Unveiling external debt dynamics: Interdependencies of macroeconomic variables in ASEAN-7

This study explores the interplay between external debt, infrastructure investment, epidemic response funding, net exports, and the consumer price index (CPI) in seven ASEAN countries—Indonesia, Myanmar, Thailand, Cambodia, Laos, the Philippines, and Vietnam—during the period from 2000 to 2020. Data were derived from the World Bank, OECD, and IMF. This research uses the autoregressive distributed lag model (ARDL) panel data approach to estimate the short-term and long-term relationships among the variables. Short-term results reveal that infrastructure investment, epidemic response funding, net exports, and the CPI do not significantly impact external debt. However, in the long-term analysis, epidemic response funding, net exports, and the CPI positively affect external debt. These findings have significant implications for policymakers in developing countries, especially within the ASEAN region

How government incentives shape consumer intention to adopt electric vehicles: A study in Batam City

This study analyzes the factors influencing the intention to purchase electric vehicles (EVs) following the implementation of financial incentives. It integrates the Theory of Planned Behavior (TPB), the Technological Acceptance Model (TAM), and extensions of TPB, including consumer knowledge, perceived risk, and financial incentives. Data were collected from 384 respondents, comprising both EV users and non-users in Batam City. The findings reveal that attitude has an insignificant effect on the intention to buy EVs, while perceived usefulness also shows an insignificant effect on attitude. In contrast, consumer knowledge and financial incentives significantly influence both attitude and purchase intention. Perceived risk negatively affects attitude and intention to buy EVs. The study highlights the need for policies providing financial incentives to consider long-term benefits for consumers to ensure sustainable adoption of EVs

Assessing the impact of oil rent on living standards in Nigeria: Evidence from an ARDL Model

Some countries are naturally endowed with abundant natural resources, which serve as a significant source of government revenue for national development. Conversely, other countries lacking such resources rely on alternative means to generate income for their developmental efforts. The disparity in development and living standards among nations, however, cannot be attributed solely to the unequal distribution of natural resources but rather to the effectiveness and efficiency with which resource revenues are utilized. This study examines the effect of oil rents on living standards in Nigeria using the Autoregressive Distributed Lag (ARDL) model. The findings reveal a positive long-run relationship between Living Standard (LS), Oil Rent (OR), and Gross Domestic Product (GDP), while a negative relationship is observed with Oil Price (OP) and Exchange Rate (ER). However, these relationships are found to be statistically insignificant. In the short run, the results show a negative and statistically significant relationship between living standards and the variables of Oil Rent, Oil Price, GDP, and Exchange Rate. These findings highlight the complex dynamics between oil rents and living standards, particularly in the context of short-run economic fluctuations. In light of these results, the study recommends that the government prioritize making all refineries operational to meet domestic fuel demand and reduce the costs associated with fuel importation, which consumes a substantial portion of the country's earnings from oil exports. Furthermore, revenues from oil exports should be channeled into productive projects that directly improve the living standards of citizens, such as investments in infrastructure, healthcare, and education. These measures are essential for ensuring that the wealth generated from natural resources translates into sustainable development and improved quality of life for the populace

Predicting future inflation in Indonesia using Dynamic Model Averaging (DMA)

The features of Indonesia's inflation data, which make it extremely susceptible to shocks like those felt in 2005 and 2008, as well as extensive potential influencing factors, lead to problems in forecasting inflation. These problems include time variation in coefficients, models that can change over time, and many predictors to consider. Dynamic Model Averaging (DMA) solves these problems since it has evolved coefficients and models that change over time. This study uses DMA to predict future inflation by involving eight macroeconomic indicators as exogenous variables. The results of the in-sample analysis show that six predictors are significant in forecasting inflation, with posterior inclusion probability (PIP) being above 40%. Although the remaining predictors have PIP means below 40%, they can still be considered important. The out-of-sample results suggest that DMA performs better than dynamic model selection and models that don’t include exogenous variables, such as autoregressive models. The forecast results indicate a consistent pattern over the 12 months studied. The attempt to control inflation can be achieved by prioritizing the money supply factor, which has the highest PIP value, indicating that it is the most important factor

The role of bank and startup fintech P2P lending in supporting financial credit for Indonesian farmers

One of the challenges faced by farmers is securing capital for the development of their agricultural businesses. Banks and peer-to-peer (P2P) lending fintech startups employ various business models to assist farmers in obtaining the necessary capital. This study investigates the credit financing schemes available to farmers through banks and P2P lending fintech startups. The research, which utilized a qualitative approach, involved collecting both primary and secondary data. Primary data were gathered through comprehensive interviews with two academic experts in the agricultural business sector and five leaders of agri-tech startup companies. Secondary data included: (1) annual financial reports from BRI, Mandiri, and BNI; (2) statistical reports on P2P lending providers from the Financial Services Authority (OJK); and (3) models of financing schemes for farmers derived from a range of empirical sources. A descriptive analysis was subsequently conducted to explore the various financing schemes available to farmers through banks and P2P lending fintech startups, as well as to assess the performance of these financing programs via data on the rate of non-performing loans (NPLs). The findings indicate that the financing schemes implemented by banks predominantly focus on economic factors to facilitate loan repayment. In contrast, P2P lending fintech startup schemes emphasize both economic and social aspects, including enhancing farmers' knowledge in implementing Good Agricultural Practices (GAP) and improving financial literacy, aiming to ensure smooth loan repayments. Furthermore, the study observed an increase in the value of Non-Performing Loans (NPL) among both banks and P2P lending fintech startups during the Covid-19 pandemic

The impact of the blue economy and renewable energy on CO2 emissions in Indonesia: An ARDL approach

Indonesia, the largest archipelagic country in the world with rich marine biodiversity, has significant potential for developing a blue economy encompassing aquaculture, sustainable fisheries, and maritime tourism. However, if not managed sustainably, these activities could increase CO2 emissions. Indonesia is also among the world's highest emitters of greenhouse gases, largely due to deforestation, forest burning for agriculture, and reliance on fossil fuels in the energy sector. Given global commitments to reducing emissions and mitigating climate change, this research explores how the blue economy and the transition to renewable energy can contribute to lowering CO2 emissions. This study examines both short- and long-term impacts using the Autoregressive Distributed Lag (ARDL) approach. The findings reveal that while increased aquaculture production initially reduces CO2 emissions due to efficiency gains and environmentally friendly technologies, its long-term effects are more complex and may lead to higher emissions. On the other hand, renewable energy consumption significantly reduces CO2 emissions in the short and long term. Conversely, higher energy intensity contributes to increased CO2 emissions, which can be mitigated through improved energy efficiency

The effect of economic growth and poverty on stunting in Indonesia

Stunting is a critical issue affecting children under five years old, characterized by inadequate growth due to chronic malnutrition and recurrent infections, especially during the crucial first 1,000 days of life (from age 0 to 23 months). Stunting impacts not only height but also vital functions such as brain development and the immune system, potentially leading to decreased intelligence levels and increased susceptibility to diseases later in life. This study examines the impact of the growth of the Gross Regional Domestic Product (GRDP) in the primary, secondary, and tertiary sectors and the level of rural poverty on stunting in Indonesia. This research, which covers time series data from 2015-2020 across 32 provinces in Indonesia, employs a panel data regression model analysis method. The findings indicate that primary sector GRDP growth has a positive effect, whereas secondary sector GRDP negatively impacts stunting. However, the tertiary sector GRDP and rural poverty do not significantly affect stunting rates in Indonesia

Determinants of Sustainable Development Goals (SDGs) in Indonesia: Mapping with Cartesius Diagram

This research aims to explore the long- and short-term relationships and the quality of interactions between variables within the framework of the Sustainable Development Concept, focusing on the social aspect (Human Development Index), economic aspect (economic growth), and environmental aspect (Environmental Quality Index) in the context of reducing poverty rates in Indonesia. The methodology employed is the Panel Vector Error Correction Model (P-VECM) analysis for panel data, combining time series (2010-2022) and cross-section data (34 provinces in Indonesia), along with a Cartesian diagram to identify which provinces have the greatest potential for achieving evenly distributed SDG progress. The results show that the Granger causality test reveals no one-way or two-way causal relationships or interactions between the human development index, economic growth, and environmental quality index. In the long-term analysis, only the human development index significantly impacts poverty, with a negative correlation. In contrast, economic growth and the environmental quality index do not have a long-term relationship with Indonesia's poverty levels. These findings suggest that improving the quality of education, healthcare, and living standards in the long term can effectively reduce poverty, especially in Indonesia. Pro-poor government policies are crucial to prevent widening inequality and ensure that economic growth benefits the upper class and the lower and middle classes through more equitable income distribution

ICT expansion and human development: Empirical evidence from Indonesia

Human development is a central focus and objective for countries worldwide. Comprehensive human development is reflected in rising living standards and easier access to essential services like education and health. Accelerating human development requires adopting technology, particularly ICT (Information and Communication Technologies), which is increasingly utilized by societies. The main objective of this study is to estimate the impact of ICT Skills, ICT Access, domestic investment, and the democracy index on the Human Development Index (HDI) using provincial-level data in Indonesia. The study employed panel data from 34 provinces from 2016-2022. Based on the Chow and Hausman test results, the fixed effect model was the best fit compared to the common and random effect models. The findings demonstrate that ICT Skills and ICT Access significantly positively affected human development throughout the research period, with their coefficients being almost equal. This highlights the rapid advancement of ICT and its vital role in the lives of the Indonesian population. The results further revealed that democracy was insignificant, while domestic investment positively and significantly impacted human development. Based on these findings, ICT development policies are essential, particularly in investment, infrastructure improvement, and the effective implementation of ICT initiatives. The use of ICT should be tailored to each province's unique characteristics and potential to promote equality and reduce disparities. A key recommendation from this study is the adoption of ICT in societal activities to enhance equality across all regions of Indonesia

Comparative analysis of entrepreneurial intentions among generations in Jambi Province: A study of Gen Bust, Millennials, and iGeneration

In the face of intense global competition, such as the introduction of the ASEAN Economic Community (AEC), the Indonesian workforce must transition from merely seeking employment to creating job opportunities. In this regard, cultivating an entrepreneurial spirit is a strategic alternative to bolster employment prospects and stimulate innovation. This study was carried out in the province of Jambi with the primary objectives of: 1) analyzing the characteristics and entrepreneurial intentions across different generations in Jambi; 2) examining the factors that influence these entrepreneurial intentions. The research compares three generations—Generation Bust (Gen Bust), Millennials, and the iGeneration—all within the productive age bracket and possesses substantial potential to drive development. The research methodology entailed collecting data through surveys administered to individuals from Gen Bust, Millennials, and the iGeneration in Jambi. The data was analyzed using descriptive statistical tools and the Structural Equation Modeling (SEM) technique. The findings reveal notable differences in entrepreneurial intentions among the generations, with Gen Bust and Millennials exhibiting stronger entrepreneurial intentions than iGeneration. Influential factors for these entrepreneurial intentions include attitudinal and contextual elements such as academic, social, and environmental support. Although individual characteristics vary among the generations, they do not consistently exert a direct and significant impact on entrepreneurial intentions, particularly for iGeneration. This research offers crucial insights into how specific factors affect entrepreneurial intentions across different generations, which can assist in developing strategies and policies to foster entrepreneurship in Jambi, especially in light of global and regional economic challenges