Returns to education in vietnam a clustered data approach

2003 did an attempt to analyze the returns to education in Vietnam by using Mincer earnings function based on the 1992–93 Vietnam Living Standards Survey VLSS data.. 2003 to re-estimate

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES

VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

RETURNS TO EDUCATION IN VIETNAM:

A CLUSTERED DATA APPROACH

BY:

NGUYEN THI NGOC THANH

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES

VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

RETURNS TO EDUCATION IN VIETNAM:

A CLUSTERED DATA APPROACH

A thesis submitted in partial fulfilment of the requirements for the degree of

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

By:

NGUYEN THI NGOC THANH

Academic Supervisor(s):

Assoc Prof Dr NGUYEN TRONG HOAI

Dr PHAM KHANH NAM

ACKNOWLEDGEMENT

First of all, I would like to express my sincere thank to the Vietnam – Netherlands Programme (VNP) for such a challenging but interesting programme, whereby I enjoyed unforgettable time beside my classmates and broadened my networking via class

I am much grateful to famous whole-hearted professors at home and abroad for advanced knowledge and updated information they gave us in class and beyond the class-time Specially, I would like to deeply thank two supervisors: Assoc Prof Dr Nguyen Trong Hoai and Dr Pham Khanh Nam for their helpful and valuable advices

on the last but utmost duty, this thesis, that helps me fulfill my study career

From the bottom of my heart, I always feel thankful to my Family for their daily care, daily worries, daily happiness with every failure or achievement I get in life I keep looking for chances to bring them happiness

To my C16 Classmates, I can say that two-year was a great memory when I am with you all Thank you for your kindness, sharing and support Especially, I cannot forget the enthusiastic disinterested help from Mr Le Anh Khang – our class “Hero” before every final exam He has inspired and motivated me a lot I would like to take this opportunity to say thanks to him formally

Life is still ahead of us, let’s just stop a moment to celebrate our achievement today and keep going forward afterward I wish you all good health, happiness and success for the coming New Year 2013 Cheers !

ABSTRACT

Moock et al (2003) did an attempt to analyze the returns to education in Vietnam

by using Mincer earnings function based on the 1992–93 Vietnam Living Standards Survey (VLSS) data In this paper, I replicate the job of Moock et al (2003) to re-estimate the returns to education by using the 2008 Vietnam Household Living Standard Survey (VHLSS) and Mincerian earnings functions, but with a different regression method, called clustered data at household level using panel commands The study reveals that (1) an additional year of schooling associates with 8.95% increasing in the average rate of return to education, comparing with only 5% in 1992/1993 In terms of gender gap, females experience higher returns to school than males (11.47% vs 8.33%) This pattern is unchanged when referring to result in 1992/1993 (6.8% vs 3.4%); (2) workers in public sector get higher rates of return to education than those in private sector (9.95% vs 5.59%) However, foreign sector is the one has the highest rates of return among the three, 11.9%; (3) university is the best option for schooling investment with the rate of return of 19% higher than upper secondary level while this number was 11% in 1992/1993 Primary level brings back 16% rate of return vs no level (13% in 1992) The rates are 10% for vocational vs primary (4% in 1992); 8% for upper secondary vs lower secondary; while only 2% for lower secondary vs primary

Key Words: return to schooling, education, Vietnam, Human Capital, Mincer earnings function, clustered data, random effect model

TABLE OF CONTENTS

CHAPTER 1 INTRODUCTION 6

1.1 Problem Statement 6

1.2 Research Objectives 7

1.3 Research Questions 8

1.4 Research Methodology 8

1.5 Structure of the Thesis 8

CHAPTER 2 LITERATURE REVIEW 9

2.1 Definition 9

2.2 A Standard Model of Human-Capital Investment 10

2.3 Empirical Studies on Estimating Returns to Education 12

2.3.1 Selective Empirical Studies in the World 12

2.3.2 Empirical Studies in Vietnam 15

2.4 Analytical Framework 19

2.5 Chapter Remarks 19

CHAPTER 3 RESEARCH METHODOLOGY 21

3.1 Data 21

3.2 Research Methodology 23

3.3 New Approach - CLUSTERED DATA APPROACH in Estimating the Returns

to Education 24

3.4 Empirical Models of the Returns to Education 27

3.5 Variable Coding 29

CHAPTER 4 RESEARCH FINDINGS AND DISCUSSION 32

4.1 Descriptive Statistics 32

4.1.1 Distribution of the Dependent and Explanatory Variables 32

4.1.2 Descriptive Statistics of the Dataset 37

4.2 Regression Results 38

4.3 Chapter Remarks 43

CHAPTER 5 CONCLUSION AND POLICY RECOMMENDATION 45

5.1 Conclusion of the Study 45

5.2 Policy Recommendation 46

5.3 Limitations of the Study 47

5.4 Suggestion for further Studies 48 REFERENCE

LIST OF TABLES

Table 2.1: Empirical studies in Vietnam utilizing Mincer earnings function over the

period 1992-2008 17

Table 3.1: Sample of cross-sectional data 26

Table 3.2: Sample of clustered data 27

Table 3.3: Description of the Variables and Variable Coding 30

Table 4.1: Descriptive statistics 38

Table 4.2: Earnings function by years of schooling 39

Table 4.3: Earnings function by sector of employment 40

Table 4.4: Earnings function with schooling levels (for all, males, and females) 41

Table 4.5: Private rates of return to schooling by level of education (%) 42

LIST OF FIGURES Figure 4.1: Histograms of log of earnings (by gender) 32

Figure 4.2: Histograms of log of earnings (by sector) 33

Figure 4.3: Histograms of years of schooling and log of hours worked/week 34

Figure 4.4: Scatterplots of monthly earnings and years of schooling 35

Figure 4.5: Scatterplots of monthly earnings and education levels 36

Figure 4.6: Scatterplots of monthly earnings and years of experience 36

LIST OF ABBREVIATIONS

VHLSS : Vietnam Household Living Standard Survey

CHAPTER 1 INTRODUCTION

This chapter explains the context of the thesis, its objectives and research questions In addition, a brief of methodology is also mentioned in this part Finally, the structure of the thesis is presented

1.1 PROBLEM STATEMENT

Education plays an important role in modern labor markets Hundreds of studies

in many different countries and time periods have confirmed that better educated individuals earn higher wage than the less-educated ones1 A variety of studies have been started with the seminal work by Mincer (1974) who was the first to derive an empirical formulation of earning over the lifecycle In his basic formulation, the logarithm of earnings can be interpreted as years of schooling, years of experience and squared years of experience

In Vietnam, since the Vietnam Living Standards Survey (VLSS) firstly conducted

in 1992–93 till present, many studies have employed the VLSS data and the Mincerian earnings function to examine rates of return to education in Vietnam, such as: Glewwe

& Patrinos, 1998; Gallup, 2002; Moock et al., 2003; Liu, 2006; Nguyen Xuan Thanh, 2006; Vu Trong Anh, 2008; Vu Thanh Liem, 2009; Doan & Gibson, 2010; etc The results are also diverse

The most cite study is from Moock et al (2003), in which the authors attempt to analyze the returns to education in Vietnam by using Mincerian earnings function based on the data of VLSS 1992–93 The authors find that the estimated rates of return are quite low (4.8%) In particular, on average, the rates of return to primary and

years; Trostel, Walker and Woodley (2002) contains estimates for 28 countries; Polachek (2007) contains estimates for 42 countries; etc

university education are 13% and 11% But these rates are just 4% and 5% at secondary and vocational levels For higher education (colleges, universities or above), the returns are higher for females (12%) than for males (10%)

Now, 20 years have passed, I return to the issue and question for now, what are the returns to education in Vietnam? How have the returns changed? Especially, in term of gender gap, between males and females who receive higher returns to education? In term of sectoral gap, among public, private, and foreign sectors, any discrepancies among these three? The findings are important implications for policy makers in directing the wage and educational policies

I would like to replicate the job of Moock et al (2003) to answer these questions

by using the Vietnam Household Living Standard Survey (VHLSS), conducted by General Statistic Office (GSO), in 2008 and Mincerian earnings function, but with a different regression method which is first time applied in this kind of estimation, called Clustered data at household level using panel commands2, instead of using a simple standard cross-sectional OLS estimator From the results, I would like to suggest some policy implications

1.2 RESEARCH OBJECTIVES

There are 03 main objectives in this study:

(1) To estimate private returns to education by years of schooling and by levels of schooling for both sexes, for males and females; and in private, public and foreign sectors recently;

(2) To assess the variation in returns to education by comparing with the findings from Moock et al (2003);

(3) To propose some policy options

1.3 RESEARCH QUESTIONS

The research questions are proposed:

(1) What are the rates of return to education by years of schooling, by levels of education for both sexes, for males and females; and in private, public, and foreign sectors recently?

(2) How are the rates of return to schooling different comparing with 15 years ago? Should the rates increase or decrease?

(3) What are policy recommendations?

1.4 RESEARCH METHODOLOGY

In the study, I use the VHLSS conducted by GSO in 2008 and the Human Capital Model developed by Mincer (1974), with the regression method so-called Clustered data at household level using panel commands, instead of using a simple standard cross-sectional OLS estimator

1.5 STRUCTURE OF THE THESIS

The paper is structured as follows: Chapter 2 provides the literature review and empirical studies over the world and in Vietnam Chapter 3 describes the data samples and specifies the research methodology The results based on descriptive statistics and econometric models are presented in Chapter 4 The last chapter comes up with conclusion, policy recommendation, limitations of the study, and suggestion for further studies

CHAPTER 2 LITERATURE REVIEW

The first part of this chapter gives preliminary definition of main terms used in the context The next part comes to provide theoretical foundation for empirical research

A standard model of human-capital investment by Mincer (1974) is introduced to briefly explain how to form up the standard Mincerian earnings function Some of selective empirical studies on returns to education in the world and in Vietnam are then recalled to summarize the empirical results found by different researchers This chapter also is going to build up analytical framework for the study

2.1 DEFINITION

Human Capital

Human capital is " the skills, knowledge, and experience possessed by an individual

or population, viewed in terms of their value or cost to an organization or country" (Oxford Dictionaries April 2010 Oxford University Press)

Rate of Return

Rate of return is " the gain or loss on an investment over a specified period, expressed as a percentage increase over the initial investment cost Gains on investments are considered to be any income received from the security plus realized capital gains" (Retrieved from http://www.investopedia.com/terms/r/rateofreturn.asp, accessed on Dec 25, 2012)

Return to Education (Schooling)

The return to education is captured only indirectly by different methods depending on which level the study is examined at Specifically, at society level, the return to education is presented as the investment in education relative to national wealth; At enterprise level, it is the investment in training in effect with enterprise

performance; In term of individual, the return to education is described as years of schooling relative to life income

At individual level, the "individual return to education" is also termed as "private return to education" to distinguish with "social return to education" at society level This study covers at individual level

2.2 A STANDARD MODEL OF HUMAN-CAPITAL INVESTMENT

Mincer (1974) proposed the standard Human Capital model in which the log of observed earnings of an individual is interpreted by years of schooling, experience in labor market and squared of experience The theoretical foundations behind this standard model are briefly presented as follows:

Mincer contends that potential earning at time t depend on investment in human capital made at time t-1 Let Et be potential earnings at time t Assuming that an individual uses kt as a share of his/her potential earnings with rt as a return in each period t to invest in human capital Then the potential earning at time t+1 is as follows:

) 1 (

j

j j

t E r k

By assuming that schooling is the number of years, s, spent in full-time investment (k0=…=ks-1=1), which is assumed to arise at the beginning of life and to produce a rate of return rs which is constant over all years of schooling (r0=…=rs-1=β) and the return to post-schooling investment is constant over time (rs=…=rt-1=λ), we can rewrite equation (2.2) as follows:

j j

Where, the last approach is for small value of β, λ, and k

To link between potential earnings and experience z from labor market, the schooling investment is assumed to be linearly decreased over time

post-) 1 ( T

z

ksz  

Where T is the last year of working life; z=t-s ≥0; and (0,1)

Replacing (2.4) into (2.3), we got:

2 0

2 2

ln

T

z T s

2 2

ln 1

T

z T T s

E T

npet ln t 1

ln  ;   ln E0 ;

T T

With final assumption that, at any time t, the observed earnings are equal to net

Replacing (2.8) into (2.7), we got the standard Mincerian earnings equation:

2

2.3 EMPIRICAL STUDIES ON ESTIMATING RETURNS TO EDUCATION 2.3.1 Selective Empirical Studies in the World

There are a huge number of studies in the world relying on the Mincerian earnings function in estimating returns to education In spite of sample selection bias as serious limitation, OLS regression are worldwide applied Numerous supplementary variables are often fitted in the estimation, such as: gender, regional dummy variables, ethnicity, race, marital status, union membership, etc These variables serve as exogenous

“control variables” which may shift the earnings function upward or downward depending on their signs

Johnson and Chow (1997) estimate rates of return to schooling in China by using OLS regression and data from a survey of Chinese individuals in 1988 The study also includes gender, race and Communist Party affiliation as control variables The authors find that the rates of return to education in China is 4.02% in the rural and that 3.29% in the urban In the urban areas, females’ rate of return is significantly higher than that for males (4.46% vs 2.78%) Additionally, members of Communist Party in urban areas have significantly lower returns to schooling than those of non-members (2.42% vs 3.68%)

Onphanhdala and Suruga (2007) assess the returns to education in Lao by using Lao Expenditure and Consumption Survey in 2002/2003 (LECS 3) Dummy variables for gender, area, ethnicity, type of business and region are included in the regression Interpreting the OLS estimator, the authors present that the returns to schooling in Lao are still very low, but have increased significantly with the economic transition (from 3.2% in 1997-1998 to 5.2% in 2002-2003) Specially, young workers obtain higher returns (7.0%) than older workers (3.9%), indicating that returns to education will continue to rise when the market reforms have full effect Although workers with high levels of education are paid large earnings premiums, but primary level still indicate it

as the most profitable investment in education Furthermore, wage differentials are found significant between public sector (2.2%) and private one (5.2%)

To correct the sample selection bias caused by nonrandom data, Heckman (1979) introduces a two-step simultaneous model which has become a popular technique in many fields of study Siphambe (2008) applies this model in his study when estimating the educational returns in Botswana in 2002-2003

Siphambe (2008) uses the Household Income and Expenditure Survey data (HIES) in 2002-2003 to examine the returns to education in Botswana The author includes such variables as age, education, and marital status in probit equation to create the selection variable, the Inverse Mill Ratio, which is then inserted into the earnings function The author then re-estimates the equation The results show that the average rates of return to education in the 2002-2003 period is 15%, representing 1% decline compared with the 1993-1994 period (16%) In term of schooling levels, details are reported that the biggest fall is for upper secondary at 28% points (8% in the later period vs 36% previously) The university education, however, has the rates of return rise at more than 50% (24% vs 11%) Except the upper secondary, the pattern of rates

of return to education keeps similar to the findings in Siphambe (2000) In term of wage differentials, the results show that the females and males enjoy the same rates of returns on education (around 15%) in 2002-2003, which is much different from Siphambe (2000), where the average rates were higher for females than for males Another critical problem when studying educational returns is the endogeneity

To deal with unobserved heterogeneity, in his review works, Card (1999) summarizes three broad approaches: (i) using instrumental variables based on institutional features

of the education system (typically, Angrist and Krueger, 1991); (ii) using family background as instrument for schooling (Ashenfelter and Rouse, 1998; Nakamuro and

Inui, 20123); (iii) estimating based on the schooling and earnings of twins (Ashenfelter and Krueger, 1994) These works generally focus on the estimation of the average impact of education on earnings, by means of both OLS and IV techniques

Angrist and Krueger (1991) reason that because of school start age policy and compulsory school attendance laws, individuals born in the beginning of the year usually start school at an older age, and can therefore drop out after completing less schooling than individuals born near the end of the year

The estimation draws on a variety of data sets constructed from the Public Use Census Data in 1970 and 1980 The samples focus on males of 16 years old born in the

US to specify the 1920-1929 corhort (in 1970 Census); and 1930-1939 corhort and 1940-1949 corhort (in 1980 Census)

Using the interaction between quarter-of-birth and year-of-birth as instrument for education, the athors evaluate the effect of compulsory schooling laws on education across cohorts After controlling for age in quadratic, race, marital status and urban residence, the difference-in-difference approach suggests that the returns to an additional year of schooling is 10% for men born in 1920-1929, 6% for 1930-1939 men, 7.8% for 1940-1949 men

Ashenfelter and Krueger (1994) use primary data collected at the Annual Twins Days Festival in Twinsburg (Ohio) in 1991 to state that the workers’ ability (or other characteristics) and schooling are uncorrelated, hence cause no direct effect on earnings The final sample contains 298 pairs of identical twins4 who are assumed to have the same ability but for some random reason vary in the amount of school they

Rouse (1998), so I add in

hence are hypothesized to share the same innate ability; vs Dizygotic twins (or fraternal twins) who come from two eggs and two sperm and are not genetically identical, hence are more likely to be affected by omitted ability bias

obtain By using each sibling’s report on his/her sibling’s education level as an instrumental variable for his/her sibling’s education level, the authors find out that an additional year of schooling raises wages by 12-16%

Ashenfelter and Rouse (1998) utilize the data conducted at the Annual Twins Days Festival in Twinsburg (Ohio) (the so-called Princeton Twins Survey) for 3 years 1991-1993, including 340 twin pairs (680 twins) of identical twins The authors control for age (rather than experience as in traditional Mincerian equation) and use the difference between twin 2's report of twin 1's education and twin 2's report of his/her own education as instrumental variable The results are fitted by fixed-effect estimator estimating that the annual returns to schooling attained for identical twins is about 9%

on average

In the very recent study, Nakamuro and Inui (2012), following Ashenfelter and Rouse (1998), measure the causal effect of education on earnings by using sample of twins in Japan The final results regressed on the data of 2,257 identical twin pairs collected through a web-based survey After correcting the measurement errors by the

IV method, the authors obtain 9.3% as the average returns to education in Japan

2.3.2 Empirical Studies in Vietnam

In Vietnam, there are a few articles written on the Mincerian function Most of the studies use OLS regression (one round or two rounds) (Glewwe & Patrinos, 1998; Gallup, 2002; Moock et al., 2003; Vu Trong Anh, 2008; Vu Thanh Liem, 2009; some use Heckman two-stage approach to correct the sample selection bias (Liu, 2006); some use Heckman one single step model (Doan & Gibson, 2010); some use the difference-in-difference approach (Nguyen Xuan Thanh, 2006)

Glewwe & Patrinos (1998) use the VLSS 1992–93 to examine the nature of attending private schools in Vietnam As a result, some important conclusions are

made: (1) among public, private and semi-public schools, better-off households tend to send their children to private schools rather than to semi-public ones; (2) of the same school attainment, individuals attending private schools get higher wages than ones from public schools; (3) the return to schooling in Vietnam is 1.6% in 1992–93

Gallup (2002), while calculating wage inequality among such controlled variables as different sectors, regions, or genders in Vietnam in 1993 and 1998, finds that although the rate of return to schooling in Vietnam increases from 2.9% in 1993 to 5.0% in 1998, it is still very low coefficient The results are retrieved from the VLSS 1992–93 and 1998 data, and two-round OLS regression

Moock et al (2003), in their attempt to analyze the returns to education in Vietnam by Mincerian function based on the VLSS 1992–93, find that the estimated rates of return are quite low (4.8%) Specifically, on average, the rates of return to primary and university education are 13% and 11% respectively However, these rates are just 4% and 5% at secondary and vocational levels For higher education (colleges, universities or above), the returns are higher for females (12%) than for males (10%)

Nguyen Xuan Thanh (2006) is the pioneer in applying the difference approach to investigate the rate of return to schooling in Vietnam The result

difference-in-is derived from the VLSS 2002 He documents that an additional year of schooling difference-in-is associated with 7.32% increase in wage in 2002

Liu (2006) exploits the data of VLSS 1992–93 with Heckman two-stage approach and the VLSS 1998 with OLS regression He reports a higher coefficient on schooling for males (5.9%) than females (4.2%) for year 1992–93, but a contrast results are seen for year 1998 when males are rewarded with 3.5% for each additional year of education, while females are rewarded with 4.8%

Vu Trong Anh (2008) uses the data of VHLSS 2004 to point out that the rate of return to schooling in Vietnam is 7.4% in 2004 For the same objective but with a different data set - VHLSS 2006, Vu Thanh Liem (2009) shows the return to be 7.63% in 2006

Different from other authors who just suggest the returns for a specific year, Doan & Gibson (2010) utilizing VLSS 1998, 2002, 2004, 2006 and 2008 examine the trend in the rate of return to schooling in Vietnam over 10 years 1998-2008, by using OLS and Heckman selection estimator (Maximum Likelihood approach) The returns are found 2.9% for year 1998, 7.6% for 2002, 8.6% for 2004, 8.8% for 2006, and 9.5% for 2008, showing a clear rising trend over the mentioned period and reach their peak around 2004-2008

The Table 2.1 below summarize the above-mentioned empirical studies in a more visual way (The research for 2009-2010 has not been found out till this study is done, therefore, not included in the study)

Table 2.1: Empirical studies in Vietnam utilizing Mincerian earnings function over the period 1992-2008 5

incorporated

employment, private, employer, HCMC, Hanoi

Moock et al

Experience, experience squared, log weekly hours worked

Experience, experience squared, gender, geography, agricultural/non-agricultural job, sectoral ownership

Experience, experience squared, married, migrant, urban, regions, majority, state employees, SOEs employees, industries

Experience, experience squared, gender, household size, non-wage income

2.4 ANALYTICAL FRAMEWORK

2.5 CHAPTER REMARKS

The study employed the standard Human Capital model developed by Mincer (1974) to build up its conceptual framework Under the framework, the logarithm of observed monthly earnings of an individual is explained by years of schooling, years of

Figure 2.1 Analytical Framework

Clustered data: VHLSS 2008

Independent variables:

(1) Schooling: divided by years of schooling

and levels of schooling including primary,

secondary, vocational education, bachelor

and above

(2) Years of experience

(3) Squared years of experience

(4) The logarithm of hours work per week

Models are fitted for all, male, and female;

private, public, and foreign sectors

Dependent variable: The logarithm of monthly earnings

Random Effects model, clustered on household

Private returns to education for all, male, female; private, public, foreign sectors

experience in labor market, squared years of experience, and the logarithm of hours work per week

In order to examine the returns to education in Vietnam, most of the existing studies use OLS regression as their first or final modeling However, this method does face problems of underestimating standard errors within the same household and ignoring the mean variation between different households

Specifically, individuals/employees in the same household are likely to share the same unobservable household characteristics such as culture, specific genetics that may affect their earnings ability Therefore, the error terms for individuals from the same household will be correlated through a common household-level component, and if ignored this may lead to substantially underestimated standard errors (Glick and Sahn,

2000, p.69-70)

Moreover, OLS estimator ignores the mean variation between households For instance, OLS results a common intercept at state level, say, all individuals have a common intercept, regardless of households It is unlikely to be true in reality as individuals from different households may hold different intercepts

By addressing the above issue, instead of using a simple standard cross-sectional OLS estimator along with cluster-robust standard errors, I transfer cross-sectional data to clustered data at household level, and then fitted by random-effect estimator By doing so, I allow for such correlation, as mentioned above, in the model through a random effect for the residuals

CHAPTER 3 RESEARCH METHODOLOGY

This chapter describes the source of data used, the way to collect and extract to final results The methodology applied to analyze the data sample is presented in the next part, followed by empirical models The introduction of new approach - CLUSTERED DATA APPROACH is the highlight of this chapter Variable coding is the final part to show how I code the dependent and independent variables from the data set

3.1 DATA

The data for this study is the Vietnam Household Living Standard Survey (VHLSS) conducted in 2008 by the General Statistical Office (GSO) of Vietnam The surveys contain detailed information of 9,189 households from 3,063 communes Samples were weighted basing on the statistics of Vietnam Population Census in 1999 with approximately 70% of Vietnamese households lived in rural areas The communes were randomly selected from a total of proximately 10,000 communes in 646 districts, and 64 provinces and cities in Vietnam, and then an average of 3 households were randomly selected for interview in each commune

In this research, I am going to estimate returns to education for only individuals who are employed for salary6 Only individuals in ages from 15 to 60 for male and 15

to 55 for female are considered Earnings are calculated by monthly earnings in labor market Earnings/month (1,000 VND) Individuals who work for their household are dropped out of the sample

In this study, I consider only wage earners Earnings are proxied only by salary/wages received, including payment in kind, from the work being done (refer to Table 3.3 “Description of the Variables and Variable Coding” for more details)

Years of schooling are collected from general education system7 This is the highest class that he/she has been completed For example, a person who is in grade 10, only recorded grade 9 is the highest grade completed Another man was in grade 9 and dropped out of school, write the grade 8 is the highest class finished For individuals who are at College level, years of schooling equal 15 years; 17 years for Bachelor; 19 years for Master; and 22 years for PhD level (Le Thi Nhat Phuong, 2008; Le Anh Khang, 2012)

While data on schooling attainments for each individual is obtainable, information

on post-school investment is not available in VHLSS Therefore, following Mincerian earning function, difference in quantities of post-school investment among employees are measured by differences in years of experience which is proxied by age of employee (in years) minus years of schooling

Hours of work per week are affixed as a compensatory instrument (Moock et al.,

2003, p.504) Mincer (1974, part 1, p.22) states that the annual earnings profile is affected when hours of work vary over the life cycle For instance, in the circumstance

of certainty where individual wealth is considered as fixed the cost of time increases with experience until reaching the peak of earning capacity If so, the ascent and descent of earning capacity is likely to trigger a corresponding pattern of working hours provided for market Thus, it seems to be overestimated of investment in human capital or the rates of return if we use the observed annual earnings as dependent variable Hours worked per week are then added as a compensatory factor for the above overestimated matter

After consolidated to remove errors and inconsistencies, the sample data remains 6,956 individuals/employees, living in 4,335 households

3.2 RESEARCH METHODOLOGY

Returns to education are estimated based on the Human Capital Model developed

by Mincer (1974) which is formulated by an earning function as follows:

i i i

i

i S EXP EXP u

2 1

.

Where,

lnYi is the logarithm of the monthly earnings for individual i

Si is the number of schooling years of individual i

EXPi is the number of years of working experience of individual i

EXPi2 is the squared of experience of individual i

ui is the error term

The squared of experience (EXP2) in equation (3.1) implies that earnings should increase along with the years of experience but at a diminishing rate and its coefficient (γ2) is expected to have a negative sign

To measure the average returns to education at different levels of schooling, various dummy variables are created by converting the continuous variable years of schooling The extended earning function is as follows:

i i i

i i

i i

i PRIM SEC VOC UNIV EXP EXP u

2 1

4 3

k k

3.3 New Approach - CLUSTERED DATA APPROACH in Estimating the Returns to Education

In term of econometric, I am using cross-sectional VHLSS data surveyed in 2008

to estimate the rates of return to education This data is grouped (nested) by households Obviously, OLS estimator is the common choice to estimate this data However, this method does face problems of underestimating standard errors within the same household and ignoring the mean variation between different households

Specifically, in terms of individuals/employees within the same household, the residual terms are unlikely to be independent, reflecting the fact that more than one individual/employee from the same household may appear in the estimating sample Whereas, individuals/employees in the same household are likely to share the same unobservable household characteristics that may affect their earnings ability For instance, the culture, genetics of a specific household make individuals in this household perform well, get more knowledge in school, more earnings in the labor