Sitemap

Original title: Bouncing Back: Sebuah Bukti Empiris Dampak Ekonomi Agregat dari Bencana Alam di Indonesia

Indonesia experiences a high frequency of disasters over the years which resulted in a devastating effect on both national and regional economies. Using the city-level data which consists of 497 cities/regencies from 2011 to 2018 which retrieved from the disaster information database (DIBI) by the National Disaster Mitigation Agency (BNPB) and regional data information system (SIMREG) by the National Planning Agency (Bappenas), the study aims to investigate the effect of the disaster on the regional economic growth per capita. Incorporating the Least Square Dummy Variable – Corrected (LSDVC) model, we explored the effect with different disaster types, economic sectors, recovery time, and we also interacted the disaster with the contingency budget to see how it helped the economy to revive from disasters. The study found that each disaster has a different effect on the economic sectors despite the general effect of a disaster shows a negative impact on the regional economy per capita by 0.008 percentage point for one in a thousand houses affected by a disaster or 0.014 percentage point for one in a thousand people affected by a disaster. The primary sector becomes the most prone to the disaster impact due to its labor-intensive characteristic. Furthermore, averagely, cities and regencies in Indonesia can rebound within a year from the impact of the disaster, while the volcanic and geologic disasters even showed a creative destruction process indicated by the positive effect on the economy a year after the disaster occurred. Besides, the contingency budget, on average, was inadequate for the economic recovery when the disaster occurred. This study suggests that the disaster mitigation should be more focused on the labor-intensive sector to improve its resilience against disaster, while the reinvestment in the recovery time could be more focused on the capital-intensive sector to optimize the creative destruction process and to offset the negative impact from the primary sector. The study also found that the model tends to robust in different estimation models.

A timely and reliable prediction of economic activities is crucial in policymaking, especially in the current COVID-19 pandemic situation, which requires real-time decisions. However, making frequent predictions is challenging due to the substantial delays in releasing aggregate economic data. This study aims to nowcast Indonesia’s economic activities during the COVID-19 pandemic using the novel high-frequency Facebook Mobility Index as a predictor. Employing mixed-frequency, mixed-data sampling, and benchmark least-squares models, we expanded the mobility index and used it to track the growth dynamics of the gross regional domestic product of provinces in Java and Bali and performed a bottom-up approach to estimate the aggregated economic growth of the provinces altogether. Our results suggested that the daily Facebook Mobility Index was a considerably reliable predictor for projecting economic activities on time. All models almost consistently produced reliable directional predictions. Notably, we found the mixed data sampling-autoregressive model to be slightly superior to the other models in terms of overall precision and directional predictive accuracy across observations.

Original title: Memetakan Kesejahteraan Dan Regenerasi Masyarakat Nelayan

Original title: Adjusting the Sails : Evaluasi Perjalanan Transformasi Struktur Ekonomi dan Efektivitas Tenaga Kerja di Indonesia

Indonesia's commitment to reducing poverty is quite hindered due to the lack of data quality which makes various social assistances often not targeted. Therefore, this study aims to determine the error pattern in social assistance, especially inclusion error, and its impact on opportunity loss. Using Susenas survey data, descriptive statistics, and multinominal logit methods, this study finds that both inclusion and exclusion errors existed and were distributed in each economic percentile for both programs. The estimation also shows that the inclusion error in the PKH program and BPNT are 34.85% and 39.21%, respectively. In addition, the determinants of errors consisting of household characteristics and regional context are discussed. We also suggest the policy recommendations based on the results. \ Keywords : Social Assistance, Opportunity Loss, Poverty