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An Identification of a Spatial Interaction Towards Rice Import in Selected ASEAN+3 Countries

Ekalia Yusiana^1*, Dedi Budiman Hakim², Tanti Novianti², Yusman Syaukat³

1Departement of Agriculture Economics, IPB University, Raya Dramaga St., Bogor, Jawa Barat 16680, Indonesia

2Department of Economics Faculty of Economics and Management, IPB University, Bogor, Raya Dramaga St., Bogor, Jawa Barat 16680, Indonesia

3Department of Resource and Environmental Economics, Faculty of Economics and Management, IPB University, Bogor, Raya Dramaga St., Bogor, Jawa Barat 16680, Indonesia

Received: 13 December 2021; Revised: 16 March 2022; Accepted: 1 April 2022

ABSTRACT

Rice turns out as significant commodity in order to meet people’s needs of food on a half of the world's population. Rice is the most consumed commodity in Asia and wold, especially countries that are members of the ASEAN+3. The need for rice that is a basic necessity must be met, thus, the supply must be maintained as well to meet the needs of staple food, and every country conducts a trade through import activities. This study aims at examining whether there is a spatial interaction of rice import in the ASEAN+3. The results of the Moran I Test show that there is a spatial interaction on rice import in the ASEAN+3 towards 11 selected countries. Trade groupings (agglomeration) were also identified in the trade area. Identification of trade groupings occurs in several countries regarding countries that are in the low-high quadrant such as Vietnam, Thailand, Republic of Korea and Indonesia.

Keywords: rice; import; spatial; analysis How to cite:

Yusiana, E., Hakim, D. B., Novianti, T., & Syaukat, Y. (2022). An Identification of a Spatial Interaction Towards Rice Import in Selected ASEAN + 3 Countries. HABITAT, 33(1), 13–23.

https://doi.org/10.21776/ub.habitat.2022.033.1.2 1. Introduction

Rice is a significant commodity towards the world community; it has an important role in agricultural commodities to meet food needs for half the world's population. Rice is the most consumed commodity by the population in Asia.

Since it becomes world’s attention, the demand for rice is estimated to increase by 1.1 percent in 2017 by 503.9 million tons to 1.3 percent in 2018 of 405.8 million tons. This growth is in line to the increasing volume of food needs for both seed and industrial consumption as well as the world's per capita food consumption are estimated to increase from 53.7 kilos in 2017 to 53.9 kilos in 2018 (FAO, 2018).

Asia is the region that dominates the second largest demand for rice after Africa for 24.38% of the total world’s demand. Rice

consumption needs in 2015 to 2019 reached 105 million tons. Consumption needs in the ASEAN+3 reached 29.1 percent of the total world population and affected the demand for rice consumption needs. In 2019, rice consumption in Indonesia reached 37.7 million tons with production about 36.5 million tons, Japan had a rice consumption need of 8.4 million tons with a production of 7.8 million tons, and the Republic of Korea rice consumption needs reached 4.4 million tons with rice production of 3.7 million tons. According to the data, rice consumption in several countries, especially the ASEAN+3 region is still high compared to the production of rice produced, thus requiring these countries to meet their domestic food needs by importing from other countries (ASEANStatistics, 2019).

That the essence of trade theory is to see why international trade can occur between countries. In international trade, a country has a comparative advantage over goods so that a country exports goods produced by using

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*Correspondence Author.

E-mail: ekalia.yusiana@faperta.unsika.ac.id Phone: +62-822-39222848

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Available online at HABITAT website: http://www.habitat.ub.ac.id production factors intensively. Explains that trade

between countries or better known as international trade includes the process of exchanging goods and services between countries. Also defines that international trade is a trading activity carried out by residents of a country with other residents on the basis of mutual agreement. The residents of the country in question are individuals with individuals, between individuals and the government of a country or the government of a country with the government of another country. In many countries, international trade is one of the main factors to increase Gross Domestic Product (GDP).

The Philippines decided to implement a policy of restricting imports and overcoming inflation by imposing tariffs through reforming production, consumption and price. That protection is shown to protect domestic production against competition for imported goods in the domestic market. The protection carried out includes tariffs (taxes) on imported goods entering the country where the tariff is a tax on imported goods. Tariffs on imported goods are one of the barriers to international trade, with tariffs, of course there are parties who benefit and those who are harmed. The reforms carried out increased the imports by 2.47 percent in 2019 which affected the decline in agricultural prices by 30.1 percent and increased rice consumption (Balié and Valera, 2020). Price is one of the factors that can affect the demand for goods. In this case, the price has a negative relationship with the quantity demanded.

Imports are still needed in various ASEAN+3 countries. The government implements an import policy to maintain reserves of domestic rice needs. China is a major consumer and producer of rice in the world. The need for irrigation and adequate temperatures are very necessary for rice cultivation but cannot be met in China, resulting in the production area and geographical discrepancy for rice demand and production. Therefore, the rice supply problems are the main and important thing in China in order to optimize rice redistributions as well as to support rice production plans in China in the future (Yang et al., 2021). The growth of rice imports in the ASEAN+3 can be seen in table 1.

Table 1. The growth of rice imports in the ASEAN Plus Three region within 2010-2018 (tons/year)

Negara Growth (%)

Indonesia 16.00

Malaysia -1.76

Philippines -3.57

Singapore -0.94

Thailand 13.74

Vietnam -2.01

Brunei Darussalam 3.19

Myanmar 10.00

Cambodia 13.13

Lao PDR 9.24

China 30.23

Japan 0.14

Republic of Korea 2.64

Source: ITC (2019)

In the 2009-2018, the volume of rice imports in the ASEAN+3 is fluctuated. Table 1 describes that Indonesia and China got the occurrence of high import growth in 2010-2018 compared to other countries such as Singapore, Brunei Darussalam, Vietnam, Thailand, Cambodia, Japan, Myanmar, Philippines and Republic of Korea. Indonesia has a relatively high percentage of import growth around 16%.

Indonesia still had to import rice because its consumption exceeded the production of rice produced. During 1998 to 2004, the price elasticity of rice imports from Vietnam and Thailand was negative; it means that if there was an increase in rice prices, the total rice imports would decrease. However, in reality, rice imports from Thailand and Vietnam were still high.

Therefore, the government should improve policies in terms of increasing domestic rice production. (Prasetyo & Anindita, 2016) also state that the problem in Indonesia is due to the increasing population in which it is impacted to the increased demand for rice, but efforts to increase domestic productivity have not been effective enough. In fact, the Indonesian government imported rice when Indonesia experienced a surplus, thus, the government's policy regarding imports continues to be debated until now. Partially Gross Domestic Product (GDP), consumption, and rice prices in the world market have a significant effect on rice imports in Indonesia. From 1997 to 2012, prices, foreign exchange reserves and population had a

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Available online at HABITAT website: http://www.habitat.ub.ac.id significant effect on rice imports in Indonesia. In

addition, imports also have a positive impact that is the increasing economic growth since the need for production factors can be obtained by importing. This is in line with the theory of production that production is the process of converting goods and services that produce inputs (factors of production) into other goods and services (outputs). Inputs or production factors are grouped into three types, namely land, labor, and capital. Based on this function, production (Y) can be increased by increasing the amount of one of the inputs used. Indonesia also plays a role in trade in goods and services in ASEAN with total trade reaching 14.2% in 2014 and falling to 12.9% in 2015. Indonesia and various countries in the world are rice traders.

One of them is China. China also had the highest import growth of 30.23% from 2010 to 2018, in addition to being the largest importing country, China is also a rice exporting country (Allo et al 2017).

An international trade is carried out by several different countries, so that it is important to look at the spatial interactions of trade between countries. ASEAN Plus Three (APT) or ASEAN+3 is a region formed to deepen East Asian cooperation in 1997 in the economic fields including finance, tourism, agriculture, forestry, energy, minerals, and Small and Medium Enterprises (SMEs). Regional cooperation covers all ASEAN countries and 3 partner countries including China, Japan and the Republic of Korea. Currently, the rice market in ASEAN countries, China, Japan and the Republic of Korea is growing rapidly in recent years. (J. W.

Lee & Oh, 2019) stated that since 1990 ASEAN has been involved in various trade agreements to position, secure and expand regional integration in order to promote socio-economic development with the establishment of ASEAN Plus. The result of integration is effective in regional trade;

those are ASEAN+3 and ASEAN+6. The distance factor affects the effectiveness of trading blocks, because long distances will result in higher costs and longer delivery times and hinder market accessibility. This is in accordance with the according to (Krugman, 2004) distance with trading partners is an important measure of geographic trading patterns. Distance can increase transportation costs. Distance is thought to have a negative relationship with bilateral trade.

The volume of exports to ASEAN+6 member countries is higher than non-member countries, thus, the policy implications are significant for creating effective and efficient trade arrangements. (H. Lee et al., 2009). Also indicated that the trade cooperation built between ASEAN+3 and ASEAN+6 through a free trade agreement or Free Trade Area (FTA) has an effect on economic welfare, trade flows and sectorial output of FTA members and other countries. The impact of FTAs for the period 2008 to 2015 through the elimination of tariffs, trade openness increases the welfare of regional economic integration. ASEAN+3 consists of ASEAN, China, Korea, Japan and ASEAN+6 consists of all ASEAN countries, Australia, New Zealand, India, China, Japan and Korea (Vallée et al, 2016). measuring economic integration in ASEAN+3 with foreign direct investment (FDI) shows that economic integration in the ASEAN+3 region can increase the total intraregional FDI inflows, in this case ASEAN+3 has a good influence on economic integration through international trade. The spatial effect is important in looking at trade activities between countries, the ECO and European Union trade cooperation shows that there is a spatial effect in trade in the two zones through export and import activities, causing the introduction of a new index that can be used to evaluate the level of agricultural development in different trade zones (Alamdarlo, 2016). This research on the identification of spatial interactions on rice imports in ASEAN+3 is important to see the spatial interactions in the ASEAN+3 region on rice imports.

2. Theoretical Underpinning

This is in line with the developed trade theory according to (Apridar, 2009). The classical theory only looks at the supply side, but the modern theory looks at the supply and demand side (Munandar, 2010). According to Heckser Ohlin (H-O) theory, international trade occurs based on different opportunity costs. (R.

Hendra Halwani, 2002). Some of the reasons that encourage international trade are differences in natural resources, capital resources, labor and technology (Ekananda, 2015). This is in line with the theory presented by (Halwani, 2002).

When the price of goods decreases, consumers will be willing to buy goods in large quantities so that the goods demanded increase, but conversely if the price of an item is high or when

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Available online at HABITAT website: http://www.habitat.ub.ac.id producers increase the price of goods, the

number of goods purchased will be reduced or slightly (Lipsey et al, 1995). According to (Widarjono, 2018), rice is a staple food for households in Indonesia. After achieving rice self-sufficiency in 1984. (Dibertin, 1986) In economic terms input is everything that is used to produce products while output is everything, namely goods and services produced in a production process. Theory that distance is a geographical factor and a variable in the gravity model, according to (Tinbergen, 1962).

3. Research Methods

This study employed secondary data in the form of time series panel data from 2005 to 2019.

The data used were rice imports, real GDP, population, production, tariffs and exchange rate data. The data was taken from several sources regarding the World Bank, Food and Agriculture Organization (FAO), World Integrated Trade Solution (WITS). The research data were 3 selected ASEAN Plus Three countries covering Indonesia, Malaysia, Philippines, Thailand, Vietnam, Brunei Darussalam, Cambodia, Lao PDR, China, Japan and Republic of Korea.

3.1. Spatial Panel Data Regression

The analysis used in this study was panel data regression analysis, in econometrics various types of regression exist, one of which was panel data regression which combined observations on several individuals (cross section) and several specific time periods (time series). Some of the advantages in using panel data according to ( Baltagi, 2005) are data that is more informative, more varied, more efficient, can avoid multicollinearity problems, can be superior in in dynamic problems, can measure unobservable effects on pure cross section data and pure time series.

Spatial panel data regression is an approach that considers spatial aspects to analyze relationships between individuals. In the spatial analysis, the first step in the analysis is to determine the weighting matrix (w) to see the effect of spatial aspects on the model. The influence is in the form of location coordinates (longitude, latitude) or weighted cross- correlation. The next test is the Morans I test to see whether or not there is a spatial effect and the positive or negative value of the spatial effect. If the test shows a probability that is smaller than the five percent real rate, it can be concluded that

there is a spatial effect on the model. In addition, this test also produces an index between -1 to 1.

If the value is negative, it indicates a negative relationship between neighboring regions, it is concluded that there is a very high inequality.

However, if the value is positive, then there is a positive spatial relationship between neighboring regions, it is shown that there is an agglomeration of phenomena in a neighboring region. In the Morans test can be formulated in the following equation:

(1) Description:

I = Global Moran Index

= A number of locations

yi,yj = Obervation Value at location to I also-j y^- = An average of observation value wij = The weight given between location I

and -j.

The Hypothesis Testing used in the Global Moran Index is:

Ho: I = 0 (There is no autocorrelation between locations)

H1: 1 ≠ 0 (There is autocorrelation between locations)

The expected value is given as follows:

(2) The Moran index value is between -1 and 1 (-1 indicates a perfect negative autocorrelation and a value of 1 indicates a perfect positive autocorrelation). If the value of i is greater than 1, it means that there is a positive autocorrelation, if the value of i<1 there is a negative autocorrelation and if the value of i = 0 then there is no spatial autocorrelation. Spatial analysis is an analysis that incorporates spatial effects into the model. Spatial data analysis cannot be done globally, meaning that each location has its own characteristics.

3.2. Model Spesification

The panel data regression analysis used in this study, generally, refers to the Alamdarlo (2016) model which is displayed in the model as follows:

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Description:

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Available online at HABITAT website: http://www.habitat.ub.ac.id i : Indonesia (an importer)

: Rice imports to Indonesia year t (tons/year)

GDP : Indonesian’s real GDP of year t (individual)

POP : Population or total population of Indonesia in year t, in natural log (ln) (Individual)

PROD: Total rice production in importing countries in year t, in natural log (ln) (tons/year)

TR : Rice import tariffs (Most Favorite Nation) applied in the importing country (%)

ER : LCU exchange rate per US Dollar in year t, in natural log (ln) (LCU/US$)

: Spatial lag variable : Spatial error variable

4. Result and Discussion

Table 2. Moran Index of Rice Imports in Selected ASEAN+3 Countries in 2005-2019

Year Moran I p-value

2005 0.3937396 0.005613*

2006 0.4342225 0.003039*

2007 0.4965233 0.001094*

2008 0.3928542 0.005686*

2009 0.4051880 0.004738*

2010 0.4263728 0.003434*

2011 0.5762457 0.0002575*

2012 0.4674573 0.001783*

2013 0.5101826 0.0008633*

2014 0.4700675 0.001708*

2015 0.4510627 0.002327*

2016 0.4392225 0.00281*

2017 0.4537657 0.002228*

2018 0.4649224 0.001859*

2019 0.4142886 0.004131*

Moran I Test is an analysis to see whether there is a spatial effect on the model. The spatial weighting matrix used to calculate the Moran index is a distance weighted matrix using the Gaussian kernel function. In the Moran I Test, it shows that the p-value <0.05 in 2005 to 2019.

Therefore, H0 is rejected or it can be concluded that at the 5% significance level. It is stated that

there is a spatial autocorrelation to the value of Rice Imports in 11 Countries from 2005 to 2019.

The value of Moran’s index is in the range 0 < I 1, this indicates that there is a fairly strong positive spatial autocorrelation. Consequently, it can be concluded that the value of rice imports in 11 countries tends to cluster in adjacent locations.

The results of the Moran I Test analysis are presented in Table 2 as follows.

The results of the Moran index obtained in 2005 were 0.3937396. Moreover, in 2019, it was 0.4142886. This indicates that the greater the value of the Moran index, the greater the spatial effect on rice imports. Because the results of the Moran index value are positive, it means that there is a relatively weak positive spatial relationship between ASEAN+3 countries in the rice trade. A positive value on the Moran index indicates an agglomeration or grouping between regions of a country that has a high trade value in mineral resources.

4.1. Moran Scatter Plot

In the Moran scatterplot, the observed location points spread between quadrants I, II, III, and IV which are depicted in each trading year from 2005 to 2019. Each quadrant in the Moran scatterplot has each meaning as follows:

a. In quadrant I, HH (High-High) indicates that areas with high observation values are surrounded by areas with high observation values.

b. In quadrant II, LH (Low-High) indicates that areas with low observation values are surrounded by areas with high observation values.

c. In quadrant III, LL (Low-low) indicates that areas with low observation values are surrounded by areas with low observation values.

d. In quadrant IV, HL (High-Low) indicates that areas with high observation values are surrounded by areas with low observation values.

Spatial autocorrelation is measuring the similarity of objects in space, spatial autocorrelation indicates that the value of a variable in a particular location is influenced by the value of that variable in other locations that are close together. If the adjacent locations are similar, then the location is positively spatially correlated, and conversely if the adjacent locations or areas are not similar, the location is negatively spatially correlated. In addition, the random shape indicates that there is no spatial

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Available online at HABITAT website: http://www.habitat.ub.ac.id autocorrelation. One of the indices used to

identify spatial autocorrelation is the Moran's I statistic or the so-called Moran index. Moran index is a statistic that is often used in determining spatial autocorrelation. At several points the location has been identified with various quadrants and depicted in the identification map of rice import locations in various countries. The identification is done by looking at the 4 quadrants which are classified into various categories including high-high, low- low, low-high and high-low.

4.2. Identification of Yearly Rice Imports in ASEAN +3 in 2005-2019

Figure 1. Moran Scattermap in 2005

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Available online at HABITAT website: http://www.habitat.ub.ac.id Figure 4. Moran Scattermap in 2008

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Figure 13. Moran Scattermap in2017

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Available online at HABITAT website: http://www.habitat.ub.ac.id The spatial interactions identified through

the Moran I Test can be seen in Figures 1 to 15. It is recognized that there is a spatial interaction on rice imports in the ASEAN+3, the spatial interaction is identified by looking at 4 quadrants including the high-high, low-high, high- low and low-low. Figures 1 2, 4 and 5 show that the rice trade through import activities in the ASEAN+3 region has agglomeration (grouping) of trade which is indicated by the results of mapping the average value of rice imports. Figure 1 shows that trade interactions are in the low-high quadrant shown in blue including Vietnam, Thailand, Republic of Korea and Indonesia.

Vietnam is a rice importing country with a low volume of trade so that countries close to Vietnam such as Thailand, Republic of Korea and Indonesia have a higher trade value than countries located further away from Vietnam. It is evident that Thailand, the Republic of Korea and Indonesia are the second, third and fourth trading countries for rice imports, respectively. In contrast to Figures 3, 7,8,9,10,11,12 and 13, Republic of Korea is a country with a low-high quadrant, while in Figure 6, rice imports in the ASEAN+3 region are shown in the low-high quadrant including Thailand, Vietnam and Republic of Korea. This indicates that rice imports in Republic of Korea have a spatial relationship with countries that are located close together such as Thailand and Vietnam compared to countries that are located far apart. Likewise, Figures 14 and 15, the countries that have close locations and are included in the low-high quadrant are Malaysia, Japan, Republic of Korea (Figure 14) and Indonesia, Vietnam, Japan and Republic of Korea (Figure 15). From the figure mentioned, it can be concluded that there is a spatial interaction on rice imports in the ASEAN+3, the spatial interaction is characterized by trade grouping based on the closest location in each country.

5. Conslusion

The purpose of this study aims at examining whether there is a spatial interaction of rice imports in the ASEAN+3. The results of the Moran I Test show that there is a spatial interaction on rice imports in the ASEAN+3 towards 11 selected countries. Trade groupings (agglomeration) were also identified in the trade area. It occurs in several countries including countries that are in the low-high quadrant such as Vietnam, Thailand, Republic of Korea and

Indonesia. This indicates that Vietnam is a rice importing country with a low volume of trade so that countries close to Vietnam such as Thailand, Republic of Korea and Indonesia have a higher trade value than countries located further away from Vietnam.

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