Analysis of Factors Influencing the Transformation of the Workforce from the Agricultural Sector to the Non-Agricultural Sector in Bagik Polak Village, West Lombok Regency - Sumarni Puspawati; Akung Daeng; Gusti Ayu Arini (Westscience Press, 2024)
This study aims to analyze the influence of income, education, age and gender on the transformation of the workforce from the agricultural sector to the non-agricultural sector in Bagik Polak Village. The type of research used in this study is the associative quantitative method. The test was carried out on 94 respondents spread across 7 hamlets, namely Karang buncu lauq, Karang buncu Barat, Karang buncu daye, Rerot, Karang kebon timur, Karang kebon Barat and Enjak. Data was obtained by distributing questionnaires and sampling research using the purposive sampling method. The analysis tool used is logistic regression (logit) which has three tests including Assessing the Overall Model Fit, Testing the Feasibility of the Regression Model (Goodness of Fit Test) and Determination Coefficient (Nagelkerke's R Square). As well as statistical tests, namely the Likelihood Ratio Test (Simultaneous Test F) and the Wald Test (Partial Test t). The results of the analysis show that purely of the four variables used in this study, there are two variables that have a positive and significant effect, namely income with a significant value of 0.045< 0.05; and education with a significant value of 0.019 < 0.05. Meanwhile, the age variables with a significant value of 0.796 > 0.05 and gender with a significant value of 0.851 > 0.05 did not have a significant effect on the transformation of the workforce from the agricultural sector to the non-agricultural sector in Bagik Polak Village. Meanwhile, simultaneously that together, the independent variable (X) has an effect on the transformation of the workforce from the agricultural sector to the non-agricultural sector with a significant chi-square value of 0.004 < 0.05. Meanwhile, Nagelkerke's R2 value shows that all independent variables are able to explain the dependent variable by 42.7% and the remaining 57.3% are explained by other variables that are not included in the model.