우병준 임소영 이두영 이형용 한보현 2018-11-15T13:13:42Z 2018-11-15T13:13:42Z 2017-12-30 979-11-6149-165-3 R838 http://repository.krei.re.kr/handle/2018.oak/22597 농업 분야의 대내외적인 환경 변화와 국가 재정의 효율적 운용 필요성이 강조되면서, 정부의 농업·농촌 정책의 내용과 지원방식의 변화가 요구되고 있다. 이러한 사회·경제적 요구를 충족하기 위해서는 다양한 자료와 통계를 활용한 종합적인 분석이 필요하다. 우리나라의 농업정책 수립과 목적 달성 여부 확인에 활용할 수 있는 자료들은 다양하게 존재한다. 그렇지만 조사 주체와 조사 목적, 조사 대상과 수집 방식 등이 각기 달라서 이들을 모두 이용한 종합적 활용에 어려움이 있다. 이 연구는 농업·농촌 정책을 지원하는 통합적 정보지원체계 구축을 위한 사전 기초연구의 성격을 갖고 있다. 이 연구는 농업경영체와 관련한 주요 지표 및 내용의 정기적·체계적·종합적인 분석 수행을 효과적인 방법들이 무엇인지를 검토하고, 농업경영체 관련 다양한 연구들을 종합적으로 정리·분류하였다. 또한, 관련 자료와 통계들을 활용하여 현황 및 실태를 파악하고, 현안 이슈에 대한 심층 분석을 위해 어떤 방식으로 관련 정보를 정리하고 객관적 지표를 개발·활용할 수 있는지를 검토하였다. Review of the Trend in Research on Agricultural Management Entities and of Analytical Frameworks This study reviewed past research by major topic related to agricultural management entities, classified them for closer analysis, and examined analytical frameworks. Most previous studies focused on the issue of agricultural management entities’ income. However, given the growth of the farm size and the increase in the number of agricultural management entities due to the expansion of capital investment since market opening, it is needed to continue researching the management situation of agricultural management entities based on the size of debts or assets, the stability issue and so on. A concrete analysis of agricultural management entities with various characteristics and their management behavior requires the classification of types that can represent Korea’s agricultural management entities, and a systematic approach based on the classification. In the US and the EU, the types of agricultural management entities are classified based on their characteristics (size) and revenue, and standards by type are applied in collecting and analyzing relevant statistics that are utilized in establishing and analyzing policies. In Korea, there is no standard for classifying agricultural management entities that is used in collecting and analyzing statistics, and some studies have developed and utilized analytical frameworks. Among them, the most representative analytical framework is the standard farm size. By calculating the standard farm size, farm household types are classified, and each type's characteristics are analyzed. This study also applies converting into the standard farm size as the analytical framework for classification. Current Status of Agricultural Management Entities Agricultural management entities mean farmers and agricultural corporations. Full-scale implementation of the agricultural corporation system began as one of the agricultural countermeasures against the Uruguay Round negotiations. Agricultural corporations include farming association corporations and agricultural corporation companies. The data on agricultural holding registration, operated by the Ministry of Agriculture, Food and Rural Affairs, show the current state of agricultural management entities. Nevertheless, the data cannot show the entire present situation exactly in that they only include agricultural management entities that voluntarily registered relevant information to receive loans and subsidies related to agriculture and rural areas. According to Statistics Korea’s Farm Household Economy Survey, farm household income, which had grown continuously, decreased in 2016, because agricultural income fell by 10.6% from KRW 11.26 million to KRW 10.07 million. The proportion of agricultural income in farm household income has declined below 30% since 2011. The continued growth of the proportion and size of nonfarm income gives important implications for policies related to farm household income. According to the results of the Farm Household Economy Survey, the farm management status seems to have improved, as the total debt of farm households has decreased since 2014, while their total assets have increased continually. Nonetheless, the debt size of young farm operators under 40 years old is also growing in the process of acquiring assets. Therefore, it is needed to identify the current situation to stabilize farm management of this age group. Changes in Major Indicators by Farm Household Type To classify farm households based on the standard farm size, this study utilized microdata from Statistics Korea’s 2008-2016 Farm Household Economy Survey. On the basis of households’ standard farm sizes calculated from the data and farm operators’ age, we divided farm households into 144 categories, and finally set nine types of farm households. The nine types consist of a combination of three age groups (40 years old and under, 41-64 years old, 65 years old and over) and three standard farm sizes (under 0.5 ha, 0.5 ha to under 2 ha, 2 ha and over). Among the nine types, the type of “65 years old and over + 0.5 ha to under 2 ha” accounts for 38.7%, the highest proportion. Particularly, the average annual increase rate of the type of “65 years old and over + under 0.5 ha” was the highest (8.35%), which indicates that the proportion of old small farmers has grown relatively fast. As for the income by farm household type, in case of the same standard farm size, the older farm operators are, the lower their income is. And the larger the standard farm size is, the higher farm household income is. In the group of old farmers, the larger their standard farm size is, the bigger their asset size is. Nevertheless, an increase in middle-aged large farmers’ assets for agriculture is stagnant. Although debts increase in proportion to the farm size in most cases, middle-aged small farmers are different. This study pointed out limits of using microdata and problems that can occur when the counting method of Statistics Korea's Farm Household Economy Survey, which uniformly classifies large animals as fixed assets and small animals as current assets, is applied to the calculation of the standard farm size. Also, the study empirically analyzed that the relative proportion of farm household income sources can change if the support system of rice variable direct payments, which are currently classified as agricultural income, changes into another form such as revenue protection insurance etc. Analysis of Farm Household Income Volatility We examined the trends in farm household income volatility after 2007 by employing the coefficient of variation and indicators of Hwang Euisik (2004). The results show that old small farmers experience higher income volatility than other groups do. Because old small farmers do not have a relatively stable income base, a decrease in the overall income level in rural areas and the growth of income volatility are expected, if aging in rural areas expands continually. In particular, as for old small farmers’ income, because its dependence level on agriculture is very low, support through agricultural subsidies is judged not to be helpful practically to the farmers. Therefore, it is necessary to increase income-based welfare support services including health insurance and national pension support. According to the analysis results, households with a larger farm size show higher income volatility. The reason seems to be that a higher level of dependence on agriculture leads to higher farm household income volatility, as agriculture is much affected by the natural environment. Accordingly, policies for stabilizing farm household income, including the direct payment program currently implemented by the government, can be more important for large farms. The cause and effect of farm household income volatility differ according to the income level. Therefore, to effectively support stabilizing farm household income, it is necessary to divide groups that need support and utilize approaches suitable to each group. In the long term, it is needed to classify farm households based on income and provide support customized to the income. At present, however, there are no data to objectively prove farm household income. Thus, it is required to consider a method that classifies groups to be supported by using data on agricultural holding registration, farmland registers and so forth. Analysis of the Level of Farm Household Income Inequality Income volatility is closely related to the income inequality level. The analysis result of this study shows that an increase in income volatility can contribute to expanding income inequality. To concretely examine the relationship between farm household income inequality and income volatility, this study analyzed the level of farm household income inequality. For this, we utilized three indices of the inequality level such as Gini’s coefficient, the Wolfson index, and Piketty’s approach by using Statistics Korea’s Farm Household Economy Survey, and calculated the contribution level to Gini’s coefficient by income source, farm operator’s age, and standard farm size to analyze inequality factors. According to the analysis result, the income inequality level of farm households is high, even when it is compared with that of urban households. The average income of farm households is lower than that of urban households, and the income inequality level of farm households is higher than that of urban households. This shows the need for policies to enhance low-income farm households’ income. In terms of Piketty’s approach, as the inequality level of farm households by income and asset bracket is lower compared to the entire economy and the inequality level has not changed much, it is judged that a big structural change in the farm household economy is not occurring. Nonetheless, this result can stem from the relatively small scale of the farm household economy in the entire economy. The analysis result of the contribution level to the level of farm household income inequality by source shows that the inequality level of agricultural and nonfarm income most affects an increase in Gini’s coefficient. Therefore, income stabilization policies that simply consider agricultural income have limits in alleviating the level of farm household income inequality. Also, according to the analysis of the contribution level to Gini’s coefficient by the farm operator’s age, the contribution level of old farmers to the income inequality level is higher than that of other age groups, and the level is increasing continuously. Given the continued aging of agriculture, the result indicates the need to systematically manage old farmers’ income. The analysis of the contribution level to Gini’s coefficient by standard farm size shows that the income inequality level of larger standard farm sizes is higher than that of small farms. This result accords with the above- mentioned analysis, indicating that if small farms expand their farm size (that is, if their dependence level on agricultural income increases), the level of farm household income inequality can also rise, with an increase in income instability. Researchers: Woo Byungjoon, Lim Soyeong, Lee Douyoung, Lee Hyungyong, Han Bohyun Research period: 2017. 8.∼ 2017. 12. E-mail address: bjwoo@krei.re.kr 제1장 서론 제2장 농업경영체 연구 동향 및 분석틀 검토 제3장 농업경영체 실태 및 현황 제4장 농가 유형별 주요 지표 변화 제5장 농가 소득변동성 분석 제6장 농가 소득불평등도 분석 제7장 결론 및 추후 연구 제언 한국농촌경제연구원 2017 농업경영체 실태 분석 An Economic Analysis of Agricultural Management Entities in Korea 2017 KREI 보고서 Woo, Byungjoon 2017 농업경영체 실태 분석