1. Introduction
Agriculture plays a crucial role in the production of essential food products and is the source of livelihood of many people by providing employment opportunities. This is also the case for countries that have a small dependency on agriculture in their economic system. Agriculture in South Korea only accounts for 1.5 percent of the national economy in 2018. Despite the low share of agriculture in the national economy, agriculture is a critical sector in terms of securing food security. For example, rice is the main staple food in South Korea and provides about 47 percent of the South Korean caloric intake. In particular, the self-sufficiency ratio for grain is about 27 percent in South Korea, and this figure dramatically decreases to 5 percent when rice is excluded [
1]. The South Korean government provides direct payments to producers to support their income, and that in turn can influence farm productivity due to the importance of the agricultural sector in securing food security. In fact, the South Korean government spent about 18 percent of its total agricultural budget on direct payments in 2018 (The rest of the total agricultural budget is used for farm-management stability such as insurance, grain management, and farm-production management such as water management, rural welfare, and development). This indicates that direct payments are an important policy for sustainable agricultural production in South Korea.
Direct payments in South Korea cover the following five categories: (1) direct payments to rice farming households, (2) direct payments to field-farming households, (3) direct payments for environment-friendly agriculture, 4) direct payments to less-favored areas and (5) direct payments for landscape preservation. In 2013, each direct-payments category was eligible for (1) rice farms larger than 1000 square meters and that had 30 million Korean won (KRW) of off-farm income, (2) field farms larger than 1000 square meters and that had 37 million KRW of off-farm income, (3) farms producing environment-friendly commodities, (4) farms located in less-favored areas, and (5) farms located in landscape-preservation areas, respectively. For each direct-payments category, the rate of payments was 0.9 million KRW/ha, 0.4 million KRW/ha, 0.98 million KRW/ha, 0.5 million KRW/ha, and 1.7 million KRW/ha, separately (1 KRW = 0.00079 USD, on 18 March 2020). A farm cannot be paid more than two types of direct payments at the same time.
There is an issue about the effect of direct payments on farm productivity. Direct payments may have a positive or negative impact on agricultural productivity. Direct payments can have a positive impact on productivity as direct payments provide an additional source of finance or affect farmers’ risk attitude [
2]. On the other hand, others argue that direct payments may negatively affect farm productivity. They argue that direct payments may distort farmers’ behavior, which leads to a decrease in allocative efficiency [
3,
4]. This indicates that the effect of direct payments on farm productivity is an open empirical question. Thus, the objective of this study is to provide empirical evidence on the effects of direct payments on agricultural productivity using a better estimation strategy.
Several studies have explored the drivers of agricultural productivity. Vigani and Kathage [
5] suggested that the impact of risk management on farm productivity can be positive or negative, depending on the risk-management strategies adopted in farms. Muyanga and Jayne [
6] and Sheng et al. [
7] evaluated the effects of farm size on productivity. Plastina and Fulginiti [
8] and Baldos et al. [
9] analyzed the impact of agricultural research and development (R&D) on the growth of agricultural productivity. Kirwan et al. [
10] and Frick and Sauer [
11] focused on the impact of quota on farm-level productivity growth. Alston et al. [
12] and Jin and Huffman [
13] evaluated the effects of research, extension, and other variables on productivity indexes using a two-step procedure.
Several papers disaggregated agricultural productivity into various components. Morrison Paul and Nehring [
14] found that scale and scope economies play an important role in explaining productivity using the stochastic frontier approach (SFA). Andersen, Alston, and Pardey [
15] concluded that omitting terms of trade and short-term seasonal growing conditions lead to an upward cyclical bias in estimates of U.S. agricultural productivity using the SFA. O’Donnell [
16] exploited data-envelopment analysis to disaggregate agricultural productivity and found that the main driver of agricultural productivity was technical progress.
Among the various factors that affect agricultural productivity, several empirical literature attempted to identify the impact of government payments on agricultural productivity. For example, Mary [
17] estimated a production function using a system generalized method of moments (GMM; [
18]) and investigated the impact of the Common Agricultural Policy (CAP) subsidies on total factor productivity (Generalized Method of Moments (GMM) is a class of estimators that can be applied to both linear and nonlinear models with applications in economics and finance. GMM estimators are constructed from exploiting orthogonality conditions where the number of equations is greater than the number of unknown parameters). The results show that subsidies have a negative impact on productivity. This paper controlled for time-invariant unobservables using the system GMM. However, there could be time-varying unobservables that affect both subsidies and productivity, which lead to an inconsistent estimate of subsidies. Rizov et al. [
19] conducted a correlation analysis to test the link between subsidies and farm productivity, but they failed to identify the causal impact of subsidies on productivity. Our paper moves beyond these papers in an important way in that we identify the impact of direct payments on agricultural productivity using a quasi-experimental econometric method.
The control function approach is widely used in estimating production functions to mitigate both the endogeneity problem and the selection problem caused by endogenous exit, that is, firm exit is not randomly assigned. Several studies applied this method to estimate a production function to examine the determinants affecting farm productivity, including decoupling policy [
20,
21], tobacco quota [
10], the Common Agricultural Policy (CAP) subsidies [
19], and market deregulation [
11].
Given the feature of the control function approach, our study examined the effect of direct payments on farm productivity. In particular, we used a two-step approach to investigate the impact of direct payments on productivity. We first constructed an individual farm-level productivity measure using the control function approach. We then estimated the effect of direct payments on farm productivity.
The rest of the paper is organized as follows. In
Section 2, we provide an overview of the data.
Section 3 presents the empirical model for production function and the identification strategy of the effects of direct payments on agricultural productivity.
Section 4 discusses the empirical results for the estimation of production function and the impact of direct payments on total factor productivity, respectively. Finally,
Section 5 provides a conclusion of the paper.
2. Data
A farm-level panel dataset from the Korean Farm Household Economy Survey was used in this analysis (Korean Farm Household Economy Survey was obtained through individual interviews about the receipts, expenses, labor-hours, and assets. The sample households allocated to the farm household economy survey were extracted after the primary extraction from the sample of annual agricultural research survey (Double sampling). For the continuity and accuracy of survey results, the stratified sampling method was applied. Using Neyman distribution, samples were distributed to nine states around the standard error of the target (3~4%) of the state). Our dataset covered the period 2008–2017, which consists of two balanced five-year panels, the period 2008–2012 and the period 2013–2017, as sampled farm household changes every five years. The dataset included detailed information about 3713 farms about farm production, financial records, and socio-economic information such as the operator’s age, sex, and education.
Output and input variables were defined following the approach of Ban and Kwon [
22]. Specifically, we defined the total sales of the farm as a single output since farms often grow multiple crops and animals. To do so, we aggregated 12 different output categories (e.g., rice, barley, other grains, bean, potatoes, vegetables, fruits, flowers, special crops, large animals, and small animals). The labor variable was an hours variable including family labor, hired labor, and communal sharing of labor. The capital variable was defined as the average of a farm’s asset including building, machine, animal, inventory, and plant. The intermediate variable included the expenses of seed, fertilizer, pesticides, forage, and light and heat. Farm revenue, capital, and intermediate were deflated by the producer price index, the producer price index of capital, and the producer price index of intermediate, respectively.
Table 1 reports the descriptive statistics of the selected variables from 2008 to 2017. The average household head was 65.86 years old, and the average number of years of education was 20.05. On average, South Korean farms earn about 38 million KRW from agricultural production a year and spend 1313 h for farming a year.
5. Conclusions
Despite the importance of direct payments for improving farm productivity, the effect of direct payments on farms’ productivity has been an open empirical question. This paper thus explores the impact of direct payments on agricultural productivity by focusing on South Korean farms. In this study, we used the control function approach proposed by Levinsohn and Petrin [
23] to estimate a production function and to obtain farm-specific, time-varying total factor productivity. This approach allows mitigating measurement issues, specification assumptions, endogeneity, multicollinearity, and endogenous exit associated with previous methods (e.g., OLS, fixed effects estimation, etc.). After estimating total factor productivity using the control function approach, we then used PSM methods to control for systematic differences between treatment and control groups and to identify the effect of direct payments on agricultural productivity.
Our results show that direct payments are associated with an increase in agricultural productivity by about 12 percent on average in South Korea. We also conducted the robustness check and found that our results are robust, which ensures the positive and statistically significant effect of direct payments on agricultural productivity. Particularly, our findings are in accordance with the previous literature [
19]. As they concluded, direct payments may increase agricultural productivity through the “credit channel”. Direct payments could have a positive impact on financial constraints and eventually impact agricultural productivity. In addition, the positive impact of direct payments on agricultural productivity could be explained by a decrease in risk aversion, which makes farms more willing to expand capital and to encourage R&D.
Despite our contributions, a caveat is worth noting. We used PSM methods to identify the effect of direct payments on agricultural productivity. However, there might exist a potential unobserved heterogeneity. For example, risk attitude could be correlated with both direct payments and agricultural productivity. Future research could attempt to mitigate potential unobserved heterogeneity concerns by conducting various robustness checks including falsification tests, which will allow policy markets to have a better understanding of the economic impacts of the direct payments.
Overall, our results suggest that direct payments play an important role in farm production in South Korea. Currently, the South Korean government revises the direct payment program to support small farms’ income and to strengthen the public function of agriculture instead of focusing on farm production, especially rice production. In terms of these changes, this study may provide important information regarding the positive relationship between the direct payments and farm productivity that the new direct payment program may need to consider.