the coefficient of SCI is also smaller than that of the same-year forecast error, indicating a lesser importance of SCI in the accuracy of two-year ahead forecasts.

Logically, the more forecasts are projected in the future, the more they carry an element of uncertainty. Therefore, information in the time of forecasting may be rendered obsolete the longer the horizon of forecasts. In addition, the risk of more unforeseen events increases, as well as their unpredictable impacts. An example is forecasts for the year 2022. Looking at forecasts made in 2021 for 2022, the focus was on post-pandemic recovery, with no knowledge of a potential conflict that would impact all international markets.

# VI. Robustness Checks

## VI.1 Alternative Measure of Data Transparency: Statistical Performance Indicator

The estimations are replicated using an alternative index—the Statistical Performance Indicator (SPI) — which, as detailed above, is more comprehensive. For instance, the SPI includes availability and frequency of labor force surveys, establishment surveys, and establishment censuses that are not included in the SCI. The SPI also covers the periodicity and timeliness of the Sustainable Development Goals (SDGs) indicators as well as indicators of data openness that were not covered in the SCI. More importantly, the SPI is more global in nature, including both developing and developed economies, while the SCI largely includes developing economies. Yet the SPI is available for a shorter period (2016–2019) than the SCI (2004–2020). Appendix Table A7 presents the findings for the lagged log of SPI indicator with both a global sample and a largely developing economies sample. These samples are determined by the availability of IMF-WEO Global sample and World Bank GEP sample respectively. The findings largely mirror those with the SCI—improvements in data transparency are positively correlated with forecast accuracy (absolute forecast errors) and reduce forecast optimism (forecast errors). However, the findings for forecast optimism are statistically significant, while the findings for forecast accuracy are not. Global sample coefficients are greater than those for just developing economies, suggesting that the effects of good data systems are much stronger in a sample that compares the performance of data systems in both advanced and developing economies. The results indicate that the positive effects of good data ecosystems are robust to the choice of the data transparency indicator.

## VI.2 Other Control Variables: Institutions, Polity, Informality, and Natural Disasters

This robustness check includes other potential factors that may affect forecast errors. They are institutions (proxied by rule of law), polity, informality, and natural disasters. The first three belong to the information set of forecasters at the time of forecasts at the beginning of the year (January), while natural disasters are considered as shocks that occurred during the year of the forecast.

The quality of political institutions may affect forecast errors. Merola and Pérez (2013) observe fiscal forecast over-optimism in election years not just in forecasts made by European governments but also, albeit to a lesser extent, in forecasts made for European countries by international organizations like the European Commission (EC) and the OECD. A system of checks and balances that restricts the power of the executive is a feature of a strong democracy (De Montesquieu, 1989). Therefore, we account for the quality of political institutions using the Polity Index and the Rule of Law Indicator. The Polity Index is defined as the lag of the combined polity score that is computed by subtracting the autocracy score from 20