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Research

Practical, policy-relevant research at the intersection of tax systems, data science, and economic development.

The research produced by Maitras.ai focuses on practical, policy-relevant solutions designed to address real-world challenges, support institutional decision-making, and remain adaptable across jurisdictions.

Research Themes

Four core areas structure the Maitras.ai research agenda.

All research outputs are designed to address real-world challenges, adapt across jurisdictions, and strengthen how institutions make public finance decisions.

1. Tax & Public Finance

This area examines structural and operational aspects of tax systems, including:

  • • Tax policy design and evaluation
  • • Revenue administration challenges
  • • Tax expenditure analysis
  • • Compliance behavior
Key Focus: Strengthening fiscal capacity in developing economies

2. Data Science & Artificial Intelligence

Research in this domain explores the application of advanced analytics in public sector environments, particularly:

  • • Machine learning models for audit selection
  • • Predictive revenue forecasting
  • • Risk-based compliance systems
  • • Data architecture for tax authorities
Key Focus: Translating complex analytics into usable government tools

3. Gender & Economic Inclusion

This theme focuses on the intersection of data and gender, including:

  • • Measurement of women’s economic participation
  • • Gender biases in tax systems
  • • Financial inclusion analytics
  • • Informal sector representation
Key Focus: Making invisible economic activity visible

4. Extractives & Resource Taxation

This area addresses challenges specific to resource-rich economies, including:

  • • Cost recovery and profit shifting
  • • Transfer pricing risks
  • • Production sharing mechanisms
  • • Revenue volatility
Key Focus: Maximizing public value from natural resources

Tax & Public Finance

Jun 2026

Domestic Resource Mobilization Under Data Constraints

Why public finance strategies in developing economies must account for fragmented tax data and implementation bottlenecks.

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