Exploring Unbalanced Growth Theory by Linear and Nonlinear Methods: Case of Indonesia

This study seeks to test the Hirschman’s theory of unbalanced growth and the viability of adopting a nonlinear model in Indonesia. It also studies the growth pattern in Indonesia by employing a suite of variable ranking algorithms to find the most significant leading sector during the study period from 1995 to 2015. To this end, an Input-Output framework is applied to detect the high linkage sector(s) (key sectors) of the Indonesian economy. Then the linear and nonlinear relationships between the extracted key sectors and GDP growth are covered with two different approaches specifically, Multiple Linear Regression and Multi-Layered Perceptron (MLP) Artificial Neural Network (ANN). Whereas detection of sector ranking is crucial for preparing a proper development plan; in the same vein, we apply two types of feature ranking methods (namely, Stepwise Regression and Ant Colony Optimization (ACO)-MLP based). Empirical results from linear and non-linear models show that the effects of different sectors on growth in GDP in Indonesia are consistent with the structure of unbalanced growth theory. In general, we found that manufacturing sector is the most strategic sector in Indonesia since it has been selected both by linear and nonlinear forms as the first rank. Therefore, its development path firstly could be reinforced by more investment in this leading sector and then followed by investment in construction, hotels and restaurants, and agriculture.