Governance Quality, Not Wealth, Influences Life Expectancy of Indonesians
Kabupaten Lamongan and Kabupaten Sukabumi are two regions with a GDP per capita far below the national average. However, residents in these areas have a longer life expectancy compared to many other districts with similar levels of economic development. The interesting fact is that neither possesses significant wealth as reflected in their Gross Regional Domestic Product (PDRB) per capita, yet both exhibit good quality of local governance.
This pattern holds true across all 514 districts and cities in Indonesia. The Regional Government Success Scorecard (RGSS)—a new data-driven tool developed by the Chandler Governance Group (CGG), supported by the Gates Foundation, and implemented for measuring local government performance in Indonesia in partnership with the University of Indonesia—shows that the quality of local governance is a far stronger predictor of citizens’ life expectancy than the wealth of the region in which they live.
Utilising the most recent year of available data from each source (primarily 2024), this pilot project employed nearly 18,000 data points from Indonesian government sources: the Central Statistics Agency (BPS), the Ministry of Finance, the Ministry of Home Affairs, the Ministry of Administrative and Bureaucratic Reform (PANRB), the Ministry of Investment/Investment Coordinating Board (BKPM), the Corruption Eradication Commission (KPK), the Government Goods/Services Procurement Policy Institute (LKPP), the National Civil Service Agency (BKN), and the National Disaster Management Agency (BNPB).
What does the data show? GDP per capita varies widely across Indonesia, from around IDR 7 million per person in Puncak Jaya Regency to over IDR 260 million in Surabaya City. One might generally assume that wealth would strongly predict how long people live. However, the correlation between GDP per capita and life expectancy is only 0.32: positive, but a moderate figure. In contrast, local government governance capability is a much stronger predictor. The RGSS measures this through four dimensions: Accountability (anti-corruption safeguards and ethical standards), Financial Management (revenue collection, budget execution, and procurement governance), Public Services (quality and efficiency of frontline administrative services), and Technology and Innovation (adoption of digital technology and administrative innovation). Each captures a different aspect of governance quality. Across all 514 jurisdictions, Technology and Innovation correlates with life expectancy at 0.63—roughly double the strength of the relationship with GDP—and this is consistent across the dataset. The strongest of the four dimensions measured are Financial Management and Accountability, each of which is more closely related to life expectancy than wealth. Thus, it is broadly clear that governance quality, not a region’s wealth, is the more reliable factor for gauging how long people live.
How does this look in practice? Kabupaten Lamongan in East Java illustrates the pattern. Its GDP per capita ranks in the 31st percentile nationally, yet it sits in the 98th percentile for Technology and Innovation, the 80th for Accountability, and the 90th for life expectancy. Meanwhile, Kabupaten Sukabumi, at the 18th percentile for GDP per capita, ranks in the 96th percentile for Technology and Innovation, the 93rd for Financial Management, and the 95th for life expectancy. In both districts, governance strengths span more than one capability—and the results appear to influence health indicators positively, despite structural economic constraints. This evidence is correlational, not purely causal. Wealth itself is linked to several capabilities, so the figures cannot completely isolate the influence of each. For Technology and Innovation, the relationship with life expectancy persists even after accounting for wealth. This association is also specific to health: the same capabilities are only weakly related to education or employment, where economic conditions matter more. Nonetheless, life expectancy reflects many linked influences, and this pattern certainly warrants further research as richer data becomes available.
What does this mean for planning, investment, and policy? The RGSS provides central ministries, development partners, and local governments with a new, more rigorous and structured basis for deciding where capacity-building investments will have the greatest impact. The complete dataset for all 514 jurisdictions is publicly available at www.regionalgovscorecard.org. The RGSS’s contribution is also partly methodological. Instead of ranking every region against a single national average, the RGSS uses Dynamic Peer Group Benchmarking to assess each district or city against structurally similar peers—regions with comparable geographic conditions and natural resource endowments—so that remote or resource-constrained areas are judged fairly against comparable regions, not against the largest cities. The RGSS is also designed as a dynamic tool: as better local indicators emerge, measures of public service outcomes and citizen-perceived results can be deepened over time. Built entirely from Indonesian government data in partnership with the University of Indonesia, the RGSS is expected to contribute significantly to providing a common foundation for central and local governments to learn from each other, including through networks and coalitions connecting districts and cities across Indonesia. Investment in local government capability is a promising, evidence-backed pathway to improving the quality of life.