IIASA at the Austrian Parliament's Day of Parliamentary Research 2024

Using machine learning algorithms, Erokhin will illustrate factors that drive tax treaty formation and make predictions for country pairs that are likely to have tax treaties. His talk, which is based on a study coauthored with Prof. Martin Zagler, will provide clear guidelines on how machine learning algorithms can inform policymakers on treaty policy.

The study delves into the predictive potential of machine learning algorithms for tax treaty formations between countries and addresses a critical gap in international economic relations. It investigates the capability of machine learning to accurately predict tax treaty formation based on economic determinants.

The findings indicate that machine learning provides a robust framework for predicting tax treaty formations and outperforms traditional econometric methods. This research innovates by integrating advanced machine learning techniques into the domain of international economic policy to significantly enhance predictive accuracy and decision-making efficiency.

“The potential relevance of this research to parliamentary practice is profound, particularly in understanding how new technologies like machine learning can enhance the capacities of parliaments. By equipping policymakers with predictive insights about tax treaty formations, this study aids in better resource allocation and strategic planning in international relations and economic policies. Furthermore, it prompts legislative bodies to consider regulatory frameworks that incorporate technological advancements to improve governance and policy effectiveness in global economic interactions. This research thus not only contributes to academic literature, but also serves as a vital tool for legislative and economic strategists and enhances the proactive capabilities of parliaments in a digitally evolving landscape,” notes Erokhin.

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