Spatial analysis on the regional and provincial rice prices in the Philippines

• Main Authors: Ko, Celine Daphne T., Ngo, Diorella Mareena F., Tan, Jasmine Kate L.
• Format: text
• Language: English
• Published: Animo Repository 2022
• Subjects: Rice—Prices > Philippines; Mathematics
• Online Access: https://animorepository.dlsu.edu.ph/etdb_math/4; https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1009&context=etdb_math
• Institution: De La Salle University

Rice is a staple in most Filipinos’ meals. Knowing that this plays a huge role in their diets, this study aims to determine if location affects rice prices, and if so, the extent of its effect. The proponents also explore the possible factors associated with rice prices across different regions. To do these, the researchers utilize regional and provincial data on rice prices to test for spatial autocorrelation, then identify possible clusters that may arise from the data. Moreover, spatial regression models are used to study the possible relationships of several economic factors and rice prices. With that said, the price of rice was revealed to exhibit spatial autocorrelation in both provincial and regional levels. While provincial rice prices manifest weakly positive spatial autocorrelation, regional rice prices demonstrate stronger negative spatial autocorrelation. Though there appeared to be no significant clusters in the regional level, provincial rice prices demonstrated clusters with peaks in agricultural areas that produce rice and drops in less accessible areas. Furthermore, socioeconomic variables relating to government support in the agricultural sector, palay production, corn production, fertilizer prices, and salary of farmers were found to have a significant correlation with rice prices. Each of these socioeconomic factors proved to have a significant effect on rice prices, through simple linear regression. Adding to that, covariates relating to the size of irrigable area as well as the average dealers' prices of complete fertilizers were concluded to have the Moran’s I with the lowest p-value, once location is taken into account, based on the spatial Durbin model and the—more appropriate—spatial error model. Knowing this, the data in the study may enable government agencies to better monitor and effectively plan policies concerning rice prices across the country.