Technical change and income inequality in China

• Author: Chen Wang,Zhi Luo,Guanghua Wan,Xun Zhang
• Date: 01 November 2017
• DOI: http://doi.org/10.1111/twec.12531
• Published date: 01 November 2017

SPECIAL ISSUE ARTICLE

Technical change and income inequality in China

Xun Zhang

1,2

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Guanghua Wan

3

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Chen Wang

4,5

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Zhi Luo

6,7

1

School of Statistics, Beijing Normal University, Beijing, China

2

Shanghai Finance Institute, Shanghai, China

3

Asian Development Bank, Manila, Philippines

4

School of Urban and Regional Science, Shanghai University of Finance and Economics, Shanghai, China

5

Leiden University, Leiden, The Netherlands

6

Economic Development Research Center of Wuhan University, Wuhan, China

7

Economic and Management School of Wuhan University, Wuhan, China

Funding information

This paper was funded by Bairen Program of Yunnan Province and the NSF Projects 71133004, 71373186 and 71603026

of the National Natural Science Foundation of China. It was also funded by Projects 15ZDA027 and 16ZDA006 of the

National Social Science Foundation of China, Major Program of Minister of Education, China (Project ID 13JZD008),

Projects 2015M580055, 2016M591645 and 2016T90048 of the China Postdoctoral Science Foundation, and Youth

Scholars Program of Beijing Normal University, and Shanghai Pujiang Program (Project 17PJC045).

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INTRODUCTION

There is a sizable and growing literature, focusing on the determinants of income inequality (e.g.

Gottschalk & Smeeding, 2000; Greenwood, Guner, Kocharkov, & Santos, 2014; Lerman & Yitz-

haki, 1985; Li, Squire, & Zou, 1998; Piketty & Saez, 2003). And more and more research atten-

tion has been directed towards the role of technical change as a major driver of income

distribution (Acemoglu, 1998).

The conventional approach to analysing the technology–inequality nexus is to identify and esti-

mate the impacts of technical change on the wage gap between skilled and unskilled labour, typi-

cally in terms of the difference in the average income between these two groups of labourers.

According to Acemoglu (1998) and Katz and Murphy (1992), new technologies lead to increases

in the productivity of skilled workers and their wages, enlarging this wage gap. Krusell, Ohanian,

R

ıos-Rull, and Violante (2000) argue that improvement in capital-embodied productivity leads to

rising demand for equipment and, when equipment is complementary with skilled labour, the wage

gap rises. This gap-enlarging finding has been confirmed by many scholars, including Aghion,

Howitt, and Violante (2002), Esquivel and Rodrıguez-L

opez (2003), Moore and Ranjan (2005),

and Van Reenen (2011). On the contrary, Goldin and Katz (1996) found that this gap was kept in

check in the USA despite significant technological progress. Card and DiNardo (2002) concluded

that wage inequality measured as the standard deviation of log wages and the 90th and 10th per-

centile wage gap was stable in the 1990s in the USA despite advances in computer technology.

DOI: 10.1111/twec.12531

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©2017 John Wiley & Sons Ltd wileyonlinelibrary.com/journal/twec World Econ. 2017;40:2378–2402.

However, wage inequality, especially the wage gap between skilled and unskilled labours, is only

one component of the overall inequality, notwithstanding its importance. By definition, total inequal-

ity can be expressed as a weighted sum of labour income and capital income (CI) inequalities.

1

On

the other hand, a driver of income distribution such as technical change may generate different

impacts on the overall inequality than its components. For example, an anti-discrimination policy

may help narrow the gender gap but may lead to higher wage inequality within male employees at

the same time. Similarly, capital-augmenting technical change may enlarge the wage gap between

the skilled and unskilled but could meanwhile help reduce inequality within the capitalists, leaving

its overall impact on the overall income inequality underdetermined. Clearly, it is insufficient to just

analyse the technical change–wage gap nexus if one is interested in the overall income inequality.

To the best of our knowledge, little has been published on the technical change–income inequal-

ity relationship, with the exception of Jaumotte, Lall, and Papageorgiou (2013) who found a positive

impact of technological progress (defined as the share of ICT capital in total capital stock) on income

inequality based on a panel data set of 51 counties over a 23-year period from 1981 to 2003.

This paper represents an early attempt to gauge the impact of technical change on the overall

inequality, not just a particular component of inequality. This is achieved by establishing that the

labour share of income is negatively correlated with overall inequality as indicated by the popular

Gini coefficient, and by modelling the labour share of income as a function of technical change.

Based on 1978–2012 provincial panel data from China, the framework of Acemoglu (2002, 2007)

will be employed to measure technical change. And the labour share of income will be then

regressed on the estimated technical change. The main empirical results show that technical change

in China had been mostly capital-biased. It contributed to the successive reductions in China’s

labour share of income and thus rapid rises in income inequality.

The rest of the paper is organised as follows. Section 2 presents our analytical frameworks,

including arguments for establishing the correlation between the labour share of income and tech-

nical change and that for measuring technical change. In Section 3, we discuss data and empirical

econometric models. Section 4 provides estimation results and discussions. Finally, Section 5 con-

cludes.

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ANALYTICAL FRAMEWORKS

2.1

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Inequality, technical change and the labour share of income

To establish the relationship between technical change and inequality, the usual econometric

approach would require specifying and estimating model (1):

Ine ¼hðTech;ZÞ;(1)

where Ine denotes an inequality indicator, Tech denotes technical change, and Zdenotes control

variables. Unfortunately, sufficient observations on inequality are not available from China to per-

mit estimation of the above econometric model.

However, it is still possible to explore the impact of technical change on inequality by analys-

ing the relationship between labour share of income and technical change. This is because the

overall inequality as indicated by the popular Gini index can be expressed as a weighted sum of

concentration indices of labour and CI, with labour and capital share of income as weights. Thus,

1

Here, the inequalities are indicated by concentration indices.

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