External and private return to education by using instrument variables approach: evidence in Vietnam with a panel data

UNIVERSITY OF ECONOMICS

ERASMUS UNVERSITY ROTTERDAM

HO CHI MINH CITY
VIETNAM

INSTITUTE OF SOCIAL STUDIES
THE NETHERLANDS

VIETNAM – THE NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

EXTERNAL AND PRIVATE RETURN TO EDUCATION BY
USING OF INSTRUMENT VARIABLE APPROACH:
EVIDENCE IN VIETNAM WITH A PANEL DATA SET

BY

MR. LE THANH HUNG

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

HO CHI MINH CITY, October 2016


UNIVERSITY OF ECONOMICS

INSTITUTE OF SOCIAL STUDIES

HO CHI MINH CITY

THE HAGUE

VIETNAM

THE NETHERLANDS

VIETNAM - NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

EXTERNAL AND PRIVATE RETURN TO EDUCATION BY
USING OF INSTRUMENT VARIABLE APPROACH:
EVIDENCE IN VIETNAM WITH A PANEL DATA SET

A thesis submitted in partial fulfilment of the requirements for the degree of
MASTER OF ARTS IN DEVELOPMENT ECONOMICS

By

MR. LE THANH HUNG

Academic Supervisor:
PROF. NGUYEN TRONG HOAI

HO CHI MINH CITY, October 2016


ACKNOWLEDGEMENT

To be able to finish this thesis, I have received the great supports from many people.

Firstly, I would like to express my appreciation and special thanks to Prof. Nguyen Trong
Hoai, my academic supervisor, who has given me many valuable guidance, advices, and
great encouragements for my thesis. Secondly, I would like to express my gratitude to
Lecturers and Staff from Vietnam – Netherlands Program at University of Economics Ho
Chi Minh city. Specially, I am indebted to Ph.D. Truong Dang Thuy, who gave me
valuable support and comments for my thesis. I am also grateful to Ph.D. Pham Thi Bich
Ngoc for her support in Stata’s commands in my thesis. Finally, I am indebted to my
family and my friends, who gave me the greatest encouragements for my study.

Page |i


DECLARATION

I declare that “External and private return to Education by using of Instrument
Variable Approach: Evidence in Vietnam with a panel data set.” is my own work.
This thesis is has not been submitted to any degree or examinations at any other
universities. In addition, all the using sources are indicated by the completed references.

P a g e | ii


ABSTRACT

The combination of Vietnam Household Living Standard Survey and Provincial Statistics
Yearbook from 2010 to 2014 provide a great opportunity for estimating the up to date
external and private return to education in Vietnam. In this paper, the Human Capital
Earning Function (Mincer, 1974) and instrument variables are adopted in order to
estimate the external and private return to education. The analysis suggests that not only
one additional schooling year have an impact on individual wage, but the increase in

proportion of skilled workers in the labor force also have an influence on the hourly wage
of individual.

P a g e | iii


TABLE OF CONTENTS
ACKNOWLEDGEMENT ............................................................................................ i
DECLARE ................................................................................................................... ii
ABSTRACT ...............................................................................................................iii
TABLE OF CONTENTS ........................................................................................... iv
LIST OF TABLES...................................................................................................... vi
LIST OF FIGURES ...................................................................................................vii
LIST OF ACRONYMS ............................................................................................viii
Chapter 1: Introduction ............................................................................................ 1
1.1 Introduction .................................................................................................... 1
1.2 Research objectives ........................................................................................ 3
1.3 Research scope ............................................................................................... 3
1.4 Structure of paper ........................................................................................... 3
Chapter 2: Literature review ................................................................................... 5
2.1 Human capital theory...................................................................................... 5
2.2 Returns to education ....................................................................................... 8
2.3 External return to education ......................................................................... 10
2.4 Chapter remark ............................................................................................. 12
Chapter 3: Research methodology ......................................................................... 14
3.1 Research methods ........................................................................................ 14

P a g e | iv

3.2 Endogeneity in Wage function .................................................................... 15
3.3 Data sources and measurement ................................................................... 16
3.4 Instrument variables .................................................................................... 18
3.5 Additional provincial control variables and interact terms ......................... 20
3.6 Data description........................................................................................... 21
3.7 Chapter remark ............................................................................................ 22
Chapter 4: Research results ................................................................................... 24
4.1 Overview about Vietnam ............................................................................ 24
4.2 Individual earning on individual characteristics ......................................... 27
4.3 Individual earning on levels of education ................................................... 28
4.4 Results on estimating returns to education.................................................. 29
4.5 Returns to education classified by dummy variables .................................. 36
4.6 Chapter remark ............................................................................................ 41
Chapter 5: Main findings and recommendations ................................................. 43
5.1 Main findings .............................................................................................. 43
5.2 Policy recommendations ............................................................................. 45
5.3 Limitations .................................................................................................. 46
REFERENCE ............................................................................................................ 47
APPENDIX ............................................................................................................... 50

Page |v


LIST OF TABLES
Table 3.3 Definitions and unit of individual-level variables ........................................17
Table 3.4 Definitions and unit of provincial level variables ........................................18
Table 3.5 Descriptive statistics for continuous variables .............................................20
Table 3.6 Descriptive statistics for dummy variables .................................................. 21
Table 4.2.1 Individual earning classified by gender .................................................... 27
Table 4.2.2 Individual earning classified by marital status ..........................................28

Table 4.2.3 Individual earning classified by type of school .........................................28
Table 4.3 Individual earning on levels of education .................................................... 29
Table 4.4.1 OLS estimate ............................................................................................. 29
Table 4.4.2 Instrument variables estimate without control variables ........................... 31
Table 4.4.3a IV estimate with additional control variables ..........................................33
Table 4.4.3b External return to education for levels of education ............................... 34
Table 4.5 IV fixed effect with additional control variables for groups of individual’s
characteristics ..............................................................................................................37
Table 4.5a External return to education for female ...................................................... 39
Table 4.5b External return to education for Married = 1 .............................................40
Table 4.5c External return to education for public school ...........................................41

P a g e | vi


LIST OF FIGURES
Figure 2.4: Analytical framework ................................................................................ 13
Figure 4.1a: GDP across six key economic regions in Vietnam 2010-2014................ 25
Figure 4.1b: Educational system in Vietnam ............................................................... 26

P a g e | vii


LIST OF ACRONYMS

GDP

Gross domestic product

VHLSS

Vietnam Household Living Standard Survey

PSY

Provincial Statistic Yearbook

HCEF

Human Capital Earning Function

P a g e | viii


External and private return to Education
by using of Instrument Variable Approach:
Evidence in Vietnam with a panel data set.
Chapter 1: Introduction
1.1

Introduction:

Human capital, physical capital, and development of technology contribute to the
economic growth of a nation. Specially, investing in human capital does not only enhance
the productivity of labor force, but it also contributes to the improvement of social
welfare, such as decreasing the criminal rate, upgrading the living standard, etc. Aristotle
- a famous Greek philosopher – said “the fate of empires depend on the education of
youth.” This statement convinces the major factor in the wealth of a nation of education,
which is a well-known representing term for human capital.
In Vietnam, the educational system has a great improvement from Doimoi policy since

1986. Before 1986, the illiteracy rate accounted more than ninety-five percent on total
population, this rate decreased at 2.7% in 2015 according to General Statistics Office.
The government considers investment in education is one of the most priority policies
and the proportion of this investment has to be at least 20% or higher on the total
National Budget. Understanding the benefits from education to social welfare provides
valuable consultations for making policies in improving the wealth of nation. The
importance of education encourages me to study the educational benefits in Vietnam with
an up-to-date information from Vietnam Living Standard Survey 2010-2014.
Nazier (2013) defined the private return to education as the productivity that individual
gains from his own investing to education. While the external return to education is
considered as the productivity that individual gains from the local human capital. In other
words, the external return to education is the influence of the share of local educated
labor force on individual’s wage. The external return to education is the sum of spillover

Page | 1


effect and demand of labor force effect. In which, the spillover effect enhance the
workers’ productivity through the sharing ideas between workers, while demand of labor
force effect speaks the higher competitive pressure on workers when increasing the
skilled workers in the labor market. In the modern economics theory, Moretti (2004)
defined the social return to education as the sum of private return to education and
external return to education.
Moretti (2004) stated that for at least a century, economists have speculated that social
return may exceed the private return to education. The external return to education is one
of the most reasonable reasons for this speculation. Understanding about human-capital
externalities or external return to education brings a good guideline for policies making
or produce the better development strategy for any country.
Economists have been paying so much in the private return to education, for instance the
works of Doan (2011), Le (2014), and Wang and Cai (2014), while there are few of

papers, which consider about external return to education. This situation encourages me
to study the external return to education in Vietnam in order to contribute to estimate the
relationship of the highly educational workers’ proportion on the individual wage by
using the lasted dataset about Vietnam. In addition, the studies conducing in Vietnamese
education usually used one year Vietnam Household Living Standard Survey (VHLSS) in
the period 1992-2008. While my paper is going to use the panel data from combining
three years of VHLSS in 2010, 2012 and 2014 for individual level and the supporting of
Provincial Statistics Yearbook of all 64 provinces/cities across Vietnam to analyze the
returns to education.
In this paper, some research questions are concerned:
-

Does share of skilled labor force impact on individual’s wage?

-

Is there any difference influence of schooling years on individual’s wage across
the regions and the share of skilled labor force?

Page | 2


1.2

Research objectives:
-

To examine the relationship between the share of skilled labor force and
individual’s wage as external return to education.

-

To examine the relationship between schooling years and wage as private return to
education for expressing the importance of education in individual’s wage.

1.3

Research scope:

The combination of Vietnam Living Standard Survey and Provincial Statistic Yearbook
from 2010 to 2014 provides a great opportunity for this research in estimating the up-todate private and external return to education in Vietnam. For private return to education,
the individual level data is used, while external return to education requires the provincial
level data. In recent decades, the micro approach in estimating the returns to education
dominates the macro one. The macro approach, which uses the cross-country data, could
be bias due to the differences in policies, orientation in educational system between
countries. Therefore, the micro approach is adopted in this paper.

1.4

Structure of paper:

This study will continue with four following chapters:
Chapter 2 illustrates in the definitions, literature, and methodologies of estimating the
return to education. The famous Mincer’s Human Capital Earning Function (1974),
which dominates the approach of estimating the main purpose of this paper. The previous
papers in this field are summarized in this chapter, too.
Chapter 3 describe the improvement from basic Mincer Human Capital Earning Function
and using instrument variables approach in order to obtain a consistent external and
private return to education.

Page | 3


Chapter 4 expresses the results when adopting the methodologies, which are discussed in
previous chapter. The results are also represented by classifying the whole sample in
gender, marital status, and types of school.
Chapter 5 interprets the main findings in the paper and some recommendations in making
polices.

Page | 4


Chapter 2: Literature Review
This chapter includes four sections, which concentrate in human capital theory, some
definitions, and related empirical studies on the human capital externalities. The first part
presents the history of human capital theory from the very early period to the present. The
next part aims to exploit the rate of return to education, including private and external
return to education. The third section focuses on the sign and size of external return to
education in the previous researches. The chapter remarks is in the final part.
2.1 Human capital theory:
According to the empirical labor economics literature, Rauch (1991) defined human
capital as the accumulation from two measurable components: education and experience
that an individual could obtain in his lifetime. In this case, the education is measured by
the number of years for completing school, while experience is calculated by the current
age minus schooling years minus the begin-attend-to-school age (or six).
In the serial book titled “The Wealth of Nations”, Adam Smith is the first economist who
found out the relationship between skilled-worker and higher earning wage in 1776. In
the 1960’s, Theodore W. Schultz and Gary Becker developed the human capital theory
by considering knowledge and skills that people obtained from vocational and technical
education as capital. Theodore W. Schultz (1961), a Nobel-winning-economist,

established the term “human capital”, which mentioned about the productivity gain from
investing in education and/or training-on-job. According to him, this type of capital is
one of the core factors for economic growth. In 1964, Becker stated that education is a
kind of investment in human capital and the individual would refer the future income
from higher level of education than the opportunity cost from being in the labor market.
In this research, Becker (1964) also provided the differences that influenced to the change
of individuals’ earnings through investment in human capital:
-

Education at school: the knowledge that individual obtains from situation which
provides education as a product.

Page | 5


-

Training at work: the knowledge that individual “collect” from work place or
learning-by-doing effect. The distinction between general and specific training is
one of the most valuable contributions from Becker to human capital theory.
o General training: the knowledge that workers can use in many firm.
However, workers have to pay all cost for this training or get the wage
lower than their current productivities.
o Specific training: the knowledge that improves workers’ productivities in a
particular firm, while the cost for this training is shared for both firm and
workers.

In 1974, Jacob Mincer used the findings of Human Capital (1964) from Becker as
conceptual framework in order to estimate the return to education by using 1960 United
State Census’ data. In this work, Mincer did create a useful methodology – Human

capital earning function – which dominated the way that economics estimate the return to
education at the micro level. This famous wage regression is developed from the human
capital accumulation model (Ben Porath, 1967), which describe the relationship between
the market wage and the skills-owned of individual in the competitive labor market. It is:
Wt = Pt.Ht (2.1)
Where:
Wt : the market wage
Pt : the price per unit of skills
Ht : the quantity of individual’s skills-owned (Human capital)
In order to develop (2.1) into a econometric model, Mincer used number of schooling
year and post-school investment instead of Ht. Then, we have:
Et+1 = Et + ct.pt = Et(1 +kt . pt) (2.2)
Where:

Page | 6


Et as the potential earnings at t period
ct = kt . Et , in which kt is the amount of time investing in human capital
pt as the return to training (or schooling)
Moving to the starting point, in which E0 as potential earning at 0, we have
Et = [

j.

p0)]E0 (2.3)

We have two period of a person in working life cycle, including in school and training, so
pt = ps for in school period
pt = p0 for training period

Consequently,
Es = [(1 + ps)s . E0 (2.4)
Et = [

j.

p0)](1 + ps)s . E0 (2.5)

Noting that ps and p0 are small, then we take logarithm of potential earning, we would
have:
lnEt = lnE0 + s.ps + p0.

j

(2.6)

Noting that experience of worker is calculated by the current age minus completingschool-age (t=T-s=X), we would have Mincer’s human capital earning function as
following:
lnW = α0 + ρs . S + β1 .X + β2 .X2 (2.7)
In which,
W as individual’s wage
S as schooling year
X as experience
Page | 7


Hence, ρs is the private return to education and β1 is represented for the relationship
between wage and experience.
The contributions from Garry Becker and Jacob Mincer are considered as the basic
conceptual framework for the modern human capital theory.

2.2 Returns to education:
Lucas (1988) developed human capital to a higher level by introducing the externalities
of human capital. In the first edition of Human capital, Becker (1962, p.45) also had a
sentence about this: “An emphasis on human capital not only help explain differences in
earning over time and among areas but also among persons or families within an area.”
According to these points of view, Nazier (2013) defined two types of return to education
as following:
-

Private return to education: the productivity that individual gains from his own
investment in human capital. This rate of return is usually estimated by the affect
of individual’s schooling years on his wage.

-

External return to education: the productivity, which spills over to the others in the
same firm, region, industry, or nation. In the other words, this type of return to
education is measured by estimating the influence of the share of skilled labor
force on the individual’s wage.

There are two main approaches estimating the external return to education, which is
distinguished by the scope of research:
-

At the macro approach:

By using the macro-level-data, this approach aims to use the differences of input factors
to explain the differences of productivity across countries (Canton, 2007). In this type of
approach, we could mention the working of Robert J. Barro in 1991 “Economic growth in

a cross section of countries”. Basing on the viewpoint of Nelson and Phelps in 1966,

Page | 8


which stated that a country with more human capital had tended to grow faster due to the
ability to catch up the new technologies, Barro (1991) estimated the relationship between
growth rate of Gross Domestic Product per Capita and school enrollment rate. In this
work, Barro used the data from Summer and Heston (1988) in 98 countries from 1960 to
1985. The school enrollment rate, which is used as the proxy for human capital, is
defined as the number of students that enroll for primary and secondary school to the
total population for the corresponding group of age. His finding implied that one percent
increased in the school enrollment rate led to the increase in growth rate of GDP per
Capita at 2.5% for primary level and 3% for secondary level. However, Barro found that
his analysis difficult to explain the performance in the below-average-growth-rate
nations. In addition, Krueger and Lindahl (2001) implied the omitted variables in the
macro approaching. For instance, each country has their own way in order to improve
educational system and the growth of economy concurrently; hence, this creates a
reasonable omitted variable in estimating the return to education for cross-country data.
-

At the micro approach:

According to the argument of Lucas (1988) about the existence of externality from
human capital investment and the human capital earning function, the second approach
estimate the external return to education through micro-level-data. The educational
externality is often estimated by the influence of the average number of schooling years
in a firm, a region, or an industry, where the individual lives in or works for, on his wage.
Strawinski (2008) stated that individual’s wage was explained by his schooling years
(private return), the average years of schooling in the related geographic area (external

return), and addition instrument variables. Some difficulties of this approach and the way
how the previous studies used to estimate the external return to education is discussed in
the next sections of this chapter.

Page | 9


2.3

External return to education:

2.3.1 Negative external return to education:
The external return to education possibly has negative value if the supply of skilled labor
exceeds the demand (Moretti 2004b). In this case, the number of high educational level
workers leads to the decrease of the average wage in labor market and create the higher
competitive pressure to the lower. However, the negative value of external return to
education is quite rare in the previous paper. For instance, the work of Acemoglu and
Angrist (2000) could be an example for negative external return to education.
Acemoglu and Angrist (2001) argued that city with higher average educational level may
also have higher wage. In order to solve this issue, they decided to estimate the returns to
education across the states in America due to the differences between the states from
1960 to 1990 in compulsory attendance laws and child labor laws. Using the combination
of these laws as instrument variables (CLSs) for human capital, they found a little
evidence for external return to education around 1-2% comparing to 7% in OLS
estimating. However, the interesting findings come from column 2 in table 11 in this
paper. there is the difference in external return to education when the authors allowed the
vary in year, the external return is 2.8% by using Child Labor Law as instrument and be
1.7% by using Compulsory Attendance Law as instrument. The external rate of return
become negative at 1.8% when the author used Child Labor Law as instrument, while this
rate of return is -3.00% when Compulsory Attendance Law instrument is adopted.

2.3.2 Positive external return to education:
The external return to education could be positive due to two following reasons:
-

According to standard neoclassical model, with the assumption that educated and
uneducated workers are imperfect substitutes, there would be a growth of
uneducated workers’ productivity when the proportion of educated workers is

Page | 10


increased. In addition, Katz and Murphy (1992) proved the assumption, which is
about imperfect substitutes of educated and uneducated workers.
-

The human spillover effect is existed (Moretti 2004b).

Rauch (1991) seems to be the first economist, who estimated the external return to
education basing on the argument about the sharing knowledge and skills when
individuals be working together. In this paper, he used the average education level in 237
Standard Metropolitan Statistic Areas (SMSAs) and the logarithm of hourly wage of
individuals in these areas to have the coefficient of external return to education at 2.8% in
the United State.
Moretti (2004) stated that the cities with higher proportion of educated labor force might
have higher level of unobserved ability. In order to control the differences among
individuals in this type of ability and the differences in returns to skill across cities,
Moretti used his owned version of National Longitudinal Survey of Youth (NLSY). He
also used the Census data in 1980 and 1990 to compare with the coefficient in NLSY and
these rates of return are similar. In the first-differenced IV estimates in Moretti’s work

stated that a one percent increases in the share of college graduates workers would lead to
the increase in wage for high-school drop-out at 1.9%, for high-school graduates at 1.6%,
for group did not finish college at 1.2% and at 0.4% for college graduates group. In the
early, almost economists focus on estimating the human capital externalities in one of the
most powerful economic country – the United State.
At the present, let us have a look on European economy. Strawinski (2008) did applied
Mincer’s Human capital earning function to estimate the external return to education in
Poland. By using main source of data from Households Budget Survey (HBS) in 1998
and 2005, Strawinski also contributed to the existence and significantly positive rate of
external return to education. Restricting empirical samples by age (16-65 for men and 1660 for women) helped to estimate external return in 1998-2005 period in the ordered of
0.7% to 1.3% for secondary education and 1.6% to 2.8% for tertiary level.

Page | 11


2.3.3 Empirical studies in developing countries:
Bakis (2010) studied the external return to education in Turkey by Household Labor
Survey in 2006, which just focused on workers in private sector. He stated that the rate of
return from human capital externalities is accounted for 2.4% with Instrument variablesOLS method and ranged from 1.3% to 3.5% with Instrument variables Quantile
regression methodology.
Liu (2007) seems to be the first economist who estimate the external return to education
in Chinese cities. There is an interesting in this paper; the rate of external return from
average schooling year across cities is range from 11% to 13%, while one percent of
college share increase lead to the raise in individual’s wage only about 1%.
In order to study the interesting findings from Liu (2007)’s work, Fan and Ma (2012)
decided to estimate external return to education in a different way, such as using
longitudinal data instead of cross-sectional data and estimating in both urban and rural
areas in China. In this paper, Fan and Ma use an instrument variable, named “211”,
which represents for the Project 211 from Chinese Government aims to “label” the
universities in this project as high-quality ones. As the result, the rate of eternal return to

education is range from 10% to 14% due to one percent increase in the share of college
graduates.
2.4

Chapter remark

This chapter provides the foundational literature for estimating external return to
education in this paper. While, the contribution from human capital theory and human
capital earning function is considered as the conceptual framework, the related studies
supply valuable lessons in order to improve the quality of analysis. Basing on the basic
Mincer’s Earning Function (equation 2.7), and the previous studies about external return
to education, this paper would used the following model to estimate the external return to
education in Vietnam, by adding the share of skilled workers in the local labor market
(Hjt):

Page | 12


LnWijt = αSijt + βHjt + ᵹXijt + μjt + ꞓijt (2.8)
In which,
Sijt is the schooling year of observation i in province/city j at t period.
Hjt is the share of skilled workers in province/city j at t period.
Xijt is vector of individual i characteristics, including (age, gender, marital status, and
type of school).
According to equation 2.8, the private return to education (α) is defined as the wage of
individual gaining by his owned schooling years, while the external return to education
(β) is considered as the increase of individual’s wage due to the proportion of educated
workers in his local area.
Fig. 2.4 Analytical framework
Mincer’s Human

capital earning
function

Human capital theory

Returns to education

Schooling years of
individual

Private return to
education

The share of skilled workers
in the labor market

External return to
education

Page | 13


Chapter 3: Research Methodology
This chapter is included six parts describe the steps using Mincer’s Human Capital
Earning Function (HCEF) to estimate the external and private return to education; and
how to calculate the variables and some arguments, which uses to apply the Instrument
Variables for controlling the differences across the cities/provinces. In the first section,
basing on introducing HCEF in chapter 2, I discuss how to apply this function in
estimating the external return to education. The second part discuss the endogeneity in
earning function. The next part provides the sources of dataset and the approach

calculating them in details. The fourth one supplies more Instrument Variables in order to
deal with the basic issues in earning function. In addition, the next part illustrates the
addition control variables, their definitions, and the way to calculate them. In the next
section, the whole sample used in this paper is described for two group, including
continuous and dummy variables. The final part of this chapter is chapter remark, which
helps to summarize the important information.
3.1 Research methods:
Moretti (2004) stated that the human capital spillovers would exist when we estimate the
influence of the share of educated or trained labor force on the worker’s productivity.
Basing on that statement and the basic Mincer Human Capital Earning Function (2.7) in
the chapter 2, I would add the share of local skilled human capital into the equation.
Hence, the external return to education across Vietnamese cities/provinces is estimated
by the influence of the local trained workers’ share on the individual’s hourly wage in
that region. Then, we have:
LnWijt = αSijt + βHjt + ᵹXijt + σPjt + μjt + ꞓijt (3.1)
In which,
LnWijt is the logarithm of hourly wage of individual i in province/city j at time t. When
Sijt stands for the individual’s schooling year, the α coefficient will explain for the private
return to education. Xijt is the vector of individual characteristics in province/city j at time
Page | 14


t, including Age, Gender, Married. Pjt denotes the provinces/cites characteristics. While,
μjt and ꞓijt are province/city error term and individual error term, respectively. β is
considered as the coefficient of capturing the effect of the skilled local human capital’s
share (Hjt), which is used to estimate the external return to education.
In this paper, the proportion of trained workers on the total working employees in
province/city j is consider as the proxy for the share of skilled labor force in that region.
According to Provincial Statistic Year Book (PSYB), trained worker has to satisfy two
conditions. Firstly, the worker has to be working in that region; even he comes from

another region. In addition, the worker is considered as trained when he has certification
or degree for short-term training, trade vocational, trade college, vocational school,
college, university and over from a school or any educational organization. This data
provides an advantage for my estimate due to its first condition. This condition helps to
solve the problem of migration instead of using the share of provincial educated people.
For instance, a worker could be graduated at his hometown, but he moves to another
province/city to work. Hence, the share of trained worker is prefer than the share of
educated people to be proxy for the share of skilled workers in the labor force (Hjt). In
this paper, the Educated variable is considered as the share of skilled workers (Hjt). Then,
model (3.1) becomes as following:
LnWijt = αSijt + βEducatedjt + ᵹXijt + σPjt + μjt + ꞓijt (3.2)

3.2 Endogeneity in Wage function:
In order to have a Best Linear Unbiased Estimates, there are 2 issues that I have to deal
with when using the earning function to estimate the returns to education. The first is
endogeneity of the individuals’ schooling years and the share of skilled human capital
(trained workers) across cites/provinces.

Page | 15