The Measurement and Influence Factors of Total Factor Productivity in Agriculture in Russia

Кандилян Марина Игоревна – магистрант Школы менеджмента Даляньского политехнического университета (КНР).

Сюй Сяо-Дун – докторант, доцент Школы менеджмента Даляньского политехнического университета (КНР).

Аннотация: Развитие сельского хозяйства России не только заложило определенный фундамент для развития внутренней экономики, но и создало исключительно благоприятные условия для дальнейшего увеличения доходов российских фермеров. В этой статье анализируются эндогенные и экзогенные факторы роста совокупной факторной производительности сельского хозяйства России с применением индекса Малмквиста.

Abstract: Russia's agricultural development not only laid a certain material foundation for its domestic economic development, but also provided extremely favorable conditions for the further increase of Russian farmers' income. And this article analyzes the EA-Malmquist index method from the two aspects of the endogenous factors of Russia's agricultural total factor growth rate and the exogenous factors of Russia's agricultural total factor growth rate. The situation is accurately measured. In the study, the "heterogeneity" of agricultural total factor productivity in various regions is used to analyze the influence of endogenous and exogenous factors on Russia's agricultural total factor productivity.

Ключевые слова: совокупная факторная производительность; сельское хозяйство; стохастический пограничный анализ.

Keywords: Total Factor Productivity; Agriculture; Stochastic Frontier Analysis

Introduction

Agriculture is known as "the future of the Russian economy" and is one of the most important sources of the Russian economy. The supply level of agricultural products has increased significantly, the comprehensive agricultural production capacity has been significantly enhanced, the industrialization of agriculture has advanced rapidly, and the income of rural residents has continued to grow. However, with the development of the economy, Russia’s agricultural development has also encountered bottlenecks. In order to better promote the growth of Russia’s agriculture, this article is based on this to study the factors affecting the increase in total factor productivity in Russia’s agriculture, and put forward relevant countermeasures and suggestions.

Judging from the existing research content, there are still some deficiencies in the research on the influence of the total factor growth rate of Russian agriculture. Based on this, this article analyzes the endogenous and exogenous factors of the total factor growth rate of Russia's agriculture. On this basis, the DEA-Malmquist index method is used to accurately measure the quantitative level and efficiency decomposition of Russia's agricultural total factor productivity from 2000 to 2019.

Analysis of Empirical Results

Agricultural total factor productivity is based on the fixed input of visible factor resources, which measures the changes in agricultural output under the influence of factors such as management, skills, and knowledge. At present, agricultural total factor productivity is becoming weaker and weaker under the driving force of agricultural factor input. Under such a general background, the realization of agricultural supply-side reform should focus on maintaining the continuous increase and stability of agricultural total factor productivity. In the process of constructing Solow’s economic growth model, total factor productivity was discovered based on the assumption of endogenous technology. Changes in agricultural total factor productivity are not only related to changes in the internal and external conditions of the economic system, but also related to factors such as technological innovation, production conditions, and resource endowments. Therefore, the factors affecting agricultural total factor productivity can be divided into endogenous factors and exogenous factors.

Influence mechanism analysis

Endogenous factors

First of all, there are many factors in agricultural production that have a direct impact on output, such as the level of rural infrastructure construction, resource utilization, land use capacity, and the efficiency of factor allocation. In addition, rural human capital has a significant impact on the diffusion and dissemination of agricultural technology, and plays an important role in improving agricultural total factor productivity.

Secondly, in the process of agricultural production, the experience accumulated by the practitioners at work can not only improve their own labor production efficiency, but also demonstrate and inspire other farmers. To achieving the scale effect of agricultural production, it can also promote the popularization of experience and agricultural technical knowledge, so that knowledge can quickly spillover.

Then, agricultural total factor productivity is affected by technological progress, which is generally abrupt. Relatively speaking, the experience accumulated in pure agricultural production and the technological progress it brings are gradual and relatively slow. Therefore, there is little effect in improving the contribution of agricultural total factor productivity, and there is no stability. The improvement of agricultural total factor productivity should be based on the talents, funds, and equipment invested in agricultural production research and development as the key driving force.

Exogenous factors

The exogenous factors affecting agricultural total factor productivity include structural factors, market factors and institutional factors. The three types of factors are briefly introduced below.

(1) Structural factors

The continuous deepening of industrial upgrading and economic reforms has promoted the development of the industrial structure to a higher level. It is inevitable that the gradual adjustment of the industrial structure's focus is also the reallocation of production and living materials and the gradual realization of labor productivity among agriculture, industry, and service industries. In addition, the upgraded industrial structure transformation enables the continuous and rapid cycle of labor factors between the agricultural and non-agricultural sectors, which can promote the coordinated development of the sectoral economy and increase the total factor productivity of agriculture.

(2) Market factors

The agricultural market includes the land market, labor market, product market, and financial market. An unreasonable agricultural market mechanism is very likely to cause an imbalance in the ratio of factor inputs, resulting in the development of agricultural total factor productivity to a bad discovery. The improvement of the income gap between agricultural practitioners, agricultural and non-agricultural practitioners, and practitioners in different fields between regions.

(3) Institutional factors

There are five main institutional factors in agricultural development: land system, organization system, price system, fiscal and tax subsidy system, and insurance system. The establishment and improvement of the land system and organizational system can enable agricultural practitioners to give full play to their abilities and promote the rapid development of modernization and large-scale agricultural production. The fiscal and tax subsidy system is gradually implemented in agricultural production in accordance with market-oriented means, and can play the role of industry in promoting agricultural development. The insurance system can better adjust the distribution of benefits between agricultural practitioners and the government, increase the income level of agricultural practitioners, and promote more active production and operation.

Analysis on the Influencing Factors of Russian Agricultural Total Factor Productivity

From the previous analysis, it can be seen that one of the important factors for the slowdown in the growth rate of Russian agricultural TFP is that the promotion of technological progress has been weakened by the decline in relative technical efficiency. So, what caused the decline in relative technical efficiency and what factors hindered the growth of Russian agricultural TFP? The following will use regression analysis to explore the internal and external factors that affect the increase in Russian agricultural TFP.

Variables and regression models

(1) Variable selection

The previous analysis shows that there are two factors that affect agricultural total factor productivity: endogenous factors and exogenous factors. That is to say, the change of agricultural total factor productivity is not only closely related to technological innovation, production status and resource endowment, but also closely related to changes in the internal and external conditions of the economic system. Based on the comprehensive consideration of theoretical analysis, data availability, and rationality of indicators, the paper selects panel data from 2000 to 2019 in various regions of Russia to empirically analyze the impact of some important factors on agricultural total factor productivity. In the analysis, rural road density, labor factor input in agricultural production, agricultural electricity efficiency, agricultural irrigation investment, disaster resistance, rural financial development level, financial support for agriculture, and rural residents’ income structure are selected as representative factors affecting agricultural total factor productivity. The variables are explained below.

Rural Road Density (Road): this indicator comprehensively investigates the influence of the area of the division and the mileage of the road, and can reflect the real situation of rural road construction more accurately.

Agricultural electricity efficiency (Power): it is difficult to count the electricity consumption of agricultural production. Therefore, the rural electricity consumption is used as a numerical substitute in this article. The agricultural electricity efficiency refers to the consumption of electricity in the increase of agricultural output value.

Agricultural Irrigation Investment (Irrigation): in order to make the data more accurate, this indicator in the article refers to the percentage of the effective irrigation area in the cultivated land area.

Agricultural labor input (Labor): this indicator is the proportion of agricultural employees in the total population, and it is a concrete reflection of the changes in labor input in the agricultural production process.

Disaster resistance (Disaster): agriculture is a relatively fragile industry in the national economy, which is obviously affected by natural conditions, geographic location, climate change and other factors. The resilience of agricultural production uses the proportion of the affected area of crops in each region in the total sown area as a proxy variable.

Rural financial development level (Loan): the level of financial development has three dimensions: development structure, scale and efficiency. This article focuses on the development scale of rural finance, and the level of financial development uses the proportion of agricultural loans in the primary industry’s GDP as a proxy variable.

Financial support for agriculture (Finance): this indicator refers to the proportion of agricultural fiscal expenditures in local fiscal expenditures, which can reflect the government's policy support in agricultural development.

Income structure of farmer residents (Wage): this indicator is the proportion of wage income in the per capita net income of farmers, which can effectively reflect the situation of non-agricultural employment.

Table 1. Variable description.

Symbol

Variable name

Description

Road

Rural road density

Highway mileage/area

Power

Agricultural electricity efficiency

Rural electricity consumption/agricultural value added

Irrigation

Agricultural irrigation investment

Effective irrigation area / arable land area

Labor

Agricultural labor input

Agricultural employees/total population

Disaster

Disaster resistance

Crops affected area/total sown area

Loan

Rural financial development level

Agricultural loans/Gross output of primary industry

Finance

Fiscal support for agriculture

Agricultural financial expenditure/financial expenditure

Wage

Income structure of farmers

Wage income/net income

(2) Regression model

According to previous management and previous research results, the three indicators of rural road density, agricultural electricity efficiency, and agricultural irrigation investment have the most obvious impact on agricultural total factor productivity. Therefore, they are used to construct a basic regression equation. These three items belong to rural infrastructure. The construction factors, analyze their effects on agricultural total factor productivity, and construct the regression equation as follows:

image001 (1)

Agricultural total factor productivity is not only affected by rural road density, agricultural electricity efficiency, and agricultural irrigation investment, but also by agricultural labor input, disaster resilience, rural financial development level, financial support for agriculture, and farmers’ income structure. Therefore, the regression model selected in the article is as follows:

image002 (2)

Experience test

(1) Unit root test

The paper selects panel data from various regions of Russia from 2000 to 2019 for empirical testing. Due to the long time span and numerous cross-sectional research objects, under the comprehensive characteristics of repeated sampling of cross-sectional data and unique records of time series random events, in order to ensure the validity of the equation regression results, it is necessary to perform the regression experiment of panel data. To examine whether the sequence stationarity can meet the requirements, the article passes empirical tests, specifically unit root tests and co-integration tests, to avoid possible model false regression problems. In order to make the unit root test have a robust result, five panel data unit root test methods are used in this article: LLC test, Breitung test, IPS test, Fisher-ADF, Fisher-PP. The results of the variable unit root test are shown in Table 2 below.

Table 2. Unit root test results.

Test

TFP

Road

Power

Irrigation

Labor

Disaster

Loan

Finance

Wage

LLC

Lag(1)
-6.587*
(0.000)

Lag(1)
-13.035*
(0.000)

Lag(1)
-10.328*
(0.000)

Lag(1)
-5.971*
(0.000)

Lag(1)
-6.085*
(0.000)

Lag(1)
-11.973*
(0.000)

Lag(1)
-7.581*
(0.000)

Lag(1)
-3.142*
(0.000)

Lag(1)
-10.786*
(0.000)

Breitung

Lag(1)
-5.334*
(0.000)

Lag(1)
-6.185*
(0.000)

Lag(1)
-12.297*
(0.000)

Lag(1)
-8.579*
(0.000)

Lag(1)
-2.726*
(0.000)

Lag(1)
-8.418*
(0.000)

Lag(1)
-3.501*
(0.000)

Lag(1)
-7.654*
(0.000)

Lag(1)
-6.687*
(0.000)

IPS

Lag(1)
-8.121*
(0.000)

Lag(1)
-17.692*
(0.000)

Lag(1)
-10.521*
(0.000)

Lag(1)
-4.610*
(0.000)

Lag(1)
-6.186*
(0.000)

Lag(1)
-11.024*
(0.000)

Lag(1)
-8.274*
(0.000)

Lag(1)
-7.786*
(0.000)

Lag(1)
-11.142*
(0.000)

Fisher-

ADF

Lag(1)
176.984
(0.000)

Lag(1)
354.633
(0.000)

Lag(1)
218.352
(0.000)

Lag(1)
116.165
(0.000)

Lag(1)
156.267
(0.000)

Lag(1)
230.686
(0.000)

Lag(1)
184.116
(0.000)

Lag(1)
166.707
(0.000)

Lag(1)
229.039*
(0.000)

Fisher-PP

Lag(1)
444.407
(0.000)

Lag(1)
2724.962*
(0.000)

Lag(1)
1202.564*
(0.000)

Lag(1)
489.671*
(0.000)

Lag(1)
617.235*
(0.000)

Lag(1)
662.628*
(0.000)

Lag(1)
826.274*
(0.000)

Lag(1)
941.073*
(0.000)

Lag(1)
1588.316*
(0.000)

Note: The P value of the first-order difference test statistic of the variable is in the parentheses, and * represents the non-stationary hypothesis of the rejection sequence at the 1% level.

It can be seen from Table 2 above that all 9 serial level tests do not reject the null hypothesis with unit root, but the first-order difference can reject the hypothesis with unit root at the 1% significance level. This shows that the nine sequences of TFP, Road, Power, Irrigation, Labor, Disaster, Loan, Finance, and Wage are all first-order single integers.

(2) Co-integration test

After the sequence unit root test is passed, in order to avoid the fact that the regression relationship is only a numerical coincidence, it is difficult to reflect the true relationship between the independent variable and the dependent variable, and the co-integration test is used to detect the evolution path between the sequences. In the process of co-integration test panel data, the three commonly used test indicators are test, test and test. In this paper, the selection test of the co-integration relationship between agricultural total factor productivity and influencing factors in various regions of Russia is tested. The results obtained show that at a significance level of 1%, each variable system rejects the hypothesis that "there is no co-integration relationship". It can be seen that the long-term stable relationship exists in each variable system.

Table 3. Cointegration test results.

Variable

Kao test

TFP, Road, Power, Irrigation

-2.704*(0.002)

TFP, Road, Power, Irrigation, Labor

-2.417*(0.007)

TFP, Road, Power, Irrigation, Labor, Disaster

-2.501*(0.005)

TFP, Road, Power, Irrigation, Labor, Disaster, Loan

-2.768*(0.002)

TFP, Road, Power, Irrigation, Labor, Disaster, Loan, Finance

-2.919*(0.001)

TFP, Road, Power, Irrigation, Labor, Disaster, Loan, Finance, Wage

-2.921*(0.001)

Note: * indicates that the null hypothesis is rejected at the 1% significance level and the alternative hypothesis is accepted. The value in parentheses is the test P value.

(3) Regression analysis

After clarifying the co-integration relationship between the variables mentioned above, regression analysis is performed. If the parameter estimation uses ordinary least squares (OLS), the existence of the result bias may be high. In order to avoid the possibility of the existence of endogeneity between variables, the paper uses the fully modified least squares method (FMOLS) in the regression analysis to clarify the correlation between variables. Moreover, in order to ensure robust parameter estimation, based on the FMOLS estimation of the model, the DOLS parameter estimation of equation (2) is also used. Finally, the obtained regression results and the relationship between the variables are shown in Table 4 below.

Table 4. Regression results of the model.

Variable

(1)

(2)

(3)

(4)

(5)

(6)

(7)

D(Road)

-0.131*
(0.000)

-0.131*
(0.000)

-0.116*
(0.000)

-0.119*
(0.000)

-0.119*
(0.000)

-0.120*
(0.000)

-0.009*
(0.000)

D(Power)

0.934*
(0.000)

0.747*
(0.000)

0.606*
(0.000)

0.463*
(0.000)

0.412*
(0.000)

0.403*
(0.000)

0.142*
(0.000)

D(Irrigation)

-0.785*
(0.000)

-0.750*
(0.000)

-0.787*
(0.000)

-0.752*
(0.000)

-0.764*
(0.000)

-0.702*
(0.000)

-0.651*
(0.000)

D(Labor)

 

-0.048*
(0.000)

-0.056*
(0.000)

-0.041*
(0.000)

-0.041*
(0.000)

-0.041*
(0.000)

-0.003*
(0.000)

D(Disaster)

   

-0.046*
(0.000)

-0.039*
(0.000)

-0.041*
(0.000)

-0.034*
(0.000)

-0.072*
(0.000)

D(Loan)

     

0.304*
(0.000)

0.304*
(0.000)

0.299*
(0.000)

0.220*
(0.000)

D(Finance)

       

0.018*
(0.601)

-0.046*
(0.212)

0.227*
(0.000)

D(Wage)

         

-0.156*
(0.078)

-0.245*
(0.000)

Note: The values in parentheses are t statistic values, *, **, *** represent the null hypothesis that the coefficient of zero is rejected at the 1%, 5%, and 10% significance levels, respectively.

It can be seen from the regression results that the coefficients of rural road density, agricultural electricity efficiency, and agricultural irrigation investment in the regression equation will not change due to the increase of control variables. Russia's agricultural total factor productivity is negatively correlated with rural road density and agricultural irrigation investment, while it is positively correlated with agricultural electricity efficiency. Among the variables in the regression equation, only the fiscal support for agriculture is insignificant, and the significance tests of other variables are all passed, which also reflects the robustness of the regression results.

Labor input has a restrictive effect on Russia's increase in agricultural total factor productivity. If the agricultural labor force inputs too much, other factor input space will be squeezed, and the function of machinery and equipment will be weakened, which is not good for improving the total factor productivity of Russia's agriculture.

There is a negative relationship between agricultural resilience and total factor productivity of agriculture in Russia. The negative impact of backward flood control and drought relief infrastructure and weak disaster resilience on agricultural total factor productivity is obvious. Therefore, improve the construction of agricultural infrastructure and promote farmers. The awareness of disaster resistance risk is imperative for the improvement of agricultural total factor productivity.

The level of rural financial development can significantly increase the total factor productivity of Russian agriculture. The improvement of rural financial service level can make agricultural asset conversion and investment have smoother channels, capital use cost and transaction cost can be reduced, and the level and quality of economic growth can be improved. Among the basic sectors of Russia’s national economy, the agricultural sector is a weak sector, and its ability to absorb and accumulate funds is relatively weak. The development process of Russia’s agricultural modernization has a large demand for external capital inflows. Therefore, the deepening of rural financial reforms, agricultural loans The increase in the proportion of loans from financial institutions can play a positive role in the realization of the scale effect of Russia's agricultural development. Fund support can be used to guarantee the introduction of technology, equipment, talent and other resources, and to increase the total factor productivity of Russia's agriculture.

Although the financial support for agriculture is not significant in the regression results, that is, it has zero effect on the total factor productivity of Russia's agriculture, the development of agricultural modernization will inevitably transform from resource and labor factor input to capital intensive. Agricultural support policies play a positive role in guaranteeing the stable development of agricultural modernization.

Therefore, in the development and construction of industrialization and urbanization, the formulation of strategies should take into consideration the scope of professionalism of practitioners.

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