Analysis of Input-Output Table: Identifying Leading Sectors in Indonesia (Case Study in 2010, 2016 and 2020)

Authors

  • Yoga Dwi Nugroho BPS Statistics

DOI:

https://doi.org/10.34123/icdsos.v2021i1.251

Abstract

According to Neoclassical theory every country has to maximize their own resources include labor, natural resources also physical resources for developing their economy. Sector-based economic development must be carried out using comprehensive economic indicators, not only looking at the economic structure but also being able to identify and analyze inter-industry relationships. One of the right indicators is through the analysis of the Input-Output Table. The I-O table used is this research are I-O Table 2010, 2016, and 2020 estimated. In this comprehensive analysis, the Forward and Backward Linkage Indexes were calculated so that the sectors that are included in the Leading sector can be identified. In addition, a good multiplier analysis is carried out include output, income, labor, and value-added multiplier to see the amount of output, income, labor, and value-added changes caused by the changes of final demand. The results of the research show that sector that is included as a lever sector is the manufacturing sector (sector 3) and the procurement of electricity and gas (sector 4). Sector 3 is the most potential sector as leading sector due to some reasons this sector has a large output, added value and input structure, and has a high multiplier for four types of multipliers and analysis of Forward linkages and Backward Linkage Indexes shows this sector has high value. Manufacturing industry is a strong leading sector, from this the recommendation is the government can increase output of the manufacturing industry by give subsidy or decrease the tax or government can decrease the price of another sector that be the intermediate sector for manufacturing industry by giving subsidy.

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Published

2022-01-04

How to Cite

Nugroho, Y. D. (2022). Analysis of Input-Output Table: Identifying Leading Sectors in Indonesia (Case Study in 2010, 2016 and 2020). Proceedings of The International Conference on Data Science and Official Statistics, 2021(1), 985–997. https://doi.org/10.34123/icdsos.v2021i1.251