Jesse Lastunen

This paper examines the experience of building and using tax-benefit microsimulation models for countries in the Global South within the EUROMOD framework, drawing on SAMOD, the SOUTHMOD model family, and EUROMOD-based models for Latin America. It reviews the research contributions these tools have enabled — spanning distributional analysis, work incentives, informal employment, and automatic stabilization — and discusses the challenges of maintaining and updating them, as well as the opportunities they open for new avenues of research to strengthen social protection and fiscal capacity in developing economies.

Ethiopia initiated a major electricity sector reform in 2024, significantly increasing tariffs and introducing a value-added tax on electricity consumption to improve the financial sustainability of its state-owned utility. This study assesses the short-term distributional, sectoral, and cost-of-living impacts of this reform. The analysis links a tax-benefit microsimulation model (ETMOD) with a price-based social accounting matrix (SAM) multiplier framework to capture both direct, household-level welfare changes and indirect, economy-wide price transmission effects. The results show the reform is regressive, increasing the national poverty rate by 3.5 percentage points in a static, short-run setting. The tariff increase is the primary driver of this effect, while the VAT has a smaller impact. Indirect effects, transmitted through higher prices for essential goods such as food and water, account for two-thirds of the total poverty impact and are larger than the direct effect of higher electricity bills. Rural and informal households are disproportionately affected. These findings demonstrate that without compensatory measures, electricity pricing reforms necessary for fiscal stability can impose substantial social costs. We conclude that pairing such reforms with targeted social protection is essential to shield vulnerable households from adverse outcomes.

This study investigates the feasibility of generating synthetic input data for the Ghanaian tax-benefit microsimulation model (GHAMOD) to address limitations posed by infrequent survey data. Current GHAMOD input datasets are derived from the 2013 and 2017 Ghana Living Standards Surveys. Reliance on such dated household data reduces the accuracy of policy analysis, particularly when failing to account for broader demographic shifts and structural changes that occur between survey rounds. This research explores methodologies ranging from linear interpolation to advanced machine learning techniques, including imputation and predictive modelling, to create synthetic data for intervening years (2014–16) and future years (2018 onwards). By combining existing survey data with complementary external data sources, this study demonstrates that synthetic data generation is a feasible, transformative approach for enhancing the timeliness of fiscal policy analysis. The research establishes a framework for effectively leveraging high-frequency complementary data to bridge temporal gaps, thereby improving the model's predictive capabilities and relevance for analysing the budgetary and distributional impacts of fiscal policies. This report details findings on suitable data and methods, discusses challenges, offers selected case studies, and provides a roadmap for integrating these techniques into national data ecosystems, with potential applications for other tax-benefit microsimulation models in Africa, Asia, and Latin America.

This report documents RWAMOD, the SOUTHMOD model developed for Rwanda. This work was carried out by UNU-WIDER, SASPRI, and the Rwanda Revenue Authority (RRA). The results presented in this report are derived using RWAMOD version 2.0, which is part of the SOUTHMOD bundle (SOUTHMOD_A4.0) and runs on EUROMOD software. The report describes the different tax-benefit policies in place, how the microsimulation model picks up these different provisions, and the database on which the model runs. It concludes with a validation of results against external data sources for Rwanda.