Inter-regional migration in Indonesia: a micro approach

Does Ethnicity Affect Ever Migrating and the Number of Migrations? The Case of Indonesia

This paper is the first to examine to what extent ethnicity affects ever migrating and the number of migrations across the lifespan for the case of internal migration in Indonesia. We use all five waves of the Indonesia Family Life Survey (IFLS) to study migration behaviour of respondents belonging to some of the largest ethnic groups in Indonesia. Our logistic regression results show that the Minangkabau, Betawi, Madurese, Balinese, Buginese and Makassarese, and Sasak, Bima and Dompu are less likely to ever migrate than the Javanese. Using only migrants and controlling for the first migration and other characteristics, truncated negative binomial regression results show that, in comparison with the Javanese, the Minangkabau and Banjarese have a higher expected number of migrations while the numbers are lower for the Betawi and Balinese. Thus, ethnicity contributes to ever migrating as well as the number of migrations, but we find that the differences between the ethnic groups diminish for the latter. These results also point out that a higher likelihood of ever migrating does not always correspond with a higher number of migrations, highlighting the importance of studying migration count to complement the study of migration as a one-time event.

Spatial differentiation and determinants of COVID-19 in Indonesia

Background

The spread of the coronavirus disease 2019 (COVID-19) has increasingly agonized daily lives worldwide. As an archipelagic country, Indonesia has various physical and social environments, which implies that each region has a different response to the pandemic. This study aims to analyze the spatial differentiation of COVID-19 in Indonesia and its interactions with socioenvironmental factors.

Methods

The socioenvironmental factors include seven variables, namely, the internet development index, literacy index, average temperature, urban index, poverty rate, population density (PD) and commuter worker (CW) rate. The multiple linear regression (MLR) and geographically weighted regression (GWR) models are used to analyze the impact of the socioenvironmental factors on COVID-19 cases. COVID-19 data is obtained from the Indonesian Ministry of Health until November 30th 2020.

Results

Results show that the COVID-19 cases in Indonesia are concentrated in Java, which is a densely populated area with high urbanization and industrialization. The other provinces with numerous confirmed COVID-19 cases include South Sulawesi, Bali, and North Sumatra. This study shows that the socioenvironmental factors, simultaneously, influence the increasing of confirmed COVID-19 cases in the 34 provinces of Indonesia. Spatial interactions between the variables in the GWR model are relatively better than those between the variables in the MLR model. The highest spatial tendency is observed outside Java, such as in East Nusa Tenggara, West Nusa Tenggara, and Bali.

Conclusion

Priority for mitigation and outbreak management should be high in areas with high PD, urbanized spaces, and CW.

Distance-Dependent Migration Intention of Villagers: Comparative Study of Peri-Urban and Remote Villages in Indonesia

Rural-to-urban migration disturbs essential factors of rural development, including labor forces, land ownership, and food production. To avoid late responses to emigration, scholars have begun investigating earlier stages of rural emigration. However, prior studies have focused on a single spatial entity only while also leaning toward trends in developed countries. Therefore, this study fills gaps by focusing on the differences in migration intention between villages in less developed settings. In observing the differences, this research takes peri-urban and remote villages as cases located at different distances from their nearest urban destination. This study treats migration intention as the dependent variable while using single-indicator place attachment and multi-indicator information sources as the independent variables. This work applies the Mann–Whitney U, ANOVA, and Brown–Forsythe tests on three hypotheses. This research also uses SEM-PLS to investigate the correlation model of the observed variables for each case. The results show that information sources negatively affect migration intentions in peri-urban settings. Remote rural areas also show similar results for the information sources variable; however, place attachment in remote settings significantly contributes to migration intention. These results show that place attachment and information sources contribute differently, depending on the distance to the urban area. We argue that access to public services and infrastructure contributes to the results. The findings suggest that an increased availability of information sources impedes the formation of migration intentions. Thus, this study suggests the necessity of improving rural infrastructure and public services to improve information literacy. It helps the government control rural emigration while fulfilling its obligation for rural development. It also offers better rural livelihoods during the development progress, providing economic incentives for villagers to stay in villages.