Poverty Macro System Dynamics Modeling Based on Simultaneous Equations Models

Poverty factors are multidimensional and complex. Currently, to predict the number of people living below the poverty line using the concept of linear thinking. It is necessary to study the causal relationships among poverty factors in form of a system dynamics model. This study aims to predict the poverty rate people in “The Golden Indonesia” 2030 using poverty macro models. The data used are time series data from 2009 to 2018 at the national level (Indonesia), and data sources from the BPS Statistics-Indonesia, and the Ministry of Environment and Forestry of the Republic of Indonesia. The research method uses a system dynamics model, where the system of thinking is created based on the two-stage least square (2SLS) simultaneous equation model. The 2SLS simultaneous equation model testing results show that there are three significant simultaneous equations, including poverty, economic growth, and human development index. Furthermore, the three simultaneous equations show a causal loop diagram (CLD) in a system dynamics model. The mean absolute percentage error (MAPE) is 2.34%, meaning that the macro poverty model is valid. The scenario formats for prediction include “optimistic” for economic growth and the “moderate” for human development index (HDI), total population, unemployment, and environmental quality index variables. The predicted percentage number of poor people in 2030 is 4.12%, a positive deviation of 0.12% from the government’s target of 4%. All parties need to work hard and together for the “optimistic” scenario to be implemented, which is to raise Indonesia’s economic growth to 7.4%. This study assumes that there is no Covid-19 problem and only predicts 10 years due to limited data used in 2010-2018. The novelty of this study is the alignment of the prediction results between the system dynamics and the simultaneous equation models. In general, the system dynamics model is valid and could answer the complexity of a phenomenon to predict poverty.