# **1. Introduction**

Economic growth continues to be a key driver of living conditions. It is the main driver of recent poverty reductions and shows a near one-to-one relationship with important welfare indicators for most countries (Dollar et al., 2015 and 2016; Crespo Cuaresma et al., 2022). It is hence not surprising that policy makers set ambitious growth targets, other societal challenges notwithstanding. Applied research can facilitate such growth agendas by providing insights into country-specific growth episodes.

Economists use several methods to investigate country-level growth performances. For example, filtering techniques allow separating cyclical shocks from long-term growth that is in line with an economy’s underlying potential (Hodrick and Prescott, 1981). Growth accounting decomposes growth into contributions from production factors, like capital and labor, and productivity growth (Hulten, 2010). Other decomposition techniques are widely used to investigate to what extent productivity improvements are due to structural change, e.g., workers moving to more productive sectors (Timmer and de Vries, 2009; McMillan et al., 2014) or firm dynamics (Brown et al., 2018; Diao et al., 2021). Most of those methods are part of a standard toolkit to analyze country-level growth performance and, for example, part of the World Bank growth diagnostics guidelines (CEM 2.0).

However, most of those country-specific methods leave critically important policy questions unanswered. Notably, the above-mentioned filtering and decomposition techniques rarely tell us anything about underlying policy drivers, let alone their relative importance.<sup>1</sup> Moreover, studies of firm dynamics are mostly limited to the formal manufacturing sector. What happens in other parts of the economy usually remains unclear.

In this paper, we make the case that growth regressions can provide policy-relevant insights into a country’s growth performance, even if they only provide descriptive correlations. Such correlations tell what policy-relevant changes in key macroeconomic variables countries typically experience in their growth process. This allows for a quantitative assessment of how special or representative the policy mix of a country’s growth performance is. For example, if typical growth correlates can explain a country’s growth performance well, this suggests that key macroeconomic variables move in line with the experience of other growing economies. Even in this case, the policy mix of this country may still be unique (see the Ethiopia case study in Moller and Wacker, 2017). Conversely, if typical growth correlates cannot explain a country’s performance well, this can be insightful in its own way. Can we learn something new for growth theory from this country? Or has the country just had “good luck”? Obtaining quantitative assessments of successful policy mixes is particularly useful today as growth policies may face trade-offs with climate goals (Haberl et al., 2020; Wollburg et al., 2023).

To facilitate the application of growth regressions, our contribution makes them readily available. In this paper we present a novel comprehensive data set and reference models. Our dataset contains about 20 variables for approximately 150 countries between 1970 and 2019 in non-overlapping 5-year averages. It largely builds upon previous work of Araujo et al. (2016) and Beyer and Wacker (2023) and their methodology but adds several climate-relevant and demographic variables, including income inequality. Our data and codes for estimation and analysis are available through the GitHub repository _KMWacker/growthdata_ and provide a rich open data source to researchers and economic policy analysts.<sup>2</sup> While our paper focuses on the application of growth regressions for country analysis, it seems

> 1 For example, McMillan et al. (2014) suggest that undervaluation and labor market flexibility promote productivity-enhancing structural change. But devaluation may make capital accumulation more expensive, and deregulation may deter labor market participation. This factor-reducing effect and associated policy tradeoffs would not be accounted for.

> 2 We plan to regularly update and expand the data set and invite scholars and analysts to comment on the code and suggest improvements; either by contacting the corresponding author or by push requests on GitHub.

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