To illustrate this weighting, consider a case where 150 original observations for log phone lines were available in 1970, and 50 observations were available for electricity. We attribute a weight 3/4 to the former and the latter gets a weight 1/4, while the remaining three infrastructure variables are not part of the composite index in 1970. This weighting reflects two aspects: statistical reliability and economic relevance. Fewer observations of a variable for a given year mean more predicted values for that variable, which are less reliable than the original observations. Statistically, one hence wants to attribute less weight to this ‘less reliable’ series. Fewer observations also reflect less relevance of the indicator. Secure internet connections or mobile phones were simply not available or not as important in the 1970s, their availability was hence not recorded in the data. This constitutes an economic reason for attributing less weight to series with fewer original observations.

Alternatively, our data also allows using those series individually (in logs), where _lphone_ has the largest coverage.

To include variables for **financial crises** , we rely on the database from Laeven and Valencia (2020), which have coverage until 2017. Our dataset contains dummy variables for individual years of banking, currency, or sovereign debt crises and a dummy variable, _dum_fincrisis_ , if either of those crises is reported. Alternatively, the variable _fincrisis_years_ captures the number of years with some financial crisis during a 5-year period (with a maximum of 3).

**Climate change** can affect economic activity through various channels (see e.g., Acevedo et al., 2020). We take data from the World Bank’s Climate Change Knowledge portal on historical monthly temperatures by country and calculate their standard deviation over a 5-year period, _sd_temperature_ . Alternatively, mean temperatures, _temperaturecelsius_ , are available in the data set as well. Both have shown to be negatively correlated with economic growth in the past (e.g., Burke et al., 2015; Kotz et al., 2021) but external validity about future causal impact of climate change is difficult to establish, due to non-linearities and the fact that countries are differently affected by global warming (e.g., Ortiz-Bobea et al., 2021).

Our dataset further includes the variables _disaster_aff_ and _disaster_death_ , which capture the number of people affected by or deceased from natural disasters, respectively, per million inhabitants. The data come from the Emergency Events Database (EM-DAT).<sup>16</sup> Since GDP does not take destruction into account and disasters are usually followed by reconstruction, which boosts GDP, the negative economic consequences of natural disasters are usually not captured in cross-country growth regressions. We therefore do not include those variables in our regression models.

To account for positive and negative shocks due to changes in international price levels, we include **terms of trade changes** , _dltot_ , calculated as changes in the log of the export price level relative to the import price level ( _pl_x/pl_m_ ), both taken from PWT10.0.

We further include the log of **government consumption** as a share of GDP, _lkg_ , calculated as the log of csh_g from PWT10.0, into our model. This variable is supposed to capture growth-reducing effects through either distortionary taxation (Afonso and Furceri, 2010), or public debt issuance. Note that the negative association with growth is motivated by the fact that we already control for the positive effects that government consumption may have on growth, e.g., through education or infrastructure spending. As our model describes long-run growth, it is also important not to conflate the short-term positive stimulus effect that increased government consumption can have during economic downturns. For similar reasons we also do not include fiscal deficit variables, which are highly cyclical and hence tend to smoothen out over 5-year averages.

Since **growth volatility** can negatively impact long-term growth (e.g., Ramey und Ramey, 1995; Hnatkovska and Loayza, 2005), our data set includes the standard deviation of annual growth rates over a 5-year period. This variable is endogenous by construction, which calls for more advanced

> 16 Due to copyright restrictions concerning republishability, those data are not directly included in our dataset and need to be manually added; see lines 435ff of _0_WBPRWP_datacr.do_ in our GitHub directory.

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