Economic openness, financial bias, and the urban–rural income gap

• Published date: 01 February 2024
• Author: Zuocheng Chen,Krishna P. Paudel,Stephen Devadoss
• Date: 01 February 2024
• DOI: http://doi.org/10.1111/rode.13052

REGULAR ARTICLE

Economic openness, financial bias, and the

urban–rural income gap

Zuocheng Chen

1

|Krishna P. Paudel

2

|Stephen Devadoss

3

1

College of Economics and Management,

Shihezi University, Shihezi, China

2

Resource and Rural Economics Division,

USDA Economic Research Service,

Kansas City, Missouri, USA

3

Department of Agricultural and Applied

Economics, Texas Tech University,

Lubbock, Texas, USA

Correspondence

Krishna P. Paudel,Resource and Rural

Economics Division, USDA Economic

Research Service, Kansas City, MO, USA.

Email: krishna.paudel@usda.gov

Funding information

National Social Science Fund of China,

Grant/Award Number: 15CMZ040;

Humanities and Social Sciences Program

of the Ministry of Education of China,

Grant/Award Number: 17XJJC790001

Abstract

We develop a theoretical model to analyze the impact

of economic openness and financial bias (i.e., open sec-

tor bias and urban area bias) on the urban–rural

income gap. Our theoretical model puts forth three

propositions, which we empirically test using a

dynamic panel data model. We use data from 30 prov-

inces in China spanning from 2004 to 2017 for our

empirical analysis. Our results reveal a U-shaped

relationship between the urban–rural income gap

and the level of regional economic openness. We also

find an inverted U-shaped relationship between the

urban–rural income gap and the open sector bias of

regional financial development. Moreover, our analy-

sis indicates a positive relationship between the

urban–rural income gap and urban area bias in

regional financial development. These three proposi-

tions hold true for both nominal and real measures

of the income gap.

KEYWORDS

China, economic openness, financial bias, urban–rural

income gap

JEL CLASSIFICATION

J61, O18, P25, R23

The findings and conclusions in this paper are those of the authors and should not be construed to represent any official

USDA or U.S. Government determination or policy.

Received: 9 September 2021Revised: 4 September 2023Accepted: 5 September 2023

DOI: 10.1111/rode.13052

© 2023 John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public

domain in the USA.

242 Rev Dev Econ. 2024;28:242–263.

wileyonlinelibrary.com/journal/rode

1|INTRODUCTION

This paper aims to understand the impact of economic openness and financial bias

1

(open sec-

tor bias and urban area bias) on the urban–rural income gap. We develop a theoretical model

and estimate the empirical model using the system generalized method of moments (system

GMM) based on the data collected from 30 provinces in China from 2004 to 2017.

Since the publication of Simon Kuznets' seminal paper on poverty and income equality in

1955, income inequality has been an oft-discussed topic in the development economics litera-

ture (Deaton, 2010; Eika et al., 2019; Greenwood & Jovanovic, 1990; Kuznets, 1955). Due to the

distinct dual economic structure (urban and rural) in developing countries, income inequality

is reflected in the urban–rural income gap. With the advance of reform and opening up of the

market in 1979, China's economy developed rapidly and became the world's second-largest

economy in 2010. In 2019, 40% of Chinese households (corresponding to 610 million people)

had an annual per capita income of 11,485 RMB.

2

At the same time, the per capita disposable

income of residents in urban areas was 30,733 RMB. The per capita income discrepancy and

overall poor living conditions in rural areas have been the source of contention among rural

residents.

With China being one of the largest manufacturing countries in the world, the rural labor

force has been temporarily migrating to work in urban areas. Since the implementation of the

reform and opening up policy, the Chinese government has set up a large number of special

economic zones (SEZs), such as state-level economic and technological development zones,

bonded areas, export processing zones, border trade zones, and, since 2013, the free trade zones

(Hu, 2020). The implementation of the SEZ policy has promoted the development of China's

trade and the inflow of foreign direct investment (FDI) and accelerated the development of an

open economy. Wang (2013) finds that SEZ program increases FDI, achieves agglomeration

economies, and increases wage rates. There are differences between urban and rural areas, as

well as between the open sector (that receives FDI) and the closed sector (that does not receive

FDI) in the ability to both obtain financial resources and the way to use financial resources.

The increased flow of financial resources to urban areas determines the difference in the ability

of the real economy to obtain financial services between urban and rural areas, resulting in

divergent levels of economic growth between urban and rural areas (Johansson & Wang, 2014).

The allocation of financial resources in open sectors is beneficial to local employment in urban

areas. With the increasing urbanization, rural laborers migrate to urban areas and find employ-

ment opportunities. Economic openness and allocation of financial resources in different

regions and sectors determine the disparity of economic development and income gap between

urban and rural areas.

Previously, researchers have studied the impact of economic openness and financial bias

on the urban–ruralincomegap(Binkai&YifuLin,2014; Chintrakarn et al., 2012;Herzer&

Nunnenkamp, 2013; Jauch & Watzka, 2016;Lin&Fu,2016;Suetal.,2019). We advance

the literature by examining the mechanism of economic openness and financial bias on the

urban–rural income gap by studying the impact of the allocation of financial resources

between rural and urban areas and between closed and open sectors on the urban–rural

income gap. We thus contribute to the existing literature by integrating economic openness

and financial bias into the same analytical framework to study their impact on urban and

rural income. We accomplish this goal by developing theoretical and empirical models to

understand the impact of economic openness and financial bias on the urban–rural income

gap in China.

CHEN ET AL.243