Analysis of Economic Ripple Effects in the Agricultural Field Using Input–Output Analysis: Drought Damage in Korea in 2018

Abstract

This study investigates the economic impact of the 2018 agricultural drought in Korea on the agricultural field through input–output analysis. Using industry linkage tables provided by the Bank of Korea, various economic impacts, including socio-economic and industry linkage effects, such as production, value added inducement effects, and employment inducement effects in the agricultural field, were analyzed. Our findings show the following: (1) It was found that an increase of 1 billion KRW (South Korean won) in output of agricultural, forestry, and fishery products induces an average of 0.6544 KRW in production inducement effects in other industries, 0.23756 KRW in value-added inducement effects, and 3.11363 in employment inducement effects per 1 billion KRW. (2) The supply shortage effect of agricultural, forestry, and fishery products was analyzed to cause a decrease in production of 2.3932 KRW across all industries, and the price inducing effect of a 10% increase in price was found to be 0.19400%, on average. The highest production inducement effects in the food and beverage industry (0.16514 KRW) and the highest value-added inducement effects (0.04391 KRW) came from agricultural, forestry, and fishery products. (3) In the industry linkage effect analysis, agricultural, forestry, and fishery products were found to have a forward linkage coefficient of 0.95652 and a backward linkage coefficient of 0.98911. It is implied by this result that the economic impact of agriculture on other industrial sectors is not significant. This study emphasizes the economic importance of agriculture by providing analytical results that can be utilized in agricultural policy formulation and economic decision-making. It can be used as an important basis for policy development for sustainable development and economic stability of the agricultural field. It can also contribute to a better understanding of how agriculture interacts with other industrial sectors and to the development of effective response strategies to natural disasters such as agricultural drought.

1. Introduction

Climate change causes a variety of natural disasters, among which droughts are very damaging in various countries and have significant socio-economic impacts as well as impacts on agriculture and water management [1,2]. The increase in the temperature and variability of precipitation due to climate change increases the frequency and intensity of droughts [3,4]. The occurrence of such droughts has far-reaching spillover effects on national economies through direct impacts such as reduced food production, water shortages, and reduced economic growth [5,6]. Therefore, assessing the economic impact of droughts serves as a basis for quantifying the damages and developing financial and policy responses. Economic modeling of the productivity losses, infrastructure damage, and labor market impacts of droughts is a necessary research area for risk management and effective climate change adaptation strategies.

Studies of the economic impacts of drought on agricultural fields have been conducted in various countries and regions. Economic modeling and analysis techniques have been used to evaluate the impact of agricultural drought on agricultural field productivity, the local economy, and society at large. Related studies have analyzed the direct economic losses and impacts of drought on agricultural productivity and proposed drought management and response strategies [7,8,9]. Regional drought impact assessments have been conducted, and vulnerabilities of the agricultural sector, in particular, have been assessed to develop drought prevention and response policies [10,11,12]. Economic modeling has been used to assess the impact of drought on economic growth and local industries, and physical and policy responses have been proposed to minimize economic losses [13,14,15]. The impact of drought on global grain markets and agricultural exports has been investigated, and strategies for international responses to drought have been explored [16,17,18]. Statistical and econometric methods have also been used to analyze the long-term effects of drought on agricultural productivity and to propose advances in sustainable agricultural technologies and irrigation systems [19,20,21]. Some studies have quantified the social costs of drought through economic impact assessments of droughts in specific regions and developed effective risk management strategies [22,23,24]. Economic damage to agricultural production occurs not only due to drought but also due to the installation of social infrastructure. In Vietnam’s Vu Gia Thu Bon River basin, economic damage was expected due to a decrease in agricultural production and expansion of reservoirs due to dam development [25].

The economic impact of drought has been studied using economic modeling techniques such as input–output analysis and computable general equilibrium models. These analyses evaluated the multifaceted impacts of drought, assessing vulnerabilities in the agricultural and energy sectors to propose effective response strategies [26,27,28,29,30,31,32]. Direct and indirect economic losses on agriculture were quantified in case studies from Colorado and Mexico [28,32]. Economic impacts were predicted, and strategic approaches for various climate and policy scenarios were proposed through scenario-based analyses [29,30]. Drought’s effects on agriculture, industry, and services were assessed using the computable general equilibrium model, focusing on economic losses and long-term growth impacts to develop mitigation policies [33,34]. The impact of agricultural drought on productivity and income was modeled and economic losses were quantified [35].

Economic modeling has also been used to study the economic impacts of water shortages and droughts. The economic impacts of drought in urban areas such as California, Mexico, and South Korea were analyzed in case studies, and policy responses for water resource management were proposed [36,37,38]. The long-term economic impacts of water shortages in urbanizing regions were investigated, and strategies for sustainable water use were explored [39,40]. The impacts of climate change on water resources were evaluated, and economic and technological regional and national policies to respond to water shortages were proposed [41,42,43,44,45].

The hydrological and environmental impacts of drought were analyzed using data from hydrological modeling and economic loss assessment. To assess the impacts of climate change and drought on water resources, relevant data were analyzed to develop models to predict the frequency and intensity of drought [46,47,48]. The impact of drought on local economies and agricultural sectors has been analyzed through economic modeling, and the economic losses due to drought has been quantified [49,50,51]. Drought response strategies were evaluated, and sustainable water management measures were proposed, particularly in agriculture and urban planning [52,53,54]. The establishment of drought information systems and the utilization of information were explored, and technological approaches to improve the effectiveness of drought prediction and warning systems were proposed [55,56].

This study analyzes the economic impact in the agricultural field based on the drought damage that occurred in 2018 in Korea. Based on the 2018 industry linkage table published by the Bank of Korea, the economic impact was assessed using the input-output analysis method. Our research details are as follows: (1) Among the various industrial classifications, we aim to evaluate the economic impact of agricultural, forestry, and fishery products, which is an industrial classification that affects the agricultural field, in relation to other industries. (2) As an analysis method, we will apply a model that analyzes demand-flow-type ripple effects and a model that has been used to analyze socio-economic ripple effects in input–output analysis in previous studies. (3) Quantitative analysis results are presented by applying various models to analyze the economic impact of agricultural drought in the agricultural field.

2. Materials and Methods

2.1. Overview of Input–Output Analysis

Input–output tables are statistical tables that present the input and output relationships of industries in a single year or a continuous year in a matrix form. Analyzing the interdependence relationship between industries using matrix input–output tables is called inter-industry analysis or input–output analysis. Inter-industry analysis is a method of economic analysis that analyzes input coefficients and production inducement coefficients calculated based on the input–output tables published by the Bank of Korea (

Table 1. 2015 Input–output tables for the major industries.

Outputs Inputs	Intermediate Demand	Final Demand	Total Output
Intermediate Demand	Quadrant I	Quadrant II
Final Demand	Quadrant III	Quadrant IV
Total Output

). Inter-industry analysis begins with the calculation of input coefficients, which represent the input composition ratio of raw materials for each industry. The production inducement coefficient analyzes the direct and indirect production ripple effects of each industry sector on final demand and represents the central analysis result in input–output analysis.

2.2. Input–Output Analysis Model

In South Korea, the Bank of Korea publishes input–output tables. The actual measurement table is prepared in five-year increments and an extension table is provided in one-year increments. Currently, the input–output tables are based on the actual measurement table as of 2015 and categorize industries into 33 large, 83 medium, 165 small, and 381 basic categories. The major categories of the input–output tables published in 2015 are shown in

Table 2. Major classifications of the 2015 Input–output tables.

No.	Industry Name
A	Agricultural, forestry, and fishery products
B	Mineral product
C	Food and beverage
D	Textiles and leather goods
E	Wood and paper, printing
F	Coal and petroleum products
G	Chemical products
H	Nonmetallic mineral products
I	Primary metal product
J	Metal processed products
K	Computer, electronic and optical instruments
L	Electrical equipment
M	Machinery and Equipment
N	Transportation equipment
O	Other manufacturing products
P	Manufacturing toll processing and repairing industrial equipment
Q	Power, gas, and steam
R	Water, waste disposal and recycling services
S	Construction
T	Wholesale and retail and commodity brokerage services
U	Transportation service
V	Restaurants and accommodation services
W	Information and communication and broadcasting services
X	Financial and insurance services
Y	Real estate services
Z	Professional, scientific, and technical services
AA	Business Support Service
AB	Public Administration, Defense, and Social Security
AC	Educational services
AD	Health and social services
AE	Arts, sports, and leisure services
AF	Other Services
AG	Other, etc.

. The actual measurement tables of the input–output tables published in 2015 apply the same classification criteria from 2015 to 2019.

In this study, we analyze the economic ripple effects on agricultural drought through input–output analysis. The analysis methods of six models for input–output analysis are presented from Section 2.2.1, Section 2.2.2, Section 2.2.3, Section 2.2.4, Section 2.2.5 and Section 2.2.6. The framework for input–output analysis is shown in Figure 1.

2.2.1. Production Inducement Effects

Production inducement effects refer to the change in output in industries other than agricultural fields when the production impact in agricultural fields increases by one unit. Production inducement effects is a method of analyzing the amount of output required to satisfy final demand conditions and is defined as shown in Equation (1).

$$\mathrm{X}={\left(\mathrm{I}-\mathrm{A}\right)}^{-1}(\mathrm{Y}-\mathrm{M})$$

(1)

Here, X represents the output matrix by industry, A is the input coefficient matrix, Y is the final demand, and M is total income. The employment inducement effects indicate how much the number of people employed in other industries increases when production increases by one unit. Multiplying the employment coefficient and the production inducement effects matrix, the employment inducement effects are calculated as shown in Equation (2).

Production inducement effects are the amount of output in industries other than the agricultural field that increases when output in the agricultural field increases by one unit. From Equation (1), using the agricultural field under study, Equation (2) is derived.

$${∆\mathrm{X}}^{\mathrm{e}}={\left(\mathrm{I}-{\mathrm{A}}^{\mathrm{e}}\right)}^{-1}({{\mathrm{A}}_{\mathrm{A}\mathrm{g}}}^{\mathrm{e}}∆{\mathrm{X}}_{\mathrm{A}\mathrm{g}})$$

(2)

where ${∆\mathrm{X}}^{\mathrm{e}}$ is the change in output of other industries except the agricultural field, and ${\left(\mathrm{I}-{\mathrm{A}}^{\mathrm{e}}\right)}^{-1}$ is the inverse of the Leontyre matrix excluding the rows and columns of the agricultural field in the input coefficient matrix A. ${{\mathrm{A}}_{\mathrm{A}\mathrm{g}}}^{\mathrm{e}}$ is the column vector of the agricultural field excluding the rows and columns of the agricultural field in the matrix A, and $∆{\mathrm{X}}_{\mathrm{A}\mathrm{g}}$ is the output of the agricultural field.

2.2.2. Value-Added Inducement Effects

The value-added inducement effects show how much the value added of other industries changes when the production sector increases by one unit. Applying the production inducement coefficient matrix and the production inducement effects in Equation (2), the value-added inducement effects are derived as shown in Equation (3).

$${∆\mathrm{V}}^{\mathrm{e}}=\widehat{{{\mathrm{A}}_v}^{\mathrm{e}}}{∆\mathrm{X}}^{\mathrm{e}}={{\mathrm{A}}_v}^{\mathrm{e}}{(I-{\mathrm{A}}^{\mathrm{e}})}^{-1}({{\mathrm{A}}_{\mathrm{A}\mathrm{g}}}^{\mathrm{e}}∆{\mathrm{X}}_{\mathrm{A}\mathrm{g}})$$

(3)

In Equation (3), ${∆\mathrm{V}}^{\mathrm{e}}$ is the impact of the change in value added in other sectors except agricultural field. $\widehat{{{\mathrm{A}}_v}^{\mathrm{e}}}$ denotes the matrix excluding rows and columns from the diagonal matrix of value-added coefficients.

2.2.3. Employment Inducement Effects

The employment inducement effects analyze the change in the number of employees in industries other than the agricultural field when the production impact of the agricultural field increases by one unit. By multiplying the employment coefficient matrix and production inducement effects, the employment inducement effects are derived as shown in Equation (4).

$${∆\mathrm{L}}^{\mathrm{e}}=\widehat{l^{\mathrm{e}}}{∆\mathrm{X}}^{\mathrm{e}}=l^{\mathrm{e}}{(I-{\mathrm{A}}^{\mathrm{e}})}^{-1}({{\mathrm{A}}_{\mathrm{A}\mathrm{g}}}^{\mathrm{e}}∆{\mathrm{X}}_{\mathrm{A}\mathrm{g}})$$

(4)

In Equation (4), ${∆\mathrm{L}}^{\mathrm{e}}$ is the rate of change in the number of employed persons by sector excluding agricultural fields, and $\widehat{l^{\mathrm{e}}}$ is the diagonalization matrix of employment coefficients, excluding the rows and columns of agricultural fields.

2.2.4. Supply Flow Model

The coefficients applied in the supply-driven model are called output coefficients, and the inverse matrix for is obtained by using the output coefficients. The supply-driven model can be used to analyze the effect of direct and indirect sectors on the supply shortage of each industry. The supply-driven model with the exogenous agricultural field under analysis is defined as in Equation (5).

$${∆\mathrm{X}}^{e^{\prime }}={{\mathrm{R}}_{\mathrm{A}\mathrm{g}}}^{\mathrm{e}}∆{\mathrm{X}}_{\mathrm{A}\mathrm{g}}{\left(\mathrm{I}-{\mathrm{R}}^{\mathrm{e}}\right)}^{-1}$$

(5)

where ${{\mathrm{R}}_{\mathrm{A}\mathrm{g}}}^{\mathrm{e}}$ is the row vector of agricultural fields with the agricultural sector elements removed, and ${\left(\mathrm{I}-{\mathrm{R}}^{\mathrm{e}}\right)}^{-1}$ is the output inverse matrix exogenous to the agricultural field. Equation (5) allows us to analyze the spillover effect of the supply shortage in the agricultural field on each industry, which is defined as the supply shortage effect.

2.2.5. Price Inducing Effect

The structure of individual industry sectors in the input–output tables represents the cost structure incurred by the production activities of each industry sector, which can be utilized to analyze the price ripple effects. In this study, the price inducing effect refers to the effect of a change in the price of the output of the agricultural field on the price of the output of other industries other than the agricultural field. The price inducing effect, which is exogenous to the agricultural field under analysis, is defined as shown in Equation (6).

$$∆\overline{{\mathrm{P}}_{\mathrm{e}}}={(\mathrm{I}-{\mathrm{A}}^{{\mathrm{e}}^{\prime }})}^{-1}{{\mathrm{A}}_{\mathrm{A}\mathrm{g}}}^{{\mathrm{e}}^{\prime }}∆\overline{{\mathrm{P}}_{\mathrm{A}\mathrm{g}}}$$

(6)

where $∆\overline{{\mathrm{P}}_{\mathrm{e}}}$ is a vector of price change rates excluding agricultural fields, and $∆\overline{{\mathrm{P}}_{\mathrm{A}\mathrm{g}}}$ is the price change rate in the agricultural field. Excluding the agricultural field component from the sector column vector of Equation (6), we can analyze the price inducing effect of an increase in the price of the agricultural field on other industries.

2.2.6. Industry Linkage Effect

The forward linkage effect of the industry linkage effect indicates the sensitivity of dispersion and is called the sensitivity coefficient. The forward linkage coefficient (${FL}_i$) is the ratio of the number of units the first industry must produce to the industry average in order to increase final demand in all sectors by one unit and is defined by Equation (7).

$${FL}_i=\frac{\frac{1}{n}\sum _{j=1}^n{\alpha }_{ij}}{\frac{1}{n^2}\sum _{i=1}^n\sum _{j=1}^n{\alpha }_{ij}}=\frac{n\sum _{j=1}^n{\alpha }_{ij}}{\sum _{i=1}^n\sum _{j=1}^n{\alpha }_{ij}}$$

(7)

The backward linkage effect of the industry linkage effect indicates the power of dispersion and is called the impact coefficient, which is the ratio of the industry-specific production inducement coefficient to the average production inducement coefficient of all industries. The backward linkage coefficient (${BL}_j$) is defined by Equation (8) for the jth industry:

$${BL}_j=\frac{\frac{1}{n}\sum _{i=1}^n{\alpha }_{ij}}{\frac{1}{n^2}\sum _{i=1}^n\sum _{j=1}^n{\alpha }_{ij}}=\frac{n\sum _{i=1}^n{\alpha }_{ij}}{\sum _{i=1}^n\sum _{j=1}^n{\alpha }_{ij}}.$$

(8)

2.3. Agricultural Drought Damage in Agricultural Fields and Industrial Impacts

Korea has a climate of four seasons and an average annual rainfall of about 1300 mm. However, 50% to 60% of the total rainfall is concentrated from June to August, causing droughts in spring and fall. The main crops grown are rice, barley, corn, potatoes, vegetables, and fruits. In the past, drought damage occurred nationwide in 1967 and 1994, and in some areas such as Chungcheongnam-do, Gyeongsangnam-do, and Jeollanam-do in 2001 and 2015.

In 2018, Korea experienced drought damage to crops products, livestock products, and fishery products in the agricultural field [57,58]. The aggregation of drought damage to crop products, livestock products, and fishery products was established, and the damage area of crop products, the number of dead livestock for livestock products, and the number of animals for fishery products were presented. Crops products were damaged in nine provinces. Livestock products and fishery products suffered damage in one province. The damage status in the agricultural field is shown in Figure 2.

The damage status of agricultural fields caused by drought in 2018 is shown in

Table 3. Agricultural drought damage in agricultural fields in 2018.

Agriculture Field	Damage Status
Crops products	Area of paddy field water drying: 5016 ha Field wither area: 28,253 ha
Livestock products	Chicken deaths: 7,291,000 Pig and cattle deaths: 544,000
Fishery products	Mortality of robber crabs, flounder, etc.: 6,860,000

. The damage area of crop products was 33,269 ha in rice paddies and fields due to water shortage. Livestock products were affected, with 7,835,000 chickens, pigs, and cattle killed. Damage to fishery products resulted in the death of 6,860,000 fish in fish farms.

For the purpose of this study, the classification of the industrial linkage table corresponding to crop products, livestock products, and fishery products in the agricultural field was categorized. The large and medium classifications of agricultural fields based on the input–output tables in 2018 and the year of agricultural drought damage, are shown in

Table 4. Input–output table major and middle categories in the agricultural sector.

Code	Integrated Major Classification	Code	Integrated Middle Classification
A	Agricultural, forestry, and fishery products	01	Crops
		02	Livestock products
		03	Forest products
		04	Marine products
		05	Agriculture, forestry, and fisheries services

. The agricultural field corresponds to agricultural, forestry, and fishery products among the 33 integrated major classifications in the input–output tables.

The integrated middle classification of agriculture, forestry, and fishery products is categorized into five industries. Agriculture, forestry, and fishery products are categorized into crops; livestock products; forest products; marine products; and agriculture, forestry, and fisheries services. Considering the status of drought damage in the agricultural field in 2018, they are all included in the integrated middle classification of the in-put–output tables. Therefore, this study sets the drought damage in the agricultural field in 2018 as agricultural, forestry, and fishery products, which is the integrated major classification of the input–output tables. In addition, the economic impact of drought damage in the agricultural field was exogenous to agricultural, forestry, and fishery products.

3. Results

3.1. Demand-Driven Ripple Effects Using Input–Output Analysis

The ripple effects in the agricultural field were analyzed by means of input–output analysis via industry classification in the demand-driven model for agricultural, forestry, and fishery products. For the analysis method, Equations (1)–(4) were applied to production inducement effects, value-added inducement effects, and employment inducement effects. Production inducement effects, value-added inducement effects, and employment inducement effects are basic analytical methods in which input factors are changed according to the characteristics of each induced effect. Production inducement effects refer to the change in output spillover from other industries when the output of agricultural, forestry, and fishery products increases by 1 KRW. The results of the exogenous production inducement effects of agricultural field products in the input–output analysis are shown in

Table 5. Analysis of production inducement effects of other industries on agricultural, forestry, and fishery products (exogenous).

No.	Industry Name	Agricultural, Forestry, and Fishery Products
		Value	Ranking
B	Mineral product	0.000431	32
C	Food and beverage	0.165144	1
D	Textiles and leather goods	0.015569	12
E	Wood and paper, printing	0.022612	9
F	Coal and petroleum products	0.039937	4
G	Chemical products	0.090287	2
H	Nonmetallic mineral products	0.002638	27
I	Primary metal product	0.006283	20
J	Metal processed products	0.008360	16
K	Computer, electronic and optical instruments	0.006892	19
L	Electrical equipment	0.006072	22
M	Machinery and Equipment	0.006979	18
N	Transportation equipment	0.007811	17
O	Other manufacturing products	0.001599	30
P	Manufacturing toll processing and repairing industrial equipment	0.017437	11
Q	Power, gas, and steam	0.027993	8
R	Water, waste disposal, and recycling services	0.004330	23
S	Construction	0.002610	28
T	Wholesale and retail and commodity brokerage services	0.066498	3
U	Transportation service	0.031748	6
V	Restaurants and accommodation services	0.019237	10
W	Information and communication and broadcasting services	0.014495	13
X	Financial and insurance services	0.031309	7
Y	Real estate services	0.010439	15
Z	Professional, scientific, and technical services	0.038060	5
AA	Business Support Service	0.011629	14
AB	Public Administration, Defense, and Social Security	0.003735	25
AC	Educational services	0.000474	31
AD	Health and social services	0.002961	26
AE	Arts, sports, and leisure services	0.002408	29
AF	Other Services	0.003958	24
AG	Other, etc.	0.006122	21
	Sum	0.676057

.

In terms of agricultural, forestry, and fishery products, the food and beverage industry was analyzed to have the highest production inducement effects at 0.16514 KRW. Other industries with high production inducement effects ranging from 0.03806 KRW to 0.09028 KRW include chemical products; wholesale and retail and commodity brokerage services; coal and petroleum products; and professional, scientific, and technical services. The industry with the lowest production inducement effects was mineral products at 0.00043 KRW. Other industries with low production inducement effects ranging from 0.0047 KRW to 0.00159 KRW include educational services; other manufacturing products; and arts, sports, and leisure services. Overall, the production inducement effects of agriculture, forestry, and fisheries were analyzed to be 0.6544 KRW for every KRW of output increase in other industries.

Value-added inducement effects are also defined as the change in output spillovers from other industries when the output of agricultural, forestry, and fishery products increases by KRW 1. Added value refers to the contribution of production factors such as labor, capital, and management created during the production process. The results of the value-added effect of exogenous agricultural, forestry, and fishery products in the agricultural field in the input–output analysis are shown in

Table 6. Analysis of value added inducement effects of other industries on agricultural, forestry, and fishery products (exogenous).

No.	Industry Name	Agricultural, Forestry, and Fishery Products
		Value	Ranking
B	Mineral product	0.000206	31
C	Food and beverage	0.041944	1
D	Textiles and leather goods	0.003127	15
E	Wood and paper, printing	0.007324	12
F	Coal and petroleum products	0.010066	7
G	Chemical products	0.024229	3
H	Nonmetallic mineral products	0.000803	28
I	Primary metal product	0.001193	26
J	Metal processed products	0.002986	16
K	Computer, electronic, and optical instruments	0.002940	17
L	Electrical equipment	0.001725	22
M	Machinery and Equipment	0.002067	20
N	Transportation equipment	0.001654	23
O	Other manufacturing products	0.000450	29
P	Manufacturing toll processing and repairing industrial equipment	0.008397	8
Q	Power, gas, and steam	0.007241	13
R	Water, waste disposal, and recycling services	0.002346	19
S	Construction	0.001141	27
T	Wholesale and retail and commodity brokerage services	0.035768	2
U	Transportation service	0.011331	6
V	Restaurants and accommodation services	0.006507	14
W	Information and communication and broadcasting services	0.007977	10
X	Financial and insurance services	0.018589	5
Y	Real estate services	0.007679	11
Z	Professional, scientific, and technical services	0.018916	4
AA	Business Support Service	0.007992	9
AB	Public Administration, Defense, and Social Security	0.002833	18
AC	Educational services	0.000340	30
AD	Health and social services	0.001566	24
AE	Arts, sports, and leisure services	0.001297	25
AF	Other Services	0.001770	21
AG	Other, etc.	0.000000	32
	Sum	0.242405

. Value added is a coefficient that reflects the structure of the industry and indicates the significance of the structural characteristics of each industry.

In terms of industries with high value-added inducement effects due to increased output of agricultural, forestry, and fishery products, the food and beverage industry was analyzed to have the highest impact with 0.04391 KRW, the same as production inducement effects. In addition, wholesale and retail and commodity brokerage services; chemical products; professional, scientific, and technical services; and financial and insurance services were analyzed to have high value-added inducement effects ranging from 0.01858 KRW to 0.03576 KRW. The industry with the lowest value-added inducement effects was other, etc. with 0.00000 KRW. In addition, mineral products, educational services, other manufacturing products, and nonmetallic mineral products were analyzed with low value-added inducement effects ranging from 0.00020 KRW to 0.00045 KRW. In terms of overall value-added inducement effects, the agriculture, forestry, and fishery industry generated 0.23756 KRW of value added for every KRW of production increase in other industries.

Employment inducement effects refer to the change of one person employed in each industry for an increase of 1 billion KRW. The results of the employment inducement effects of exogenous agricultural, forestry, and fishery products in the agricultural field in the input–output analysis are shown in

Table 7. Analysis of employment inducement effects of other industries on agricultural, forestry, and fishery products (exogenous).

No.	Industry Name	Agricultural, Forestry, and Fishery Products
		Value	Ranking
B	Mineral product	0.001673	31
C	Food and beverage	0.419456	2
D	Textiles and leather goods	0.057592	13
E	Wood and paper, printing	0.083221	10
F	Coal and petroleum products	0.002986	30
G	Chemical products	0.141875	7
H	Nonmetallic mineral products	0.006681	27
I	Primary metal product	0.005641	29
J	Metal processed products	0.028870	16
K	Computer, electronic, and optical instruments	0.008274	26
L	Electrical equipment	0.014226	24
M	Machinery and Equipment	0.019742	20
N	Transportation equipment	0.015782	23
O	Other manufacturing products	0.009768	25
P	Manufacturing toll processing and repairing industrial equipment	0.126464	8
Q	Power, gas, and steam	0.018319	21
R	Water, waste disposal, and recycling services	0.025699	17
S	Construction	0.017326	22
T	Wholesale and retail and commodity brokerage services	0.814048	1
U	Transportation service	0.296480	3
V	Restaurants and accommodation services	0.228124	5
W	Information and communication and broadcasting services	0.065044	12
X	Financial and insurance services	0.118509	9
Y	Real estate services	0.025448	18
Z	Professional, scientific, and technical services	0.250522	4
AA	Business Support Service	0.143677	6
AB	Public Administration, Defense, and Social Security	0.029680	15
AC	Educational services	0.005769	28
AD	Health and social services	0.033100	14
AE	Arts, sports, and leisure services	0.021664	19
AF	Other Services	0.077971	11
AG	Other, etc.	0.000000	32
	Sum	3.113632

. The employment inducement effects of agricultural, forestry, and fishery products were analyzed with the highest impact of wholesale and retail and commodity brokerage services at 0.81404 person. In addition, high employment inducement effects ranging from 0.22812 person to 0.41945 person were analyzed in the food and beverage industry; transportation services; professional, scientific, and technical services; and restaurants and accommodation services. The lowest employment inducement effects were analyzed in the other, etc. industry with 0 person. In addition, low employment inducement effects ranging from 0.0.00167 person to 0.005769 person were analyzed in mining, coal, and petroleum products; primary metal products; and educational services. The overall employment inducement effects are analyzed to increase by 10 billion KRW, while other industries are analyzed to induce employment of 3.11363 person.

3.2. Socio-Economic Ripple Effects Using Input–Output Analysis

The spillover effect analysis analyzes the input coefficients of intermediate inputs and value-added inputs to total inputs using the balanced supply and demand equation of each industry. And, the supply shortage effect is an analysis method that organizes the allocation of intermediate and final demand to the total supply. The supply shortage effect of agricultural, forestry, and fishery products using the supply-driven model was analyzed using the Leonty price model. The results of the supply shortage effect analysis for agricultural, forestry, and fishery products are shown in

Table 8. Socio-economic ripple effect of supply shortage effect of agricultural, forestry, and fishery products.

No.	Industry Name	Supply Shortage Effect
		Value	Ranking
B	Mineral product	0.000217	32
C	Food and beverage	0.217257	1
D	Textiles and leather goods	0.002128	23
E	Wood and paper, printing	0.013890	4
F	Coal and petroleum products	0.004487	15
G	Chemical products	0.012052	6
H	Nonmetallic mineral products	0.001820	27
I	Primary metal product	0.001844	26
J	Metal processed products	0.004231	17
K	Computer, electronic, and optical instruments	0.001985	25
L	Electrical equipment	0.001707	28
M	Machinery and Equipment	0.001262	30
N	Transportation equipment	0.001371	29
O	Other manufacturing products	0.002200	21
P	Manufacturing toll processing and repairing industrial equipment	0.012847	5
Q	Power, gas, and steam	0.006138	12
R	Water, waste disposal, and recycling services	0.002158	22
S	Construction	0.002350	20
T	Wholesale and retail and commodity brokerage services	0.022181	3
U	Transportation service	0.011079	7
V	Restaurants and accommodation services	0.056660	2
W	Information and communication and broadcasting services	0.004242	16
X	Financial and insurance services	0.006162	11
Y	Real estate services	0.006248	10
Z	Professional, scientific, and technical services	0.010968	8
AA	Business Support Service	0.004157	18
AB	Public Administration, Defense, and Social Security	0.005448	14
AC	Educational services	0.000808	31
AD	Health and social services	0.007473	9
AE	Arts, sports, and leisure services	0.003465	19
AF	Other Services	0.002112	24
AG	Other, etc.	0.005615	13
	Sum	0.436559

. The supply shortage effect is the decrease in production in an industry due to a decrease in output of 1 KRW for agricultural, forestry, and fishery products.

A supply disruption of 1 KRW in agricultural, forestry, and fishery products results in a supply shortage effect of 2.3932 KRW across all industries. If the supply disruption is more than 1 KRW, it can be judged that agricultural, forestry, and fishery products are highly related to other industries. It means that the effect on other industries when agricultural, forestry, and fishery products are not supplied smoothly as intermediate goods for other industries is not small. The supply shortage effect of agricultural, forestry, and fishery products was analyzed to be the highest for the food and beverage industry with 0.217257 KRW. In addition, restaurants and accommodation services; wholesale and retail and commodity brokerage services; wood and paper, printing; manufacturing toll processing; and repairing industrial equipment were analyzed with high supply shortage effects ranging from 0.01284 KRW to 0.0566 KRW. The industry with the lowest supply shortage effect was mineral products with 0.00021 KRW. Other industries with low supply shortage effects ranging from 0.00080 KRW to 0.00170 KRW include educational services, machinery and equipment, transportation equipment, and electrical equipment. The overall supply shortage effect is that for every 1 KRW increase in the production of agricultural, forestry, and fishery products, other industries cause 0.43655 KRW in production inducement effects.

Price inducing effect is a method that analyzes the impact of price changes in raw materials on the price of each production product by industry. The price inducing effect is presented as a percentage of the price increase in the industrial sector using the Leonty price model. The price inducing effect of agricultural, forestry, and fishery products in the agricultural field is analyzed as shown in

Table 9. Socio-economic ripple effects of price inducing effects on agricultural, forestry, and fishery products.

No.	Industry Name	Price Inducing Effect
		Value (%)	Ranking
B	Mineral product	0.07765	12
C	Food and beverage	2.61923	1
D	Textiles and leather goods	0.05394	19
E	Wood and paper, printing	0.14623	7
F	Coal and petroleum products	0.01235	32
G	Chemical products	0.05985	16
H	Nonmetallic mineral products	0.05298	20
I	Primary metal product	0.03577	28
J	Metal processed products	0.04929	23
K	Computer, electronic, and optical instruments	0.01981	30
L	Electrical equipment	0.03865	27
M	Machinery and Equipment	0.04643	24
N	Transportation equipment	0.04142	26
O	Other manufacturing products	0.07237	13
P	Manufacturing toll processing and repairing industrial equipment	0.14798	6
Q	Power, gas, and steam	0.01872	31
R	Water, waste disposal, and recycling services	0.05270	21
S	Construction	0.05451	18
T	Wholesale and retail and commodity brokerage services	0.08591	11
U	Transportation service	0.04488	25
V	Restaurants and accommodation services	1.24350	2
W	Information and communication and broadcasting services	0.05069	22
X	Financial and insurance services	0.05739	17
Y	Real estate services	0.02286	29
Z	Professional, scientific, and technical services	0.09775	9
AA	Business Support Service	0.06118	15
AB	Public Administration, Defense, and Social Security	0.06796	14
AC	Educational services	0.09695	10
AD	Health and social services	0.16012	5
AE	Arts, sports, and leisure services	0.20696	4
AF	Other Services	0.12750	8
AG	Other, etc.	0.28476	3
	Average	0.10909

, which shows the price inducing effect of each industry in % when the price of output products increases by 10% due to the water shortage in 2015.

A 10% increase in the price of agricultural, forestry, and fishery products generate an average price inducing effect of 0.19400% across all industries. The industry with the largest price inducing effect is the food and beverage industry, where a 10% increase in the price of agricultural, forestry, and fishery products increases output by 2.61923%. Food and accommodation services are analyzed as 1.24350%; other services as 0.284767%; arts, sports, and leisure services as 0.20696%; and health and social services as 0.16012%. The industries that are not affected by the increase in the price of agricultural, forestry, and fishery products are coal and petroleum products; power, gas, and steam; and computer, electronic, and optical instruments, ranging from 0.01235% to 0.01981%. In addition, changes in real estate services of 0.02286% and primary metal products of 0.03577% were analyzed.

By analyzing the forward linkage effect and backward linkage effect by industry, the relative position of agricultural, forestry, and fishery products by industry was analyzed as shown in Figure 3. The sensitivity coefficient, which indicates the forward linkage effect, was analyzed to be 0.95652, which is close to 1 for agricultural, forestry, and fishery products. A forward linkage coefficient less than 1 means that agricultural, forestry, and fishery products are less stimulated by industrial growth than other industries. The backward linkage coefficient, which indicates the backward linkage effect, is 0.98911, which is close to 1 for agricultural, forestry, and fishery products. A backward linkage coefficient of less than 1 means that other industries are less influential. Agricultural, forestry, and fishery products are located in the III quadrant in the diagram of backward linkage effect by industry. This is the case when both the influence coefficient and the forward linkage coefficient are smaller than 1. Industries in this quadrant have low influence and low sensitivity. They are mostly independent industries, which means that the occurrence of agricultural drought affects the whole society.

4. Discussion

This study presents a quantitative analysis of the economic impact of agricultural fields using input–output analysis. The object of analysis was to analyze the economic importance of each industry and the impact of natural disasters on agricultural fields, whose damage status was investigated during the agricultural drought that occurred in 2018 in Korea. The industry linkage tables of the agricultural field for input–output analysis are agricultural, forestry, and fishery products of integrated major classification and include all the damaged crops products, livestock products, and fishery products.

This study analyzes the economic spillovers of the agricultural sector using input–output analysis. In the past, input–output analysis has been used to quantitatively evaluate the impact of various sectors such as manufacturing, services, and transportation [9,12,18,19,27,32,34,37,41,46,52,54]. It is considered that the input–output analysis and model applied in this study is an appropriate method for economic evaluation of industrial sectors. In addition, the regional scope of the existing studies analyzed the socio-economic ripple effects in countries such as Europe and North America [32,34,37,50]; however, this study analyzed the specific socio-economic ripple effects in the agricultural field in Korea. Some studies have analyzed economic models through theoretical models or limited case analysis and used them to make policy recommendations [20,29,38,44].

The socio-economic impacts of agricultural drought were presented in terms of the amount of damage, percentage of GDP, etc. In 2011, a drought in Texas caused USD 16.9 billion in socio-economic damages and 106,000 unemployment [7]. In addition, a decrease in agricultural production of USD 183 million was analyzed in southern Colorado, USA. It was analyzed that the agricultural drought resulted in a 20% decrease in the GDP of the region and a loss of 2000 jobs [17,18]. The production inducement effects of agricultural, forestry, and fishery products were mainly concentrated in the food and beverage industry; chemical products; wholesale and retail and commodity brokerage services; coal and petroleum products; and professional, scientific, and technical services. The food and beverage sector had the highest production inducement effects at 0.16514 KRW, and the overall production induced effects amounted to 0.6544 KRW. The value-added inducement effects for a 1 KRW increase in output of agricultural, forestry, and fishery products was 0.23756 KRW, suggesting that the value added inducement capacity of agricultural, forestry, and fishery products is significantly higher than that of other industries. The food and beverage sector had the highest effect at 0.04391 KRW. It was analyzed that drought damage occurs in the agricultural sector and socio-economic damage is estimated at 77,783 billion KRW as of 2018. Additionally, the added value was analyzed to be 27,577 billion KRW. In terms of employment inducement effects, it was analyzed that an increase of 1 billion KRW would induce 3.11363 jobs. In particular, the wholesale and retail and commodity brokerage services sector showed the highest induced effect (0.81404).

CGE models have been used to analyze the economics of Iran and Mexico [21,22,32]. In Iran, it was estimated that a drought could reduce GDP by 7%, resulting in a 12% decrease in income. In Mexico, agriculture accounts for only 4% of the economy, but 15% of the labor force works in agricultural fields. The United Kingdom estimated a reduction in total output of 0.35% to 4%, depending on the severity of the drought and policies [30]. In the Castile and Leon region, a 1% increase in GDP is associated with an estimated 1.4% increase in water use in the manufacturing, energy, and services sectors [43]. In the event of a drought in the agricultural sector, the socio-economic impact was analyzed to be approximately 3.16% of the total production as of 2018. In addition, the value added was analyzed to have an impact of approximately 1.47% on the entire industry. In other countries, the cost of agricultural drought has been estimated to be up to 7% of GDP. However, as Korea is a country that exports industrial products, the economic impact of agriculture is judged to be smaller than that of other industries. Food and beverage, chemical products, and wholesale and retail and commodity brokerage services showed the highest sensitivity in terms of industry-specific impact through the forward linkage effect and backward linkage effect of socio-economic ripple effects. It can be seen that products produced in the agricultural field are used in other industries such as agricultural and livestock processing, cosmetics, and wholesale and retail. These results are a sensitive industrial category in the event of an agricultural drought and require preemptive response.

The socio-economic ripple effects were analyzed in terms of supply shortage effect, price inducing effect, and industry linkage effect. In the case of supply disruption of agricultural, forestry, and fishery products due to drought, production decreased by 2.3932 billion KRW across all industries. This shows that disruptions in the supply of agricultural, forestry, and fishery products have a significant impact on food and beverage, accommodation, and restaurants and accommodation services. Furthermore, the average price inducing effect of a 10% increase in the price of agricultural, forestry, and fishery products on all industries was calculated to be 0.19400%. The largest effect (2.61923%) was seen in the food and beverage industry, reflecting the direct impact of price changes in agricultural, forestry, and fishery products on consumer prices.

In this study, we analyzed the socio-economic impacts of agricultural fields based on the drought damage that occurred in 2018 in Korea. Previous studies have analyzed the GDP ratio or job impacts for economic models. This study analyzed the production inducement effects and value-added inducement effects of an increase of 1 KRW in agricultural, forestry, and fishery products as demand-driven ripple effects due to the occurrence of agricultural drought. In addition, the socio-economic ripple effects of supply shortage effect and price inducing effect were analyzed, and the backward and forward industry linkage effects were also presented. For future research, we would like to propose an economic impact evaluation method by applying multi-year industrial cascade analysis to water use or precipitation. Evaluate the quantitative impact of multi-year water use planning and rainfall fluctuation characteristics over multiple years. We develop measures to alleviate not only the socioeconomic damage caused by the current agricultural drought, but also regional damage through water distribution.

5. Conclusions

This study analyzes the spillover effects of the 2018 agricultural drought in Korea on the national economy. Various economic models were analyzed based on input–output analysis as a research method. The input–output tables were based on the integrated major classification as of 2018, and agricultural, forestry, and fishery products were selected to represent the economic impact of the agricultural field. The interdependence between the agricultural field and other industries was investigated, and the economic impact of agricultural, forestry, and fishery products was analyzed systematically.

The demand-driven ripple effects and socio-economic ripple effects of agricultural, forestry, and fishery products were mainly analyzed in the research methodology. Production inducement effects, value added inducement effects, and employment inducement effects were included in the demand-driven ripple effects. Our findings show that the following: (1) Production inducement effects of 0.6544 KRW and value-added inducement effects of 0.23756 KRW were analyzed for every 1 KRW increase in agricultural, forestry, and fishery products. (2) Employment inducement effects were analyzed to be 3.11363 persons in other industries per 10 billion KRW. (3) The price inducing effect of a 10% increase in the price of agricultural, forestry, and fishery products due to the occurrence of drought was analyzed, with an average price increase of 0.194%. An increase of 2.619% was analyzed for food and beverages, where the use of agricultural and forestry products is the highest. (4) The forward linkage coefficient of agricultural, forestry, and fishery products was found to be 0.95652, and the backward linkage coefficient was 0.98911. This means that agricultural, forestry, and fishery products are sensitive to changes in other industries and at the same time have a significant impact on other industries.

By analyzing the economic impact of the 2018 agricultural drought in Korea on the agricultural field, this study demonstrates the important impact of agriculture on the national economy. In particular, it emphasizes the importance of agriculture through economic ripple effects, such as production, value added, and employment, and provides indicators that can be utilized by policy makers and researchers. The impact of agricultural drought on other industrial sectors can be checked to enable preemptive responses in sensitive industrial sectors. It can be used for disaster prevention or recovery, such as importing agricultural and livestock products used in the industrial field or using stored products. Also, considering the impact of the agricultural field on other industries in the event of drought, it is expected that more effective agriculture drought support policies can be developed and disaster response strategies can be prepared.