The Development of Economic Infrastructure in Western and Eastern Indonesia to Support People’s Welfare Improvement

Abstract

Economic infrastructure development remains a strategic choice to accelerate economic growth and ensure equitable welfare. While those goals demand an ever-increasing budget, the infrastructure development budget allocation has been limited due to the COVID-19 outbreak. This research aims to analyze: (1) the impacts of Length of Roads, Number of Motor Vehicles, Electricity Distributed, and Percentage of Households with Internet Access on GRDP per Capita at Constant Prices and (2) the annual trend in infrastructure expenditure budget. GRDP per Capita at Constant Prices is used herein to measure welfare. This research employs Statistical Method, Pooled Data Regression Model, and Least Square Trend Method, and uses secondary data series throughout 2015-2019 from 10 provinces across Central and Eastern Indonesia. The results show that the Number of Motor Vehicles has a negative and significant impact on welfare improvement, while the Percentage of Households with Internet Access makes a positive and significant impact. The impacts of Length of Roads and Electricity Distributed, however, are not significant. The projected infrastructure expenditure budget in 2024 amounts to Rp453.5 trillion with an average annual increase of Rp18.6 trillion, much lower than the expenditure needs. In light of these findings, support from the government is needed to improve domestic connectivity, develop public transport in urban areas, and accelerate digital transformation while training available human resources. The government also needs to involve the public and encourage them to make an investment while collaborating with business entities to overcome the substantial financing gap.

New Digital Infrastructure, Financial Resource Allocation and High Quality Economic Development in the Internet Era

“…Information entropy can represent the degree of disorder of the measured information, and the entropy method is used to weight the indicators according to the amount of information provided by the entropy [17]. The number of details presented and the weighting increase proportionately to the index's degree of fluctuation.…”