Effects of fiscal support for agriculture on grain production technical efficiency: Empirical evidence from Chinese farms

Wang, Zu; Wu, Zhihao; Wei, Longbao Article Effects of fiscal support for agriculture on grain production technical efficiency: Empirical evidence from Chinese farms Journal of Applied Economics Provided in Cooperation with: University of CEMA, Buenos Aires Suggested Citation: Wang, Zu; Wu, Zhihao; Wei, Longbao (2024) : Effects of fiscal support for agriculture on grain production technical efficiency: Empirical evidence from Chinese farms, Journal of Applied Economics, ISSN 1667-6726, Taylor & Francis, Abingdon, Vol. 27, Iss. 1, pp. 1-29, https://doi.org/10.1080/15140326.2024.2347687 This Version is available at: https://hdl.handle.net/10419/314272 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc/4.0/ Journal of Applied Economics ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/recs20 Effects of fiscal support for agriculture on grain production technical efficiency: empirical evidence from Chinese farms Zu Wang, Zhihao Wu & Longbao Wei To cite this article: Zu Wang, Zhihao Wu & Longbao Wei (2024) Effects of fiscal support for agriculture on grain production technical efficiency: empirical evidence from Chinese farms, Journal of Applied Economics, 27:1, 2347687, DOI: 10.1080/15140326.2024.2347687 To link to this article: https://doi.org/10.1080/15140326.2024.2347687 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 29 Apr 2024. Submit your article to this journal Article views: 750 View related articles View Crossmark data Citing articles: 1 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=recs20 RESEARCH ARTICLE Effects of fiscal support for agriculture on grain production technical efficiency: empirical evidence from Chinese farms Zu Wang a,b , Zhihao Wu a,b and Longbao Wei a,b a China Academy for Rural Development, Zhejiang University, Hangzhou, China; b School of Public Affairs, Zhejiang University, Hangzhou, China ABSTRACT This study utilizes micro-level farm panel data from 2007 to 2012 to measure the technical efficiency of grain production among farms using a stochastic frontier analysis model. Additionally, it employs a two-way fixed effects model to empirically investigate the impact of fiscal support for agriculture on grain production technical efficiency and its underlying mechanisms. The results reveal significant room for improvement in the technical efficiency of grain production among Chinese farms, with increased fiscal support for agriculture demonstrating a substantial enhancement of their efficiency. This promoting effect only exists in the major grainproducing areas and increases with the increase of farm size. The augmentation of fiscal support for agriculture achieves this goal by augmenting modern agricultural input factors, alleviating financing constraints faced by farms, and optimizing agricultural production infrastructure. Our findings provide guidance for optimizing fiscal policy to support agriculture, promoting agricultural modernization, and achieving food security. ARTICLE HISTORY Received 7 August 2023 Accepted 22 April 2024 KEYWORDS China; fiscal support for agriculture; grain production; technical efficiency 1. Introduction Food security is a critical issue with significant implications for human survival. The United Nations (UN) has identified “eradicating hunger and achieving food security” as one of the seventeen Sustainable Development Goals (SDGs). Ensuring consistent and stable growth in grain yield is of significance for the economic development and social stability of developing countries. China is the largest developing country and one of the major agricultural countries, studying China’s food security issues holds valuable insights for other developing countries worldwide. Nowadays China’s food security is confronted with several challenges including the rising demands for grain and food consumption, the non-agricultural transfer of crucial input factors such as labour and land, and the mounting constraints imposed by limited resources and environmental concerns. Therefore, ensuring food security primarily hinges upon enhancing the grain production efficiency, rather than increasing input factors for grain production (Zhang et al., 2021). CONTACT Zhihao Wu [email protected] China Academy for Rural Development, Zhejiang University, 866 Yuhangtang Road, Hangzhou, Zhejiang 310058, China JOURNAL OF APPLIED ECONOMICS 2024, VOL. 27, NO. 1, 2347687 https://doi.org/10.1080/15140326.2024.2347687 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http:// creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. There exists an extensive body of literature that has quantitatively assessed grain production technical efficiency at macro levels, including provincial and county scales (Odeck, 2007; Zheng et al., 2023), and some of the literature used small-scale micro-level data for empirical analysis (Gong et al., 2019; Z. Liu & Zhuang, 2000). However, there is a lack of research based on national data on the technical efficiency of grain production on farms, which are the main body of grain production. Chari et al. (2021) studied the impact of land property reform on farmer productivity by using the national fixed point (NFP) survey data at the farmer level. Although they used data on farmers at the national level, they did not examine the impact of fiscal support for agriculture on technical efficiency. Compared to their study, the measurement of technical efficiency in this study is further accurate to the farmer-crop level. On the other hand, considering the public attributes and externalities associated with agriculture and its low profitability as a vulnerable industry, government fiscal support plays a pivotal role in agricultural development. Agricultural protection and subsidies are prevalent practices globally (Byerlee & Sain, 1986; Mendelsohn, 2003). In China, the government places great importance on fiscal support and subsidies for agriculture. From 2016 to 2019, China’s general public budget expenditure allocated a cumulative expenditure of 6.07 trillion yuan for agricultural and rural purposes, with an average annual growth rate of 8.8%, surpassing the average growth rate of the general public budget expenditure. 1 The government aims to improve grain production technical efficiency by fostering the enthusiasm of farmers through increasing fiscal support for agriculture, so as to ensure a stable grain supply and enhance the international competitiveness of grain products. Therefore, examining grain production technical efficiency at the farm level and investigating the impact and mechanism of fiscal support for agriculture on China’s grain production technical efficiency hold significant theoretical and policy implications for ensuring food security and promoting sustainable agricultural development in China and other developing countries. Research on the economic impacts of fiscal support for agriculture primarily focused on aspects such as farmer income (Gao et al., 2013), agricultural economic growth (Stads & Beintema, 2015), and farmer consumption (Dercon et al., 2009; Fan et al., 2000). Existing studies have found that fiscal support for agriculture can effectively improve the agricultural production environment and conditions, enhance agricultural production capacity, and consequently promote income growth for farmers (Gao et al., 2013). Fiscal support for agriculture plays a significant role in stimulating agricultural economic growth in lowand middle-income countries (Idoko & Jatto, 2018). Fiscal support for agriculture elevates the consumption level of farmers, although there exist temporal and spatial heterogeneities (Blancard et al., 2006). On the other hand, extensive research examined the factors influencing grain production technical efficiency from both micro and macro perspectives. At the micro level, factors such as land input (Wu et al., 2005), fertilizer input (Liefert et al., 2003), machinery input (Monchuk et al., 2010; Wu et al., 2021), effective irrigation area (Hassan et al., 2000), labour input, and characteristics of farmers (Li & Sicular, 2013; Tian & Wan, 2000) have been found to affect grain production technical efficiency. At the macro level, existing research mainly focused on regional economic development levels (Kuang et al., 2021), environmental pollution (Hoang & 1 Information source: Portal of the Central People’s Government of the People’s Republic of China (https://www.gov.cn/ xinwen/2020–12/23/content_5572857.htm). 2Z. WANG ET AL. Coelli, 2011; Li et al., 2022), climate conditions (Chen & Gong, 2021), technological changes (Jin et al., 2002, 2010), and agricultural infrastructure (Chen & Ding, 2007; Teruel & Kuroda, 2005) as factors influencing grain production efficiency. For instance, S. Chen and Gong (2021) utilized 35 years of county-level panel data in China to assess the impact of global warming on China’s agricultural total factor productivity (TFP) and found that extremely high temperatures reduce China’s agricultural TFP, while climate adaptation can mitigate this negative impact. Although some studies have attempted to establish a linkage between fiscal support for agriculture and macro-level grain production efficiency (Zhang et al., 2021), they have overlooked the investigation of micro-level farms’ grain production technical efficiency and the exploration of underlying mechanisms. Based on the literature above, despite a considerable number of empirical studies that have extensively examined fiscal support for agriculture and grain production technical efficiency, there are several shortcomings in the existing research. Firstly, previous studies on grain production technical efficiency primarily focused on macro-level measurements at the provincial and county levels, lacking investigation of grain production technology efficiency at the farm level of a national sample. Secondly, the examinations of the effects of fiscal support for agriculture in existing research are still not comprehensive, mainly concentrating on farmer income, consumption, and agricultural economic growth. There is a lack of research evaluating the economic consequences of fiscal support for agriculture from the perspective of grain production technical efficiency. Thirdly, although some studies explored the relationship between fiscal support for agriculture and grain production technical efficiency, there is still a lack of research that empirically examines the mechanisms, such as input factors, financing constraints, and infrastructure, through which fiscal support for agriculture affects grain production technical efficiency. This study presents an empirical analysis of the effect of fiscal support for agriculture on farm-level grain production technical efficiency in China. By constructing a panel dataset at the farm level from 2007 to 2012, and employing the stochastic frontier analysis model, we measure the Chinese farm’s grain production technical efficiency. Furthermore, we investigate the effects and mechanisms through which fiscal support for agriculture affects farm-level grain production technical efficiency by utilizing a twoway fixed effects model. This study makes several significant contributions to the literature. Firstly, this study computes grain production technical efficiency using micro-level data, allowing us to examine the effect of city-level fiscal support for agriculture on farm-level grain production technical efficiency, thus addressing the limitations of existing macro-level studies. Secondly, this study enriches and expands the empirical research on the impacts of fiscal support for agriculture, providing valuable insights for a comprehensive assessment of the policy design of fiscal support for agriculture. These findings bear strong policy implications for government efforts in promoting agricultural modernization and sustainable growth in grain production. Lastly, this study elucidates the mechanisms through which fiscal support for agriculture affects grain production technical efficiency, thus contributing to the existing literature on the impact of public policies on grain production efficiency and offering valuable evidence from a developing country context regarding the relationship between fiscal support for agriculture and grain production technical efficiency. JOURNAL OF APPLIED ECONOMICS 3 The remainder of the article is organized as follows. Section 2 presents the conceptual framework, outlining the theoretical foundation of this study. Section 3 describes the empirical strategy and econometric model. Section 4 provides a comprehensive description of the data and variables in this study. Section 5 presents the results of baseline estimations, robustness checks, heterogeneity analyses, mechanism tests, and our discussion. Section 6 is our conclusion. 2. Conceptual framework Agricultural production serves as the foundation for a nation’s sustenance and development (Ellis, 2008). Nevertheless, agriculture is inherently characterized by externalities and dual risk attributes, making it a sector with weakened competitiveness (Reardon et al., 1994). Smith (1776) argued that agriculture cannot function under a strictly specialized system. Nurkse (1952) identified the existence of a poverty-driven cycle in underdeveloped regions. The Lewis dualistic economic model presupposes agriculture as a lowquality sector (Lewis, 1954). Schultz (1964), in contrast to prior scholars, proposed that modern agriculture could promote economic growth, contingent upon the modernization of the traditional “poor and inefficient” agricultural sector. However, the profitdriven dynamics of markets suggest that the market mechanism alone cannot facilitate the transition from traditional to modern agriculture (David et al., 2000; Timmer, 1995). Therefore, increasing government regulation in the agricultural sector to compensate for the inherent deficiencies of the market mechanism has become an important area of study in modern public fiscal theory (Kelly et al., 2003; Rubenstein et al., 2003). Although agricultural development can bring substantial social benefits, due to the non-rival and non-excludable characteristics of the public goods it provides, the private sector will lack the incentive to invest in it (Fan & Zhang, 2008). Consequently, government support through fiscal measures in agriculture has emerged as a critical approach to ensuring national food security, particularly in the face of increasing food demand and resource and environmental constraints (Fan et al., 2021; Qaim, 2020). The aforementioned theories of public fiscal and agricultural externalities provide theoretical support for fiscal policies aimed at improving agricultural productivity and reinforcing international food competitiveness. Fiscal support for agriculture can affect the grain production technical efficiency through various channels. To start with, it contributes to enhancing modern input factors utilized in grain production. Against the backdrop of industrialization and urbanization, due to the higher returns of non-agricultural industries, there has been an overall outflow of agricultural labor. The substitution of modern input factors represented by agricultural machinery for traditional factors has become an inevitable trend in agricultural production (Huang et al., 2012). The process of agricultural development in China since the reform and opening up also reflects the above-mentioned pattern (Gong, 2018a). Firstly, fiscal support for agriculture can alleviate problems such as insufficient factor investment due to farmers’ financial constraints and risk expectations (Karlan et al., 2014), thus effectively augmenting the modern input factors in agricultural production processes. For instance, subsidies for the purchase of agricultural machinery can guide farmers in engaging specialized service providers to deliver mechanized operations essential for the entire grain production process, enabling farmers to efficiently 4Z. WANG ET AL. accomplish their tasks (Labarthe & Laurent, 2013; Ma et al., 2018). Secondly, fiscal expenditures, serving as investments in technological research and development, can stimulate technological advancements, thereby enhancing the quality of modern agricultural input factors like machinery and fertilizers (McArthur & McCord, 2017). Thirdly, fiscal support for agriculture can alter the relative prices of agricultural input factors through various subsidy methods, further enhancing the allocation ratio of modern agricultural input factors (Adamopoulos & Restuccia, 2014; Deaton & Deaton, 2020), thereby optimizing the combination among machinery, land, labour, and other different factors. Previous studies suggest that modern input factors such as agricultural mechanization can improve the efficiency of resource allocation, reduce the average grain production costs, enhance specialized production practices, ultimately fostering improvements in both technological levels and productivity in grain production (Ma et al., 2018; Wang et al., 2020). Consequently, fiscal support for agriculture can facilitate improved grain production technical efficiency by elevating modern agricultural input factors. Furthermore, fiscal support for agriculture contributes to increasing household income levels and facilitates asset accumulation among farmers, thereby reducing their financial constraints and improving accessibility to loans (Islam & Luo, 2018; Kassouri & Kacou, 2022). Due to the vulnerability of agricultural production and agricultural operators themselves, the profit-seeking nature of financial institutions, and the imbalance in the allocation of funds between urban and rural areas, agriculture faces serious financing constraints, hindering agricultural development and transformation (Guirkinger & Boucher, 2008). Agricultural credit subsidies within fiscal support for agriculture provide financial information for various stages such as production, procurement, warehousing, and transportation of grains. By offering timely and effective financing channels for grain producers, agricultural credit subsidies can reduce the financing costs associated with grain production. Numerous empirical studies indicate that agricultural credit plays a facilitating role in enhancing the grain production technical efficiency (Ali et al., 2014; Balana et al., 2022; Z. Liu & Zhuang, 2000). For instance, Liu and Zhuang (2000) explained the importance of rural credit to farmers’ technical efficiency by relaxing liquidity constraints. Consequently, fiscal support for agriculture can enhance the grain production technical efficiency by alleviating credit constraints. Finally, as a fundamental tool for national regulation for agricultural production, fiscal support for agriculture can effectively address the issue of inadequate supply of public goods in the agricultural production process (Hazell & Varangis, 2020). Compared to the needs of agricultural and rural modernization, there is insufficient investment in agricultural infrastructure, and issues of misallocation exist. The incidence of agricultural water and drought disasters remains relatively high (Pinstrup-Andersen & Shimokawa, 2006). Fiscal support for agriculture implies the flow of financial and fiscal resources from urban and non-agricultural sectors towards agriculture sector. One crucial direction of this support involves amplifying investments in agricultural infrastructure such as farmland and irrigation. This serves to enhance the agricultural production environment and elevate the level of agricultural infrastructure construction (Zhong et al., 2019). Agricultural infrastructure has both scale expansion effects, increasing the quantity and quality of arable land, and technological progress effects, optimizing the input structure of grain production to achieve higher crop yields with fewer expensive inputs (Finger et JOURNAL OF APPLIED ECONOMICS 5 al., 2019; Pan et al., 2021). Infrastructure plays a crucial role in economic growth, and the agricultural sector, which heavily relies on external financial support, particularly benefits from agricultural infrastructure construction as a precursor to development (Donaldson, 2018; Shamdasani, 2021). Several empirical studies suggest that improving agricultural infrastructure construction effectively lowers agricultural production costs, promotes an increase in grain production efficiency, subsequently raising grain yield and competitiveness (Teruel & Kuroda, 2005; Yuan et al., 2021). Therefore, fiscal support for agriculture can enhance grain production technical efficiency by elevating the level of agricultural infrastructure (as shown in Figure 1). In summary, fiscal support for agriculture is crucial for agricultural development. Based on the conceptual framework above, fiscal support for agriculture might enhance input factors, alleviate financial constraints, and strengthen infrastructure, thereby fostering an improvement in grain production technical efficiency. However, whether fiscal support for agriculture inevitably enhances grain production technical efficiency, and whether the corresponding increase in agricultural fiscal expenditure inevitably promotes the improvement of grain production technical efficiency through the enhancement of these three channels, requires further empirical verification through quantitative analytical tools. 3. Empirical strategy and econometric model 3.1. Stochastic frontier analysis Stochastic frontier analysis (SFA) and data envelopment analysis (DEA), are the most popular statistical tools in the line of efficiency analysis (Coelli & Rao, 2005; Ruttan, 2002). Some studies (Rezitis, 2010; Suhariyanto & Thirtle, 2008) have applied the DEA to estimate agricultural efficiency. However, some scholars (Headey et al., 2010; Nin et al., Fiscal support for agriculture Alleviate insufficient modern factors Enhance modern factors quality Improve factors allocation ratio Realize asset accumulation Improve loan accessibility Financial flow to public goods Invest farmland and irrigation Grain production technical efficiency Increase modern input factors Ease financing constraints Improve infrastructure Figure 1. Conceptual framework. Notes: Conceptual framework of the impact of fiscal support for agriculture on grain production technical efficiency. Source: Produced by the authors. 6Z. WANG ET AL. 2003) also claimed that DEA-based efficiency measures always draw anomalous results as compared with those from other measures of agricultural development, since DEA cannot distinguish efficiency from white noise and measurement error. Yuan et al. (2021) stated that white noise and measurement error are significant challenges and problems in agricultural efficiency analysis. Nevertheless, neither of the two issues can be fully addressed by DEA. Compared with DEA, SFA imposes assumptions of parametric functional forms and can carry out statistical tests on the results (Nguyen et al., 2016), which means that SFA is able to deal with both problems and rule them out. Headey et al. (2010) also pointed out that SFA estimations are significantly more stable and plausible than those derived by DEA. In summary, SFA model is more suitable for this study to estimate grain production technical efficiency. The original SFA model was initially proposed by Aigner et al. (1977) and Meeusen and van Den Broeck (1977), then developed by Schmidt and Sickles (1984) under a panel data setting: Where yit means the output value of farm i at time t in logarithms; and xkit measures the kth input of farm i at time t in logarithms; Xit is the vector of all K types of inputs, including Labour (number of days of labour required), Fertilizer (total amount of pure fertilizer used), Machinery (cost of mechanical operation), and Other intermediate input (includes management cost, financial cost, insurance cost, seed cost, pesticide cost, and irrigation cost).ui is the non-negative random term that indicates the technical inefficiency;vit is the typical disturbance; αi¼αui. fXit ð Þ is the production function, which describes the production frontier. The Transcendental Logarithmic (T-L) production function and the Cobb Douglas (C-D) production function are two widely used production functions. Hence, it is important to adopt scientific and rigorous tests to decide whether to choose the T-L production function or the C-D production function as the production function of this paper. Wallach and Goffinet (1989) pointed out that the mean squared error (MSE) is a reasonable criterion of model quality and can be used to determine model selection. The MSE is defined by: MSE ¼1 nPn i¼1ðyib yiÞ2, where yi is the observed values of the variable, with b yi being the predicted values. Similar to the MSE, there are also the mean absolute error (MAE ¼1 nPn i¼1yib yi ð Þj j) and the mean absolute percentage error (MAPE ¼1 nPn i¼1 yibyi  � yi ��������). The MSE, MAE and MAPE can be used to reflect the errors between the observed values and the predicted values of the model, and then reflect the accuracy of the model. Many studies have used these indicators to consider the selection of models (De Myttenaere et al., 2016; Köksoy, 2006; Willmott & Matsuura, 2005). Therefore, this paper also adopts the MSE, MAE and MAPE to determine the production function. By calculating and comparing these three indicators of the two functions’ estimation results, we find that the C-D production function has less errors and is more suitable for the data in this paper (See Table A1 in Appendix). Equation (1) shows the SFA model with the time-invariant technical inefficiency. However, it is not appropriate to assume that technical inefficiency is time-invariant when analysing the panel data with a long-time span. Hence, many scholars have JOURNAL OF APPLIED ECONOMICS 7 ratio is significantly positive. After gradually adding control variables and fixed effects, the coefficient remains to be significantly positive. This indicates that the impact of fiscal support for agriculture on technical efficiency is robust. We prefer the coefficient in column (7) because the regression result in column (7) has the most comprehensive control variables. Specifically, a 1% increase in the fiscal support ratio results in a 0.2573% increase in grain production technical efficiency. In our study sample, the mean grain production technical efficiency is 0.545, suggesting that a 10% increase in the fiscal support ratio leads to a 0.014 increase in grain production technical efficiency. Our findings are consistent with B. Gong’s (2018a) estimates based on provincial-level data, where he suggests that a 1% increase in agricultural public expenditure could lead to a 0.006% improvement in agricultural productivity. Moreover, by utilizing farm-level data, we effectively control for Table 1. Summary statistics. Var. Definition (Unit) Mean S.D. Min Max Output variable Output value Output value (thousand yuan/hectare) 11.419 3.284 3.642 22.413 Input variables Labour Number of days of labour required (day/hectare) 123.492 49.897 36.750 342.300 Fertilizer Total amount of pure fertilizer used (kilogram/ hectare) 315.282 100.306 96.300 651.150 Machinery Cost of mechanical operation (yuan/hectare) 1,032.622 652.896 16.500 3,080.148 Other intermediate input Other cost except mechanical cost (yuan/hectare) 2,668.922 928.006 1,181.195 6,701.064 Independent variable Fiscal support ratio Fiscal support for agriculture expenditure/Total budget expenditure 0.116 0.037 0.030 0.445 Control variables Income Net income of rural residents (thousand yuan/per capita) 5.142 1.595 1.595 13.457 Primary industry ratio Added value of primary industry/GDP 0.175 0.079 0.014 0.417 Employee ratio Number of employees in primary industry/Total employment 0.438 0.124 0.054 0.919 Cultivated area Cultivated area (thousand hectare) 360.229 281.330 22.570 2,237.300 Irrigation area Effective irrigation area (thousand hectare) 225.116 132.250 37.310 562.325 Obs. = 22,879. Machinery is adjusted by agricultural machinery price index. Other intermediate input is adjusted by agricultural means of production price index. Income is adjusted by consumer price index. Output value is adjusted by consumer price index. Other intermediate input includes management cost, financial cost, insurance cost, seed cost, pesticide cost, and irrigation cost. Table 2. Estimation results of the production function. Dep. Var. Ln(Output value) Est. S.E. Ln(Labour) 0.0716*** (0.0126) Ln(Fertilizer) 0.0390*** (0.0097) Ln(Machinery) 0.0308*** (0.0041) Ln(Other intermediate input) 0.0887*** (0.0141) σ2 u0.0779 σ2 v0.0174 γ¼σ2 u=σ2 uþσ2 v  � 0.8177 Obs. = 22,879. σ2 u is the variance of technical inefficiency. σ2 v is the variance of typical disturbance. Standard errors are given in parentheses. Asterisks *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. 14 Z. WANG ET AL. Table 3. Main regression results. Dep. Var. Ln(Technical efficiency) (1) (2) (3) (4) (5) (6) (7) Fiscal support ratio 0.2746*** 0.3794*** 0.6429*** 0.5011*** 0.6087*** 0.4487*** 0.2573*** (0.0478) (0.0530) (0.0496) (0.0526) (0.0536) (0.0572) (0.0512) Ln(Income) 0.1806*** 0.2149*** 0.2051*** 0.1585*** 0.2913*** (0.0058) (0.0081) (0.0080) (0.0096) (0.0262) Primary industry ratio 0.0399 0.0169 −0.0023 −0.1350** (0.0353) (0.0355) (0.0357) (0.0627) Employee ratio 0.1210*** 0.1493*** 0.1041*** 0.0766*** (0.0225) (0.0225) (0.0229) (0.0264) Ln(Cultivated area) −0.0624*** −0.0611*** 0.0131 (0.0047) (0.0047) (0.0096) Ln(Irrigation area) 0.0531*** 0.0568*** 0.1076*** (0.0048) (0.0049) (0.0130) Intercept −0.6724*** −0.6846*** −2.2498*** −2.5846*** −2.4462*** −2.0353*** −3.7920*** (0.0057) (0.0061) (0.0751) (0.0739) (0.0886) (0.2328) (0.0061) Individual FE NO YES NO NO NO NO YES Year FE NO YES NO NO NO YES YES Obs. 22,879 22,879 22,879 22,879 22,879 22,879 22,879 R-squared 0.0014 0.8692 0.0433 0.0451 0.0536 0.0636 0.8715 Robust standard errors are given in parentheses. Asterisks *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. JOURNAL OF APPLIED ECONOMICS 15 individual fixed effects, thereby mitigating the underestimation issue caused by omitted variables. Furthermore, the R-squared of the regression in column (7) is 0.8715, indicating that the regression model can explain 87.15% of the variation in grain production technical efficiency and highlighting the accuracy of the regression model. 5.3. Robustness checks In this section, we conduct a series of robustness checks to validate our baseline results and mitigate concerns regarding potential biases. Table 4 presents the results of our robustness checks. Firstly, in our baseline regression, we use the ratio of fiscal support for agriculture expenditure in total budget expenditure (Fiscal support ratio) as the independent variable to measure fiscal support for agriculture. However, different ways of measuring fiscal support for agriculture may lead to different estimation results. To verify whether our estimation results are affected by the measurement of the independent variable, we use Table 4. Robustness checks. Ln(Technical efficiency) Alternative measurement of fiscal support Cluster at county Cluster at prefecturelevel city T-L BC95 Dep. Var. (1) (2) (3) (4) (5) (6) Fiscal support ratio 0.2573** 0.2573** 0.2680*** 0.1590*** (0.1166) (0.1184) (0.0508) (0.0387) Ln(Fiscal support) 0.0191*** (0.0057) Fiscal support ratio’ 0.2335*** (0.0361) Ln(Income) 0.2946*** 0.2980*** 0.2913*** 0.2913*** 0.2782*** 0.1266*** (0.0261) (0.0260) (0.0709) (0.0763) (0.0259) (0.0214) Primary industry ratio −0.1350** −0.0546 −0.1350 −0.1350 −0.0926 0.1795*** (0.0626) (0.0632) (0.2116) (0.2237) (0.0629) (0.0494) Employee ratio 0.0747*** 0.0800*** 0.0766 0.0766 0.0697*** 0.0702*** (0.0264) (0.0265) (0.0655) (0.0714) (0.0270) (0.0225) Ln(Cultivated area) 0.0128 0.0130 0.0131 0.0131 0.0095 −0.0342*** (0.0095) (0.0096) (0.0220) (0.0239) (0.0094) (0.0070) Ln(Irrigation area) 0.1030*** 0.1032*** 0.1076*** 0.1076*** 0.0967*** 0.0707*** (0.0134) (0.0131) (0.0353) (0.0390) (0.0132) (0.0084) Intercept −3.8108*** −3.8401*** −3.7920*** −3.7920*** −3.6209*** −1.5534*** (0.2336) (0.2323) (0.6409) (0.7080) (0.2305) (0.1917) Individual FE YES YES YES YES YES YES Year FE YES YES YES YES YES YES Obs. 22,879 22,879 22,879 22,879 22,879 22,879 R-squared 0.8713 0.8716 0.8715 0.8715 0.8696 0.7767 The dependent variable is Ln(Technical efficiency) derived from the model (CD-SFA/CSS) for column (1)-(4), Ln(Technical efficiency) derived from the model (TL-SFA/CSS) for column (5), and Ln(Technical efficiency) derived from the model (CD-SFA/BC95) for column (6). The robust standard errors are adopted for column (1), (2), (5) and (6). The standard errors clustered at county level are adopted for column (3). The standard errors clustered at prefecture-city level are adopted for column (4). Fiscal support ratio’ is derived by Fiscal support for agriculture expenditure/Added value of primary industry. Standard errors are given in parentheses. Asterisks *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. 16 Z. WANG ET AL. the logarithm of fiscal support for agriculture expenditure (Ln(Fiscal support)) and the ratio of fiscal support for agriculture expenditure in the added value of the primary industry (Fiscal support ratio’) as alternative proxies for fiscal support for agriculture in columns (1) and (2) of Table 4. The results in columns (1) and (2) indicate that both the logarithm of fiscal support for agriculture expenditure and the ratio of fiscal support for agriculture expenditure in the added value of the primary industry still have a significantly positive impact on grain production technical efficiency. This suggests that our baseline results are robust and not driven by the measurement of the independent variable. Secondly, in our baseline regression, heteroscedasticity-robust standard errors are adopted to determine the significance of the estimated coefficients, as these standard errors do not rely on any specific assumptions under large sample sizes. To check the robustness of coefficient significance, we make different assumptions about the variance distribution of the error term of the regression model in columns (3) and (4) of Table 4. In column (3), we assume that the variance of the error term is heteroscedastic across counties and use clustered standard errors at county level. In column (4), we assume that the variance of the error term is heteroscedastic across prefecture-level cities and use clustered standard errors at the prefecture-city level. The results in columns (3) and (4) indicate that the impact of fiscal support for agriculture on the grain production technical efficiency is still significantly positive at 5% level. This suggests that our baseline results are robust and not driven by the assumptions about the variance distribution of the error term of the regression model. Lastly, we use the SFA model based on the C-D production function to estimate the grain production technical efficiency in our baseline regression. Hence, in column (5) of Table 4, we use the SFA model based on the T-L production function to estimate the new grain production technical efficiency and replace the original dependent variable with the new T-L technical efficiency. Furthermore, in our baseline regression, we employ the CSS estimator to estimate the SFA model based on the C-D production function. To examine whether our empirical results are affected by the choice of estimator, we employ the BC95 estimator (Battese & Coelli, 1995) and adopt size (the acreage of the farm) and variety (the dummy variable of crop varieties) to define the technical inefficiency effects. Then we use the BC95 estimator to estimate the SFA model based on the C-D production function and obtain the new grain production technical efficiency. In column (6) of Table 4, we replace the original dependent variable with the new BC95 technical efficiency. The results in columns (5) and (6) confirm our baseline results once again. After using new technical efficiency, the impact of fiscal support for agriculture on the grain production technical efficiency is still significantly positive. This indicates that our baseline results are robust and not driven by the measurement of the dependent variable. 5.4. Heterogeneity analyses In this section, we conduct heterogeneity analyses on the results of baseline regression by introducing interaction terms. The results are shown in Table 5. On the basis of considering the differences in resource endowments and the development of grain production among provinces, the Chinese government divided each province into major grain-producing areas (MPGA) or non-major grain-producing JOURNAL OF APPLIED ECONOMICS 17 areas. The major grain-producing areas undertake the main grain production tasks and play a core role in ensuring national food security. Hence, the impact of fiscal support for agriculture on the grain production technical efficiency may be different between major grain-producing areas and non-major grain-producing areas. Results in column (1) of Table 5 show that the coefficient of the interaction term of fiscal support ratio and MGPA is significantly positive, while the coefficient of fiscal support ratio is not significant. These results suggest that the promotion effect of fiscal support for agriculture on grain production technical efficiency is only present in major grain-producing areas. In major grain-producing areas, where grain production is the primary focus, government fiscal support for agriculture can effectively implement measures that enhance grain production, thereby improving grain production technical efficiency. These findings underscore the importance of targeted policies that consider regional heterogeneity in promoting sustainable and efficient grain production in China’s agricultural sector. The relationship between farm size and agricultural production technical efficiency has been a focal point in the field of agricultural economics, but a consensus has not been reached. Small-scale farms have the advantage of providing more family labour and the ability to adjust agricultural production strategies, thereby improving agricultural production technical efficiency (Bevis & Barrett, 2020; Rada & Fuglie, 2019). However, largeTable 5. Heterogeneity results by major grain-producing areas and by farm size. Ln(Technical efficiency) MGPA Farm size MGPA and Farm size Dep. Var. (1) (2) (3) Fiscal support ratio 0.0764 0.0626 −0.1191 (0.0837) (0.0622) (0.0937) MGPA Fiscal support ratio × MGPA 0.2842*** 0.2805*** (0.0263) (0.0991) Size −0.0023*** −0.0024*** (0.0008) (0.0008) Fiscal support ratio × Size 0.0279*** 0.0285*** (0.0056) (0.0054) Ln(Income) 0.2842*** 0.2328*** 0.2263*** (0.0263) (0.0295) (0.0295) Primary industry ratio −0.1316** −0.2122*** −0.2090*** (0.0627) (0.0629) (0.0630) Employee ratio 0.0787*** 0.0689*** 0.0710*** (0.0264) (0.0265) (0.0265) Ln(Cultivated area) 0.0098 0.0061 0.0028 (0.0096) (0.0095) (0.0096) Ln(Irrigation area) 0.1057*** 0.0916*** 0.0899*** (0.0130) (0.0135) (0.0135) Intercept −3.7095*** −3.1376*** −3.1067*** (0.2344) (0.2661) (0.2635) Individual FE YES YES YES Year FE YES YES YES Obs. 22,879 22,479 22,479 R-squared 0.8715 0.8720 0.8720 Notes: MGPA = 1 if farms are in the major grain-producing areas; MGPA = 0 if farms are not in the major grain-producing areas. Size is measured by the cultivated area of the farms and is considered as a continuous variable. The coefficient of MGPA is absorbed by the individual FE. Robust standard errors are given in parentheses. Asterisks *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. 18 Z. WANG ET AL. scale farms have greater scale-related budget capabilities and the ability to invest in advanced machinery, allowing them to better benefit from technological advancements (Sheng & Chancellor, 2019; Tan et al., 2010; Wang et al., 2015). Overall, the improvement of technical efficiency growth that farms benefit from fiscal support for agriculture may vary with their farm size. Results in column (2) of Table 5 show that the coefficient of the interaction term of fiscal support ratio and size is significantly positive. This indicates that larger farms benefit more from the promotional effect of fiscal support for agriculture on grain production technical efficiency. Moreover, we take the MGPA and Size into the same regression in column (3) and find that the coefficients of interaction terms don’t change much. This indicates that the heterogeneity in MGPA and farm size of the impact of fiscal support for agriculture on technical efficiency is robust. 5.5. Mechanisms We have already identified the positive impact of government fiscal support for agriculture on grain production technical efficiency in our baseline regression. In this section, we will further explore the specific mechanisms through which fiscal support for agriculture affects grain production technical efficiency. Table 6 presents the results of our mechanisms tests. Firstly, an increase in the proportion of modern agricultural production input factors contributes to the improvement of grain production technical efficiency (Y. Zhang & Brümmer, 2011). We measure the modern agricultural production input factors using the natural logarithm of total agricultural machinery power. The results reported in column (1) of Table 6 indicate that for every 1% increase in the fiscal support ratio, there is a corresponding 0.2459% increase in the agricultural machinery power. It indicates that increased government fiscal support for agriculture enhances grain production technical efficiency by facilitating the allocation of modern agricultural input factors and optimizing the allocation of agricultural production resources. We did not include modern agricultural production factors as control variables in our baseline regression, because in this study, fiscal support for agriculture can affect grain production technical efficiency by influencing modern agricultural production factors. Including the mechanism variables in the regression would lead to an underestimation of our baseline estimation results. Furthermore, due to the high risk and low liquidity of agricultural activities, as well as the lack of high-quality collateral for agricultural credit, many farmers often face difficulties in obtaining sufficient credit loans, which hinders their ability to engage in optimal agricultural production and improve agricultural production technical efficiency (Ali et al., 2014; Balana et al., 2022). We measure the financing constraint faced by farmers using the agricultural loan rate, which is calculated as the ratio of agricultural loans to the total loan amount. The results reported in column (2) of Table 6 demonstrate that a 1% increase in the fiscal support ratio leads to a 0.0232% increase in the agricultural loan ratio. The increase in government fiscal support for agriculture alleviates financing constraints faced by farmers, enabling them to make grain production decisions without being limited by financial constraints. Consequently, this enhances grain production technical efficiency. JOURNAL OF APPLIED ECONOMICS 19 Lastly, the improvement in agricultural infrastructure contributes to enhancing agricultural production conditions, reducing production costs, and further improving grain production technical efficiency (Teruel & Kuroda, 2005; Yuan et al., 2021). We employ two variables, rural electric power consumption and per capita highway mileage, to measure the level of infrastructure construction in the respective regions, which are logarithmically transformed prior to regression analysis. The results in columns (3) and (4) of Table 6 demonstrate that a 1% increase in the fiscal support ratio leads to a 0.7521% increase in rural electricity power consumption and a 0.1893% increase in per capita highway mileage. The increase in government fiscal support for agriculture promotes the development of agriculture-related infrastructure, thereby enhancing grain production technical efficiency. 6. Conclusion Based on the panel data of farms and prefecture-level cities from 2007 to 2012, we employ the SFA model to estimate the grain production technical efficiency at the farm level. Additionally, we use a two-way fixed effects model to empirically examine the impact of government fiscal support for agriculture on grain production technical efficiency. Furthermore, a series of robustness checks are conducted to ensure the stability of the baseline estimates. We also do heterogeneity analyses by major grain-producing areas and farm size. Finally, following the conceptual framework, we explore the mechanisms through which government fiscal support for agriculture affects grain production technical efficiency, focusing on three aspects: modern agricultural input factors, financing constraints, and agricultural production infrastructure. Table 6. Mechanism tests. Dep. Var. Input factor Financing constraint Infrastructure Ln(Machinery power) Loan ratio Ln(Electric power) Ln(Highway mileage) (1) (2) (3) (4) Fiscal support ratio 0.2459*** 0.0232*** 0.7521*** 0.1893*** (0.0344) (0.0090) (0.0738) (0.0540) Ln(Income) 0.0303** 0.0152** −0.1290*** −0.3868*** (0.0134) (0.0067) (0.0269) (0.0402) Primary industry ratio −1.0895*** 0.0738*** −1.2137*** −2.6408*** (0.0569) (0.0278) (0.0748) (0.0706) Employees ratio 0.0957*** 0.0034 −0.1239*** 0.3510*** (0.0209) (0.0074) (0.0414) (0.0548) Ln(Cultivated area) 0.1330*** 0.0135*** 0.0997*** 0.0328*** (0.0106) (0.0017) (0.0112) (0.0101) Ln(Irrigation area) 0.1842*** 0.1436*** 0.5447*** 0.2369*** (0.0149) (0.0082) (0.0185) (0.0133) Intercept 3.7012*** −0.8225*** 9.1414*** 5.5482*** (0.1263) (0.0533) (0.2236) (0.3603) Individual FE YES YES YES YES Year FE YES YES YES YES Obs. 16,363 10,329 22,176 12,412 R-squared 0.9866 0.8938 0.9760 0.9669 The dependent variable is the logarithm of the total power of agricultural machinery for column (1), Loan ratio derived by Agricultural loans/Total loans for column (2), the logarithm of rural electric power consumption for column (3), and the logarithm of per capita highway mileage for column (4). Robust standard errors are given in parentheses. Asterisks *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. 20 Z. WANG ET AL. We find that during the period of 2007–2012, the average grain production technical efficiency of Chinese farms was 0.545 significantly below the maximum value of 1. This indicates that there is substantial room for improvement in the grain production technical efficiency of Chinese farms. Furthermore, the results of the baseline regression show that government fiscal support for agriculture can significantly enhance grain production technical efficiency. For 1% increase in the ratio of fiscal support for agriculture expenditure to the total budget expenditure, the grain production technical efficiency is observed to improve by 0.2573%. Moreover, the robustness checks demonstrate that the baseline regression results remain robust under different measures of fiscal support for agriculture, variance assumptions, and technical efficiency estimation methods. Additionally, the heterogeneity analyses reveal that the promotion effect of government fiscal support on grain production technical efficiency is only significant in major grain-producing areas. Furthermore, larger-scale farms experience a greater promotion effect. Lastly, the mechanism analysis indicates that government fiscal support for agriculture can enhance grain production technical efficiency by promoting modern agricultural production input factors, alleviating financing constraints faced by households, and improving agricultural production infrastructure. This study addresses the limitations of existing literature by utilizing unique farmlevel data to estimate grain production technical efficiency, thereby providing a more comprehensive understanding of agricultural production technical efficiency at the micro level. In addition to measuring grain production technical efficiency at the farm level, this study empirically examines the impact and mechanisms of government fiscal support for agriculture on grain production technical efficiency. It complements the existing literature on the economic effects of government fiscal support for agriculture and the factors influencing grain production technical efficiency. Furthermore, this study deepens our understanding of the relationship between government fiscal support for agriculture and grain production technical efficiency. The findings highlight the significance of fiscal support in grain production and offer valuable insights for other developing countries seeking to enhance grain production technical efficiency and formulate agricultural fiscal support policies. Acknowledgments The authors acknowledge Zhejiang University for its support of this study. Disclosure statement No potential conflict of interest was reported by the author(s). Funding This study was supported by the National Natural Science Fund of China (Grant No. 71973123) and the National Social Science Fund of China (Grant No. 22&ZD081). JOURNAL OF APPLIED ECONOMICS 21 Notes on contributors Zu Wang is a Ph.D. candidate in Agricultural Economics and Management from Zhejiang University. His research interests include agricultural technical progress and food security. Zhihao Wu is a PhD candidate in Agricultural Economics and Management from Zhejiang University. Her research interests include agricultural trade, digital agriculture, and rural development. Longbao Wei is a distinguished professor at Zhejiang University, whose research field mainly focuses on food economics, agribusiness management, and digital economy. ORCID Zhihao Wu http://orcid.org/0000-0002-6249-2069 Author contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Zu Wang. The first draft of the manuscript was written by Zhihao Wu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Availability of data and materials The first dataset is available from the first author on reasonable request. The second dataset analysed during the current study is available in the China City Statistical Yearbook repository, https://data.stats.gov.cn/easyquery.htm?cn=C01. References Adamopoulos, T., & Restuccia, D. (2014). 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