Source: Authors’ calculations based on the World Bank’s January _Global Economic Prospects_ , the International Monetary Fund’s January _World Economic Outlook_ , January Consensus Forecasts, January Focus Economics Forecasts, the World Bank's _Statistical Capacity Indicator,_ the World Bank’s _World Development Indicators_ , the Export Commodity Price Shocks data from Gruss and Kebhaji (2019), and the UCDP-PRIO Dataset for Conflict.

Note: Statistical significance level 0.01 - ***; 0.05 - **; 0.1 - *. Robust standard errors clustered at the country level. All regressions include year fixed effects, and the reference year is 2015. Columns (1) and (4) samples refer to same-year World Bank GEP January Forecasts sample from 2010 to 2020, covering 126 countries. This common sample is used to define the columns (2) and (5) samples, for same-year IMF-WEO January Forecasts from 2010 to 2020. Columns (3) and (6) samples refer to same-year Consensus January Forecasts and the MENA region Focus Economics January Forecasts, from 2015 to 2020, covering 56 countries, and are not forced to be a common sample with WB-GEP and IMF-WEO. Log of SCI (lagged) is the one-year lagged log of Statistical Capacity Indicator. Growth Volatility (lagged) is the one-year lagged rolling 10-year standard deviation of realized growth rates. Log of GDP Per Capita (lagged) is the one-year lagged log of GDP per capita in constant 2010 US dollars. Absolute Value of Export Commodity Price Shocks (T) is the same-year absolute value of the log change in export commodity prices, it captures both exposure to commodity exports and the fluctuations of the international prices. Internal Conflicts Shocks Dummy (T) is the same-year dummy variable for internal conflict country-year pairs - as defined using the UCDP-Prio Dataset. Log of Total Population (lagged) is the one-year lagged log of population and is considered a proxy for the country size. Boom Dummy if Above Median Growth (lagged) is a one-year lagged dummy variable for economic booms if growth is greater than or equal to the previous 10-year median growth. Note that the unforeseen shocks taken into account in this regression occurred during the same year of the forecast, after the GDP growth forecasts were published in January.

The coefficient of growth volatility, measured as the standard deviation of GDP growth (lagged rolling 10year average), is positive and statistically significant at the 1 percent level across the three samples (see columns 4, 5, and 6). As expected, countries experiencing larger growth volatility have larger absolute forecast errors. Commodity price shocks and internal conflict shocks are positively correlated with larger absolute forecast errors. This is consistent with the hypothesized relationships between unexpected shocks and forecast errors. Absolute forecast errors are smaller after economic booms, measured as the economic growth above the previous 10-year median, with coefficients that are statistically significant for both the IMF and the Consensus/Focus Economics samples. This is consistent with the finding that forecasters have a harder time predicting recessions (Eicher et al., 2019). The level of development has no statistically significant bearing on the accuracy of forecasts. A persistent finding is that small countries (proxied by population size) have larger absolute growth forecast errors. However, this might reflect the tendency of small countries to be more open and thus vulnerable to shocks that cause volatility. When growth volatility is accounted for, the relationship between country size (population) and absolute forecast errors is no longer statistically significant (see Table 3 columns 4 through 6).

As previously established, the MENA region tends to have the largest forecast errors. In the base regressions (Table 3 columns 1 through 3), where several factors are accounted for, the coefficient of the MENA dummy is statistically insignificant, but of considerable magnitude with a coefficient of 0.906 for World Bank absolute growth forecast errors, and 1.032 for IMF absolute growth forecast errors. One possible interpretation could be that weak statistical capacity as well as the prevalence of internal conflict and exposure to commodity shocks explains the higher growth forecast errors for the MENA region relative to the world, and thus accounting for these factors results in the coefficient of the MENA dummy losing statistical significance. However, the large magnitude of the MENA dummy coefficients weakens this argument. Furthermore, when growth volatility is accounted for (see Table 3 columns 4 through 6), the coefficient of the MENA dummy is statistically significant, at least at the 10% level, although the magnitude is somewhat lower. The more plausible interpretation is that while several of the factors accounted for explain why MENA has higher forecast errors, they do not fully explain why the growth forecasts are inaccurate in the MENA region.

Table 4 replicates Table 3, with the forecast error as the outcome variable, to capture optimism or pessimism bias. Countries with better data ecosystems (SCI) tend to have less optimistic growth forecasts. This finding is statistically significant across international institutions’ forecast errors (see Table 4, columns 1 and 2). The relationship between the SCI score and forecast errors holds even after considering growth volatility (columns 4 through 6).

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