4.1 Data and variables
Based on data availability and the results of the final model, we collected data for 2016–2020. Since the central financial poverty alleviation funds do not cover Beijing, Tianjin, and Shanghai, and the data for Tibet are seriously missing, we finally chose to use panel data to conduct an empirical study on 27 provinces, autonomous regions, and municipalities (excluding Hong Kong, Macao, and Taiwan) in China, with the raw data from the China Statistical Yearbook, the China Agricultural Yearbook, and the China Rural Statistics Yearbook in previous years. Yearbook, China Agricultural Yearbook, and China Rural Statistics Yearbook. The exogenous variable rainfall was obtained from the China National Meteorological Information Database. Details of the variables used in the study are shown in Table 2:
Table 2
Input and output variables
Stage
|
Variable
|
Unit
|
Stage1
|
Input
|
Crop sown area
|
thousand hectares
|
Input
|
Agricultural business entity
|
10,000
|
Output
|
Agricultural GDP
|
100 million RMB
|
Output
|
Per capita disposable income of rural residents
|
10000 RMB
|
|
Link
|
Per capita disposable income of rural residents
|
10000 RMB
|
Stage2
|
Input
|
Financial support for agriculture
|
100 million RMB
|
Output
|
Infrastructure for sustainable agricultural development
|
/
|
Output
|
Number of rural residents guaranteed minimum subsistence allowance
|
10,000 person
|
|
Carryover
|
Number of agricultural large and medium-sized tractors
|
10000
|
|
Exogenous
|
Annual rainfall
|
100 million cubic meter
|
Data source: China Statistical Yearbook database, rainfall data from the National Meteorological Information Database.
Stage I: Agricultural Production stage
Input variables:
(A) Crop sown area (CA) is the area actually sown or transplanted with crops
(B) Agricultural business entity (ABE) is any individual or organization directly or indirectly engaged in the production, processing, marketing, and service of agricultural products. service of agricultural products.
(C) Agricultural GDP (AGDP) is the total amount of all products of agriculture, forestry, animal husbandry, and fishery expressed in monetary terms within a certain period (usually one year). It reflects the total scale and results of agricultural production. It reflects the total scale and results of agricultural production.
(D) Per capita disposable income of rural residents (RRDI) is the combination of final consumption expenditure and savings available to rural survey households, i.e., the income that survey households can use for discretionary purposes. Per capita disposable income of rural residents (RRDI) is the combination of final consumption expenditure and savings available to rural survey households, i.e., the income that survey households can use for discretionary purposes. Disposable income includes both cash and in-kind income.
Stage II: Government Poverty Reduction Stage
(E) Financial support for agriculture (FSA) is China's national financial support for agriculture, rural areas, and farmers, the main means of national financial support for agriculture, rural areas, and farmers, and one of the important elements of the distribution relationship between the State and farmers. Financial support for agriculture (FSA) is China's national financial support for agriculture, rural areas, and farmers, the main means of national financial support for agriculture, rural areas and farmers, and one of the important elements of the distribution relationship between the State and agriculture (FSA) is China's national financial support for agriculture, rural areas and farmers, the main means of national financial support for agriculture, rural areas and farmers, and one of the important elements of the distribution relationship between the State and farmers, whose main forms of expression are capital input preferential policies and institutional construction.
Financial support for agriculture = central fiscal special funds for poverty alleviation + local government fiscal expenditure for agriculture.
(F) Infrastructure for sustainable agricultural development (ISA) is a comprehensive evaluation indicator synthesized by the entropy method: (
1) postal and telecommunication facilities; (
2) ecological facilities; (
3) water resources, water supply, and drainage facilities; (
4) energy and power facilities. (
1) postal and telecommunication facilities; (
2) ecological facilities; (
3) water resources, water supply and drainage facilities; (
4) energy and power facilities; and (
5) road transportation facilities. (
3) water resources, water supply, and drainage facilities; (
4) energy and power facilities; and (
5) road transportation facilities.
(G) Number of rural residents guaranteed minimum subsistence allowance (MSA) is a livelihood protection system introduced by the Chinese Government for rural residents whose annual per capita net household income is below the local minimum subsistence standard. A straightforward explanation is that "low security" is the same as the minimum subsistence guarantee. A straightforward explanation is that "low security" is the same as the minimum subsistence guarantee.
Carryover
(H) Number of agricultural large and medium-sized tractors (ALMT) is the number of medium and large tractors used for agricultural production and other related farming activities.
Exogenous
(I) Annual rainfall(R) is a measure of how much precipitation falls on an area. Specifically, it is the depth to which liquid and solid (melted) precipitation falling from the sky to the ground has accumulated on the horizontal plane without evaporation, infiltration, or loss. Specifically, it is the depth to which liquid and solid (melted) precipitation falling from the sky to the ground has accumulated on the horizontal plane without evaporation, infiltration, or loss.
We used a modified non-expectation two-stage dynamic DDF model to analyze the estimation bias in the two-stage analysis using annual rainfall as an exogenous variable and disposable income per capita of rural residents as an intermediate linking variable. Based on these assumptions, we designed a Meta-frontier two-stage non-expectation dynamic DDF model under consideration of the effects of exogenous variables (see Fig. 1).
4.2 Descriptive Statistics of Relevant Indicators such as Inputs and Outputs
Figure 2 presents input and output indicators, including input indicators for the agricultural production stage: total sown area of crops, number of legal entities in agriculture, and output indicators: gross agricultural product and disposable income per rural resident. The results of the statistical analysis of the Government's additional input indicators for the poverty reduction stage: fiscal expenditure and output indicators: infrastructure for sustainable agricultural development and the number of rural inhabitants covered by the minimum subsistence guarantee.
The main reason for the slow growth of the average total sown area (inputs) of crops in 2016–2020 is affected by the 1.8 billion mu of arable land red line policy. In 2016, China's Ministry of Land and Resources issued the Adjustment Programme for the Outline of the National Overall Land Use Plan (2006–2020), in which it adjusted the indicators of arable land retention, basic farmland protection area, and the total scale of land used for construction at the national level and in all provinces (autonomous regions and municipalities), requiring that by 2020 the national arable land retention will be more than 1,865 million mu and The basic farmland protection area will be over 1.546 billion mu, and the total scale of construction land will be controlled within 40.71939 million hectares (610.79 million mu). From the perspective of Agricultural business entity (input), the average and maximum values have increased year by year, while the minimum value has not changed significantly in the rest of the years, except for a slight increase in 2017. The maximum and average values of Agricultural GDP have shown a trend of faster growth, while the minimum value has grown slowly and fluctuated slightly. Per capita disposable income of rural residents' maximum value, average value, and minimum value all show an increasing trend year by year. Financial support for agriculture's maximum value, average value, and minimum value continue to increase, and the gap between the maximum value and the minimum value is getting bigger and bigger, which indicates that Financial support for agriculture has maintained a certain degree of growth trend. In terms of Infrastructure for sustainable agricultural development, the maximum value fluctuates from 2019–2020, the index started to increase in 2016, but in 2020 the index fell and hit a new low. Both its minimum and average values also fluctuate slightly. The maximum value of the non-expected output Number of rural residents guaranteed minimum subsistence allowance shows a gradual decline in 2016–2018, but then rebounds in 2019–2020, and the performance of the minimum value also fluctuates slightly, but in terms of the overall performance of the average value 2016–2019 declined yearly but regressed to the 2018 level in 2020 due to the new crown epidemic. The maximum, minimum, and average values of the carry-over variable Number of agricultural large and medium-sized tractors all show a year-on-year upward trend and peak in 2020, which also reflects the increase in the degree of mechanization of agricultural production in China.
This paper takes the Qinling-Huaihe line as the regional demarcation line between the north and the south of China, and according to the latitude of the provinces and the difference in the degree of regional economic development, the 27 provinces of China are explored based on retaining the complete provincial administrative units, and the statistics of input-output indexes of each province are shown in Table 3:
Table 3
Division of Regions in China
Region
|
Provinces, municipalities, and autonomous regions
|
South
|
Fujian、Guangdong、Guangxi、Guizhou、Hainan、Hunan、Jiangsu、Jiangxi、Sichuan、Yunnan、Zhejiang、Chongqing
|
North
|
Anhui、Gansu、Hebei、Henan、Heilongjiang、Hubei、Jilin、Liaoning、Inner Mongolia、Ningxia、Qinghai、Shandong、Shanxi、Shaanxi、Xinjiang
|
Table 4 shows a comparison of key input-output indicators for the two regions for 2016–2020. We see that the Northern Area's average Agricultural business entity inputs increase year on year, while Southern Areas fluctuate slightly. In terms of average Crop sown area inputs, Northern Areas has a clear comparative advantage over Southern Areas. In stage 1, Southern Areas outperform Northern Areas in terms of average agricultural GDP and per capita disposable income of rural residents, while in stage 2, the additional financial support for agriculture inputs, Northern Areas are significantly more favorable than Northern Areas. support for agriculture inputs, Northern Areas is larger than Southern Areas, while in terms of output performance Southern Areas' desired output Infrastructure for sustainable agricultural development is higher than that of Northern Areas in 2016, except that it is higher than that of Northern Areas in 2016. development lags behind Northern Areas in 2017–2020, except for a slight lead in 2016, while in terms of non-desired outputs Rural residents guaranteed minimum subsistence allowance, Southern Areas' farm household performance of the exogenous variable Rainfall is significantly higher in Southern Areas than in Northern Areas.
Table 4
Input and output variables from 2016–2020 between Northern Areas and Southern Areas
Year
|
Region
|
ABE
|
CA
|
AGDP
|
RRDI
|
FSA
|
ALMT
|
ISA
|
MSA
|
Rainfall
|
2016
|
South
|
2.70
|
5103.67
|
2044.92
|
1.18
|
4621.46
|
30.92
|
37.56
|
128.56
|
2905.57
|
North
|
2.22
|
6969.26
|
2039.67
|
1.21
|
5275.23
|
17.27
|
36.75
|
199.99
|
1744.68
|
2017
|
South
|
3.51
|
5109.91
|
2121.45
|
1.27
|
4980.85
|
32.11
|
34.54
|
106.10
|
2822.71
|
|
North
|
3.09
|
6927.57
|
2137.63
|
1.31
|
5786.14
|
17.93
|
36.43
|
182.15
|
1771.89
|
2018
|
South
|
3.42
|
5086.63
|
2223.64
|
1.38
|
5397.13
|
33.63
|
35.45
|
92.70
|
2739.84
|
|
North
|
3.24
|
6918.47
|
2252.90
|
1.43
|
6330.09
|
18.86
|
37.76
|
158.40
|
1799.10
|
2019
|
South
|
3.25
|
5097.29
|
2355.37
|
1.51
|
5793.29
|
35.07
|
35.45
|
88.93
|
2656.98
|
|
North
|
3.26
|
6915.43
|
2483.73
|
1.57
|
6977.25
|
19.73
|
37.76
|
157.44
|
1826.30
|
2020
|
South
|
3.40
|
5174.34
|
2572.21
|
1.61
|
6138.96
|
36.51
|
36.39
|
94.29
|
2574.17
|
|
North
|
3.63
|
6956.69
|
2686.89
|
1.69
|
7160.81
|
20.60
|
36.70
|
164.17
|
1853.53
|
4.4 Efficiency Analysis between the Agricultural production stage and Government support for agriculture stage
Southern and northern provinces are generally more efficient at the agricultural production stage than at the government poverty reduction stage. The overall efficiency value of the southern provinces in the agricultural production stage is 0.743, with room for improvement. In the following, we will analyze the efficiency performance of these two stages in terms of efficiency. Agricultural production efficiency is an important criterion for evaluating the efficiency of sustainable agricultural development, so it is necessary to analyze the efficiency of the agricultural production stage. Table 6 shows that the performance of the south and the north during the period of 2016–2020 is good, with an overall average value of 0.787, which is closely related to China's "Three Rural" policy regulation, the improvement of agricultural mechanization and the rapid development of rural revitalization. The efficiency of the agricultural production stage in the northern provinces is significantly higher than that in the southern provinces, with an average efficiency of 0.822. This is mainly since the northern provinces are China's main grain-producing areas, for example, according to the data published by the National Bureau of Statistics of China: in 2021, the total population of the seven grain-producing provinces in the north of China was 398 million people, which accounted for 28 percent of the country's total population. The total grain output, however, is as high as 683.118 billion jin, accounting for about 50 percent of the country. In terms of topography, China's major plains are concentrated in the northern region, while the terrain in the southern region is mostly hilly. In addition, the area of arable land, per capita area of arable land, the degree of agricultural mechanization, agricultural population, and per capita food ownership have all contributed to the increase in agricultural GDP, which has led to a better performance in terms of efficiency in the agricultural production phase.
Effective allocation of government financial resources and macroeconomic growth affect sustainable agricultural development, and numerous literatures have found that government intervention is an important factor contributing to the variability of regional economic development in China. We introduce indicators related to sustainable agricultural development (fiscal expenditure, Infrastructure for sustainable agricultural development, and the number of rural residents with minimum subsistence guarantee) into the input and output elements of the government's poverty reduction stage to assess the Chinese government's public service function and sustainability. As can be seen from Table 7, the overall average efficiency of the government's poverty reduction stage is 0.666 lower than that of the agricultural production stage. From the evaluation results, the government poverty reduction efficiency of 0.679 in the southern provinces has a large room for improvement, and the efficiency value shows a U-shaped curve trend of decreasing and then increasing. The performance of the government's poverty reduction efficiency in the northern provinces is 0.656, and its actual performance is also lower than expected compared to the more financial funds received from the Chinese government for poverty reduction, which actually contributes to the widening of the economic gap between the north and the south that has long been present in the Chinese economy.
An important feature of local government intervention in the microeconomic sector at all levels in China is administrative intervention in or control of the allocation and pricing of key factor markets within their jurisdictions, leading to distortions in factor markets as factor market reforms lag behind product market reforms. Specifically, due to the greater integration of southern China into the global industrial chain, supply chain, and value chain division of labour and trade system, under the dual effect of the historical tradition of being "relatively far away from the political center" and the mechanism of "external openness to force the reform of the internal market", the factor market distortion is caused by the lagging behind of factor market reform compared with product market reform. The two factors are mutually reinforcing. In the Southern Plate, the government's intervention and control of financial subsidies, financial markets, land markets, and other key factor resources are more in line with the principle of fair competition in the market, so that the development and operation mechanism of key factor markets are relatively perfect, and the dominant role of the market competition mechanism in the operation of the national economy is more prominent. On the contrary, in the face of the competitive pressure from the economic development and industrial development advantages of the southern sector, the government of the northern sector of China, in the process of attracting investments and promoting industrial development, is more inclined to adopt preferential policies and government subsidies that are contrary to the market competition mechanism, such as intervening in and controlling the distribution and pricing of specific key factors in the region. As a result, the market-oriented reform process, including product market-oriented reform and factor market-oriented reform, has lagged behind that of the Southern China region, which has resulted in the Southern China government's poverty reduction efficiency being significantly higher than that of the Northern China government.
Table 6
Efficiency of the Agricultural production stage between the Northern Areas and Southern Areas, 2016–2020
Cluster
|
Mean
|
2016
|
2017
|
2018
|
2019
|
2020
|
South
North
|
0.743
|
0.791
|
0.714
|
0.692
|
0.775
|
0.742
|
0.822
|
0.898
|
0.791
|
0.828
|
0.816
|
0.778
|
Table 7
Efficiency of the Government support for agriculture stage between the Northern Areas and Southern Areas, 2016–2020
Cluster
|
Mean
|
2016
|
2017
|
2018
|
2019
|
2020
|
South
North
|
0.679
|
0.697
|
0.675
|
0.673
|
0.632
|
0.718
|
0.656
|
0.709
|
0.687
|
0.652
|
0.632
|
0.599
|
As can be seen in Fig. 4, 13 of the 27 provinces are less efficient in the second stage than in the first stage, and 14 provinces are more efficient in the second stage than or the same as in the first stage. In the second stage, Yunnan province has the largest improvement in efficiency value of 0.712, which is 0.167 higher than the first stage. Jilin province has the largest regression in efficiency from an efficiency value of 1.000 in the agricultural production stage to 0.448 in the second stage. for both stages, Guangdong and Ningxia have an efficiency value of 1.