Indonesia's Economic Growth: Role of Human Capital, Physical Capital, and Social Infrastructure

This study aims to analyze whether economic growth in Indonesia is driven by physical or human capital using panel data analysis consisting of all provinces over the last nine years. The estimation results show that the Indonesian economy is more likely to be driven by physical than human capital. The formation of human capital that has a significant positive effect on economic growth is health. However, the education variable represented by the mean years schooling has no significant effect on economic growth when including the control variable in the research model. To improve the quality of education, the state requires the government to provide substantial educational spending. However, the budget has not been used optimally so that the expected achievements of graduates are not achieved. In addition, education spending has not met the criteria for quality spending. In contrast to education spending, an increase in health spending will increase economic growth by improving the quality of health and life expectancy. A healthier society will have a high level of productivity that impacts the regional and national economy.

Digital economy has a major contribution to Indonesia's Gross Domestic Product (GDP). Digital economy sector that has the highest growth is e-commerce. The development of e-commerce and rapid improvements in internet infrastructure are expected to encourage public consumption, so as to increase GDP growth. The purpose of the research examines the general overview of economic growth and several e-commerce indicators in Indonesia and analyzes the effect of several e-commerce indicators on economic growth in Indonesia from 2016 to 2018. Economic growth and several e-commerce indicators in Indonesia have a positive trend. DKI Jakarta has some of the highest e-commerce indicators compared to other provinces, while East Nusa Tenggara has the biggest number of additional points internet access locations from 2016 to 2018. The results of panel data regression analysis show that the variable computer users, e-commerce users, internet access, and expenditure on information technology communication have a positive and significant effect on economic growth in Indonesia and the e-commerce indicators that have the greatest influence is computer users. Thus, the results of this study indicate the importance of utilizing the role of e-commerce in promoting economic growth in Indonesia. Keywords: economic growth; e-commerce; panel

This study examines inflation, consumption, and economic growth before COVID-19 to see a causal relationship between inflation, consumption, and economic growth in Indonesia so as to provide an overview of the economic impact of post-covid-19 inflation. This research examines data from 2000 until 2020 to be able to produce "autoregressive vectors" that may be used to evaluate the causal link between variables. Based on secondary data from the World Bank, the following multivariate regression model was used to investigate the causal link between Inflation, Gross Domestic Product, and Consumption expenditure in Indonesia. We found that inflation has a significant impact on economic growth and the real sector in Indonesia. Indonesia with a large population contributes greatly to economic growth. The results of this study are quite surprising where inflation actually encourages economic growth and consumption in Indonesia. However, the results of this study need to be confirmed regarding people's purchasing power during the research period. From the period 2000 to 2020, Indonesia has escaped the 1997 Asian crisis so Indonesia's economy is quite stable during the study period. And in 2020 when covid 19 began to spread in Indonesia, online-based purchases supported Indonesian consumption, so inflation during this research period actually boosted economic growth and consumption in Indonesia.

Green technology innovation can bring about dual benefits, i.e., technological progress and energy conservation, as well as emission reduction, which are regarded as effective means to achieve economic development and environmental protection. The influencing factors of green technology innovation have been studied from multiple angles. In order to promote the level of green technology innovation in China from a new perspective, this paper selected human capital as the independent variable, and empirically investigated the direct impact of educational and healthy human capital on green technology innovation, based on the panel data of 30 Chinese provinces (excluding Hong Kong, Macao, Taiwan and Tibet) from 2006 to 2016. Meanwhile, considering the current environmental policy system in China, this paper took environmental regulations as moderating variables, and analyzed the moderating role of three environmental regulations, namely, command-and-control environmental regulations, market-incentivized environmental regulations, and public voluntary environmental regulations, in the impact of human capital on green technology innovation. It was found that (1) educational human capital, with a three-period lag, and healthy human capital significantly promotes green technology innovation; (2) command-and-control environmental regulations, with a one-period lag, and market-incentivized environmental regulations promote green technology innovation, while public voluntary environmental regulations have an insignificant impact on green technology innovation; (3) the moderating effect of command-and-control and market-incentivized environmental regulations in the impact of human capital on green technology innovation is not significant. For public voluntary environmental regulations, the moderating effect between educational human capital and green technology innovation is significantly negative, while the moderating effect of healthy human capital on green technology innovation is not significant.

The Covid-19 pandemic is a serious threat to the world's economy, including Indonesia. This study aims to determine: 1. Will Covid-19 worsen/increase economic growth in Indonesia. 2. Whether the ongoing pandemic news will worsen economic growth in Indonesia and 3. Can both bitcoin and gold commodities improve/worsen the economy in Indonesia. The method used in this research is descriptive qualitative method, which is more specifically, using interactive data analysis method (Miles and Huberman 1992:20 model). Which consists of 4 stages as follows: 1. Data collection, 2. Data presentation, 3. Data reduction and 4. Conclusion. Data collection techniques were carried out using literature studies derived from scientific articles that were relevant to the research problem/title from articles indexed by Scopus. The data analysis technique uses interactive data analysis/descriptive analysis from previous research and phenomena during the pandemic, namely March 2020 s.d. July 2021, using Milles and Huberman model analysis. The results of this study are: 1. The occurrence of the Covid 19 outbreak, pandemic news has a negative effect on economic growth in the world, including in Indonesia. While bitcoin and gold can have a positive effect on economic growth in Indonesia. The conclusion is that the corona virus 19, pandemic news has a negative impact on economic growth in Indonesia, while bitcoin and gold have a positive effect on economic growth in Indonesia. Suggestions to the government, in order to quickly recover the economy, the government accelerates the booster vaccine for its people, while the advice for the public is to quickly follow the booster vaccine, and always obey health protocols so that the economy in Indonesia can recover quickly. As for bitcoin and gold, they can have a positive effect on the economy during a pandemic, so the government needs to promote and promote it.

This study investigates the impact of human capital in the context of health and education on Indonesia's economic growth, which includes physical capital investment and trade openness as control variables. Using time series data from 1981 to 2022 and employing econometric techniques by applying the ARDL model. The findings reveal that education, investment, and trade openness have a positive and statistically significant impact on Indonesia's economic growth. Surprisingly, life expectancy has a negative and significant impact on Indonesia's economic growth. This unexpected result warrants further investigation to identify potential confounding factors or data limitations. Despite this finding, the study emphasizes the crucial role of health in human capital and long-run economic prosperity. Among the policy suggestions are enhancing nutrition, guaranteeing access to high-quality healthcare, and maximizing health transformation through the development of public health services. Concurrently, investments in education, particularly in improving quality, accessibility, and alignment with labor market demands, are essential. These findings underscore the need for a comprehensive approach to economic development that prioritizes human capital development while addressing the complexities of health-growth relationships.

The aim of this research is to determine the influence of Domestic Investment, Labor, Exports and Government Expenditures on Economic Growth in Indonesia. This research explains the influence of Domestic Investment (X1), Labor (X2), Exports (X3), and Government Expenditures (X4) on Economic Growth (Y) in Indonesia. The data used is time series data and was obtained from the Indonesian Central Bureau of Statistics (BPS) for 2006-2020. The data analysis method uses multiple linear regression analysis using eviews 10 software. The research results show that partially domestic investment has a positive and significant effect on economic growth in Indonesia. Partially, labor has a positive and insignificant effect on economic growth in Indonesia. Simultaneously domestic investment, labor, exports and government spending have a positive and significant effect on economic growth in Indonesia.

This study aims to analyze and determine the effect of: (1) government spending on economic growth in Indonesia, (2) taxes on growth in Indonesia, (3) money supply on economic growth in Indonesia and (4) interest rates on economic growth . This study used the Ordinary Least Squared (OLS) analysis tool method. The dependent variable in this study is economic growth. While the independent variables in this study are government spending, taxes, money supply and interest rates. The results of the study conclude that (1) government spending has a significant and positive effect on economic growth in Indonesia. (2) taxes have a significant and positive effect on economic growth in Indonesia. (3) the money supply also has no positive effect on economic growth in Indonesia. (4) interest rates have no effect on economic growth in Indonesia and are negative. Based on these results, the policy that can be suggested by the Government of Indonesia is the need for local government efforts or policy makers to increase government spending (Fiscal Expansion Policy). The government is expected to be able to regulate the appropriate tax allocation so as not to undermine consumers' purchasing power. Central Government and Bank Indonesia in order to maintain liquidity or availability of money in the economy in Indonesia. The government together with the banking sector, especially Bank Indonesia, should maintain a healthy interest rate so that it does not have an impact on reducing investor interest in investing.

The purpose of this study is to analyze the effect of world oil prices and inflation on economic growth in Indonesia. The type of data used is time series data from 1987 to 2020 taken from FRED Economic Data, the World Bank and the Central Statistics Agency. This type of research is descriptive and associative research. The data analysis used is descriptive analysis and inductive analysis. Stationarity test is one of the tests used in inductive analysis; (2) Cointegration Test; (3) Multiple Linear Regression Test and Error Correction Model (ECM); (4) Classical Assumption Test; (5) T test and F test. The results of this study reveal that: (1) world oil prices (X1) have an insignificant negative effect on economic growth (Y1) in Indonesia in the long term and in the short term world oil prices (X1) have an insignificant negative effect on economic growth (Y1) in the long term. positive and significant to economic growth (Y1) in Indonesia; (2) inflation (X2) has an insignificant negative effect on economic growth (Y1) in Indonesia in the long term while in the short term inflation (X2) has an insignificant negative effect on economic growth (Y1) in Indonesia.

We study the accumulation of human capital and the behavior of consumption and earnings in a life cycle equilibrium model with endogenous borrowing constraints. Constraints arise endogenously from the inalienability of human capital and the limited punishments that creditors are able to impose on those who default. The endogeneity of borrowing constraints produces a number of interesting relationships. First, efficient borrowing limits are functions of individual observable characteristics and choices, especially ability and human capital investments. The connection between human capital investments and borrowing limits creates additional incentives to invest beyond those present in models with exogenous constraints. Second, government policies affect the incentives to default and, hence, the limits on private borrowing. As opposed to exogenous constraint models, additional subsidies for investment in human capital should be accompanied by increases in credit, since borrowers are more able to re-pay higher debts. Finally, general equilibrium considerations have an additional role, since borrowing limits depend on the returns to physical and human capital. We calibrate the model to U.S. data and are able to replicate key features of the economy regarding human capital investment, earnings, and consumption. The calibrated model is then used to study the steady state impacts of changes in government policies. We find that changes in bankruptcy laws can have sizeable effects on the accumulation of both human and physical capital. At the aggregate level, general equilibrium forces are important and can reverse the results predicted in partial equilibrium. Government subsidies to education (financed with a proportional tax on earnings) cause lenders to increase credit limits and substantially increase aggregate human and physical capital. Most importantly, we show that the implications of our model are very different from those of standard exogenous constraint models. For example, the effects of increases in initial wealth and government subsidies on investment are substantially greater in our model than in a similar model with exogenous constraints.

South Korea is a case of impressive economic growth: a previously underdeveloped country that, after the 1960s, embarked on a process to achieve development before other underdeveloped countries. South Korea is also a case where innovation processes move from imitation to self-creation thanks to a quick updating or “catching up” process. South Korea’s journey from underdevelopment to development has sparked a rich and well-founded debate within economic theory. These debates weigh the roles of productive factors (Physical, Human, Social, and Financial Capital, Labor, resources, environment), economic agents (State, Firms, Banks), and international trade factors (FDI, Imports, Exports) on its growth process. The central argument of this article establishes that Capital is the central variable that explains the successful outcome of the Korean growth miracle. However, Capital composition is even more important. The impact of Human Capital on the growth process evinces a synergy with Knowledge development. We modify the Solow model using Human, Physical Capital, and Total Factor Productivity as independent variables in a Multivariable Regression Model for the period between 1960 and 1979 on Output per worker. We conclude that Human Capital and Productivity are just as important as Physical Capital for explaining growth per worker in South Korea due their synergistic properties. The study is restricted to the years prior to Park Chung-Hee’s rise to power and ends with his assassination.

Natural resource dependence and the accumulation of physical and human capital in Latin America

In formulating policies, no nation can dispute the significance of investing in human capital in attracting physical capital and fostering economic expansion. Economic Growth has a significant link with Physical Resources and Human Capital. The main aim of this study is to examine the relationship between economic growth and both physical and human capital. The study's main objective is to examine how Pakistan's economic growth has been impacted by both people and physical capital. This study attempts to evaluate the connection between Pakistan's total economic growth, human capital, and physical capital using World Bank time series data from 1990 to 2020. To determine the influence of physical capital and human resources on the nation's economic expansion, descriptive statistics are utilized. The significance of both human and physical capital for Pakistan's growth is highlighted by this study. life expectancy influences human capital, gross fixed capital formation influences physical capital. The average life expectancy in Pakistan is 63 years, according to the results. Politically related flux is also revealed by gross fixed capital formation. It increased to 4.9 percent in 2020. Variations were also seen in the GDP growth rate. The pandemic of 2019 causes a reduction in gross fixed capital formation and life expectancy. The policy implications indicate the need for increased gross fixed capital investment. It is also recommended that the government set aside a sizable portion of its budget. In addition, to achieve economic growth, public awareness campaigns about investments in human and physical capital should be conducted. Keywords: Physical Capital, Human Capital, Economic Growth, Descriptive Analysis, Pakistan

I. Introduction Can technological progress explain differences in growth rates? Interest in technological progress has been revived in recent years by the so-called new growth theory (Fagerberg 1994). This contribution compares long-run economic growth in Indonesia and Thailand in relation to technological progress. It is argued that technological progress as shaped by official policies and the institutional framework of absorption provides an explanation why outcomes have differed so much despite apparently similar conditions under which economic growth took place. Or, more generally formulated, macroeconomic policies need to pay explicit attention to the acquisition of modern technologies in order for rapid economic growth to be sustained (Pack 1992, p. 300). Our comparison between Indonesia and Thailand is based on number of similarities and differences in initial conditions and subsequent performance. In the early 1990s, Thailand and Indonesia were both included in the World Bank's category of so-called Highly Performing Asian Economies (HPAEs) characterized by what then appeared to be sustainable path of steeply increasing levels of GDP per capita underpinned by rapid capital accumulation and spectacular enlargement of exports (World Bank 1993, p. 12). The point of departure in both countries in the 1950s was also similar, in particular with regard to per capita levels of GDP and economic structure. In both countries, nearly four-fifths of the labour force found employment in agriculture whereas the share of manufacturing in GDP amounted to mere 10 per cent (ILO 1996, pp. 214-16; U.N. 1965, pp. 396, 729). The endowment of natural resources was, and still is, considerably richer in Indonesia than in Thailand, which obviously does not explain why Indonesia should lag behind. The chief difference between the two countries lies in the speed and stability of economic growth. Factor accumulation was rapid in both countries but Thailand has apparently been capable of putting resources to use in more efficient and productive way. There are two likely explanations for this difference. The first one is technical efficiency, i.e., the rate of technological development optimizing output under given input constraints. The second one, institutional efficiency, refers to the development of institutions that may reduce transaction costs and facilitate economic change. These two types of efficiency are complements rather than substitutes of each other. Our aim is to gain an insight into major differences in technical and institutional efficiency between Indonesia and Thailand. Technology is conventionally defined as a collection of physical processes that transform inputs into outputs and knowledge and skills that structure the activities involved in carrying out these transformations (Kim 1997, p. 4). Several factors determine the rate at which technological progress occurs: the openness of the economy as reflected by foreign trade and investment, human capital development, infrastructure and business institutions, competitive environment and institutionalization of national research and development (R&D) efforts (Hill 2004b, pp. 356-57). Some of these receive ample attention below, in particular, manifestations of the economy's openness, the institutional environment, and national R&D policies. The structure of the paper is as follows. Section II analyses long-run economic development in growth accounting framework, whereas in section III changes in economic structure are related to technological upgrading. Sections IV and V are devoted to foreign imports of capital goods and FDI respectively. In section VI we turn to the organization of domestic R&D, and in section VII institutions and national policies vis-a-vis technology are reviewed. Section VIII offers summary and conclusions. II. Factor Accumulation and Factor Productivity Long-run paths of economic development may be compared using the ratio between GDP per capita in Indonesia and Thailand. …

This study aims to determine the long-run impact of physical and human capital on GDP by using the panel data set of 13 developed and 11 developing countries over the period 1970–2010. Gross fixed capital formation is used as physical capital indicator while education expenditures and life expectancy at birth are used as human capital indicators. Panel DOLS and FMOLS panel cointegrated regression models are exploited to detect the magnitude and sign of the cointegration relationship and compare the effect of these physical and human capital variables according to these two different country groups. As a consequence of panels DOLS and FMOLS models, the impact of physical capital and education expenditures on GDP in the developed countries is determined as higher than the impact in the developing countries. On the other hand, the impact of life expectancy at birth on GDP is determined as higher in the developing countries.