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The Significance of Educational Attainment in Understanding Human Capital

Discuss about the Wage Returns To the Education Over The Life-Cycle.

Human Capital is often measured in terms of levels of attainment at school.  School education or the levels of attainment at school is the most commonly used proxy to understand human capital (Edwards, 2004).  In the 1950s, Gary Becker demonstrated that the returns to educational attainment showed significant variability even among people with similar IQs . In the years to follow, Becker highlighted the need for investment in education in order to achieve higher levels of investment. (Teixeira, 2014)

 In many ways, productivity of a person can be very enhanced sue to school attainment. Mincer has described “level of schooling” in such a way that schools provide endowment to students in the form of “ability” which then, in turn, improves their productivity, thereby, indirectly improving their earnings, thus explaining the causality between educational attainment and increased productivity. (Edwards, 2004)

In order to develop Human Capital, more than just physical strength of labour is required. In order to make the physical strength or physical more productive, investment in human capital is required. This investment is not just in the form of knowledge or skills but also, in the form of better overall well being. Gary Becker described this as general human capital. Schooling helps improve the general human capital i.e. development of skills and knowledge in a generic sense that would allow the person to be more productive in any area, instead of having a specific skill in a particular area.  For example, if a country has plenty of farmer who have a great knowledge of farming, then according to Becker’s theory, then it can be described as specified human capital. However, if a country has farmers who have the ability to read and write well, then those farmers can use those skills to do their farming with greater efficiency. Similarly, a person with knowledge of specific computer programming skills  (Teixeira, 2014)

Hence, in the context of Mincer’s theory, schooling is a investment which has a rate of return. The investment made in terms of schooling is made in terms of financial investment made in the achieving  a desired level of schooling as well as the time foregone , in order to achieve schooling , which could be used in directly productive work. Oreopoulos and Petronijevic (2013) described this decision as borrowing from the future income.  Thus, Mincer’s equation provides as estimation of the “average monetary returns of one additional year of education” .

The Positive Correlation between Productivity and Investment in General Human Capital

The earning potential of a person can be increased due to schooling as schooling does provide once with skills like “critical thinking”, understanding of language, basic ability to read and write and much more. These, however, are skills provided by primary education. Generally, a large percentage of populations invest in beyond the mandatory and necessary levels of educational attainment and invest in tertiary education. In many cases, tertiary education provides with “specified knowledge” and may make a person more efficient and armed with a level of skills as soon as they enter college. Thus, tertiary education can act as a foundation to increase the efficiency of a person.  A degree can signal the proficiency and skills and can provide a prospective employer or contractor detailed information about one’s “ability” in a specified field or in general. Hence, a degree is a signal to prospective employers and contractors.(Teixeira, 2014)

College transcripts, scores from competitive tests etc. are some of the tools that persons with tertiary college attainment signal their ability. Additionally, college degrees often provide with “specified knowledge” which may be useful as a signal to prospective employers about the skills, proficiency and productivity of a person in an objective manner.  In an imperfect market, signals such these can help a college student be more competitive. In context of Spence’s signaling process, a college degree itself may be an endowment , instead of being a medium to achieve endowment or productivity.(Oreopoulos & Petronijevic, 2013) Some may call this the “Sheepskin Effect”

The logical question that follows is whether the returns on such an investment are worth the investment made and whether the investment in a degree is a bad investment. In order to evaluate the validity of an investment the rates of return must be evaluated. Mincer proposed an equation to evaluation the Internal Rate of Return on schooling. The rates to return on schooling were measured by the average increase in earnings that would be accrued for every increase in a schooling year. The equation can be used at all levels of schooling primary, secondary as well as tertiary.  However, it must be noted that a vast majority of these studies simply estimate the relationship between the earnings and additional schooling. This, does not imply that there is a causality between the two variables.  Moreover, as the economy of the world becomes more digital, the way in which education is consumed and delivered may render Mincer’s equation obsolete. (Patrinos, 2016)

The Impact of Schooling and Earning Potential on a Person's Productivity

The issue is further explored by (Grenet, 2013) who carried out a comparative analysis of two important schooling reforms related to School Leaving Age which were adopted in Europe.  France introduced in 1967 Berthoin a legistation that raised the minimum School Leaving Age for students from age 14 years to 16 years.  In 1972, England extended the minimum School Leaving age from the age of 15 years to 16 years for the regions of England and Wales. The legislation known as “Raising of School Leaving Age” (here onwards known as RoSLA)  seems to have has an impact on the earnings of the English population, in general. Acoording to Grenet, the effect of  RoLSA on the English population was greater than the effect that its counterpart legislation had in France. There could be a number of reasons for this. Grenet however, proposed that these were differences were simply due to the fact that the likelihood of French students staying in school, even without the legislation, was greater than the likelihood of the English population staying in school. Thus, the legislation seeks to understand the impact of RoSLA on earnings in UK.

The issue of the School Leaving Age and it’s impact on the earning in UK has been reasearched widely.

Oreopoulos and Petronijevic, 2013 measured the returns to schooling in a more secular way than just , in terms of money. They calculated the cost benefit of attaining a tertiary degree. They also proposed to  account for the benefit of “better signaling” as one of the attainment of tertiary effect of attainment of an additional schooling as way of  providing to prospective employers or other people that one has a attained a certain level of intelligence. The authors, also, described the “Non-pecuniary Benefits” of attending college. These include experiences that would not be possible to achieve while a person is engaged in competition for better earnings. As such, colleges act as incubators for students have the time and the freedom to make themselves more efficient by engaging in various extra curricular activities. This also, provides them , if possible , the time to accumulate intellectual capital that may make them more creative. Thus, according to  Oreopoulos, college students may enter the work force as more efficient and more creative human capital which may increase their productivity and consequently their earnings.(Oreopoulos & Petronijevic, 2013) The study however, fails to take into account the role of vocational education. Students who take degree primarily from the point of view of consumption, such as Bachelors in Anthropology do not necessarily gain higher earnings due to the degree but due to their own ability to find jobs. Similarly, students who gain vocational degrees do not seem to have been included in the study, even though the impact on earnings in those cases would be higher.

Evaluating the Rates of Return on Schooling and the Implications of the Mincer Equation

Several studies conducted to understand the relationship between “earnings” and “School Level Attainment”,  try to use the  Mercer’s Equation(Patrinos, 2016). Mincer’s equation suggests that earnings are a function of school years and the labour market experience. According to some studies such as Crespo and Reis (2009) have found that the returns are higher for an additional year, if that year signifies the attainment of some qualification or some degree.  This is known as the sheepskin effect. If the sheepskin effect comes into the picture, then the impact of an additional year would be higher.

lny = lny+ rS + β1X + where

Lny =  the earnings from an additional year or marginal earnings

lny0  =  earnings without any education

S = Total number of years of schooling attained

X -  total number of years of experience in the labour market

In this case, earnings are not just dependent on schooling alone but also labour market experience. Schooling is an endogenous variable to earnings. There is also, a case of a minimum earning potential, regardless of whether an individual has attained schooling or not. This equation accounts for schooling but also for work experience. For example, if one were to be an entrepreneur, then the earning potential would have a very weak co-relation with schooling but the earning potential would still be high due to work experience. (Rodel & Arvin, 2017)

The equation can be used at all levels of schooling primary, secondary as well as tertiary.  However, it must be noted that a vast majority of these studies simply estimate the relationship between the earnings and additional schooling. This, does not imply that there is a causality between the two variables.  Moreover, as the economy of the world becomes more digital, the way in which education is consumed and delivered may render Mincer’s education obsolete. (Patrinos, 2016)

Devereux and Hart (2010) tried to analyse the impact of RoSLA on the average earnings and found no increase in retuns for women while the returns of additional year of schooling for men has a modest return of 4% -7 % . This implies that RoSLA may have not been as impactful as it is believed to be and put forth the prospect of other insitutions within the society that may enhance the benefits of compulsory education. 

Buscha and Dickson (2015) found that the returns could vary from 0% t 7% , varying based on the method used for estimation. This puts a question mark on various studies conducted and highlights the point that any study on returns cannot be taken at face value and must be viewed in a certain context.

The Role of Vocational Education in Earnings Potential

The gains of an investment in an additional year of schooling can be best understood by comparing those who would have other wise not been able to enter another level at school versus those who were able to get in only due to some intervention like a policy intervention, financial support, etc. (Fack & Grenet, 2013)

 Fack and Grenet (2013) compared the earnings difference between the “marginal students” and “infra marginal students”. Marginal students who was the cohort of students would not have been able to gain additional education without financial support and infra marginal student or students who had the ability to pay and would have gained additional education regardless of financial support.  The analysis in the paper uses the same logic. The comparison is between the students at the margins of the policy or right after the policy and the generations of the students that followed.

Clark and Royer (2013)Another impact of the additional year of schooling that has been studied is who explored the impact of RoSLA on health. Health could be an endogenous variable to earnings . By avoiding getting to the work force earlier than required, the policy may help workers prolong their life span as well improve their earnings. Thus, they questioned the idea of returns beyond hourly wages.

Sturgis and Buscha (2015)went further ahead and questioned the intra-generational mobility that such a policy would be able to bring about. However, it is difficult to identify whether a complex phenomenon like inter generational mobility can be brought about by a very simple and basic policy like RoSLA

Dickson  and Smith (2011) have quesstioned the practicality of the policy and point out the Easter Leaving Rule. Students who were of age 16 years by the end of the Easter tern tended to drop out of the school after the Easter term instead of staying in school to complete the rest of the term. Students who did not celebrate their birthdays before the beginning of the summer term tended to end stay until the end of the year. Thus, the RoSLA policy only extends the school for drop outs for a few months. Those students who were motivated to drop out or demotivated to continue would not end up staying more than a few months that they normally would have. Thus, the policy is not practical in the sense that it is based on the age of the student instead of being based on the minimum number of years that a student must gain. It also raises a question about the practicality for those people who started late or had to take a break during their schooling. This is more true if the student has completed some kind of qualification such as a preparatory test and gained some qualification.

Other Factors Affecting Earnings Potential including Work Experience and Gender

Bono and Galindo-Rueda (2006) found results that supported the Easter Rule. However,m the results of the study found that the  Easter Rule was more true for women than for men in the UK.

We approach the analysis by trying to understand the problem by applying a treatment effect , or  the causal effects of  “yes or no” or “binary differences. OLS estimates on their own present very confounding results, results that are way off the base. Hence, a probit model would be a better solution. According to (Harmon, et al., 2003), a probit model was found to have generated better results than Ordinary Least Squares, since OLS estimate often give linear estimations while in reality, linear and isolated relationships are rare.

As identified earlier, education is an Instrumental Variable that determines earning. However, it cannot be the only factor.  As mentioned earlier, there are other institutions that play a role in the determination of earnings. These covariates could be some of the conditional variables or variables that act as catalysts in increasing the earnings of an individual Health is an example of such a covariate. It is difficult to isolate the impact of the dependent variable from such co-variates and it is not possible to conduct an experiment to understand the effect of education on the earnings.  Hence, a Regression Discontinuity Design (RDD) model is a good option to isolate the effects of an additional year of education on earnings.

An RDD helps understand the effect of a treatment on a population. The causal effect of a ‘yes or no’ variable can be understood using RDD. It helps compare the results between those who received a certain “treatment” and those who did not receive the same treatment  (or in this case, the application of the RoSLA mandate).

The most common metric of the treatment effect is the estimatioon of the “average effect of treatment”. The average effect for a localized or specified cohort among a larger population is called a local average treatment effect (LATE). (Gelman & Imbens, 2014.)

Two assumptions are generally made while estimating the WALD estimator. It is assumed that the impact of the Independent variable or the treatment (in this case the increase in minimum SLA) is jointly distributed among various cohorts and that the impact is monotonic. However, the impact of the treatment (RoSLA) is not smooth or monotonic. This implies that the impact of the policy change may have encouraged all students in school for another year. In reality, this cannot be true. Some students, who would have otherwise dropped out, may have been encouraged to stay in school while some others who may have given up. As it is clear, the impact of RoSLA was not consistent for every student. Hence, the RDD proposed in this analysis is a Fuzzy Regression Discontinuity Design.   (Gelman & Imbens, 2014.)(Imbens & Lemieux, 2008)

The Wald estimator consistently estimates LATE or the average effect as written formally as

E [Y1i − Y0i|W1i > W0i]

It is important to note that all the values estimated by Wald Estimators are expected values. They can only define the likelihood of increased or decreased earnings and not  reflect the actual estimated earnings. (Gelman & Imbens, 2014.) (Imbens & Lemieux, 2008)

In general, the Average Treatment Effect is the most common measure used. The idea is that a comparision between those just below the threshold date of birth and those just above the threshold date of birth will help understand the effects of policy reform better. The Wald Estimator is a predictor of a smooth curve. However, when the curve is not smooth there is an indication of a cut off. This cut off indicates that there is a causality between the treatment or RoSLA and the earnings. (Gelman & Imbens, 2014.) (Imbens & Lemieux, 2008)

This predictor may itself be associated with the potential outcomes, but this association is assumed to be smooth, and so any discontinuity of the conditional distribution (or of a feature of this conditional distribution such as the conditional expectation) of the outcome as a function of this covariate at the cutoff value is interpreted as evidence of a causal effect of the treatment. (Gelman & Imbens, 2014.) (Imbens & Lemieux, 2008).

The data was taken from the British Household Survey Panel. Observations for this “Date of Birth Year” or the running variable were taken from this panel. The variable “rosla” was created to encode the  Date of Birth in Binary terms.

The equation taken is as follows:

tf   =  {lim E x↓c (Y?X = x) -  lim E x ↑c (Y?X = x) } / lim E x↓c (D?X = x) -  lim E x ↑c (D?X = x)



Y = actual earnings

 X = variable date-of-birth as x takes the value of dob

 c= RoSLA threshold or the cut off date of birth which defines the cohort

 D is the years’ of schooling

The earnings estimates were taken from the pooled data of the Labour Force Survey. Hourly wages were taken into consideration and the lo fog hourly wages was used as a variable. In order to understand the effect on real wages and not just nominal wages, the  The British Household Panel survey was used by 

 The Second Reform or the RoSLA Act is assumed to started to have an impact on students who entered school from 1958 onwards.

The outcome variable or the dependent variable is taken as the log of the hourly wages. The RD command was executed in order to gain the WALD estimators.

Given below are the WALD estimates of the “log of Hourly wages” at various bandwidths.


There is causal relationship between spending an “additional year in school” and earnings in Britain. However, this relationship wanes and waxes as the Year of Birth gets further and further away from the base or the year 1958. This implies that there may be more variables within the economy that may be affecting the earnings. The qualifications of the participants were not considered in these tests. There is room to improve the testing for more factors such as family income, educational background and more. Moreover, this analysis only contains the effects the policy on earnings. Earnings are subjective and may not necessarily include quality of life estimates. Moreover, the reported earnings merely depict the average effect and do not account for income inequality, regional inequality and many other factors. There can be regional inequalities that affect earning. For example, the overall earnings average for an additional school year maybe higher in London than in Reading. Additionally, given that the way information is being consumed in the current scenario, it is possible that the distance between earnings and  time spent in school may be increasing.

The study also, highlighted the need to understand returns on investment in education by students, families and government in terms that go beyond earnings and wages.

Lastly, the overwhelming positive impact of RoSlA and earnings makes case for raising the minimum School Leaving Age to a higher age such as 17 or 18 years.

Bono, E. D. & Galindo-Rueda, F., 2006. The long term impacts of compulsory schooling: evidence from a natural experiment in school leaving, Colchester, Essex, UK: Institute for Social and Economic Research, University of Essex.

Buscha, F. & Dickson, M., 2015. The Wage Returns to Education over the Life-Cycle: Heterogeneity and the Role of Experience, Bonn, Germany: Institute for the Study of Labor (IZA).

Clark, D. & Royer, H., 2013. The Effect of Education on Adult Mortality and Health: Evidence from Britain. American Economic Review, 103(6), pp. 2087-2120.

Crespo, A. & Reis, M. C., 2009. Sheepskin effects and the relationship between earnings and education: analyzing their evolution over time in Brazil. Revista Brasileira de Economia, 63(3), pp. 209-231.

Devereux, P. J. & Hart, R. A., 2010. Forced to be Rich? Returns to Compulsory Schooling in Britain, Bonn: Institute for the Study of Labor (IZA) .

Dickson , M. & Smith, S., 2011. What determines the return to education: an extra year or hurdle cleared?, Bristol: THE CENTRE FOR MARKET AND PUBLIC ORGANISATION.

Edwards, T., 2004. The Mankiw-Romer-Weil growth model and the valuation of human capital., Warwick: Centre for the Study of Globalisation and Regionalisation.

Fack, G. & Grenet, J., 2013. Improving College Access and Success for Low-Income Students: Evidence from a Large Need-based Grant Program. s.l., s.n.

Gelman, A. & Imbens, G., 2014.. Local Polynomial Order in Regression Discontinuity Designs, Washington DC, USA: National Bureau of Economic Research, Inc..

Grenet, J., 2013. Is Extending Compulsory Schooling Alone Enough to Raise Earnings? Evidence from French and British Compulsory Schooling Laws. Scandinavian Journal of Economics, 115(1), p. 176–210.

Harmon, C., Walker, I. & Oosterbeek, H., 2003. THE RETURNS TO EDUCATION: MICROECONOMICS. JOURNAL OF ECONOMIC SURVEYS, 17(2), pp. 115-155.

Imbens, G. W. & Lemieux, T., 2008. Regression discontinuity designs: A guide to practice. Journal of Econometrics, Volume 142, pp. 615-635.

Oreopoulos, P. & Petronijevic, U., 2013. Making College Worth It: A Review of the Returns to Higher Education. Future Child, 23(1), pp. 41-65.

Patrinos, H. A., 2016. Estimating the return to schooling using the Mincer equation. IZA World of Labor , 1 July, 278(278), pp. 1-14.

Rodel, S. & Arvin, H., 2017. Determinants of Earnings: A Commentary on Mincer’s Earning Function. EUROPEAN JOURNAL OF CONTEMPORARY RESEARCH, 6(1), pp. 269-275.

Sturgis, P. & Buscha, F., 2015. Increasing inter?generational social mobility: is educational expansion the answer?. The British Journal of Sociology, 66(3), pp. 512-533.

Teixeira, P. N., 2014. Gary Becker’s early work on human capital – collaborations and distinctiveness. IZA Journal of Labor Economics, 3(12), pp. 1-20.

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