Browse

You are looking at 1 - 10 of 63 items for :

Clear All
Inter-American Center of Tax Administrations, International Monetary Fund, Intra-European Organisation of Tax Administrations, and Organization for Economic Co-operation and Development

Abstract

This guide is part of a series of Virtual Training to Advance Revenue Administration (VITARA) reference guides that has been developed based on the contents of the VITARA online modules. This reference guide focuses on international good practices in organizational design. It explains how tax administrations can organize people, processes, and work effectively. It builds knowledge and understanding of critical features and dependencies in the organizational design of tax administrations, defining the concept of organizational design, and explaining why it is important. This guide also identifies the main organizational models (function-based, tax type-based, segment-based, and hybrid) used in the design of tax administrations and explains the advantages and disadvantages of the different models. It compares the roles of headquarters and field operations in a tax administration's organizational structure and also describes the importance of special units and functions within a tax administration's organizational structure. The guide helps tax administration leaders better understand how tax administration organizational models can be adapted to accommodate new responsibilities and roles.

Andras Komaromi, Xiaomin Wu, Ran Pan, Yang Liu, Pablo Cisneros, Anchal Manocha, and Hiba El Oirghi
The International Monetary Fund (IMF) has expanded its online learning program, offering over 100 Massive Open Online Courses (MOOCs) to support economic and financial policymaking worldwide. This paper explores the application of Artificial Intelligence (AI), specifically Large Language Models (LLMs), to analyze qualitative feedback from participants in these courses. By fine-tuning a pre-trained LLM on expert-annotated text data, we develop models that efficiently classify open-ended survey responses with accuracy comparable to human coders. The models’ robust performance across multiple languages, including English, French, and Spanish, demonstrates its versatility. Key insights from the analysis include a preference for shorter, modular content, with variations across genders, and the significant impact of language barriers on learning outcomes. These and other findings from unstructured learner feedback inform the continuous improvement of the IMF's online courses, aligning with its capacity development goals to enhance economic and financial expertise globally.
Fernanda Brollo, Era Dabla-Norris, Ruud de Mooij, Daniel Garcia-Macia, Tibor Hanappi, Li Liu, and Anh D. M. Nguyen
Generative artificial intelligence (gen AI) holds immense potential to boost productivity growth and advance public service delivery, but it also raises profound concerns about massive labor disruptions and rising inequality. This note discusses how fiscal policies can be employed to steer the technology and its deployment in ways that serve humanity best while cushioning the negative labor market and distributional effects to broaden the gains. Given the vast uncertainty about the nature, impact, and speed of developments in gen AI, governments should take an agile approach that prepares them for both business as usual and highly disruptive scenarios.
International Monetary Fund. Western Hemisphere Dept.

Peru has been diverging from advanced economies due to sluggish productivity growth. While multiple factors contributed to it, constrained technology and knowledge diffusion is one of them. Addressing these barriers could unlock the considerable potential of the digital revolution, including digital technologies and artificial intelligence (AI),2and boost productivity primarily in the formal sector, especially in the financial, government, education, health, IT, trade, and real estate sectors. Current advancements in the use of technology within the financial sector (FinTech) and the government sector (GovTech) are steps in the right direction.

Fernanda Brollo
Fernanda Brollo
This paper investigates the impact of automation on the U.S. labor market from 2000 to 2007, specifically examining whether more generous social protection programs can mitigate negative effects. Following Acemoglu and Restrepo (2020), the study finds that areas with higher robot adoption reduced employment and wages, in particular for workers without collegue degree. Notably, the paper exploits differences in social protection generosity across states and finds that areas with more generous unemployment insurance (UI) alleviated the negative effects on wages, especially for less-skilled workers. The results suggest that UI allowed displaced workers to find better matches The findings emphasize the importance of robust social protection policies in addressing the challenges posed by automation, contributing valuable insights for policymakers.