This study examines whether income inequality and poverty are determinants of crime rates across 34 provinces in Indonesia. Three indicators of income inequality and four poverty measures are tested to examine whether the dimension and degree of unequal welfare distribution are linked to crime occurrences. We use panel data from 2010 to 2019 with the Generalized Method of Moments (GMM) approach. The findings indicate that higher income levels and wider income inequality are associated with higher crime rates. Our first indicator of income inequality, non-food expenditure, has a larger impact on crime rates than our second and third indicators, i.e., the gap in food expenditure and the Gini ratio. Poverty is also positively associated with crime. The wider the poverty gap (a measure of poverty) and the severity index, the higher the deprivation levels among the poor, which lead to more crime. The significant and positive effect of poverty on crime rates, and the positive nexus between crime, income, and inequality suggest that Indonesia will face a higher crime risk as the country becomes increasingly more affluent. In such a scenario, policymakers can leverage education and investment (domestic and foreign) to minimize the crime rate. The government could also strengthen crime prevention programs, crime settlement systems, and policing in Indonesia, and raise the budget for social assistance.

In this study, we investigated the effects of foreign direct investment (FDI) spillovers on firm production and technical efficiency in Indonesia’s manufacturing sector from 2010 to 2015. We scrutinized three different channels of horizontal spillover, namely, demonstration, technology adoption, and competition channels. We also captured the heterogeneous effect of firms by categorizing them based on their technological intensity (low, medium, or high). Using time-varying stochastic production frontier analysis, we found that manufacturing firms in each technological group benefit from FDI, either from productivity improvement or technical efficiency improvement. High-technology firms mainly benefit from FDI due to their high technological absorption capability. Meanwhile, medium-high and medium-low tech firms increase productivity through the demonstration effect and the technology adoption channel, despite underperforming in technical efficiency. Finally, low-tech firms, which primarily employ unskilled workers, suffer from large inefficiency. We found that increased FDI, combined with improvements in technology absorption capacity, can help revitalize the productivity of low-tech firms. FDI, firm size, and access to foreign inputs all support production effects but have no direct positive impact on firms’ technical efficiency. The results of this study suggest that the government should ensure that domestic firms can absorb the technical capability of a foreign presence. Open innovation can help strengthen foreign–domestic linkages to contribute to growth through the transfer of knowledge, skills, and technologies.

We analyzed the relationship between tourism destination competitiveness (TDC) and economic performance using the Travel and Tourism Development Index (TTDI) and various tourism metrics. Employing a Structural Equation Model (SEM) and the latest available 2021 data, we focused on factors such as connectivity, infrastructure, demand drivers, sustainability, and the enabling environment. The findings indicated a positive correlation between these factors and the pillars of destination competitiveness. Key elements such as connectivity, infrastructure, demand drivers, sustainability, and enabling environment significantly influence TDC in Asia. Our research demonstrates that these indicators strongly affect various aspects of performance in Asian countries, including tourism arrivals, revenue, and job creation. We found a negative correlation between certain aspects of destination performance and tourism growth, suggesting that highly competitive destinations may experience lower growth rates than less competitive ones, thereby affecting the overall development of the sector. This highlights the necessity of specific strategies to leverage the positive influence of competitive tourist destinations on economic dynamics and job creation within a destination. This study highlights the crucial role of core resources and infrastructure in enhancing tourism performance and competitiveness, and advocates targeted efforts for sustainable and resilient sector growth. These findings offer valuable insights into the relationship between competitiveness and performance in the Asian tourism industry.

The objectives of this paper are to estimate technical efficiency in rice production and to assess the effect offarm-specific socio-economic factors on the technical efficiency using survey data from 15 provinces inIndonesia, collected in 2008. A stochastic frontier production function model is used to estimate the technicalefficiency of rice farms in each province, and using the model, the influence of socio-economic factors onefficiency is also measured. This study finds that there is a sizeable degree of variation of inefficiency betweenthe 15 provinces. It also finds that factors like land size, income and source of funding are influentialdeterminants of technical efficiency. In terms of age, it also found that younger farmers tend to be more efficient.Expanding the agricultural area, especially outside Java and Sumatera Islands, improving farmers’ income andgiving an incentive to young people to work in the agricultural sector will enhance technical efficiency and thusproductivity, as well as the overall rice output.

This study analyzes whether ongoing liberalization has resulted in more profound trade expansion for Indonesia versus regional partner countries. A gravity model is first employed to find whether regional trade agreements resulted in more significant intra-regional exports or diverted trade. This study applies the generalized method of moment (GMM-sys) and the Poisson pseudo-maximum likelihood (PPML) estimator. Four groups of manufactured products are aggregated according to technology intensity, and two nature-based groups of products are estimated in the dynamic panel model. Additionally, revealed comparative advantage (RCA) and a trade balance index (TBI) for 5120 products are used to map goods based on specialization–advantage patterns. The gravity model indicates that regional trade agreements supported trade in manufactured and naturally sourced goods but not in high-tech and primary goods. Additionally, export expansion took place in goods that revealed comparative advantage and export specialization. Competition has increased between Indonesia and four regional trade partners: China, India, Thailand, and Vietnam. Indonesia gained from trading primary materials while losing in some low-cost manufacturing sectors. The potential for trade expansion remains large, as trade patterns differ among regional partners.

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