Education stock and its implication for income inequality: The case of Asian economies

• Author: Noman Arshed,Muhammad Shahid Hassan,Samra Bukhari,Awais Anwar
• Date: 01 May 2019
• Published date: 01 May 2019
• DOI: http://doi.org/10.1111/rode.12585

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wileyonlinelibrary.com/journal/rode Rev Dev Econ. 2019;23:1050–1066.

© 2019 John Wiley & Sons Ltd

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INTRODUCTION

The persistent rise in inequality around the globe over the last two decades from 1990 to 2010 led to

a growing concern for worldwide policy makers (Wolf, 2015). The unequal distribution of economic

resources among individuals and flow resources that are increasing between the rich and the poor is

known as income inequality (Todaro & Smith, 2015). Oxfam (2017) reveals that 1% of the richest peo-

ple owned more wealth than the rest of planet. However, the income of the poorest 10% has increased

DOI: 10.1111/rode.12585

REGULAR ARTICLE

Education stock and its implication for income

inequality: The case of Asian economies

NomanArshed1

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AwaisAnwar2

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Muhammad ShahidHassan1

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SamraBukhari1

1Department of Economics,University

of Management and Technology, Lahore,

Pakistan

2Department of Economics,The University

of Lahore, Lahore, Pakistan

Correspondence

Awais Anwar, Department of Economics,

The University of Lahore, Lahore,

Pakistan.

Email: awaisanwar007@yahoo.com

Abstract

This study develops a quadratic relationship between educa-

tion and income inequality among Asian developing econo-

mies for the period from 1960 to 2015. Panel cointegration

and fully modified OLS is applied for the estimation of

long- run coefficients. The results show that initial, primary,

secondary, and tertiary enrollment increases inequality.

However, the effect of education on income inequality be-

comes negative after a certain threshold level (i.e., 97.5%

for primary, 43.5% for secondary, and 11% for tertiary).

Thus, this result proves the Kuznets phenomenon of an in-

verted U- shape relationship for primary, secondary, and ter-

tiary enrollments.

JEL CLASSIFICATION

I24, O15, O53

KEYWORDS

education—income inequality Kuznets curve, panel cointegration, panel

FMOLS

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ARSHED EtAl.

by only $3.00, while the income of the richest 1% have increased 182 times. According to a study of

Vietnam, the richest man earns more in a day that a poor man earns in 10 years. Byanyima (2017)

confirms that 35 people have more resources than 50% of the world's population. However, the pres-

ent situation shows that eight richest people own the same amount of resources as 50% of the world's

population. Benabou (1996) shows that half of the world's poor population owns less than 10% of its

wealth. However, several studies indicate that developing economies can reduce income inequality by

increasing the level of education in their country. Housing prices in urban areas are erratic in the EU

because of increasing income inequality (Inchauste, Karver, Kim, & Jelil, 2018), which is the highest

over the past three decades from 1980 to 2015 (Cohen & Ladaique, 2018).

Development theory mainly discusses the disparity in income, health, education, and living stan-

dards among people. People cannot reach their optimal potential, so they cannot obtain more resources

(Harriet, 2010; Hills, 2010). Furthermore, inequality only favors those who have political muscle,

while creating negative externalities for others (Krueger, 2018). At the initial stages of development,

inequality increases owing to an increase in economic growth stemming from increases in the in-

comes of people in business, but as time passes, the trickle- down effect reduces the level of inequality

because businesses pay higher taxes, which are then spent on public goods, or because they demand

more labor for expansion. This implies a nonlinear relationship between income inequality and devel-

opment. This phenomenon creates a difficulty in handling economic growth for countries that have a

low level of development (Aghion, Caroli, & Garcia- Penalosa, 1999; Kuznets, 1955).

However, changes in economic growth are not the only factors that affect income inequality; skilled

and nonskilled labor forces also exhibit certain variations in income inequality. A higher proportion

of a skilled labor force will reduce wage premiums because of the shortage of labor that decreases the

level of wage inequality (Krueger, 2018; Lakner & Milanovic, 2016; Loayza & Raddatz, 2010; Ruffin,

2009). Additionally, there are two main scenarios: one is the scenario of education, and the other is the

scenario of noneducation. An increase in education will lead to a decrease in the income of a country

because of technology and innovation. This scenario will decrease the difference between wages and

reduce income inequality. Hopefully, the proportion of skilled labor force is likely to rise up to 45.5%

by 2030 around the globe. Furthermore, an increase in unskilled labor affects the schooling and ageing

of the population in the world labor market, and this will lead to an increase in the skill premium in

the scenario of increasing education and, thus, further increase the income inequality (Dixon, 2003).

The objective of our study is to present a dynamic panel investigation of the effect of education

on income inequality. This objective was selected because an increase in the skilled labor force will

decrease the level of income inequality, but in the case of Asian economies, income inequality and

skilled labor force have been increasing for the past few decades from 1995 to 2014.1 A key feature of

our study is the inclusion of a specific level of education and its impact on income inequality because

the skilled labor force includes those people who attain 9, or more than 9, years of education.2 In

our model, we also include the labor force that attains less than 9 years of education. The study also

investigates the law of diminishing returns and its implications for the education and income inequal-

ity relationship, bearing in mind the specific levels of education. The effect of education on income

inequality has been studied by many researchers (Arshed, Anwar, Kousar, & Bukhari, 2018; Barro,

1999; Checchi, 2001; Gregorio & Lee, 2002; Karim, 2015), but they did not discuss different levels of

education and the implication of the law of diminishing returns. They also did not incorporate specific

levels of education in their respective econometric models. This study tries to bridge this gap.

The study is organized as follows. Section 2 presents the literature review. Section 3 describes the

data and presents the explanation of the estimation results, which contribute a deeper understanding

of how education affects income inequality in Asian developing countries. Section 4 concludes the

study with policy implications.