Elsevier

Economic Modelling

Volume 60, January 2017, Pages 424-438
Economic Modelling

Growth versus equity: A CGE analysis for effects of factor-biased technical progress on economic growth and employment

https://doi.org/10.1016/j.econmod.2016.10.014Get rights and content

Highlights

  • We examine the economy-wide impacts of factor-biased technical changes, focusing on the Korean economy.

  • Simulation results from a CGE model are analyzed in terms of economic growth, employment, and distribution.

  • Results show that technological innovation drives economic growth with productivity improvements.

  • However, it is found that factor-biased technical change increases demand for capital and high-skilled labor.

  • A shift in the value-added composition is found to increase income inequality in the economy.

Abstract

With factor-biased technical progress described as labor-saving and skill-biased technical changes, there are concerns that technological innovation can lead to unemployment and widen inequality in the economy. This study explores impacts of factor-biased technical changes on the economic system in terms of economic growth, employment, and distribution, using a computable general equilibrium (CGE) model. The results show that technological innovation contributes to higher level of economic growth with productivity improvements. However, our analysis suggests that economic growth accompanied by skill- and capital-biased technical progress disproportionately increases demand for capital and high-skilled labor over skilled and unskilled labor. This shift in the value-added composition is found to deepen income inequality, as more people in higher income groups benefit from skill premium and capital earnings. Our results suggest that policymakers should prepare a wide range of policy measures, such as reforms in educational programs and taxation systems, in order to ensure sustainable growth.

Introduction

During the 1990s and the early 2000s, many countries have witnessed “jobless growth”, in which economies experience economic growth while decreasing their employment levels. In other words, increases in economic outputs come predominantly from higher productivities of already employed workers, rather than from the expansion of the labor force. As a result, the unemployment rates show high levels for a prolonged period, despite economic growth (Usanov and Chivot, 2013). There are concerns that jobless growth is not just a cyclical phenomenon, but also a structural problem driven by technological progress.

Brynjolfsson and McAfee (2014) pointed out that firms in the United States (U.S.) did not expand hiring after the Great Recession (from 2007 to 2008), and U.S. economy having shown the highest unemployment rate since the postwar period. They stated that wide deployment of new technologies (i.e., digital technologies) is one of the most important driving forces behind higher structural unemployment rates in recent years. They also argued that newly adopted machines and automation devices with higher productivity replace workers, leading to slower job creation. Furthermore, they describe this situation as being one in which many people are losing the race against the machine (Brynjolfsson and McAfee, 2014, Brynjolfsson and McAfee, 2012). Stiglitz (2014) also argued that productivity improvements from technological innovations in U.S. manufacturing sectors have been coupled with decreased employment and wages, leading to the current economic slowdown.

Concerns about the role of the technological innovations behind the labor market are not new. The Industrial Revolution during the late 18th and early 19th centuries initially drew attention to the relationship between technology and the labor market, as English workers lost their jobs to newly developed machines during that period (Bessen, 2015, Katz and Margo, 2013). Debates on the impacts of technological innovations on the labor market and employment have been sparked again in recent years, with the advent of emerging technologies, such as robotics and artificial intelligence. However, there are substantial disagreements between studies, in relation to the effects of innovations and technological progress on employment structure.

As described above, there is a growing body of evidence from studies about “technological unemployment” that rapid advances in technologies, and productivity improvements from technological innovations, displace many workers (Marchant et al., 2014). Those studies highlight the fact that technological unemployment in recent years is deeply associated with job polarization and social inequality in the economy. For example, Usanov and Chivot (2013) found that the digital revolution has favored high skilled labor, as technical progress usually replaces tasks traditionally carried out by unskilled labor. Mallick and Sousa (in press) also uncovered a positive relationship between technological progress and the skilled–unskilled labor ratio, which supports “skill-biased” characteristics of technical change. From these findings, they stressed that divergent trends in the wages for skilled and unskilled labor is a main contributor behind income inequality in recent years.

Another strand of the studies emphasized that technological unemployment is the direct effect of technological progress, and that indirect effects or compensating mechanisms should be considered, to fully understand the employment impact of technological change in the economic system (Piva et al., 2005). Vivarelli, 2014, Vivarelli, 2012 highlighted the fact that, from a macroeconomic view, technological progress has several second-order effects on employment, such as income (increase of income) and price mechanisms (decrease of commodities' prices), which could counterbalance direct employment impacts of technological change (i.e., technological unemployment). Those studies suggest that final impacts of innovations on employment can vary, depending on various economic factors in macroeconomic conditions.

In this regard, from an economy-wide perspective, it is essential to consider both direct effects of technological change and market compensation forces in analyzing final outcomes of technological progress in terms of employment. Despite a growing body of theoretical literature on the employment impact of technological change, there is a lack of quantitative analysis for this issue. In particular, most quantitative studies have focused on direct effects of new technologies on the number of employees and on wages, based on the firm- and industry-level analysis.

This study aims to quantitatively assess the macroeconomic impacts of innovations on employment structure and economic growth, with an economy-wide aspect, using a computable general equilibrium (CGE) model. We focus on the economy in Korea (South Korea), and simulation results for policy scenarios are analyzed in terms of employment structure, economic growth, and social inequality. For the analysis, we reflect innovation-related activities (i.e., endogenously determined R&D investments), characteristics of knowledge (i.e., spillover effects from knowledge accumulation), and factor-biased technological change in the model. The economic intuition behind these methodological approaches is that current labor-saving and skill-biased technological change from innovations shapes the employment structure in the economy by interacting with market mechanisms. Our study is significant, in that it is devoted to a macroeconomic analysis in investigating the link between technological innovations and the labor market, with understanding of both direct and indirect effects of technological change on the economy.

The rest of the paper is structured as follows: Section 2 provides a brief review of the relevant literature, which focuses on the relationship between innovations and employment; Section 3 contains general descriptions of the CGE model used for the analyses; Section 4 explains the scenario settings; the main results are presented in Section 5; and, lastly, the summary and concluding remarks are provided in Section 6.

Section snippets

Literature review

Technological innovations are deeply involved in the issue of growth and distribution, which are like two sides of a coin. Although technological innovation promotes economic growth via productivity improvements, it may favor skilled labor over unskilled labor, which may gradually worsen the labor market conditions. This “factor-biased” technological change suggests a rise in the skill premium, which implies a growing income gap between workers. Research into the relationship between

CGE modeling

In this paper, we use a CGE model to quantitatively assess the macroeconomic impacts of innovations on employment structure and economic growth. It is important to incorporate innovation-related activities (e.g., research and development) and characteristics of knowledge (e.g., knowledge accumulation and knowledge spillover effects) into the CGE model, in order to represent the indirect employment effects resulting from spillover effects of innovation and compensation. In this context, we

Implementation of factor-biased technological change

Model settings, as discussed in the previous chapter, give us information on methodological foundations to examine direct and indirect employment effects arising from innovations. A brief representation of the methodological approaches is shown in Fig. 1. To account for indirect effects of technological innovations on employment structure, we have introduced R&D investments and characteristics of knowledge into our model. In this context, the CGE model is set up so that spillover effects from

Effects on the employment structure

Based on the scenario settings presented in the previous section, we firstly examine the changes of aggregate labor demand by scenario type. Table 4 illustrates the rate of changes in the aggregate labor demand between a base year (2010) and a target year (2030) for each scenario, as well as the changes of aggregate employment levels in 2030 relative to the BAU scenario (for SCN1 and SCN2 scenarios). Market clearance conditions of the general equilibrium framework imply that flows of goods and

Discussion and conclusions

In this paper, we examined the economy-wide effects of innovation, in terms of employment structure and economic growth. For the analysis, we used the knowledge-based CGE model, focusing on the Korean economy. To incorporate characteristics of innovation within the CGE framework, R&D investments and knowledge capital stocks were introduced in the model. The knowledge stock as one of the factor inputs is assumed to be accumulated through R&D investments. In addition, to deal with spillover

Conflicts of interest

The authors declare no conflict of interest.

Acknowledgments

The authors are grateful to the editor and two anonymous reviewers who dedicated their considerable time and provided valuable comments and suggestions to improve the quality of the paper. This work is supported by the National Research Foundation of Korea Grant, funded by the Korean Government (NRF-2013R1A2A2A03014744).

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