of Hungarian data, Mike and Kiss (2019) have shown that latent class analysis (LCA) is particularly suitable for this task.<sup>4</sup> Thus, a second contribution of this paper is to develop the application of LCA for the discovery and estimation of governance structures, and apply it to the six-country dataset. We characterize the governance structures, estimate the prevalence of each, and show how the use of each varies across countries, producing results that are exactly comparable across countries. Our characterization of governance structures is data-driven: it does not rely on an a priori conception of which governance structures are used in practice.

A third contribution is to provide illustrative examples of the use of the information generated by our method. We present descriptive statistics on the variation of the use of governance structures across different subsets of firms, for example, in different sectors, or for firms of varying sizes, or for firms with different types of management practices. These are descriptive in the sense that they do not isolate the ceteris paribus causal effects of single variables. We do, however, use techniques previously developed for LCA that produce consistent estimates of the descriptive parameters. The examples point the way to potential areas of further research and possible policy conclusions.

A fourth contribution is to develop a set of tools for other researchers who can use our data and estimates of governance structures to advance their own research agendas.<sup>5</sup> We provide a dataset of the predicted posterior probabilities that each firm in our sample uses each estimated archetypal governance structure. These predicted probabilities could then be used in combination with existing survey data produced by the World Bank Enterprise Surveys (WBES) to investigate the causal determinants of firm choice of governance structures. Or, more broadly, researchers could use our predicted probabilities in combination with their own datasets in order to address a broad range of research issues by using a Rajan-Zingales (1998) style methodology.

Going even further, readers could add different countries, or regions, or sectors to those we have studied here. Given any set of responses to the questions that we lay out in Section II, even responses from very few firms, a researcher could use the information we provide to obtain the predicted probabilities that the respondent firms use particular governance structures. The results could then be directly compared to those for the six South American countries. This could be particularly useful for researchers who have limited resources and can only sample a small number of firms, yet have an interest in obtaining reliable statistical results to use in a crosscountry comparable setting.

These contributions show the feasibility of constructing and implementing a methodology that produces cross-country data characterizing the mix of governance structures that are

> <sup>4</sup> LCA has been used a great deal in the social sciences in general, but not much in economics.  For an intuitive introduction, see Collins and Lanza (2010), for a precise description of the decisions to be made in practically implementing LCA and the criteria to be used in making them, see Masyn (2013), and for a comprehensive technical introduction to the statistical theories and methods included in the software used to produce the results for this paper, see Vermunt and Magidson (2016).  The applications most similar to ours in economics are Ebers and Oerlemans (2016) and Mike and Kiss (2019).

> <sup>5</sup> The very last paragraph of the paper provides details on how to access the relevant datasets and tools.

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