Statistics Indonesia

Indonesia’s economic transformation and persistent regional disparities highlight the need for comprehensive analytical tools to evaluate structural change and policy impacts. This paper develops Indonesia’s Interregional Social Accounting Matrix (IRSAM) 2019, a major update from the previous 2005 version. IRSAM 2019 features six regions (Sumatra, Java, Bali, Kalimantan, Sulawesi, and Nusa Tenggara–Maluku–Papua), 67 sectors designed to capture emerging industries and policy-relevant activities, and household classifications by income decile. The table is constructed using a top-down hybrid method that combines the RAS approach with survey-style evidence to incorporate new and emerging sectors such as biodiesel, electric vehicles (EVs), and seaweed products. IRSAM 2019 captures interregional flows of labor, capital, and trade, and can be applied to analyze regional and income disparities as well as broader economic transitions. This paper contributes by documenting the evolution of Indonesia’s SAM and IRSAM, detailing the structure and compilation process of IRSAM 2019, and presenting an overview of its aggregated results.

The Special Region of Yogyakarta was the first province in Indonesia to experience population aging. This study analyzed the participation rate of older adults in the workforce and the factors that influenced their decision to work. It used data from the 2024 National Labor Force Survey (Sakernas). Descriptive analysis showed that 69.26% of older adults remained active in the labor market, with the majority working in agriculture (48.25%) and holding informal jobs (84.84%). Their average income was far below the Provincial Minimum Wage. A total of 23.43% of older workers worked more than 48 hours per week, which could pose health risks. Logistic regression revealed that older men were 2.297 times more likely to work than women. Those with less education were 2.984 times more likely to remain employed than those with more education. Older people in rural areas were 2.940 times more likely to work than those in urban areas. However, the likelihood of older adults working decreased by 0.879 times with each additional year of age. Overall, the findings showed that older adults in Yogyakarta were highly dependent on informal employment, which tended to be inadequate in terms of job protection, working hours, and income.

This study introduces the Quantum Becker Model (QBM), a novel quantum-classical hybrid framework that integrates rational choice theory with parameterized quantum neural networks. We created this model to better capture the complex, nonlinear cognitive dynamics that support corrupt decision-making in Indonesia, which conventional economic modeling approaches frequently fail to address. By representing individual choices as evolving qubit states modulated through rotation gates, the QBM naturally accounts for psychological superposition and abrupt behavioral shifts. We calibrated the model using empirical Indonesian data, including the 2024 Anti-Corruption Behavior Index (IPAK = 3.85), low detection probabilities, and sentencing records from 1,768 court decisions. Simulations revealed a near-total collapse of the cognitive state into the corrupt basis (offense probability 99.43 %) under current enforcement conditions. Analysis of the three-dimensional social loss landscape showed that increases in punishment severity yield little deterrence when the probability of apprehension remains low. Optimal policy configurations need significant improvements in detection capabilities rather than relying solely on harsher penalties. The QBM therefore provides policymakers with a robust computational tool for evaluating policy trade-offs and designing more effective, evidence-based anti-corruption strategies. Using quantum machine learning, law and economics, and computational public policy analysis, the proposed approach establishes a novel interdisciplinary connection. Its findings will contribute to quantum economics modeling theory as well as provide practical insights into designing more effective anti-corruption policies based on evidence.

Small Micro Enterprises (MSEs) are populist businesses that have the potential to continue to be developed. To develop the business, MSEs should increase the product capacity needed so that it is requiring much capital. Lack of capital and the ability and knowledge MSE managers make MSEs unable to keep up with changes in customer satisfaction and global competitiveness. The purpose of this study is to analyze the factors that influence the low access of MSEs to capital resources in West Sumatra Province using the logistic regression method. The results of the study indicate that MSEs incorporated in cooperatives have three times greater opportunities to gain access to capital from formal financial institutions compared to businesses that are not members of cooperatives. MSEs that have a business development plan is twice as likely than other businesses that do not have a development plan can access capital from formal financial institutions. From the results, it can be seen from MSEs whose managers are male, less education than high school, locations in rural areas, that work more than five years and have more money to get funds from formal financial institutions.