Just Get Out

The data behind Indonesia’s youth exodus

In early 2025 a hashtag started trending across Indonesian social media: #KaburAjaDulu, which roughly translates to “just get out first.”

It wasn’t really a political protest. It was more a feeling that a lot of young Indonesians seemed to share, that things at home weren’t going to get better fast enough to be worth waiting around for. The message was simple: leave, build a life somewhere else, and send money back home.

This report looks at the data behind that movement.

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Part 1: Stuck at the Starting Line

Indonesia’s youth unemployment is sitting at around 13%. On its own that number is hard to read, so it helps to put Indonesia next to its neighbours.

This chart shows youth unemployment for five Southeast Asian countries from 2005 to 2024. Hover over any line to see the exact figures.

Most of the region brought youth unemployment down over these two decades. Indonesia, in red, started the highest and has barely improved.

Vietnam is the clearest contrast. It kept its youth unemployment low and fairly steady the whole time, while Indonesia stayed stuck near the top of the chart.

So when Indonesian graduates say they feel stuck, the data mostly backs them up.

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Part 2: The Cost of Leaving

When young people leave a country, it shows up in the migration figures. Indonesia has had negative net migration for decades, meaning more people leave than arrive.

The top panel shows that outflow. It had been easing for years, but since 2020 it has turned upward again. So #KaburAjaDulu isn’t only a social media trend, you can see it in the numbers too.

The part that surprised me is the bottom panel. The people who leave send money back.

In 2024 Indonesia received about US$16 billion in remittances, more than ten times what it got in 2000. The government has even called migrant workers “heroes of foreign exchange.”

It’s a fair title, but it also raises an uncomfortable question. Is a $16 billion lifeline a sign of strength, or a sign that the opportunities just aren’t there at home?

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Part 3: The Jobs That Aren’t There

So why do so many young people leave? A lot of it comes down to the kind of work on offer at home.

Vulnerable employment counts people in informal or insecure work, often with no contract and no steady income. In Indonesia it has been stuck above 50% for the past decade.

Malaysia sits at around 22%. Indonesia has barely moved. That means about half of all workers, including graduates who studied for years, are in insecure informal jobs.

The jobs technically exist, they’re just not the kind of jobs people trained for.

Just get out first. Honestly, can you blame them?

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All data in this article comes from the World Bank’s World Development Indicators, pulled directly into R using the WDI package (Arel-Bundock et al., 2022). The full list of indicators is available at data.worldbank.org.

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Acknowledgements

I used Claude (Anthropic, 2026) to help me set up and troubleshoot the WDI function used to pull the World Bank data into R, as well as to troubleshoot R code for the scrollytelling.

I also followed Steve’s Guide to Scrollytelling to learn the Closeread extension.

References

Data

World Bank. (2025). World development indicators [Data set]. The World Bank Group. https://databank.worldbank.org/source/world-development-indicators

Indicators used: SL.UEM.1524.ZS, NY.GDP.PCAP.CD, SM.POP.NETM, BX.TRF.PWKR.CD.DT, SL.EMP.VULN.ZS

Software and tools

Arel-Bundock, V. (2022). WDI: World development indicators and other World Bank data [R package]. https://CRAN.R-project.org/package=WDI

qmd-lab. (2024). Closeread (Version 1.0.1) [Quarto extension]. GitHub. https://github.com/qmd-lab/closeread

Posit, PBC. (2024). Quarto (Version 1.9.38) [Computer software]. https://quarto.org

R Core Team. (2025). R: A language and environment for statistical computing [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org/

Sievert, C. (2020). Interactive web-based data visualization with R, plotly, and shiny. Chapman & Hall/CRC. https://plotly-r.com

Wickham, H. (2016). ggplot2: Elegant graphics for data analysis (2nd ed.). Springer.

Anthropic. (2026). Claude [Large language model]. https://claude.ai