2010; Berry, Gaynor and Scott Morton, 2019) to answer important questions in development economics.

Given its economic importance and methodological appeal, a large empirical literature has studied the cement industry. Many studies have sought to describe the industry equilibrium in a specific economy, for instance as in Brazil (Salvo, 2010), India (Bhayani, 2010), Norway (R ̈oller and Steen, 2006) and the United States (Jans and Rosenbaum, 1996; Newmark, 1998; Ryan, 2012; Miller and Osborne, 2014; Fowlie, Reguant and Ryan, 2016; Miller, Osborne, Sheu and Sileo, 2022). In contrast, our goal is to explain sources of variation in prices across countries and contribute to macroeconomic debates on the price of investment (Easterly, 1993; Jones, 1994; Hsieh and Klenow, 2007).

Three closely related papers also study cross-country differences in the cement industry. None of these papers, however, estimates costs (and thus markups) and their underlying drivers. Ghemawat and Thomas (2008) use the global cement directory to describe the strategic decision of multinational cement firms to enter national markets. The World Bank (2016) highlights high prices of cement (and other products) in Africa, using the 2011 IPC data.<sup>5</sup> Beirne and Kirchberger (2021) combine the global cement directory with IPC data like we do and show that cement prices are higher in more concentrated markets. The authors embed an oligopoly model of the cement industry into a general equilibrium model and show that the steady-state capital stock in Africa is most sensitive to changes in markups in the cement industry. Despite using the same data, our paper differs from, and complements theirs, in three ways. First, we estimate a structural model rather than a reduced-form relationship between market concentration and prices.<sup>6</sup> Second, differences in modeling approach (and identification strategy), lead to a distinct focus: while we do not link concentration in the cement industry to the aggregate capital stock, our model allows us to disentangle drivers of costs and markups in the industry and perform rich counterfactuals. Finally, we highlight the rapid decline of prices in Africa, rather than exclusively cross-sectional differences in price levels. These distinctions, supported by the model’s estimates, lead to rather different conclusions with respect to drivers of cross-economy price dispersion, and how it may (or may not) be addressed by public policy.

The rest of the paper unfolds as follows. Section 2 presents the data, motivating facts, and industry background. Section 3 introduces the model. Section 4 applies the model

> 5Hassan (2016) finds that the overall price level falls with national income among low-income economies in Africa. Higher prices in low-income economies have been documented for generic pharmaceuticals (Silverman, Keller, Glassman and Chalkidou, 2019) and mobile internet (Faccio and Zingales, 2017).

> 6See Berry et al. (2019) for a discussion of the difficulties in interpreting reduced-form relationships between market structure and markups (and, a fortiori, prices) in the spirit of the older ‘structure-conductperformance’ paradigm.

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