# **4. Variables and Data Sources**

Our dataset has a panel structure (repeated observations for each country); ‘geo’ and ‘period’ are the variables that identify country and time, respectively. The former is an encoded variable: it ranges from 2 (Albania) to 208 (Zimbabwe) but STATA displays country names instead of numbers. Our dataset additionally contains a ‘country’ and ‘NAMES_STD’ variable<sup>13</sup> and their ‘iso3’ code as a string variable. Additionally, ‘region_un’ classifies each country’s region, based on UN standards.

The ‘period’ variable indicates 5-year average periods, where period 1 encompasses the 19701974 data, while period 10 contains the 2015-2019 data. Note that the inclusion of those timefixed effects accounts for all global factors that affect all countries equally (in proportionate terms, as the dependent variable is logged). They could reflect global commodity prices or the global interest rate environment, or global advances in technology.<sup>14</sup>

## **4.1 Income Level**

The dependent variable we aim to explain is _lrgdpna_pc_ , the real (inflation-corrected) **income level per capita** , in logarithmic terms. The variable is based on PWT10.0 and constructed by dividing the series _rgdpna_ by _pop_ . The natural log is taken so that changes in the explanatory variables can be interpreted as percent changes in per capita income levels.

Although national income data are converted into (constant 2017) US$, note that this variable does not allow for proper comparison of welfare across countries because it does not account for differences in price levels across countries. If goods are half the price in one country compared to others, half the income level (in US$) is needed for the same consumption welfare. Such comparisons are facilitated through PPP adjustments, as in the PWT series _rgdpo_ or _rgdpe_ (Feenstra et al., 2015), which are also contained in our data set. PPP adjustments, however, can cause short-term country-specific fluctuations in the PPP-income variable (Inklaar et al., 2022), which can distort analysis of country-specific growth contributions.

Our model and results based on _lrgdpna_pc_ still have a proper welfare interpretation because they control for cross-fixed effects and hence differences in price levels across countries. But for cross-country comparison of welfare levels or analyses without country fixed effects, we recommend the variables _lrgdpe_pc_ (or _lrgdpo_pc_ ), which are also contained in our data set.

## **4.2 Correlates of Growth**

In table 1, we provide an overview of variables contained in our dataset and models. Variables are ordered by their availability: variables in the “small model” are widely available, hence allowing for a sample of approximately 1,500 observations (168 countries). The “small model” hence has the broadest coverage and should therefore provide most precise parameter estimates. However, it does not contain some potentially relevant variables that previous research has found to correlate with growth but which are only available for fewer countries. We hence include those additional variables in a separate “medium” and “large” model. For those models, the sample decreases to 967 (149 countries) and 635 observations (128 countries), respectively. The variables, their construction, and rationale for inclusion are discussed below the table. While the table focuses on variables we include in different model variants, the text also highlights alternative variables included in the data set, where available.

> 13 The NAMES_STD is a standardized country identification from STATA’s ‘kountry’ ado command. 14 Some of the mentioned factors may impact country groups differently (e.g., commodity prices for importers vs. exporters). It is possible to interact the period dummies with dummies for country groups (by region, income level, commodity dependence etc.) if one is interested in this aspect.

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