This research aims to prove the existence of a convergence process and analyse the effect of investment and energy infrastructure on the convergence process on Sumatra Island by including the element of space to understand spatial convergence better. The dataset used in panel data consists of 154 regions (district/municipality) from 2010 to 2020. The analytical tools used with a spatial econometric approach consist of Spatial Autoregressive (SAR) and Spatial Error Model (SEM). The results of the convergence test prove that there is convergence in both absolute and conditional convergence, and there is a difference in the speed of convergence for the two equations. Meanwhile, the results of the spatial approach state that there are spatial dependencies so that neighbouring regions influence the region. The estimation results of conditional β-convergence reveal that investment and government spending in infrastructure has a positive and significant effect, in contrast to energy infrastructure, which has a negative and significant relationship, and only human capital is not significant to the convergence process in Sumatra.

Small and Medium Industries (SMIs) play an important role in the industrial sector of the Lima Puluh Kota Regency, even though the growth rate has fluctuated in the last six years. The purpose of this study is to identify the potential of Small and Medium Industries (SMIs) Lima Puluh Kota Regency that have comparative and competitive advantages. The analytical methods are location quotient (LQ) and shift-share analysis. Using a variable value of industrial production based on Indonesian Standard Business Classification in five digits of industrial commodity was found, the basic organic chemical industry for raw materials for dyes and pigments, embroidery industry, wood container industry, garment industry of leather, industrial stone goods for home use stairs and displays, the gypsum industry, the components and equipment industry of two-and three-wheeled motorcycles, the soft drink industry and the coconut oil industry are industries that have a comparative as well as a competitive advantage. These industries are suggested to be lead industries in the development of Small and Medium Industries (SMIs) in Lima Puluh Kota Regency.

This study analysed the effect of agglomeration on profits, and price efficiency. This study was carried out in and outside the Production Central Area (PCA) of Payakumbuh. Sixty-seven breeders in Payakumbuh PCA and 67 breeders outside Payakumbuh PCA were involved. Multiple linear regression was used for analysis. The results showed that there were two agglomeration profits in PCA: large scale economies (SE) and localization economies (LE). Urbanization economies (UE) were not found. The agglomeration profits had a significant effect on price efficiency. The difference in business location between PCA and outside PCA had no significant effect. The establishment of a PCA should take into account the agglomeration profits, which can be profitable for entrepreneurs with lower production and transportation costs due to the use of shared facilities. It is important to equip the PCA with supporting infrastructure to develop livestock business with the aim to reduce production costs, and make output competitive.

This study aims to analyze the level of economic disparity and the influence of several economic and social variables on the convergence process between regions on Sumatra Island by using oil & gas and non-oil data to support the achievement of the 10th goal of sustainable development or SDGs 2030. The dataset used is panel data totaling 154 Regencies/Cities from 2010-2020. The analytical tool used is the Theil index, conventional panel data regression approaches, panel spatial data, and comparisons with both approaches to produce the best model. The results of the Theil index show that the disparity trend is decreasing for both data. The disparity value of oil & gas is more remarkable than without oil & gas. Furthermore, comparing the panel data method produces a superior and realistic spatial model. From the spatial model using the SEM approach, the convergence speed for oil & gas is 4.01% with a half-life of about 17 years, and for non-oil & gas, it is 5.37% with a half-life of about 12 years. All variables significantly affect the convergence process and are valid for oil & gas and non-oil & gas data.

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