# V. Extensions

## V.1 On the Sub-Components of Data Capacity and Transparency

Certain elements of the data ecosystem may matter more for forecasting growth than others. To test this, the SCI index is deconstructed into three subcomponents: (i) directly related to GDP forecasting; (ii) indirectly related through the macroeconomic framework; and (iii) other elements of the data ecosystem including the periodicity of micro and macro indicators, which capture overall quality of the data ecosystem.<sup>11, 12</sup> Direct elements include the periodicity of GDP growth data, import and export price indices, the industrial production index, and the updating of base years for national accounts and the consumer price index. The indirect elements related to the macroeconomic framework comprise standards for external debt reporting, government finance accounting, updated balance of payments manual, and subscription to the IMF’s Special Data Dissemination Standards (SDDS). Regression results show that of the three subcomponents, the “other elements” of the data ecosystem, are statistically significantly correlated with lower forecast errors and absolute forecast errors, while the direct and indirect elements are not (see Appendix Table A2). This finding implies that for countries to improve their forecasting accuracy, the whole data ecosystem needs to be upgraded, not just certain elements of it. A limitation of this finding is that there is far less variation in the direct element of the SCI indicator, which may explain the lack of statistical strength.

## V.2 Forecast Errors between Different Forecasters

We investigate the role of forecasters in explaining forecast errors. The type of forecaster may influence the magnitude of forecast errors or the optimism of forecasts. To take this into account, the data is restructured with each unit of observation being a forecaster for a country and year. Dummy variables are created to account for the different categorizations of forecasters.

Forecasters are divided into three categories based on the _institution_ sources. These include the IMF (WEO dataset), the World Bank (GEP dataset) and private forecasters (Consensus/Focus Economics datasets). January GDP growth forecasts are used for the analysis across all forecaster types. Private forecasts are obtained from two sources. Forecasts for the MENA region are obtained from Focus Economics, while the private forecasts from the rest of the world are source from the Consensus Economics dataset. Equations (1) and (2) are altered to include them as follows: A F 1 L L 2 L L L 3 A + 4 I I 5 L L L 6 7 I + ii,tt,ff ii,tt−1 ii,tt−1 ii,tt 8AAFL AAF = αα+ ββ LLII + ββ LLFF + ββ AA∆L LLFFL (3) LLFFppFFppAA ii,tt ii,tt−1 ii,tt−1 ff ββ FFppFFI IIL IIppF + ββ + ββ BBL mm + ββ FF_LLDDm yy ff tt ii,tt ββ IIAAppIIAADDAA_LLDDm yy + ττ + εε

> 11 Each indicator in the SCI is a score from 0 to 100. Each sub-component is calculated as the simple average of its indicator scores divided by 100, to adjust the scale from 0 to 1.

> 12 For a cross-section of 145 countries over the period (2004-2020): (i) the correlation between the SCI and SCI GDP Direct sub-component is 0.72; (ii) the correlation between the SCI and SCI GDP Indirect sub-component is 0.78; and (iii) the correlation between the SCI and SCI Other Data Ecosystem (Other) sub-component is 0.93.

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