disaster occurrence dummy was created using the EM-DAT’s International Natural Disaster Database created by the Center for research on Epidemiology of Disasters.

# III.4 Empirical Specification of the Baseline Regression

In the baseline regressions, the relationship between statistical capacity and the magnitude of same-year forecast errors is explored using two models across three different samples of forecasters. The first sample is from the World Bank’s _Global Economic Prospects_ January Forecasts (GEP), the second sample is from the International Monetary Fund’s _World Economic Outlook_ January Forecasts (WEO). Both samples cover the same 126 countries from 2010 to 2020. A panel model is estimated using the absolute value of GDP growth forecast error of same-year forecasts as the outcome variable regressed on the lagged log of statistical capacity and other covariates as presented in equation (1): A F 1 L L 2 L L L 3 A + 4 I I 5 L L L 6 (1) ii,tt ii,tt−1 ii,tt−1 ii,tt AAF AAF = αα+ ββ LLII + ββ LLFF + ββ AA∆L LLFFL LLFFppFFppAA ii,tt ii,tt−1 ii,tt−1 tt ii,tt Where ββ FFppFFI is for country and IIL IIppF + ββ is for year. The dependent variable is the absolute value of GDP growth + ββ BBL mm + ττ + εε forecast errors ( A F ), defined as forecasted GDP growth minus actual GDP growth. A negative forecast error signifies that forecasted GDP growth was below actual GDP growth. The main regressor is pp FF the log of lagged Statistical Capacity Index ( AAF AAF L L , as measured by the World Bank Statistical Capacity Indicator (SCI). Following Eicher et al. (2019), we expect lower quality data to drive larger forecast errors. The identification strategy leverages cross-country variation across time in both forecast errors and the LLII) SCI. There are two main concerns of endogeneity. One is the possibility of simultaneity bias or reverse causality. It seems unlikely that the forecast errors will affect the quality of the data systems. However, one cannot rule out the possibility that as a response to growth forecast errors, policy makers may enact reforms to improve data systems. To limit this, we use the lagged value of the SCI. The second concern is that of omitted variable bias. We address this by accounting for as many relevant covariates as possible, as discussed below. We cannot completely obviate the endogeneity concerns in the study, but we do alleviate them as much as possible.

To account for omitted variable bias, explanatory variables employed include several covariates. The log lagged GDP per capita in constant 2010 US dollars ( L L L ) is included as a proxy for the level of economic development. According to Loungani (2001), on average, forecast errors are positive LLFF (optimistic), and more pronounced in developing countries. Due to their greater susceptibility to shocks, smaller economies may exhibit greater volatility than larger ones. Developing economies may be more susceptible to severe shocks that might result in significant forecast errors since they may be less diversified and dependent on the fluctuations of commodity prices (Eicher et al., 2019). Therefore, we include the lagged log of the total number of population in an economy (L L L ) as a proxy for country size, and the absolute value of log change in export commodity prices ( A ), which captures both exposure to commodity exports and the fluctuations of the international prices. An internal conflict dummy variable ( I I ) for internal conflict country-year pairs - as defined using the AA∆L LLFFL LLFFppFFppAA UCDP-Prio Dataset is included in our analysis. Given the significant macroeconomic repercussions of FFppFFI IIL IIppF conflict, growth in countries that frequently experience conflict and social unrest may be more difficult to forecast with accuracy (Novta and Pugacheva, 2021). We also include a lagged dummy variable for economic booms which is a dummy variable if growth is greater than or equal to the previous 10-year median growth. Frankel (2011) found that official forecasters tend to be overly optimistic during (BBL mm) booms and busts, more so than when GDP is at its long-run trend. They overestimate the durability of booms and underestimate the transitory nature of busts (Frankel, 2011). Recessions are more difficult for 12