(iii) Interestingly, the strongest dominance is attributed to human capital. This appears conflicting with the conclusion from several growth regressions that stress the statistical insignificance of this variable, which also cannot be distinguished from a 0-correlation in our regression. But statistical insignificance can be the result of poor measurement of that variable, which has been pointed out in the literature (Pritchett 2001; Hanushek et al. 2017) and should not be confused with economic irrelevance (that dominance criteria aim to measure). More sophisticated measurement approaches usually confirm the importance of human capital for growth (e.g., Hanushek and Woessmann 2012; Maloney and Valencia Caicedo, 2022). In line with the potential problem of measurement error (which biases estimated coefficients towards 0), the standardized beta coefficient suggests a more modest economic relevance than the dominance statistics. This may also reflect that human capital is usually quite persistent and shows little variability over time, which lowers the standardized beta coefficients by construction (see previous footnote).

(iv) To a lesser extent, FDI and credit/GDP provide a good fit for income per capita levels in the sample but only show modest partial correlations with the latter (which cannot be distinguished from 0 in a statistical sense, except for FDI in the medium model, see Online Appendix A.2).

(v) Conversely, trade diversification,<sup>21</sup> the exchange rate, political violence, trade openness, and inflation show relevant partial correlations with income but are attributed a low dominance. In the medium model (see Online Appendix A.2), the partial correlation further increases for trade openness and financial crises but declines for the exchange rate.

Our overall reading of those results is that: _(i)_ they confirm the potentially important role of infrastructure for growth, which the recent literature has emphasized, _(ii)_ typically insignificant parameter estimates of human capital in growth regressions shall not lead to the conclusion that education does not matter, and _(iii)_ export diversification may potentially play a large role that should be investigated in further research.

Note that this cross-country perspective on economic relevance does not mean that those variables are also most likely growth correlates in individual countries. For example, the partial correlation between major episodes of political violence and income levels may be large but there may not be much variability of such events across the whole sample. For a conflict-affected country, only a country-specific growth analysis, as outlined in section 6, will reveal its importance.

# **5.3 How Stable Are Parameters across Country Groups?**

A policy-relevant concern with growth regressions is that parameters are not identical across country groups. Regional heterogeneities were extensively discussed in the context of the “Africa dummy” in growth regressions (see, e.g., Easterly and Levine 1997; Sachs and Warner 1997). Furthermore, it is also plausible that growth correlates are systematically different across country groups with different income levels.

In country-specific analysis, we therefore advise to statistically test if parameters of the country under investigation are systematically different, or if parameters for a reasonably similar peer group are systematically different from the overall sample. Such a peer group could consist of countries with similar geographic or socio-economic features. Empirically, such tests can be implemented with simple interaction of country- or group-specific dummy variables with the relevant model variables. Examples for such group-specific interactions in regression commands (for income and regional groups) are provided in the code that is available in the GitHub repository _KMWacker/growthdata_ accompanying this paper. Parameter estimates that are significantly different

> 21 Because EDI consists of the linear and squared term, we take the average of both (absolute) coefficients for the display of the standardized beta coefficient and group them together as a so-called “set” in the dominance analysis.

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