In order to analyze the influencing factors of TFP (the abbreviation of Total Factor Productivity) deeply, this paper calculates and decomposes the TFP of the main grain producing provinces in China from 2006 to 2015 by the DEA-Malmquist index model. On the basis of this, the grey correlation analysis model based on super-efficiency DEA is used to quantitatively analyze the influencing factors of total factor productivity, technological progress and technical efficiency. The results show that the proportion of grain sowing, the level of food economic development, the level of grain mechanization, the average scale of operations and the level of fertilizer using have the most influence on grain total factor productivity. Finally, according to the results of the analysis, some suggestions are put forward to improve the TFP of different provinces in the main grain producing areas.
The grain issue is always the foundation for the development of the country’s economy and social stability and an important issue concerning international livelihood for a populous country that accounts for about 20% of the world’s population with 7% of the world’s land. It’s an important issue that how to achieve sustained growth of grain yield in the process of full urbanization and industrialization with the continued rising prices of means of production, the more and more serious supply of water resources and other resources and the insufficient promotion of agricultural science and technology innovation. TFP is considered as an important comprehensive measure of the growth of food production sexual indicators and the growth of TFP is an important means to promote the steady growth of grain output and achieve the transformation of food production mode. The main grain producing provinces provide about 75% of the grain for our country every year, which is a powerful backing of grain security and the main source of grain yield in China. Therefore, it is necessary to conduct a quantitative study on grain total factor productivity in main grain producing areas of our country. Grasping its sources of growth and its characteristics and understanding the impact of various factors on total factor productivity of grain have some practical significance to promote the improvement of grain yield and formulate relevant policies for the government.
Many scholars have conducted a multi-faceted study in the field of total factor productivity of grain recent years. Fan calculated the provincial grain total factor productivity from 1978 to 2012 and studied the dynamic evolution of regional disparity in China by the SBM-Malmquist productivity index [
The Malmquist index just studied in the theoretical phase and was not initially used in total factor productivity analysis when it was proposed by Sten Malmquist firstly. Then the Malmquist productivity index gradually became a tool to study dynamic efficiency changes while Caves [
Malmquist is defined as follows:
M i t , t + 1 = E C i t , t + 1 T C i t , t + 1 , i = 1 , 2 , ⋯ , n ; t = 1 , 2 , ⋯ , n
where,
E C i t , t + 1 = D i t + 1 ( x i t + 1 + y i t + 1 ) D i t ( x i t + y i t )
T C i t , t + 1 = D i t ( x i t + 1 , y i t + 1 ) D i t + 1 ( x i t + 1 + y i t + 1 ) × D i t ( x i t + y i t ) D i t + 1 ( x i t + y i t )
Among them, x i t and y i t represent the input and output of the region of i in the period of t. D i t ( x i t + y i t ) and D i t ( x i t + 1 + y i t + 1 ) are the distance function relative to the technical reference in period of t. The Malmquist index of production indicates the degree of change in the productivity of a decision-making unit from one period to another. It indicated that the total factor productivity has been raised during this period when the M i t , t + 1 ≥ 1 . On the contrary, it indicated that the total factor productivity has been declined. E C i t , t + 1 indicates the change of technical efficiency of decision-making unit from one period to another and describe the degree of improvement for decision-making unit which relatives to the optimal production front during this period. E C i t , t + 1 also describes the contribution of the change of technical efficiency to total factor productivity in this period. T C i t , t + 1 represents the movement of technical frontiers of the decision-making unit. In other words, it represents the contribution of the production frontier movement to the total factor productivity.
1) Grey Correlation Analysis
Grey correlation analysis is regarded as an important branch of the grey system theory, the basic idea of the geometric shape of the data sequence is used to determine whether the contact is close, the closer the geometric shape of the sequence curve is, the greater the degree of grey correlation between them is.
Grey system theory is a new method proposed by professor Deng to study the problem of the less data and uncertain system. It is based on the evolution of the things with little sample, incomplete information. It can correctly describe and effectively monitor the behavior and evolution of the system through the “part” of the known information generation, development. Grey correlation analysis refers to the quantitative description and comparison of a system development and change situation. The basic idea is to determine whether the relationship between curves is close by determining the similarity between the reference series and the geometric shapes of several comparative series degree [
Step 1: Determine the analysis sequence.
Set as the reference sequence:
X 0 = ( x 0 ( 1 ) , x 0 ( 2 ) , ⋯ , x 0 ( n ) ) (1)
Set as the comparison sequence:
X i = ( x i ( 1 ) , x i ( 2 ) ， ⋯ , x i ( n ) ) ( i = 1 , 2 , ⋯ , m ) (2)
Step 2: Nondimensionalize the original data.
The initialized reference sequence is:
X ′ 0 = x 0 ( k ) / x 0 ( 1 ) = ( x 0 ( 1 ) , x 0 ( 2 ) , ⋯ , x 0 ( n ) ) ( i = 1 , 2 , ⋯ , m ) (3)
The initialized comparison sequence is:
X ′ i = X i ( k ) / x i ( 1 ) = ( x ′ i ( 1 ) , x ′ i ( 2 ) , ⋯ , x ′ i ( n ) ) ( i = 1 , 2 , ⋯ , m ) (4)
Step 3: Calculate difference sequence.
The calculation formula of sequence difference is:
Δ 0 i ( k ) = | x 0 ( k ) − x i ( k ) | ( i = 1 , 2 , 3 , ⋯ , m , k = 1 , 2 , 3 , ⋯ , n ) (5)
The maximum and minimum calculated by Formula (5) are:
Maximum: M = max i max k Δ i ( k ) = Δ max .
Minimum: m = min i min k Δ i ( k ) = Δ min .
Step 4: Calculate grey correlation coefficient.
The calculation formula of grey correlation coefficient is:
γ 0 i ( k ) = m + ρ M Δ 0 i ( k ) + ρ M (6)
where, ρ ∈ ( 0 , 1 ) , i = 1 , 2 , 3 , ⋯ , m , ρ is called resolution coefficient, set ρ = 0.5 usually.
Step 5: Calculate grey correlation degree.
The calculation formula of grey correlation degree is:
γ 0 i = 1 n ∑ k = 1 n γ 0 i ( k ) (7)
The γ 0 i is the comparison sequence x i to reference sequence x 0 , which can reveal the degree of their association.
2) Super Efficiency DEA
The DEA model is a non-parametric measurement method that measures the level of efficiency of a production unit by calculating the distance between the actual output of the production unit and the production frontiers. However, the traditional DEA model cannot be further evaluated when there are multiple effective decision making units. Therefore, Andersen [
{ max h 0 = ∑ r = 1 s μ r y r 0 s .t . ∑ i = 1 m ω i x 0 = 1 ∑ i = 1 m ω i x i j − ∑ r = 1 s μ r y r j ≥ 0 ω i , μ r ≥ 0 , ∀ i , r ( j = 1 , 2 , 3 , ⋯ , n ) (8)
3) Grey Correlation Model Based on Super Efficiency DEA
Li and Liu [
{ max r 0 , i = ∑ t = 1 l ω t × ξ 0 , 1 ( t ) s .t . ∑ t = 1 l ω t ξ 0 , i ≤ 1 i = 1 , 2 , ⋯ , n ( i ≠ i 0 ) ω t ≥ 0 , ∀ t (9)
We can get the weight and the optimal grey correlation of the i 0 by solving the formula above. The corresponding weight vector of each factor has been given in (9), which makes the analysis results more objective compared with the traditional grey correlation analysis.
The data in this paper are the panel data which related to the input, output and influencing factors of grain production of main grain producing areas in China from 2006 to 2015. Some data in the paper come from “China Statistical Yearbook”, “China Rural Statistical Yearbook” and “Compilation of Rural Data in the Past Thirty Years from Reform and Opening Up”, some data are calculated based on the data of statistical yearbook.
These indexes in the paper are chosen by referencing to the existing research literature and the indexes are measurable or accessible [
The Malmquist productivity indexs in 13 provinces of the main grain-producing areas from 2006 to 2015 are shown in
| Efficiency change | Technological change | Pure technological efficiency change | Scale efficiency change | Total factor efficiency change | |
|---|---|---|---|---|---|
| Anhui | 1.010 | 1.008 | 1.007 | 1.003 | 1.018 |
| Hebei | 1.007 | 1.011 | 1.006 | 1.001 | 1.018 |
| Heilongjiang | 1.000 | 1.016 | 1.000 | 1.000 | 1.016 |
| Hubei | 1.000 | 1.029 | 1.000 | 1.000 | 1.029 |
| Hunan | 0.994 | 1.010 | 0.995 | 0.999 | 1.003 |
| Jilin | 1.000 | 0.991 | 1.000 | 1.000 | 0.991 |
| Jiangsu | 1.000 | 1.010 | 1.000 | 1.000 | 1.010 |
| Jiangxi | 1.002 | 1.006 | 1.025 | 0.978 | 1.009 |
| Liaoning | 0.994 | 1.008 | 1.000 | 0.994 | 1.001 |
| Inner Mongolia | 1.000 | 0.986 | 1.000 | 1.000 | 0.986 |
| Shandong | 0.998 | 1.011 | 1.000 | 0.998 | 1.008 |
| Sichuan | 1.016 | 1.005 | 1.000 | 1.016 | 1.021 |
| Henan | 0.998 | 1.014 | 1.000 | 0.998 | 1.011 |
| Mean | 1.001 | 1.008 | 1.002 | 0.999 | 1.009 |
Note: The data in
From
In terms of the decomposition of total factor productivity, the technological progress in Hubei Province was 2.9% from 2006 to 2015, which was the fastest in main grain producing provinces and Heilongjiang Province was 1.6% followed. Only technological progress of Jilin Province and Inner Mongolia decreased by 0.9% and 1.4% respectively. The technical efficiency of Anhui Province, Hebei Province, Jiangxi Province and Sichuan Province increased by an average of 1%, 0.7%, 0.2% and 1.6% respectively; Technical efficiency of Hunan Province, Liaoning Province, Shandong Province and Henan Province decreased by 0.6%, 0.6%, 0.2% and 0.2% respectively; The technical efficiency of Heilongjiang Province, Hubei Province, Jilin Province, Jiangsu Province and Inner Mongolia remained unchanged. The technical efficiency can be further decomposed into pure technical efficiency and scale efficiency. It can be seen that the purely technological efficiency increases in Anhui Province, Hebei Province and Jiangxi Province by 0.7%, 0.6% and 2.5% respectively; while purely technological efficiency declined by 0.5% in Hunan Province and the remaining province have not changed. The scale efficiency increased by 0.3%, 0.1% and 1.6% in Anhui Province, Hebei Province, Sichuan Province and decreased by 0.1%, 2.2%, 0.6% and 0.2% in Hunan Province, Jiangxi Province, Liaoning Province, Shandong Province and Henan Province, the remaining provinces have not changed.
1) Grain yield per unit area ( X 1 ): It’s expressed as the output of grain in a certain area divided by the total sown area of grain. The increasing of grain yield per unit area means the grain production efficiency increasing. Therefore, there are relevance among the per unit area of grain production and total factor productivity of grain, food production efficiency and technological change.
2) Proportion of grain sown ( X 2 ): It is expressed by the proportion of sown area of grain in the total sown area of the crop. It reflects the level of enthusiasm for sowing grain in the area.
3) Average labor management scale ( X 3 ): It is expressed that the sown area of grain in a certain area divided by the number of grain laborers (the number of grain laborers = the number of agricultural workers * the sown area of grain/the sown area of crops).
4) The level of fertilizer using per unit area ( X 4 ): It is expressed by the amount of pure fertilizers divided by the sown area of the crop in a region. Within a reasonable range, the higher the proportion is, the higher the growth of grain crops will be.
5) The development level of grain economy ( X 5 ): It is expressed as the ratio of the gross value of agricultural output to the total output value of agriculture, forestry, husbandry and fishery in a certain region and then multiplied by the ratio of sown area of food crops to sown area of crops. The higher the ratio is, the higher the level of grain economy development will be. In general, the higher the level of grain economy development, the more developed the grain industry and the higher the total factor productivity of food will be.
6) The level of afflicted crops ( X 6 ): It is measured by the ratio of the disaster area of crops to total sown area of crops in an area. In order to accurately measure the level of disaster of food crops, the affected area of crops multiplied by the ratio of the sown area of grain crop to the sown area of crop. The higher the ratio is, the more serious the disaster is and the less promotion to the improvement of grain production efficiency.
7) The level of grain mechanization ( X 7 ): It is expressed by the ratio of the total agricultural machinery to the total crop sown area. This index reflects the substitution ability of labor resources. The higher the ratio is, the more obvious the substitution of labor resources will be.
According to the selection of Index and grey correlation method based on super efficiency DEA, we can calculate the correlation degree and the ranking of factors affecting the Total Factor Productivity of Grain, Technological Progress and Technical Efficiency. The results are shown in
According to the ranking of influencing factors on total factor productivity of the main grain producing areas, Anhui Province, Hubei Province, Inner Mongolia, Sichuan Province and Henan Province were most affected by the sown proportion of grain; Hebei Province, Heilongjiang Province, Hunan Province and Jiangsu Province, Jilin Province and Liaoning Province were most affected by the level of grain economy development; Jilin Province and Liaoning Province were most affected by the level of grain mechanization; Jiangxi Province was most affected by the average labor management scale; Shandong Province was most affected by the level of fertilizer using per unit area.
According to the ranking of the influencing factors on technological progress province of the main grain producing areas, Anhui Province, Jiangxi Province and Sichuan Province were most affected by the average labor management scale; Heilongjiang Province, Hunan Province, Jiangsu Province and Henan Province were most affected by the level of grain economy development; Hubei Province, Liaoning Province, Inner Mongolia and Shandong Province were most affected by the proportion of grain sown; Hebei Province was most affected by the level of fertilizer using per unit area; Jilin Province was most affected by the level of grain mechanization.
| Province | γ 1 | γ 2 | γ 3 | γ 4 | γ 5 | γ 6 | γ 7 | |
|---|---|---|---|---|---|---|---|---|
| Anhui | tfpch | 0.705 | 0.920 | 0.904 | 0.906 | 0.919 | 0.794 | 0.834 |
| techch | 0.659 | 0.964 | 0.982 | 0.910 | 0.964 | 0.729 | 0.760 | |
| effch | 0.711 | 0.908 | 0.893 | 0.908 | 0.908 | 0.802 | 0.843 | |
| Hebei | tfpch | 0.641 | 0.970 | 0.886 | 0.959 | 0.985 | 0.867 | 0.863 |
| techch | 0.655 | 0.928 | 0.923 | 0.959 | 0.955 | 0.896 | 0.898 | |
| effch | 0.668 | 0.896 | 0.931 | 0.929 | 0.920 | 0.892 | 0.931 | |
| Heilongjiang | tfpch | 0.622 | 0.819 | 0.903 | 0.844 | 0.929 | 0.813 | 0.863 |
| techch | 0.622 | 0.819 | 0.903 | 0.844 | 0.929 | 0.813 | 0.863 | |
| effch | 0.575 | 0.975 | 0.893 | 0.863 | 0.798 | 0.775 | 0.719 | |
| Hubei | tfpch | 0.712 | 0.905 | 0.828 | 0.835 | 0.857 | 0.850 | 0.895 |
| techch | 0.712 | 0.905 | 0.828 | 0.835 | 0.857 | 0.850 | 0.895 | |
| effch | 0.689 | 0.899 | 0.990 | 0.980 | 0.952 | 0.725 | 0.754 | |
| Hunan | tfpch | 0.729 | 0.950 | 0.959 | 0.897 | 0.960 | 0.716 | 0.874 |
| techch | 0.729 | 0.950 | 0.959 | 0.897 | 0.960 | 0.716 | 0.874 | |
| effch | 0.731 | 0.970 | 0.943 | 0.969 | 0.962 | 0.682 | 0.816 | |
| Jilin | tfpch | 0.709 | 0.815 | 0.860 | 0.852 | 0.810 | 0.814 | 0.888 |
| techch | 0.709 | 0.815 | 0.860 | 0.852 | 0.810 | 0.814 | 0.888 | |
| effch | 0.613 | 0.920 | 0.881 | 0.859 | 0.929 | 0.742 | 0.728 | |
| Jiangsu | tfpch | 0.714 | 0.962 | 0.896 | 0.955 | 0.976 | 0.927 | 0.832 |
| techch | 0.715 | 0.962 | 0.896 | 0.954 | 0.976 | 0.927 | 0.833 | |
| effch | 0.690 | 0.979 | 0.850 | 0.987 | 0.950 | 0.918 | 0.794 | |
| Jiangxi | tfpch | 0.746 | 0.825 | 0.892 | 0.841 | 0.819 | 0.683 | 0.825 |
| techch | 0.744 | 0.814 | 0.900 | 0.831 | 0.809 | 0.691 | 0.836 | |
| effch | 0.671 | 0.733 | 0.885 | 0.744 | 0.729 | 0.753 | 0.953 | |
| Liaoning | tfpch | 0.664 | 0.840 | 0.916 | 0.858 | 0.865 | 0.871 | 0.933 |
| techch | 0.617 | 0.983 | 0.931 | 0.936 | 0.956 | 0.873 | 0.813 | |
| effch | 0.652 | 0.869 | 0.938 | 0.887 | 0.895 | 0.875 | 0.911 | |
| Inner Mongolia | tfpch | 0.603 | 0.984 | 0.906 | 0.877 | 0.954 | 0.948 | 0.867 |
| techch | 0.603 | 0.984 | 0.906 | 0.877 | 0.954 | 0.948 | 0.867 | |
| effch | 0.591 | 0.954 | 0.885 | 0.879 | 0.984 | 0.903 | 0.823 | |
| Shandong | tfpch | 0.729 | 0.963 | 0.875 | 0.964 | 0.899 | 0.696 | 0.805 |
| techch | 0.744 | 0.988 | 0.900 | 0.964 | 0.926 | 0.709 | 0.825 | |
| effch | 0.725 | 0.956 | 0.868 | 0.964 | 0.893 | 0.693 | 0.800 | |
| Sichuan | tfpch | 0.706 | 0.994 | 0.839 | 0.985 | 0.982 | 0.672 | 0.755 |
| techch | 0.695 | 0.817 | 0.926 | 0.805 | 0.803 | 0.777 | 0.880 | |
| effch | 0.678 | 0.802 | 0.912 | 0.790 | 0.788 | 0.786 | 0.891 | |
| Henan | tfpch | 0.743 | 0.983 | 0.813 | 0.916 | 0.971 | 0.869 | 0.867 |
| techch | 0.748 | 0.974 | 0.801 | 0.916 | 0.977 | 0.873 | 0.853 | |
| effch | 0.723 | 0.974 | 0.769 | 0.923 | 0.958 | 0.835 | 0.816 |
| Province | Correlation degree ranking of influential factors | |
|---|---|---|
| Anhui | tfpch | γ 2 ≻ γ 5 ≻ γ 4 ≻ γ 3 ≻ γ 7 ≻ γ 6 ≻ γ 1 |
| techch | γ 3 ≻ γ 2 ≻ γ 5 ≻ γ 4 ≻ γ 7 ≻ γ 6 ≻ γ 1 | |
| effch | γ 4 ≻ γ 2 ≻ γ 5 ≻ γ 3 ≻ γ 7 ≻ γ 6 ≻ γ 1 | |
| Hebei | tfpch | γ 5 ≻ γ 2 ≻ γ 4 ≻ γ 3 ≻ γ 6 ≻ γ 7 ≻ γ 1 |
| techch | γ 4 ≻ γ 5 ≻ γ 2 ≻ γ 3 ≻ γ 7 ≻ γ 6 ≻ γ 1 | |
| effch | γ 3 ≻ γ 7 ≻ γ 4 ≻ γ 5 ≻ γ 2 ≻ γ 6 ≻ γ 1 | |
| Heilongjiang | tfpch | γ 2 ≻ γ 7 ≻ γ 5 ≻ γ 6 ≻ γ 4 ≻ γ 3 ≻ γ 1 |
| techch | γ 5 ≻ γ 3 ≻ γ 7 ≻ γ 4 ≻ γ 2 ≻ γ 6 ≻ γ 1 | |
| effch | γ 2 ≻ γ 3 ≻ γ 4 ≻ γ 5 ≻ γ 6 ≻ γ 7 ≻ γ 1 | |
| Hubei | tfpch | γ 2 ≻ γ 7 ≻ γ 5 ≻ γ 6 ≻ γ 4 ≻ γ 3 ≻ γ 1 |
| techch | γ 2 ≻ γ 7 ≻ γ 5 ≻ γ 6 ≻ γ 4 ≻ γ 3 ≻ γ 1 | |
| effch | γ 3 ≻ γ 4 ≻ γ 5 ≻ γ 2 ≻ γ 7 ≻ γ 6 ≻ γ 1 | |
| Hunan | tfpch | γ 2 ≻ γ 5 ≻ γ 4 ≻ γ 3 ≻ γ 7 ≻ γ 6 ≻ γ 1 |
| techch | γ 5 ≻ γ 3 ≻ γ 2 ≻ γ 4 ≻ γ 7 ≻ γ 1 ≻ γ 6 | |
| effch | γ 2 ≻ γ 4 ≻ γ 5 ≻ γ 3 ≻ γ 7 ≻ γ 1 ≻ γ 6 | |
| Jilin | tfpch | γ 7 ≻ γ 3 ≻ γ 4 ≻ γ 2 ≻ γ 6 ≻ γ 5 ≻ γ 1 |
| techch | γ 7 ≻ γ 3 ≻ γ 4 ≻ γ 2 ≻ γ 6 ≻ γ 5 ≻ γ 1 | |
| effch | γ 5 ≻ γ 2 ≻ γ 3 ≻ γ 4 ≻ γ 6 ≻ γ 7 ≻ γ 1 | |
| Jiangsu | tfpch | γ 5 ≻ γ 2 ≻ γ 4 ≻ γ 6 ≻ γ 3 ≻ γ 7 ≻ γ 1 |
| techch | γ 5 ≻ γ 2 ≻ γ 4 ≻ γ 6 ≻ γ 3 ≻ γ 7 ≻ γ 1 | |
| effch | γ 4 ≻ γ 6 ≻ γ 3 ≻ γ 2 ≻ γ 5 ≻ γ 1 ≻ γ 7 | |
| Jiangxi | tfpch | γ 3 ≻ γ 4 ≻ γ 7 ≻ γ 2 ≻ γ 5 ≻ γ 1 ≻ γ 6 |
| techch | γ 3 ≻ γ 7 ≻ γ 4 ≻ γ 2 ≻ γ 5 ≻ γ 1 ≻ γ 6 | |
| effch | γ 7 ≻ γ 3 ≻ γ 6 ≻ γ 4 ≻ γ 2 ≻ γ 5 ≻ γ 1 | |
| Liaoning | tfpch | γ 7 ≻ γ 3 ≻ γ 6 ≻ γ 5 ≻ γ 4 ≻ γ 2 ≻ γ 1 |
| techch | γ 2 ≻ γ 5 ≻ γ 4 ≻ γ 3 ≻ γ 6 ≻ γ 7 ≻ γ 1 | |
| effch | γ 3 ≻ γ 7 ≻ γ 5 ≻ γ 4 ≻ γ 6 ≻ γ 2 ≻ γ 1 | |
| Inner Mongolia | tfpch | γ 2 ≻ γ 5 ≻ γ 6 ≻ γ 3 ≻ γ 4 ≻ γ 7 ≻ γ 1 |
| techch | γ 2 ≻ γ 5 ≻ γ 6 ≻ γ 3 ≻ γ 4 ≻ γ 7 ≻ γ 1 | |
| effch | γ 5 ≻ γ 2 ≻ γ 6 ≻ γ 3 ≻ γ 4 ≻ γ 7 ≻ γ 1 |
| Shandong | tfpch | γ 4 ≻ γ 2 ≻ γ 5 ≻ γ 3 ≻ γ 7 ≻ γ 1 ≻ γ 6 |
|---|---|---|
| techch | γ 2 ≻ γ 4 ≻ γ 5 ≻ γ 3 ≻ γ 7 ≻ γ 1 ≻ γ 6 | |
| effch | γ 4 ≻ γ 2 ≻ γ 5 ≻ γ 3 ≻ γ 7 ≻ γ 1 ≻ γ 6 | |
| Sichuan | tfpch | γ 2 ≻ γ 4 ≻ γ 5 ≻ γ 3 ≻ γ 7 ≻ γ 1 ≻ γ 6 |
| techch | γ 3 ≻ γ 7 ≻ γ 2 ≻ γ 4 ≻ γ 5 ≻ γ 6 ≻ γ 1 | |
| effch | γ 3 ≻ γ 7 ≻ γ 2 ≻ γ 4 ≻ γ 5 ≻ γ 6 ≻ γ 1 | |
| Henan | tfpch | γ 2 ≻ γ 5 ≻ γ 4 ≻ γ 6 ≻ γ 7 ≻ γ 3 ≻ γ 1 |
| techch | γ 5 ≻ γ 1 ≻ γ 4 ≻ γ 6 ≻ γ 7 ≻ γ 3 ≻ γ 1 | |
| effch | γ 2 ≻ γ 5 ≻ γ 4 ≻ γ 6 ≻ γ 7 ≻ γ 3 ≻ γ 1 |
Note: In
In terms of the ranking of the influencing factors on technical efficiency of the main grain producing areas, Hebei Province, Hubei Province, Liaoning Province and Sichuan Province were most affected by the average labor management scale; Heilongjiang Province, Hunan Province and Henan Province were greatest impacted by the proportion of grain sown; Jilin Province, Inner Mongolia were most affected by the level of grain economy development; Anhui Province, Jiangsu Province and Shandong Province were most impacted by the level of fertilizer using per unit area; Jiangxi Province was greatest impacted by the level of grain mechanization.
According to the tendency of the overall ranking of the various factors, the trend of technological progress and total factor productivity are the closest. It shows that the total factor productivity is “dominated by technological progress” on the other hand. All local governments can make corresponding adjustments according to the specific sequencing of the province and improve the grain total factor productivity overall.
It is possible to make a comprehensive judgment of the factors that affect the TFP of main grain producing provinces from various angles and make various policies according to the local conditions through the multilevel grey correlation analysis of the decomposition of total factor productivity and the influencing factors.
In order to further promote the grain total factor productivity in the main grain producing areas, improvements can be made in the following aspects: The government of Anhui Province, Hubei Province, Inner Mongolia, Sichuan Province and Henan Province should optimize the grain production structure and increase grain sown area. It is necessary to make more rational use of land for grain production based on comprehensive consideration of the production conditions of climate, land and resources in various regions, the actual production all over the country. In order to increase the proportion of grain sown, these provinces should make full use of land resources; Hebei Province, Heilongjiang Province, Hunan Province and Jiangsu Province should increase the gross value of agricultural production, increase their investment in scientific research on grain production, establish a sound grain subsidy system, raise the overall level of grain economy and promote the development of grain industry; Jilin Province and Liaoning Province should focus on improving the scale and mechanization of grain production to increase the grain production efficiency; Jiangxi Province should step up its efforts to support grain production and increase various kinds of policies to benefit farmers so as to raise farmers’ enthusiasm for grain cultivation and actively create various kinds of jobs and training for peasants; Shandong Province should increase the scientific and technological research of grain and the cultivation of new varieties. The government should guide peasants to use chemical fertilizers in a scientific and rational manner to increase grain yield per unit area.
In addition, in order to achieve the “double-layer” promotion of technological progress and technological efficiency for all-provincial grain total factor productivity, governments at all levels can prioritize technological progress and enhance technological efficiency according to the ranking of factors influencing technological progress and the factors influencing technological efficiency. On the basis of the research in this paper, other scholars can further study the relationship between TFP and technological progress and technical efficiency so as to better solve the problem of TFP’ increase.
The work was supported in part by the Soft-science Foundation of Henan Province (172400410015); Philosophy and Social Science Program of Henan Province (2016BJJ022).
Li, B.J., Yang, W.M. and Zhu, X.X. (2018) Multilevel Correlation Analysis of Influencing Factors on Grain Total Factor Productivity in Main Grain Producing Provinces of China. Open Journal of Applied Sciences, 8, 12-24. https://doi.org/10.4236/ojapps.2018.81002