significantly underperform relative to predicted growth and check for economic policy changes during this period.

# **6.5 Grouping of Variables**

With an increasing number of explanatory _x_ variables in the model, it becomes increasingly difficult to display the results in an easy-to-access fashion for several periods. The studies cited at the beginning of this section hence aggregate those variables into different groups. In the above illustration for Bangladesh, for example, one could group the growth contributions of inflation, the real exchange rate, financial crises, government consumption, and growth volatility into a “macro stabilization” category. Grouping choices will depend on the question or hypothesis one is interested in. For example, Araujo et al. (2016) were particularly interested in whether the solid growth performance in most Latin American and Caribbean countries in the early 2000s was due to good policies or a favorable external environment, which motivated their variable grouping into structural policies, stabilization policies, and external conditions.

# **6.6 Extending the Model to More Recent Periods**

Our data set ends with period 10, covering the years 2015-2019. If one wants to analyze a more recent period for an individual country, one does not necessarily need to update the whole data set (although we plan for regular updates in the GitHub repository). Remember that country-specific growth analysis is performed with changes of variables. One can hence rely on the parameter estimates from models like those in table 2 and apply them also to more recent changes in variables, even if they are not fully consistent with the level variables in the model. For example, one may not be able to re-calculate the full infrastructure index in our dataset for more recent periods. But one could alternatively create a similar index for the country under investigation with available variables that resembles the index in the dataset for the pre-2019 periods as good as possible and then take changes beyond 2019 for country-specific growth analysis.

# **6.7 Creation of Growth Scenarios and Benchmarking**

A likely question that arises in a policy context is: what growth could a country expect if a certain explanatory variable _xk_ changes? Recall that the model is not suited to provide an estimate of causal effects for such a scenario, but rather a thought experiment along the lines: what income changes did countries usually experience during periods when _x_ changed, holding several other aspects constant?

Usually, benchmarking to an aspirational peer country provides a meaningful magnitude for a potential improvement in a growth correlate _xk_ . For example, column (3) of Table 3 shows that the infrastructure index for Bangladesh stands at 0.154 in period 8. From the data set in the GitHub repository, we can further infer that the infrastructure index in Indonesia stood at 0.655 in the same period. What growth did countries usually observe when they closed the remaining gap of 0.5 units? For simplicity, we assume that the closure of the whole gap happens in one period.<sup>27</sup> The model then implies an associated increase in income per capita levels of Δ _xk·β_ = 0.5∙0.08=4.1% in the short run and Δ _xk·β/(1-θ)_ = 0.04/(1-0.8)= 20.2% in the long run.

Such benchmarking exercises can help prioritize broad areas of reform because they combine partial growth correlations (captured in the parameters _β_ ) with magnitudes of gaps in typical growth correlates _xk_ . In other words, section 5.2 highlights how important _β_ possibly is across countries, but

> 27 This is a possible but very ambitious improvement. The average (median) change in the infra index per period over all sample observations is 0.12 (0.16). The 95<sup>th</sup> (99<sup>th</sup> ) percentile change is 0.43 (0.63).

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