The Variation Tendency Analysis on Contribution Rate to Economic Growth by Produ

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Abstract

The economic growth is influenced by two factors， input of production factors and social production efficiency. The input of production factors is the premise and foundation of economic growth. The previous academic studies generally use the production functions to derive quantitative description of contributions and all kinds of production factors input have contributed to economic growth. Based on translog production function， measuring the contribution rate of capital， labour and energy to economic growth in China， will absolutely be able to reveal their variation trendencies， as well as the changing path of the growth model in China.

Key words： Production factors； Production Functions； Contribution rate； Variation tendency

INTRODUCTION

With the successive deepening of reform and opening up， China has achieved economic bloom and high increasing rates of development. In this process， all kinds of production factors input structure， organization and allocation efficiency are adjusted and changed profoundly. The contributions to economic growth exhibit shifting tendencies. A scientific analysis on these tendencies may help us understand the driving force of “China style” economic growth and comprehend its inherent characteristics. In this circumstance， the present study analyzes the contribution rate of the variation tendency on economic growth.

1. ANALYSIS ON ECONOMIC GROWTH INFLUENTIAL FACTORS The economic growth is a macro and long-term development concept， usually referring to increasing amount of total output for a specific country or region in a certain period. The economic growth is related to many aspects， such as social production， consumption and distribution. It is also influenced by many factors， like economy， politics， culture and environment. These factors that mutually cross and permeate have comprehensive impact on the entire social and economic development process. It will affect not only the quantity but also the quality of economic growth. Therefore， the research on the influencing factors of economic growth has always been a hot topic in the field of economics.

Having been referring the economic growth

experiences and features of developed capitalism countries， the receiver of 1971 Nobel Prize in Economics Simon Kuznets pointed out that the economic growth influential factors for one nation include 3 aspects， which are the increment of knowledge stock， improvement of production efficiency and industry structure modification（Barro & Martin， 2012）.

The receiver of 1987 Nobel Prize in Economics Robert Solo summarized the factors of influencing economic growth to three aspects： capital， labor force and technological progress， having a particular emphasis on the key role of technological progress on economic growth.

2.4. The Translog Production Function （TPF function）

Due to that the above production function are established under certain assumptions of input factors substitution elasticity， so it has significant limitations in practical application. Regarding this problem， in 1973 Christensen， Jorgenson and Laura proposed a more general production function that has variable elasticity of substitution： Translog Production Function， the basic form is：

The ridge trace and goodness of fit corresponding to Table 2 are shown in Figure 1 and 2. It can be seen from Figure 1 that with the increment of K value， the standardized regression coefficient of each independent variable approaches stable. when K=0.1， it is basically stable， and it is not shown in Figure 1 that the regression coefficient changes with K’s value increment.

It can be seen from Figure 2 that as the increment of K value， the coefficient of determination of regression equation （RSQ） gradually decreased. In addition， when K≥ 0.1， it approaches to be stable， after which no obvious fluctuation appears. So the 3 points before which it approaches to be stable are selected. Their average value K =0.15 is regarded as ridge coefficient for regression analysis， the results shown in Figure 3 and 4.

The Figure 3 shows that the determinant coefficient of regression formula（Adj RSqu）is 0.9818， close to 1， when K=0.15. This implies the equation fits better. The F statistic value of significance test of regression equation is 67.0345， the corresponding value of P is 0.01478

The Figure 4 shows that B is the non-standardized regression coefficient when K=0.15， the SEB is standard errors， B/SEB is the T value of regression coefficient significance test. The ridge regression formula derived from that Figure is followed：

InY=-2.4840+0.1247InK+0.2940InL+0.1615InE+0.00 49（InK）2+0.01304（InL）2+0.0067（InE）2+0.0105InK・InL+0. 0059InK・InE+0.01291InL・InE

Checking T value distribution table， when the freedom is 9， α=0.01presents T=3.36， Figure 4 shows each regression coefficient T test values are all bigger than 3.36， it implies the regression coefficient is significant under the level α=0.01.

4.3 Contribution Rate Estimation

Each factor’s output elasticity can be calculated by using the formula 2-4. Each factor’s annual input growth rates are calculated according to Table 1， then the contribution rate to economic growth of each factor can be calculated by using formula 7-8， the results are below：

The Development Variation Tendencies of contribution rate of Capital， Labor Force and Energy during 2001-2011 are shown in Figure 5.

It can be seen from Table 3 and Figure 5， the capital contribution rate basically maintained stable in the years of 2001-2007， with an average value 42.59%. In 2008， it rose to nearly 60%， its peaking value 88.19% appeared in 2009. It indicates that the 4，000 billion investment plan implemented in China during the past two years has played a key role on overcoming the negative influence of international economic crisis and maintaining the sustainable and rapid economic growth. Regarding the situation in 2010 and 2011， the capital contribution rate showed a declining trend， but maintained above 55%， the average value was 56.56%， which was about 14 percentage points higher than the average level before the crisis. It may indicate that in the post-crisis time， the investment driving effect on China’ economic development will be further strengthened maintaining the high level comparing to the level prior to crisis. The labor contribution rate showed a downward trend continued in the years 2001-2011， getting down from 9.65% in 2001 to 2.64% in 2011， compared to Table 1， in which the total labor force has increased year by year， it can be seen that simple expansion of labor supply does not behave promoting economic growth. In 2003， the energy contribution rate reached the maximum 61.08% ， then it decreased year by year， reaching its lowest value 15.82% in 2008， after which， it presented the upward trend of shock. The residual value of contribution rate besides the input factors （referred as the efficiency contribution rate） occurred significant fluctuation in 2001-2011， had negative values in some years. The reasons may be decline of production efficiency due to low utilization ratio of input factors， the error resource allocation， and weakness of management level. In addition， as it can be seen from Figure 5， it has reverse fluctuation tendencies between the energy contribution rate and efficiency contribution rate. It notes that the energy utilization efficiency occupies an important position in the total social production efficiency.

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