Analysis of Geographically Weighted Regression (GWR) on Retail Prices of Medium Rice and Red Chili in Java

Jan Piter Sinaga,Idha Widi Arsanti,Muhammad Firdaus,Akhmad Fauzi,Agung Hendriadi

doi:10.17358/jma.18.2.144

Abstract

Research using a global regression model might not be appropriate to find out the factors that influence strategic food prices based on spatial characteristics. To analyze the spatial effect, Geographically Weighted Regression (GWR) was employed. GWR models are better than OLS, which is indicated by the higher R2 GWR and lower AIC values. The GWR analysis provides the following findings: (1) the wholesale price most influential on the retail price of medium rice and red chili both during the main harvest and non-harvest periods; (2) the harvest pattern results in the effect of production and producer prices on the retail prices of the major harvest and non-harvest periods. Management of inter-regional distribution must be carried out to maintain supply stability and disparity in food prices between regions; (3) producer prices are integrated with trader prices in the district of production centers and surrounding areas while the integration of food prices at the consumer level occurs in the main economic center area of the region. These aspects have different effects for each region and district because the estimated parameters can be positive or negative. Testing during the harvest season (April) and non-harvest can also produce estimates that vary according to the specific characteristics of each location. Keywords: spatial analysis, Geographically Weighted Regression (GWR), retail prices, wholesale price, spatial distribution patterns

Highlights

Production disparities between time and region, natural disasters, distribution, storage, and limited information are the main factors causing price fluctuations (Udoh and Sunday, 2007)
Because spatial dependency and heterogeneity effects were reported from those analyses, the use of the Geographically Weighted Regression (GWR) model in equation 2 is recommended
Factor analysis using global regression is unsuitable for site-specific analysis in each district/city, so this study uses the Geographically Weighted Regression (GWR) model

Summary

Introduction

Production disparities between time and region, natural disasters, distribution, storage, and limited information are the main factors causing price fluctuations (Udoh and Sunday, 2007). Controlling disparities and high price fluctuations of strategic food prices must be carried out by the government through a more effective and efficient interregional trade process. Barrett and Li (2002) defined market integration as the ability to sell products between markets where demand, supply, and transaction costs in different markets determine prices and trade flows simultaneously and transmitting price shocks. According to Sonogo and Amadou (2010), market integration is the flow of goods and information, prices, distances, shapes, and times or defined as trade relations between markets in the process of forming and transmitting prices from market to market

Objectives

Findings

Conclusion