Surakarta, Indonesia — Integrating individual population records with Family Card (Kartu Keluarga) data significantly improves local tax revenue performance and development planning accuracy, according to a 2026 study by Sarsiti, Tamam Rosid, Juli Prastyorini, and Putri Nilam Aisyah. Conducted across three Indonesian districts and published in the International Journal of Finance and Business Management, the research shows that unified data systems can raise tax compliance, expand the taxpayer base, reduce administrative costs, and support more equitable regional development.
The research was carried out between 2021 and 2024 in urban, peri-urban, and rural districts in Indonesia. The authors are affiliated with the University of Surakarta, Muhammadiyah University of Berau (East Kalimantan), and STIAMAK Barunawati Surabaya. Their findings matter as Indonesian local governments continue to face funding gaps, uneven development, and limited fiscal capacity under decentralization.
Why Data Integration Matters for Local Governments
Local tax revenue is a critical pillar of regional autonomy in Indonesia. District governments rely on local taxes to fund infrastructure, education, health services, and social protection programs. However, fragmented administrative systems have long limited revenue collection. Population registries, Family Card databases, and tax records often operate in isolation, making it difficult to identify potential taxpayers or accurately assess tax capacity.
This disconnect contributes to a persistent gap between potential and actual tax revenue, especially in regions with large informal economies. As digital government reforms accelerate, policymakers are increasingly looking at data integration as a way to modernize tax administration while improving transparency and planning.
How the Study Was Conducted
The researchers analyzed administrative data from three districts representing different socioeconomic conditions: one urban district, one peri-urban district, and one rural district. The dataset included:
- More than 2.2 million individual population records
- Around 587,000 family units
- Approximately 1.47 million local tax accounts
The study combined quantitative analysis of tax performance data with predictive analytics and qualitative interviews with local officials. Instead of relying solely on traditional tax records, the researchers linked individual demographic data with household-level information from Family Cards, then connected these records to tax administration systems.
Advanced analytics were used to assess tax compliance patterns, identify unregistered taxpayers, and predict payment behavior. The approach allowed local governments to view taxpayers not just as individuals, but as members of households with specific socioeconomic characteristics.
Key Findings: Higher Revenue, Better Compliance
The results show clear and measurable benefits from integrating population and Family Card data into local tax systems.
Across the three districts studied:
- Tax compliance rates increased by 23–31 percent
- The registered taxpayer base expanded by 18–26 percent
- Total local tax revenue grew by up to 48.3 percent
- Administrative collection costs fell by 35–42 percent
- Revenue forecasting accuracy improved from about 68 percent to 89 percent
In practical terms, one urban district saw tax revenue rise from roughly IDR 487 billion to IDR 723 billion after integration. Similar growth patterns were observed in peri-urban and rural areas, indicating that the benefits are not limited to large cities.
Predictive Analytics Strengthen Tax Administration
A notable contribution of the study is its use of predictive analytics to support tax administration. By applying machine learning models to integrated datasets, the researchers achieved 84–85 percent accuracy in identifying potential taxpayers and predicting payment behavior.
Household-level variables derived from Family Card data—such as education level, occupation, asset ownership, and number of working family members—accounted for more than 40 percent of the model’s predictive power. This confirms that household data adds substantial value beyond standard tax records.
According to the authors, predictive tools enable local governments to move away from broad, inefficient enforcement strategies toward more targeted and preventive approaches.
Sarsiti of the University of Surakarta explains that integrating household data “allows tax authorities to better understand economic capacity at the family level, not just individual income, which is especially important in informal and mixed-income households.”
Impact on Development Planning and Public Services
Beyond taxation, integrated data systems significantly improved regional development planning. With more accurate revenue forecasts, local governments reduced budget deviations and gained greater confidence in multi-year development plans.
The study reports:
- Budget variance dropped from over 30 percent to around 10–14 percent
- Infrastructure project completion rates increased substantially
- Allocation of development funds became more spatially equitable
- Social assistance programs became more accurately targeted
By linking demographic, household, and fiscal data, planners were better able to direct resources to underserved communities. Leakage in social protection programs declined by more than 20 percentage points, while coverage of eligible households increased.
Challenges and Policy Considerations
The researchers note that data integration is not without challenges. Legal frameworks for data sharing, data privacy protections, system interoperability, and staff capacity all require careful attention. Initial investment costs were significant, but the study estimates a payback period of just two to three years due to efficiency gains and higher revenue.
The authors emphasize that technology alone is not enough. Strong political commitment, inter-agency coordination, staff training, and clear communication with the public are essential for successful implementation.
Author Profiles
Sarsiti
Faculty of Economics, University of Surakarta
Expertise: Public sector accounting and local fiscal policy
Tamam Rosid
Muhammadiyah University of Berau, East Kalimantan
Expertise: Public financial management
Juli Prastyorini
STIAMAK Barunawati Surabaya
Expertise: Government accounting
Putri Nilam Aisyah
Faculty of Economics, University of Surakarta
Expertise: Public finance data analysis
Source
Journal Article: Integration of Individual Data and Family Cards in Optimizing Tax Revenue: A Public Accounting and Predictive Analytics Approach to Regional Development Planning
Journal: International Journal of Finance and Business Management
Year: 2026
DOI: https://doi.org/10.59890/ijfbm.v4i1.176
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