New parameter dimension of poverty modelling to improve FDI inflows in agriculture sector using fixed and random effects

Abstract

This study applies the empirical analysis and focuses on a new parameter dimension of poverty modelling to improve FDI inflows focusing on the agriculture sector. Data from 2005 to 2019 were collected to determine the relationship between the variables. The models that were being used are the Random and Fixed Effects Method. The test was performed to examine the causal relationship between poverty and FDI in agriculture, inflation and FDI in agriculture, technology advancement and FDI in agriculture and infrastructure, and FDI in agriculture. Random Effects Method is used to analyze the data where there is an assumption that no fixed effects and used to make inferences about the population from where the variables were taken. While the Fixed Effects Method was used to estimate the coefficient of the model of all the variables. According to the results of econometric estimations based on the Random and Fixed effects Model, poverty has positive effects and is statistically significant in the OECD developing region.