Data Transparency and GDP Growth Forecast Errors

Roberta V. Gatti, Daniel Lederman, Asif Islam, Ha Nguyen, Rana Mohamed Amr Mohamed Nabil Lotfi and Mennatallah Emam Mohamed Sayed Mousa

No 10406, Policy Research Working Paper Series from The World Bank

Abstract: This paper examines the role of a country’s data transparency in explaining gross domestic product growth forecast errors. It reports four sets of results that have not been previously reported in the existing literature. First, forecast errors—the difference between forecasted and realized gross domestic product growth—are large. Globally, between 2010 and 2020, the average same-year forecast error was 1.3 percentage points for the World Bank’s forecasts published in January of each year, and 1.5 percentage points for the International Monetary Fund’s January forecasts. Second, the Middle East and North Africa region has the largest forecast errors compared to other regions. Third, data capacity and transparency significantly explain forecast errors. On average, an improvement in a country’s Statistical Capacity Index, a measure of data capacity and transparency, is associated with a decline in absolute forecast errors. A one standard deviation increase in the log of the Statistical Capacity Index is associated with a decline in absolute forecast errors by 0.44 percentage point for World Bank forecasts and 0.49 percentage point for International Monetary Fund forecasts. The results are robust to a battery of control variables and robustness checks. Fourth, the role of the overall data ecosystem, not just those elements related to gross domestic product growth forecasting, is important for the accuracy of gross domestic product growth forecasts. Finally, gross domestic product growth forecasts from the World Bank are more accurate and less optimistic than those from the International Monetary Fund and the private sector.

Date: 2023-04-12
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Related works:
Journal Article: Data transparency and GDP growth forecast errors (2024)
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