Middle East and Central Asia > Qatar

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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.
Mr. Paul A Austin
,
Mr. Marco Marini
,
Alberto Sanchez
,
Chima Simpson-Bell
, and
James Tebrake
As the pandemic heigthened policymakers’ demand for more frequent and timely indicators to assess economic activities, traditional data collection and compilation methods to produce official indicators are falling short—triggering stronger interest in real time data to provide early signals of turning points in economic activity. In this paper, we examine how data extracted from the Google Places API and Google Trends can be used to develop high frequency indicators aligned to the statistical concepts, classifications, and definitions used in producing official measures. The approach is illustrated by use of Google data-derived indicators that predict well the GDP trajectories of selected countries during the early stage of COVID-19. To this end, we developed a methodological toolkit for national compilers interested in using Google data to enhance the timeliness and frequency of economic indicators.
Mr. Andrew Baer
,
Mr. Kwangwon Lee
, and
James Tebrake
Digitalization and the innovative use of digital technologies is changing the way we work, learn, communicate, buy and sell products. One emerging digital technology of growing importance is cloud computing. More and more businesses, governments and households are purchasing hardware and software services from a small number of large cloud computing providers. This change is having an impact on how macroeconomic data are compiled and how they are interpreted by users. Specifically, this is changing the information and communication technology (ICT) investment pattern from one where ICT investment was diversified across many industries to a more concentrated investment pattern. Additionally, this is having an impact on cross-border flows of commercial services since the cloud service provider does not need to be located in the same economic territory as the purchaser of cloud services. This paper will outline some of the methodological and compilation challenges facing statisticians and analysts, provide some tools that can be used to overcome these challenges and highlight some of the implications these changes are having on the way users of national accounts data look at investment and trade in commercial services.