Factors Affecting Productivity of Upland and Lowland Rice Farms in Matalom, Leyte: A Quantile Regression Approach

This study investigates the determinants of productivity in selected upland and lowland rice farms in Matalom, Leyte using quantile regression approach. Data on rice production are obtained from 40 upland and 40 lowland rice farming households which are randomly selected across all 30 barangays in Matalom, Leyte. Quantile regression analysis is used to provide complete characterization of the determinants of productivity at the higher and lower tails of the distribution. Results show that the factors affecting rice production differ across distribution. In the lower quantile, labor cost and fertilizer cost are the main determinants of rice production while in the median quantile, labor cost, fertilizer cost and farm area positively affect rice production. Moreover, in the upper quantile there are more determinants positively affecting rice production. These include labor cost, fertilizer cost, farm area, household size, male household head, and technical assistance provided to farmers. In addition, results of quantile regression are compared with ordinary least squares (OLS) estimation. The comparative analysis shows that there are some factors which do not have significant coefficient in the OLS estimation but are found to have significant effects with quantile regression. This shows that the coefficients estimated through OLS only provide a partial view of the determinants of rice productivity. By performing quantile regression, we are able to identify significant determinants of productivity which cannot be detected using the mean based regression approach.