one can calculate that males earn returns of 3.40%, 5.38%, 8.11%, 6.81% and 16.11% for an increase in year of educational attainment for primary, middle, secondary, higher secondary and graduate schooling. In comparison females receive returns of 2.55%, 3.64%, 10.80%. 13.69% and 15.31% for an increase in year of educational attainment for primary, middle, secondary, higher secondary and graduate schooling.

The results obtained in Tables 1, 2 and 3 are worth looking at and discussing about. The returns to educational attainment for males, females and for both genders indicate that the rewards for primary and middle schooling levels are lower than those in comparison to secondary and higher levels of education. This finding contradicts the regular pattern observed in previous studies worldwide that primary education earns the highest returns in developing countries as reported in Psacharopoulos(1994). But there are recent empirical recent studies for several developing nations with low income which demonstrate that the returns to primary education is lesser when compared to secondary education (Moll, 1996). Studies conducted by Kingdon (1997) for India, Mwabu and Schultz (2000) for South Africa, and Siphambe (2000) for Botswana have confirmed Moll's findings. Mwabu and Schultz (2000) show that supply of graduates significantly determines the market returns to education. Additionally, the lower returns to primary schooling can be attributed to the slump in the quality of education and also to the fluctuations in demand and supply factors. India became an independent nation in 1947 and since then, the infrastructure of education has increased multifold and in a colossal manner. This can be a prime cause in the decline of the quality of education because an inverse relationship is observed between quantity and quality of education Duraisamy (2002). Policy makers should proceed with extreme caution while dealing with the estimated low returns to primary schooling. This is because the key advantages of education, especially for primary education, are in the form of better political awareness, improved health conditions, family planning and reduction of poverty and such non-market advantages are not reflected in the obtained estimates (Agarwal, 2011). Additionally, the estimates are restricted to individuals who are wage earners and hence, the estimates cannot be generalized as returns for primary education across all the sectors.

An obvious observation one can make by looking at the tables is that the returns to schooling varies by gender. It is interesting to note that the returns for females are much higher for attaining secondary, higher secondary and graduation. The reason behind this may be attributed to the supply side argument. Supply-side economics is a school of macroeconomic thought that argues that economic growth can be most effectively created by lowering barriers for people. This is true for women with educational attainment of secondary, higher secondary and graduate level of schooling because the supply of women in these levels of educational attainment is generally less. To get a clearer picture more work has to be done to understand the reasons for such differences. Another interesting finding on comparing the returns to males with that of females is that the yield to an additional year of women's education is higher than that of males for schooling at secondary and higher secondary levels and the returns are quite close together for graduate level. The difference is most prominent in the higher secondary level with the returns to education of females being just more than twice of what it is to males.

The study also indicates a difference in returns based on the area of residence. The urban-rural disparity should not, however, be inferred as pointing out the differences in the schooling infrastructure between urban and rural sectors due to migration from rural to urban areas. Thus, it may be possible that a sizeable population in the urban areas might have undergone schooling in rural parts of the country. One can thus safely say that the urban estimates, apart from indicating the school quality in urban locations, reflect the quality of schooling in the rural areas to some extent.

The finding of increase in returns with educational attainment suggests a relationship between the returns and the social status of a household. Families which are economically not so well off and educate their children at the primary level have a possibility of facing lower returns as compared to richer families who can afford to educate their children to levels beyond secondary schooling, thereby earning higher returns. This affects the way in which families invest in education with poorer families not being spurred on the idea of investing more on education as compared to that with comparatively richer families. In addition, families are more likely to invest on schooling of the children who have better ability, thereby having a higher probability of attaining higher levels of education and earning higher returns. This may cause inequality among education and earnings. This disparity may widen over time both within families and between families. Another factor which cause low returns to education is the quality of education. A study by Moll(1996) conducted for schooling in South Africa explains that various qualitative factors such as poorly qualified teachers, high student-teacher ratios and low funding are reasons behind the low returns to primary education. A similar study in the Indian milieu by Duraisamy(2002) argues that the low returns to primary schooling may be due to the waning quality of primary education in the country. The rates of return to education can be expected to decline in the future because of the dissemblance between of demand and supply of labor in the labor market. The rates of return will decline if the supply continues to exceed the demand. The labor market elevates minimum job requirements as a reaction to the sudden increase in the supply of graduates. (Siphambe, 2000). Swaminathan (2005) points out that a sudden increase in supply of technical graduates in India is the primary reason leading to under-employment and, to some extent, even unemployment. As a result, highly qualified and skilled graduates are being forced to move down the pecking order. High returns to higher education motivate individuals to continue schooling and attain still higher levels of education. As a result, the supply of individuals with higher degrees would increase multifold and would cause a downward shift on returns to higher education (Blom et al., 2001). One can understand that the productivity of higher educational levels may not be credible as graduates with higher education like an MBA or MSC are forced to do under paying jobs. For instance, many cases in India have been reported where graduate engineers are doing the work of diploma holders.

Table 1: Returns to Education - All Genders

VARIABLES

No controls

(1)

Age Cohort Control

(2)

Location Control

(3)

Ability Control

(4)

Family Education Control

(5)

Primary

0.2497484

(26.45)

0.2368404

(26.86)

0.2071997

(22.89)

0.2378034

(27.09)

0.2782332

(26.40)

Middle

0.4335421

(43.74)

0.4191295

(44.67)

0.3513792

(36.76)

0.4208944

(45.05)

0.4300905

(36.93)

Secondary

0.7135043

(75.17)

0.6520917

(71.94)

0.5940556

(69.18)

0.5989204

(61.15)

0.5543759

(43.38)

Higher Secondary

0.9482433

(70.44)

0.8531017

(66.86)

0.7952779

(60.79)

0.7170862

(45.98)

0.6384542

(33.52)

Graduate and above

1.58222

(133.94)

1.355241

(116.89)

1.33263

(111.81)

1.15047

(71.56)

0.9401833

(44.31)

(Ref. Rural)

Urban Residence

0.4279616

(63.02)

0.4566176

(65.56)

0.4160533

(61.29)

0.4061752

(59.78)

(Ref. Age - 18 to 25)

Age 26-40

0.2003079

(25.18)

0.1988026

(25.08)

0.1953868

(24.70)

Age 41-50

0.3966665

(42.37)

0.3946916

(42.32)

0.3831875

(40.98)

Age 51-65

0.4724426

(44.40)

0.4687027

(44.21)

0.4418657

(41.15)

(Ref. SSLC Division

III)

SSLC Division I

0.3495841

(19.86)

0.1374303

(10.73)

SSLC Division II

0.1481644

(11.54)

0.3381233

(19.26)

Ref. No Education

Primary

-0.0915321

(-8.06)

Middle

-0.0600676

(-5.06)

Secondary

0.0100598

(0.84)

Higher Secondary

0.0343978

(2.28)

Graduate and above

0.2125261

(12.77)

Adjusted R2

0.3258

0.4180

0.3837

0.4230

0.4713

No. of Observations

45, 769

45, 769

45, 769

45, 769

45, 769

Table 2: Returns to Education - Male

VARIABLES

No controls

(1)

Cohort Control

(2)

Location Control

(3)

Ability Control

(4)

Family Education Control

(5)

Primary

0.136035

(12.10)

0.1273244

(12.25)

0.1013644

(9.44)

0.1281789

(12.40)

0.1262503

(9.13)

Middle

0.2975209

(25.96)

0.293926

(27.39)

0.2251389

(20.47)

0.2954434

(27.69)

0.2395953

(16.33)

Secondary

0.5409618

(49.82)

0.4932116

(48.13)

0.4342244

(41.27)

0.4414384

(40.38)

0.3105609

(19.67)

Higher Secondary

0.7453191

(49.98)

0.6681543

(47.78)

0.6121425

(42.49)

0.5374975

(32.19)

0.3574459

(16.27)

Graduate and above

1.389706

(103.20)

1.1807

(90.88)

1.165409

(86.84)

0.9792514

(56.45)

0.6682683

(27.51)

(Ref. Rural)

Urban Residence

0.4183016

(55.99)

0.4439912

(57.62)

0.4048747

(54.26)

0.3881458

(51.94)

(Ref. Age - 18 to 25)

Age 26-40

0.2244199

(24.61)

0.2219311

(24.46)

0.2114894

(23.35)

Age 41-50

0.42055851

(39.49)

0.4166008

(39.31)

0.3971704

(37.44)

Age 51-65

0.502931

(41.68)

0.4963637

(41.33)

0.4439268

(36.16)

SSLC (Ref. Division III)

SSLC Division I

0.1412215

(10.62)

0.1307833

(9.87)

SSLC Division II

0.3611561

(19.32)

0.3519941

(18.92)

Ref. No Education

Primary

-0.0275172

(-1.81)

Middle

0.0286365

(1.86)

Secondary

0.1267107

(8.17)

Higher Secondary

0.1737065

(9.27)

Graduate and above

0.3462105

(17.07)

Adjusted R2

0.2753

0.3839

0.3408

0.3909

0.4713

No. of Observations

33,367

33,367

33367

33,367

33,367

Table 3: Returns to Education - Female

VARIABLES

No controls

(1)

Cohort Control

(2)

Location Control

(3)

Ability Control

(4)

Family Education Control

(5)

Primary

0.1020289

(6.15)

0.1034181

(6.45)

0.0696841 (4.34)

0.1041741

(6.52)

0.0888199

(5.21)

Middle

0.2112948

(10.40)

0.2057759

(10.37)

0.1485091

(7.52)

0.2071555

(10.47)

0.1658011

(7.73)

Secondary

0.5352127

(23.13)

0.4948405

(21.88)

0.4373714

(19.36)

0.4244929

(17.11)

0.3331924

(12.35)

Higher Secondary

1.08294

(34.93)

0.9725986

(32.02)

0.91233294

(29.92)

0.778689

(19.08)

0.6364669

(14.65)

Graduate and above

1.695137

(71.27)

1.471111

(60.09)

1.43084

(58.09)

1.215722

(30.99)

0.9213572

(20.25)

(Ref. Rural)

Urban Residence

0.383131

(28.10)

0.4082456

(29.80)

0.3760655

(27.61)

0.3645755

(26.87)

(Ref. Age - 18 to 25)

Age 26-40

0.1275641

(9.11)

0.1275309

(9.14)

0.1219651

(8.75)

Age 41-50

0.2484292

(14.75)

0.2500897

(14.88)

0.2218498

(13.15)

Age 51-65

0.2647376

(13.64)

0.2660278

(13.74)

0.2426365

(12.55)

(Ref. SSLC Division III)

SSLC Division I

0.2028746

(5.72)

0.1801896

(5.10)

SSLC Division II

0.3663332

(8.64)

0.3443846

(8.16)

Ref. No Education

Primary

-0.0062154

(-0.40)

Middle

0.018653

(1.08)

Secondary

0.0630296

(3.50)

Higher Secondary

0.0867161

(3.57)

Graduate and above

0.343236

(12.18)

Adjusted R2

0.3363

0.3944

0.3806

0.3980

0.4055

No. of Observations

12,402

12,402

12,402

12,402

12,402

Conclusion:

This thesis estimates the returns to schooling across different educational levels in India. This study presents estimates for returns to schooling in India by accounting for demographic characteristics, state of residence, individuals' ability and family's education. Ordinary Least Squares estimates show that the degree of economic returns to education is significant in India. The estimates indicate that returns to education in India increases with increase in level of schooling. The results in the study thus contradict the hypothesis of depreciating returns to education. The increasing pattern of returns by level of education could be attributed to the quality of schooling. One can expect the quality of education to alleviate as an individual moves up in the educational hierarchy. Another reason which could explain this observable fact is ability of the people. Higher rates of return will be a product of higher ability if individuals with higher ability complete higher levels of schooling. The growing rates of return motivate people to invest more in attainment of higher schooling levels. This study also reports that the returns to primary education in India are the lowest when compared to all the other levels of education. The low returns to primary education may be due to the declining quality of primary schooling in India. Policy makers should look into this and take immediate measures to improve the quality of primary education in India. This is because primary education forms the base of one's further education and it is very important to have a strong foundation. This will help an individual to acquire strong skills and gain higher returns in the future. Other benefits of primary education include non-market gains (Wolfe and Haveman, ----) and high returns to basic education of farmers in the rural sectors (Duraisamy, 1992), which are not reported by the estimates of this study. This study also reports that returns to education differ by gender and location. The returns of wage workers residing in the rural areas earn lesser returns as compares to their urban counterparts. This indicates that it is important to reach out to the people residing in rural areas and make efforts to improve the quality of schooling in the rural sectors. The results for wages show that there is a sizeable difference in returns for education for males and females. An interesting finding of this study for males and females is that females with educational attainment of secondary, higher secondary and graduation have higher returns for an additional year of schooling as compared to that of males. Another important control variable used in this study is the effect of family on returns to years of schooling. The family background, corresponding to the member of the family with the highest educational qualification, is an important explanatory variable in explaining the wage equation. One can observe that omitting the family background characteristics may cause upward biased returns. The returns of education can also be explained by one's ability. An individual with greater ability will generally fare better and earn higher returns in comparison to those with lesser ability. The results confirm this theory and one can observe that people with better aptitude scores earn higher returns to education. The ability of an individual can have an independent positive effect on earnings apart from the human capital variables normally taken into consideration like the years of schooling attended.

Estimating the true effect of years of education on earnings in the Indian milieu has been a real challenge. First, in most of the developing countries including India, richer families can afford to educate their children and can help them secure well paid jobs. It is not straightforward to unweave which part of the earnings is due to the family background and which part is due to one's educational attainment. Second, schooling in India is not consistent in terms of quality and this can result in biased estimates and may mislead results. Third, a person with better ability would always be expected to earn higher returns irrespective of the amount of schooling. Fourth, there is a possibility of a measurement error because people sometimes forget their exact years of schooling attended. By not accounting for certain unobservables, like a child's health status, investment in schooling and mother's education, this study may have underestimated the economic returns to schooling. The magnitude of the estimates is higher than what it is for developed countries and what has been reported previously for studies about India. However, in spite of these limitations in the estimation, years of education is an immensely important part of human capital. Its positive relationship with wages and earnings plays a very significant role in an individual's well being. It enables an individual to have a better and comfortable lifestyle and to be politically aware (Dee, 2004 and Milligan et al, 2004) and also in having better health (Mazumder, 2008 and Oreoupolos, 2006), reduction in crime rates (Lochner and Moretti, 2004) and having smaller families. Additionally, it is very important for people in India to know and be aware about the economic benefits of education so that they can have a clearer picture about investing in human capital.

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