estimation approaches if one is interested in investigating this link. To somewhat mitigate this problem in our data, we lag growth rates by two years (e.g., the 5-year period 1990-1994 includes the standard deviation of growth rates between 1987-1988 and 1991-1992).

Our data further contains **changes in the share of urban population** of the total population, _durbanpop_ , from WDI. We include this variable in differences as we think that fast urbanization periods are correlated with growth spells. Our dataset further contains population density, _popdens_ , from WDI.

# _Variables in the “Medium Model”_

To measure countries’ **financial development** , we use the log of domestic credit to the private sector as a percentage of GDP, _lcredit_ , taken from WDI. The literature on financial development and its relationship to growth has grown extensively over the last decades (e.g., Levine, 2005; Arcand et al., 2015) and various studies and data sets on more detailed aspects of the issue have appeared (e.g., Svirydzenka, 2016). Credit over GDP, however, has the clear advantage of wide availability across countries and over time and has been used by several seminal studies in the literature on finance and growth. Brunnermeier et al. (2021) also confirm that credit expansion is rather a sign of financial deepening than the prelude for financial busts.

**Foreign direct investment** acts as a proxy for the activity of multinational corporations (Casella et al., 2023). We take the inward FDI stock as a percentage of GDP from UNCTAD. Since no stock data are reported before 1980, we fill the missing decade of data with an interpolation from FDI flow data (as a percentage of gross fixed capital formation). The sample correlation of both variables is high, 0.87. The variable is logged and labeled _lFDIstock_ip_ .

For **export diversification** , we rely on the export diversification index of the IMF (Papageorgiou et al., 2015), which is a Theil index. A higher value hence indicates lower diversification. Since this index stops in 2014, we interpolate the missing years with the Herfindahl-Hirschmann Product Index from UNCTAD. The correlation between both series in the sample is high, 0.90. The variable is logged and labeled _lEDI_ipol_ . Moreover, we take the square of the variable because a non-linear or U-shaped relationship is expected (see Imbs and Wazciarg, 2003, and Papageorgiou et al., 2015: Fig. 3).

Institutions are widely acknowledged as a key ultimate source of cross-country income differences (e.g., Acemoglu et al., 2001; 2005). However, their measurement problems have also been highlighted (e.g., Glaeser et al., 2004; Voigt, 2013) and deep institutional aspects barely vary over time, which is the key dimension we use for identification of our model parameters (due to the presence of country fixed effects). Moreover, most common measures of institutional quality are not available for the whole sample we aim to investigate. Instead of using a fine measure of institutional quality, we hence rely on data about major **episodes of political violence** from Systemicpeace.org., which has the advantages of wide coverage and considerable variation over time. In the regression models, we rely on _actotal_ , which aims to capture the magnitude of major events. Our dataset further contains a breakdown of this variable for interstate and societal violence ( _inttot_ and _civtot_ ), as well as a dummy variable, _dum_mepv_ , which equals 1 for any period that contains a major episode of political violence. Note that Systemicpeace.org data does not include data for 2019.<sup>17</sup>

> 17 In future updates of the dataset, we plan to include data from the Varieties of Democracy Project to capture institutional quality.

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