Although growth regressions cannot substitute for causal identification of growth policies, they can still be useful for comparative country analysis.<sup>6</sup> Early attempts to use growth regressions in a policy context originated from two companion studies at the World Bank for Latin America and Chile, respectively (Loayza et al., 2005; Gallego and Loayza, 2002). Although those studies still followed a causal interpretation, they used their estimated drivers of growth to gauge the relative importance of domestic policies compared to external factors, which highlighted the benefit of growth regressions for policy analysis. Moreover, they illustrated how those estimates can be used to assess the role of policy complementarities, an approach that was furthered by Chang et al. (2009).

Araujo et al. (2016) revived this policy-driven approach when they studied whether a period of catch-up growth in the early 2000s in Latin America and the Caribbean (LAC) was driven by ‘good policies’ or ‘good luck’ from high commodity prices. Other analyses built on Araujo et al (2016) to explore country- and region-specific growth episodes. For example, results from Haile and Moller (2018) pointed to the importance of financial deepening, macro policy, and infrastructure for previously poorly understood growth accelerations in the West African Economic and Monetary Union (WAEMU). Geiger et al. (2019) revealed that typical growth correlates explain the exceptional growth performance of Ghana up to 2010 but not afterwards. They interpret this as an abatement of structural improvements in the economy and an increasingly cyclical growth performance.<sup>7</sup> Moller and Wacker (2017) illustrated how growth regressions can provide a solid underpinning for understanding a country’s macroeconomic policy mix. They confirmed that typical correlates of growth can explain Ethiopia’s decade-long growth acceleration after 2003. They then showed that what made Ethiopia exceptional was its outstanding performance in essential growth correlates, notably infrastructure, and its heterodox macroeconomic policy mix. Analyzing typical correlation patterns with a cluster analysis, they showed that Ethiopia benefitted from a unique combination of overvalued exchange rates and financial repression that facilitated infrastructure spending.

Similarly, Beyer and Wacker (2023) built on growth regressions to explain decades of solid growth in Bangladesh. By putting growth correlates into a comparative perspective, they showed that Bangladesh’s improvements in growth correlates between 1990 and 2004 were in the global top 5 percent for any 15-year period since 1970. Further comparing Bangladesh to other countries with periods of rapid improvements in key macroeconomic variables, they showed that Bangladesh does not show the typical pattern of mean-reversion in growth rates, which they attributed to outstanding macroeconomic stability.

This paper builds on the above-mentioned country studies from Latin America, Africa, and South Asia. Specifically, we provide a dataset that is comparable to the data employed in the above studies but can be freely accessed through the GitHub repository _KMWacker/growthdata_ . We further discuss the methods that are needed for such country analysis on a more conceptual level.

> 6  In our view, growth regressions additionally provide a useful cross-check for causal identification approaches. E.g., if education and microfinance interventions improve incomes at the micro level, should we not also check for reasonable correlations of education and financial development with income at a highly aggregated macro level before rolling out large-scale programs?

> 7 Ghana’s debt default in 2022 suggests they had a point.

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