## _Variables in the “Large Model”_

For **human capital** (education) we rely on the log human capital index, _lhc_ , from PWT10.0, which linearly interpolates data on educational attainment from Barro and Lee (2013) and assumes a certain rate of return for primary, secondary, and tertiary education. Human capital is widely assumed to be a key driver of cross-country income differences (Mankiw et al., 1992; Glaseser et al., 2004; Hanushek and Woessmann, 2015) but problems in adequately measuring human capital across a wide range of countries have also been pointed out in the literature (e.g., Pritchett, 2001; Hanushek and Woessman, 2012).

To measure **inequality** , we take the Gini coefficient of market and disposable incomes, _gini_mkt_ and _gini_disp_ , respectively, from the Standardized World Income Inequality Database (Solt, 2019). It should be noted that this data is severely over-imputed.<sup>18</sup> Following the literature (e.g., Bruckner and Lederman, 2018; Scholl and Klasen; 2019), we do not log-transform this variable. As discussed in those studies, there are several approaches to address apparent endogeneity concerns for this variable and several arguments suggest various functional forms (changes vs. levels, quadratic and interaction terms; e.g., Gründler and Scheuermeyer, 2018; Blotevogel et al., 2022). We opt for changes in the Gini of market incomes, _dgini_ , in our regression models.

# **5. Reference Models and Results**

## **5.1 Baseline Results from Least Squares Estimation**

Table 2 contains the results of the fixed effect regression, as specified in equation (1), with the variables introduced above. The “small model” in column (1) captures fewer variables, which allows for a larger sample size (~1,500 observations from 168 countries). At each column of table 2, the model is sequentially extended, shrinking the sample accordingly.

The results in table 2 can be summarized as follows: (i) First, the autoregressive parameter is in the expected range, hovering around 0.8. Note that this parameter is likely downward biased, as further discussed in section 5.4.

(ii) Second, a number of correlations is quite robust: infrastructure and trade are positively correlated with income per capita levels, financial crises, inflation, and inequality changes negatively. All those correlations are statistically different from 0 across all specifications (at conventional levels of significance).

(iii) Third, a number of correlations are in line with expectations and show reasonable standard errors but cannot be reliably distinguished from a 0-correlation in most models. This concerns the negative coefficient estimates for temperature variability, terms of trade changes, and growth volatility, as well as the positive parameter estimate for credit/GDP, FDI, human capital, and the non-linear U-shaped relationship with export concentration. The latter suggests that medium specialization (around the sample mean) is associated with the lowest income levels and growth strategies can either be successful through diversification (e.g., United States, Netherlands) or specialization in goods with outstanding comparative or factor advantage (e.g., Saudi Arabia, United Arab Emirates).

> 18 For this reason, we plan to include data from pip.worldbank.org in future updates of the dataset instead.

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