Previous studies on the relationship between government debt and economic growth have produced very diverse findings. This study examines the relationship between public debt and economic growth in developing countries using a quantile regression approach with fixed effects and bootstrapping on the 10% to 90% quantile distribution. The quantile grouping is based on specific percentiles of economic growth in developing countries. This study uses panel data from 127 developing countries for the period 2012 to 2019. Data were obtained from the World Development Indicators, the World Bank, and Transparency International. The results of this study indicate that public debt is not friendly to economic growth. Public debt actually hinders economic growth in developing countries, especially in the 30% to 90% quantile. Other factors that influence economic growth in developing countries are trade, inflation rates, government spending, corruption, and net foreign direct investment. Trade and net direct investment significantly increase economic growth in developing countries. Meanwhile, public debt, the inflation rate, government spending, and corruption actually inhibit economic growth in developing countries. On the other hand, education spending, private debt, tax revenues, and labor force participation do not contribute significantly to economic growth in developing countries. These findings confirm that public debt governance and governance are very important in driving economic growth in developing countries. This paper provides empirical and policy contributions to the assessment of institutional effectiveness in relation to the impact of public debt management on economic growth in developing countries.

Labor market dualism has caused a persistent wage gap between formal and informal workers in Indonesia. This study aimed to decompose the wage difference between formal and informal workers based on cohorts in Indonesia and further analyze the factors that influence this gap. This allowed us to identify the structure of the labor market and whether it tends to be segmented or competitive. By controlling the cohort, the estimation result was compatible with the human capital accumulation argument. The data sourced from the 2010 and 2019 National Labor Surveys comprised 581,312 and 503,313 workers, respectively. This study used the threefold Heckman selection-biased corrected Blinder–Oaxaca decomposition technique. The results showed that the wage gap between formal and informal workers is decreasing, especially among the younger cohort. Among this cohort, the labor market is still segmented, although the degree of segmentation is lower. In contrast, the older cohort labor market is more competitive but at a decreasing rate. A further decomposition showed that education has affected the wage gap between young and old cohorts. However, this effect is diminishing. In addition, differences in institutional labor market settings such as the wage system and occupational health and safety systems have increased the wage gap. The findings imply that equitable access to education for the younger cohort and the improvement of labor market institutions can reduce the gap between formal and informal wages.

We employ a Markov-Switching regression model using monthly US data from April 1987 to August 2022 to investigate the effect of climate policy uncertainty (CPU) on renewable energy consumption (REC). The main findings suggest the presence of a nonlinear relationship between CPU and REC. The baseline analysis using the CPU index reveals an adverse effect in regimes characterized by high levels of uncertainty. However, the effect is not statistically significant in a low uncertainty regime. To account for potential variations in the results, we perform a robustness analysis that considers the effect of CPU on REC, which may fluctuate based on the authorities' contextual perspectives (i.e., being in favor or against climate policy) and also the effect of CPU on REC by household. In addition, we incorporate a robustness check by utilizing the environmental policy uncertainty index developed by Noailly et al. (2022). The robustness test results confirm the results obtained from the baseline estimation.