_Human capital_ is approximated by the initial gross rate of secondary schooling (in logs) and the data is obtained from Barro and Lee (2001).<sup>5</sup> _Financial development_ is measured by the ratio of domestic credit to the private sector to GDP and the data is collected from Beck, Demirgüç‐Kunt and Levine (2000), Beck and Demirgüç‐Kunt (2009), and updated using data from the IMF’s International Financial Statistics and the World Bank’s WDI. For the sake of robustness, we use other proxies of financial development: domestic credit provided by domestic money banks, and liquid liabilities of the financial sector. Both variables are expressed as a percentage of GDP and in logs. _Institutional quality_ , on the other hand, comprises different dimensions such as absence of corruption, rule of law, enforcement of contracts, quality of the bureaucracy, democratic accountability, among others. We use the ICRG index of political risk as our indicators of institutional quality. The data is published in the International Risk Country Guide (ICRG) by the Political Risk Services (PRS) Group. The _lack of price stability_ is approximated by the average CPI inflation rate. This variable typically reflects the quality of monetary and fiscal policies and is directly related to other indicators of poor macroeconomic management. The data on the inflation rate is gathered from the IMF’s International Financial Statistics.

_Infrastructure_ is a multi‐dimensional concept; however, most empirical studies have focused on a single‐sector approach partly due to: (i) the difficulty of capturing the multiple dimensions of infrastructure in a simple way, and (ii) the high correlation often found among indicators of different types of infrastructure assets (Calderón and Servén, 2004).<sup>6</sup> To overcome this problem, while accounting for the multi‐dimensionality of infrastructure, we use principal component analysis to build synthetic indices summarizing information on the quantity of different types of infrastructure assets as well as the quality of services in different infrastructure sectors.<sup>7</sup> These synthetic indices combine information on three core infrastructure sectors ‐‐ telecommunications, power, and roads ‐‐ and help address the problem of high co‐linearity among their individual indicators.<sup>8</sup> We denote IK the synthetic quantity

> 5 This “flow” measure captures more closely current policies on schooling and human capital investment than “stock” measures related with educational attainment of the adult population or life expectancy (Loayza et al. 2005).

> 6 Calderón and Servén (2004) find that the sample correlation between standard measures of telephone density and power generation capacity (measured respectively by a country’s total number of telephone lines, and its total power generation capacity, in both cases relative to the number of workers) exceeds 0.90, which makes it hard to disentangle in a regression framework the separate roles of the two types of assets.

> 7 Alesina and Perotti (1996) used principal component analysis to create a measure of political instability, while Sánchez‐Robles (1998) employed it to build an aggregate index of infrastructure stocks.

> 8 We should point out that the sector‐specific indicators of infrastructure quantity and quality employed below, while standard in the literature, are subject to caveats regarding their homogeneity and international comparability. For example, the quality and condition of a ‘paved road’ can vary substantially across countries – even within the same country. More homogeneous measures of infrastructure performance would be clearly

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