Indonesian maize imports: a gravity approach

Abstract

The implementation of the ASEAN Economic Community (AEC) will affect the trade flow of goods and services in the ASEAN region. The flow of food from surplus countries to the food-insecure countries will improve national food security. The purpose of this study is to investigate the factors that affect Indonesia’s maize Import in ASEAN trade. The study analyzed the annual data from 1990 to 2016 and cross-section ASEAN member countries, as well as Indonesia’s trading partners such as Malaysia, the Philippines, Thailand, Vietnam, Myanmar, and Singapore. The study applies the Gravity model approach to identify factors that influence maize import in Indonesia. The panel data regression applied to the fixed-effect method. The results show that Indonesian maize imports were influenced by growth in GDP per capita, economic distance, import tariffs, the exchange rate of the Rupiah against the Dollar, the implementation of AEC (ASEAN Economic Community), non-tariff barriers, and the population growth. All the independent variables together affected Indonesian maize imports from Southeast Asia. The empirical results of this study reveal the implications for the development of the production and competitiveness of Indonesian maize. The Indonesian government should strive to increase farm productivity and efficiency to increase maize production.

Hofstede’s cultural dimensions in the gravity model using mixed-effect model

“…Kristjánsdóttir et al (2017) modified the gravity model by changing the distance between countries with Hofstede's cultural dimensions, whereas DiRienzo & Das (2020) do not apply the gravity model. The focus of some studies is not on a certain commodity (Aryani et al, 2020;Effendi, 2014;Supriana, 2011;Waristi, 2014), but some studies only inquire about a specific commodity (Bato, 2014;Lembang & Pratomo, 2013;Mardiah, 2020;Purnamasari et al, 2020;Rizal, 2018;Suryanta, 2012).…”

“…Some studies use the gravity model to explain the trade between Indonesia and its other main trade partners from some countries and do not limit it with a certain region (Bato, 2014;Effendi, 2014;Hermawan, 2011;Lembang & Pratomo, 2013;Ridwannulloh & Sunaryati, 2018;Rizal, 2018). However, some studies utilize the gravity model to explain the trade between Indonesia and its main trade partners in a specific area, such as Asia Pacific, ASEAN, RCEP, and China ASEAN Free Trade Area (Ambarita & Sirait, 2020;Aryani et al, 2020;Mahendra & Solikin, 2019;Mardiah, 2020;Purnamasari et al, 2020;Supriana, 2011;Suryanta, 2012;Waristi, 2014).…”

“…Despite that, in most of those researches, the gravity model is proved, that is, the distance affects the trade negatively (Ambarita & Sirait, 2020;Bato, 2014;Effendi, 2014;Mahendra & Solikin, 2019;Mardiah, 2020;Purnamasari et al, 2020;Ridwannulloh & Sunaryati, 2018;Rizal, 2018;Supriana, 2011;Suryanta, 2012). Nonetheless, some studies found that the distance affects the trade positively (Aryani et al, 2020;Lembang & Pratomo, 2013;Waristi, 2014), which might be due to the development of technology and communication so that the distance in the distribution process does not become the main resistance because the distribution becomes easier with the technology advances.…”

“…The previous studies employed various trade indicators. Aryani et al (2020); Effendi (2014); Hermawan (2011); Mardiah (2020); Ridwannulloh & Sunaryati (2018) use export value, Lembang & Pratomo (2013) utilizes export volume while Purnamasari et al (2020) make use of import value. Suryanta (2012) uses net export (export minus import), Ambarita & Sirait (2020); Mahendra & Solikin (2019) make use of export value, and import value, Supriana (2011) utilize export volume and import volume, whereas Waristi (2014) employs export value plus import value and Kristjánsdóttir et al (2017) utilizes the ratio of the trade value to GDP.…”

“…The last difference is that the prior studies employ the stochastic frontier analysis (Effendi, 2014), the estimated generalized least square (EGLS) (Bato, 2014;Effendi, 2014;Hermawan, 2011;Lembang & Pratomo, 2013;Rizal, 2018), the multiple regression model (DiRienzo & Das, 2020), the pooled least square or the common effect model (Bato, 2014;Effendi, 2014;Hermawan, 2011;Lembang & Pratomo, 2013;Rizal, 2018;Suryanta, 2012;Waristi, 2014), the fixed effect model (FEM) (Aryani et al, 2020;Mahendra & Solikin, 2019;Purnamasari et al, 2020;Ridwannulloh & Sunaryati, 2018;Waristi, 2014) or the random effect model (REM) (Ambarita & Sirait, 2020;Lembang & Pratomo, 2013;Mahendra & Solikin, 2019;Ridwannulloh & Sunaryati, 2018;Rizal, 2018;Waristi, 2014), in which studies that use FEM and REM do not estimate the difference on the constant model and the random effect within countries being studied. Meanwhile, this study applies the mixedeffect model, which enables us to differentiate the constant model and the regression coefficients within the studied countries, both for variables with constant and various values within time.…”