A) and absolute GDP growth forecast errors (Panel B) from private forecasters. However, same-year growth forecast data from private sector forecasters are available only since 2015 and for a smaller set of countries. On average, between 2015 and 2020, the average same-year forecast error from the private sector is 1.7 percentage points, while the average same-year absolute forecast error is about 2.4 percentage points (Appendix Figure A1), not too far off the GEP’s figures. The forecast error and absolute forecast error are also largest for MENA: about 2.8 and 3.4 percentage points on average respectively.

The timing of forecasts also matters. Appendix Figure A2 using same-year GDP growth forecasts from January, April, June and October from the WEO shows that forecast errors for the year tend to fall in magnitude and increase in precision as the forecasting month shifts from January to October. As the forecasting date approaches the end of the year, forecasters have more information and can thus more accurately forecast growth for that year. Utilizing a longer time series, Appendix Figure A3 visualizes the IMF’s April and October GDP growth forecast errors from 1990 to 2020 and finds that the October forecast errors are generally lower.

The longer the forecast horizon, the larger the forecast errors and absolute forecast errors. Appendix Figure A4 shows the GEP’s one-year and two-year ahead GDP growth forecast errors and absolute GDP growth forecast errors. Two-year ahead forecast errors and absolute forecast errors are larger than oneyear ahead ones. In turn, one-year ahead ones are larger than same-year ones. This feature holds even when we impose the same sample size for different horizons (see Appendix Table A1). This finding is consistent with prior results reported by Ho and Mauro (2016).

# III.2.Data Capacity and Transparency

The main variable of interest in our paper, data capacity and transparency, is proxied by the World Bank’s Statistical Capacity Index (SCI). The overall SCI score is based on a diagnostic framework to assess the capacity of national statistical systems over time. The framework has three dimensions: source data; methodology; and the periodicity and timeliness of socioeconomic indicators. A composite score for each dimension and an overall score combining all three dimensions are derived for each country on a scale of 0–100. A score of 100 indicates that the country meets all criteria. Each dimension is evaluated on criteria based on metadata information obtained from the World Bank, International Monetary Fund, United Nations, the UN Educational, Scientific and Cultural Organization (UNESCO), and the World Health Organization (WHO).

The overall SCI score is the average of the three sub-indicators calculated for each dimension (see Table 1). The source data dimension reflects whether a country conducts data collection activity in line with internationally recommended frequency (periodicity), and whether data from administrative systems are available and reliable for statistical estimation purposes. This dimension covers the micro-data aspect of data transparency because microdata is at the foundation of a country’s data system. Specifically, the criteria used are the frequency of population and agricultural censuses and of poverty- and health-related surveys, and completeness of vital registration system coverage. A country can achieve a perfect score if it has conducted at least one population census in the past 10 years, one or more agricultural censuses in the past 10 years, three or more health surveys in the past 10 years, and has a complete vital registration system.

The statistical methodology dimension measures a country’s ability to adhere to internationally recommended standards and methods. This aspect assesses guidelines and procedures used to compile macroeconomic statistics and for social data reporting and estimation practices. This dimension measures 7