Modeling the Influence of Expropriated Farmers' Determinants on Compensation Payments Using Multiple Regression

Abstract

In recent years, Rwanda's economic shift has been triggered by expropriation for land conversion in areas of urbanization, roadways, modern village settlements, and agricultural modernization. Even though various studies on expropriation have been carried out to elucidate constraints associated with expropriation, as far as we know, no Multiple Linear Regression (MLR) analysis models have been used to determine the land-lost farmers' profiles' association with compensatory payments. This study was carried out in the Eastern Province of Rwanda. This study investigated how the expropriated farmers' profiles can influence both the compensatory payment appraisal and expropriation for land conversion. The MLR model was utilized to ascertain the relationships between the response variable (compensatory payments) and the explanatory variables (expropriated farmer profiles). Data were obtained using a questionnaire administered to 90 expropriated farmers selected using purposive and multi-stage sampling techniques and analyzed using STATA. The MLR showed a good fit of the model (R² = 0.6900) with the results that farmer's age, means of acquiring land (the fact of owning land from inheritance), cropping systems (the fact of mono-cropping practice), and satisfaction (the fact of being satisfied) showed statistically significant (p £ 0.05) association with compensatory payments; whereas "ubudehe" (the fact of being a high-income earner) and civil status (the fact of being married) were statistically significant at 10%. An important implication of these results is that in the perspective of expropriation for infrastructure developments that affect farmers' properties, the MLR model can solve several issues associated with this process. As a recommendation, governments, investors, expropriating agencies, and property valuers are encouraged to carry out the process of land expropriation by exploring and controlling the significant factors influencing the process.

Received: 8 April 2023 | Revised: 24 May 2023 | Accepted: 26 May 2023

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data available on request from the corresponding author upon reasonable request.