Growth versus equity: A CGE analysis for macroeconomic policy mix for the sustainable economic growth

With factor-biased technical progress described as labor-saving and skill-biased technical changes (SBTC), there are concerns that technological innovation can lead to unemployment and widen inequality in the economy. In the previous work, we have tried to explore 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 showed that technological innovation contributes to higher level of economic growth with productivity improvements. However, our analysis suggested that economic growth accompanied by skill- and capital-biased technical progress disproportionately could increase demand for capital and high-skilled labor over skilled and unskilled labor. This shift in the value-added composition has been found to deepen income inequality, as more people in higher income groups benefit from skill premium and capital earnings. Our analysis implies that technological innovations pose both opportunities and challenges in the society. Over the past four decades, technological advances have driven productivity upward, improved living standards, and stimulated economic growth by creating new growth opportunities. However, economic growth accompanied by rapid technological change has brought new challenges in terms of employment and inequality. In this regard, our analysis demonstrates that economic growth driven by technological innovations does not automatically benefit everyone in a society. We have found that technological advances may create both winners and losers via SBTC, and by capital-biased technical change. A substantial increase in inequality does not bode well for social and political stability, and faster technological progress may intensify a strong relationship between technological change and higher inequality. Therefore, there should be faster adjustments by policymakers and institutions to address the risk of job polarization (by replacing skilled and unskilled jobs with higher skilled jobs) and the income inequality behind innovation-driven economic growth. In this paper, we use a CGE model to quantitatively assess the macroeconomic impacts of technological innovation on employment structure and economic growth, furthermore to explore the role of macroeconomic policy-mix for ensuring both the quality of economic growth and the quantitative expansion. It is important to incorporate innovation-related activities (e.g., research and development activities) and characteristics of knowledge (e.g., knowledge accumulation and positive 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 construct the knowledge-based CGE model by adding R&D descriptions and characteristics of knowledge, with a series of equations based on a knowledge-based Social Accounting Matrix (SAM). It is also essential to classify the labor into occupational categories by skill level, to examine the changes in employment structure arising from technological innovations via SBTC and capital-biased technical change. From this perspective, the labor input for production of final goods and knowledge production is classified into three types of labor, based on the educational attainment level: high-skilled, skilled, and unskilled labor. Furthermore, households are classified into 20 quantiles, based on income levels, using micro data of household level survey data-sets to investigate the income distribution impacts arising from changes in employment structure. There should be faster adjustments by policymakers and institutions to address the risk of job polarization (by replacing skilled and unskilled jobs with higher skilled jobs) and the income inequality behind innovation-driven economic growth. The policy implications, in terms of employment and inequality challenges posed by technological innovations, can be summarized as the need to adopt a broad perspective when preparing policies dealing with these issues, rather than just focusing on labor market measures. In this regard, we propose policy recommendations in various dimensions, ranging from employment policy to fiscal policy. And we are considering those policy recommendations proposed by previous works as policy scenarios for this study as described below. Firstly, in terms of employment policy, we can consider policy scenario of investments in educational programs which focus on "up-skilling" and "re-training". This policy scenario enables workers to keep their competences in quickly adjusting to the rapid technological changes, SBTC. Those educational programs also can facilitate smooth transitions of workers, either from unskilled to skilled workers or from skilled to high-skilled workers, in line with changing labor market demand. Secondly, in terms of fiscal policy, problems of income polarization and inequality, driven by factor-bias technological change, can be mitigated with supplementary fiscal policy instruments. From the previous analysis, it is found that people at higher income groups are relatively more engaged in high-skilled works and benefit more from higher levels of skill premium and capital earnings than do other groups of people. In this context, policy instruments related to tax and transfer mechanisms can play important roles in achieving redistribution of income and reducing inequality. For example, increase in tax rates for capital earnings and progressive labor income taxation could be policy options for resolving income inequality problems. 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. In addition, from this study we expect that we can highlight the role of the policy-mix for ensuring the sustainable economic growth driven by technological innovation, which captures both the qualitative and quantitative economic growth.