Structural Transforamtion And Evolution Under The Change Of Economic Growth In China

This paper focus on economic activity and resource of three sectors in China and its provinces.The manufacturing share of 21 countries (both developed and developing) from 1800 to 2010 is an sign for the turning point of structural transformation and that share of employment is always beyond output. When a country's per capita GDP is 6413 international dollars, its industrial employment share climb to the inflection point of the hump shape while the value added share is 7133 international dollars. As a developing country, China is from the "industrial" to "de-industrialization" stage with the industry share exceeds the inflection point of inverted U-shaped measured by output and employment. At the same time, China's structural transformation of agriculture has not finished yet. China's structural transformation demonstrates a composite Kuznets-type. Since the reform and opening up, the 31 provinces'structural transformation is different. The turning point of the industrial employment share was 24,343 Yuan per capita GDP (in 1978 prices). Until 2012 only Tibet, Gansu, Guizhou and Yunnan haven't reached. The industrial output share is 63,850 Yuan (1978 price). Three municipalities Beijing, Shanghai and Tianjin have achieved until 2012. In addition, only the same three cities complete structural transformation in agriculture.Aims to regional difference, this paper estimates the three sectors' capital stock of 31 provinces from 1978 to 2012 on the basis of variable depreciation rate under the accounting framework of the national economy by perpetual inventory method. By the method of Stochastic Frontier Analysis model to evaluate TFP.1. Measured by efficiency parameters, the primary industry was accompanied by increased efficiency since the reform and opening up while the secondary and tertiary industry increased. 2. Infrastructure, marketization, government actions and openness, as four factors of inefficiency have different effects in opposite directions on the three sectors.3. When decompose TFP growth into three parts including technological progress, the technical efficiency growth and allocate efficiency growth, the last one is dominant and impedes TFP growth of the same sector.4.There are systematic divergences in TFP growth rates across sectors which appear to be consistent with what is needed to obtain the observed reallocation of employment out of agriculture and manufacturing into the service sector. Both manufacturing and service sector have more obvious granger causality between TFP growth or their differences and structural transformation.Under the law of diminishing returns on capital and labor productivity and technology spillover to raise the capital return, the rate of return on capital of three sectors among 31 provinces are all declined with a L-shaped downward trend with the growing of capital stock, which is in accordance with Kaldor Facts. However the total capital return of the economy both in China and regions are like U-shape as time goes in other studies. The differences between prices of capital goods and output are the main factor fluctuating the actual return rate of capital. In terms of capital allocation efficiency, agriculture is effective. Manufacturing is independent of capital allocation. Service is relatively mismatched. There are many factors that influecing capital return:TFP growth, capital-output ratio, structural changes (the output share and employment share), the market and government behavior. Wherein, TFP growth through technological progress and technical efficiency effect on capital return through vary aspect; capital-output ratio and output shares are in favor while employment share, market and government behavior is not conducive.Under different endowment structure and certain endowment conditions, how to upgrade industrial structure through technology choice? By constructing a model among technology choice, industrial upgrading and economic growth on regional panel data, we found that:technology choice index has distinctive effect on three sectors. It will promote agriculture 0.21 percentage points and manufacturing 0.36 respectively with 1 percentage point of technology choice. However technology choice dose not affect the service sector. Provinces are supposed to adopt different strategies on institutional arrangements to accelerate capital deepening, higher capital-labor ratio and increasing labor productivity, according to their own natural resources and structures.