the democracy score, hence, the resulting unified polity scale ranges from +10 (strongly democratic) to - 10 (strongly autocratic). The Rule of law Indicator is the lagged rule of law indicator which is one of the six Worldwide Governance Indicators. It captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence.

We also check the robustness of our results to the informal sector. Blanchard and Leigh (2013) find that errors in official forecasts of fiscal consolidation effects are prevalent across many countries. Such errors, according to Pappa et al. (2015), are caused by the absence of the informal sector in standard models. When taxes increase in these models, agents may opt to work and invest less. Agents do, however, have an alternative option in an economy with an informal sector, which is to shift their activities to the shadow economy. This makes it easier for more resources to leave the formal sector, which causes recorded output and tax income to decline sharply. A recession may turn out to be worse than forecasted, but it may also not be as bad as it seems because the resources that leave the formal sector are employed in the informal sector. As a result, the decline in true (formal plus informal) output can be much smaller than that in recorded output. Dellas et al. (2017) found that the standard modeling practice of overlooking the informal sector is largely responsible for the significant forecast errors associated with the fiscal consolidation in Greece during the Euro Area Debt Crisis. In this sense, informality can affect GDP growth forecast errors, and we account for it using self-employment as a proxy, which is defined as the lag of total self-employed as a percentage of the total employment based on modeled ILO estimates.

Our findings are robust to the inclusion of natural disasters. Unpredictable and costly occurrences like natural disasters can have a detrimental impact on many economies. Celasun et al. (2021) found that the IMF’s WEO GDP growth forecasts have a tendency to be upwardly biased in part due to the rarity of unforeseen growth booms compared to significant growth collapses brought on by natural disasters, armed conflicts, and systemic financial crises. Loayza et al. (2012) investigated the effects of the different natural disasters (i.e., droughts, floods, earthquakes, and storms) separately by economic sector (agriculture, industry, and services) and found that natural disasters have a greater impact on growth in developing countries than in developed ones, with more industries being impacted and more significant economic repercussions. Depending on the type of disaster and the affected economic sectors, disasters can have positive or negative effects on economic growth. Severe disasters have no beneficial growth impact, even though moderate calamities (such as mild floods) can spur growth in some industries (Loayza et al., 2012). In a recent study, Kong et al. (2021) examined the changes in analyst optimism degree in earnings forecasts following earthquake events in China. They revealed that analysts utilize heuristics to forecast earnings and become irrationally pessimistic when an earthquake occurrence is salient. Analysts do, however, rectify the bias after the initial irrational predictions. Hence, we include in our analysis a natural disasters shocks dummy that is equal to 1 when severe natural disasters had occurred in a country. Severe natural disasters are those that generate damages equivalent to at least 1 percent of the GDP of the country affected that year (based on the total estimated damages reported by EM-DAT). Disasters include floods, earthquakes, droughts, storms, landslides, volcanic activities, extreme temperatures, and wildfires.

Appendix Table A8 shows that the relationship between SCI and forecast error remains robust, although the sample size shrinks. After accounting for the aforementioned additional factors, the quality of the data ecosystem seems to matter less for the forecast accuracy, as the magnitude of the SCI decreases and is less statistically significant across the World Bank, IMF, and private sector’s samples (columns 1 through 3), given the smaller sample size. The coefficient of the MENA dummy is also statistically insignificant, possibly suggesting that MENA has high absolute forecast errors due to these additional factors. However, data capacity reduces the forecast optimism bias (forecast errors) and remains statistically significant at the 5 percent level for both the World Bank and the IMF’s samples (columns 4 and 5). The coefficient of 21