Our key independent variable of interest is the political connectivity of each firm. We follow Faccio (2006), and others, and code a firm as politically connected when it has at least one board member or CEO who is a politician, a close relative of one, or a publicly known friend. For that purpose, we leverage online business directories and Lebanon’s commercial registry to look up the name of each firm’s board members in addition to collecting data on their size, age, and paid-in capital.

Our approach to identify political connections takes into account that political connections are a complex phenomenon in a country like Lebanon (Leenders, 2012; Diwan and Haidar, 2020). We go beyond previous studies, which establish objective criteria for the identification of connections, such as by name matching of a company’s shareholder or CEO names with those of political actors. As such approaches have tended to underestimate results,<sup>7</sup> we instead review each firm in our data set manually via an approach outlined in detail in Mahmalat, Atallah and Maktabi (2021). For each firm, we go through a multi-layered search process that relies on media searches on the names of each board member of a firm with a corresponding name of a politician or their political party. This approach allows us to carefully assess a number of common issues in the identification of political connections, such as whether individuals with matching names are related, connections are “deep” enough to matter, or relevant during the period of investigation. We augment and validate the findings with our key informant interviews and code a firm we have not found reliable information on as connected when multiple interviewees correspond in their assessment of a particular firm.<sup>8</sup> Moreover, we review the commercial registries as well as the companies’ websites to identify firm characteristics, notably their age, size (in number of employees), and paid-in capital. As these directories fail to report some of the characteristics for some firms (Table 3), we use multiple imputations to estimate the missing values for these observations. The goal of using multiple imputations is to maximize the use of available information, minimize estimation bias, and obtain appropriate standard errors (Enders, 2010). We use multiple imputation, rather than other available techniques such as stochastic or deterministic imputation, to minimize the bias of standard errors in our regression analyses. We leverage the _mi estimate_ command in Stata using a multivariate normal distribution with 10 imputations and take the contract value as an auxiliary variable.<sup>9</sup> Table 3: Number of incomplete observations of supervision and design consultants

|**Supervisors**|**Complete**|**Incomplete**|**Total**|**Percent missing**|
|---|---|---|---|---|
|Age|340|21|361|5.8%|
|Size|332|29|361|8.0%|
|Paid-in Capital|260|101|361|28.0%|
|**Designers**|||||
|Age|207|26|233|11.2%|
|Size|203|30|233|12.9%|
|Paid-in Capital|159|74|233|31.8%|

# **Descriptive statistics**

> 7 The widely-cited work of Faccio (2006), for example, uses a data set of firms worldwide and finds no politically connected firms in Zimbabwe and Venezuela—two countries with an arguably weak record for the control of corruption. Even for the United States, where the author’s data set includes more than 7,000 firms, her approach only identifies 14 connected firms (p. 374), a number that other works have found to be much higher (Goldman, Rocholl and So, 2009).

> 8 Note that the differentiation between PCF1 and PCF2 is mutually exclusive. In the few cases in which we find connections to both circles of elites, we code the firm according to its superior connection (i.e., PCF1) as such firms would prefer invoking their direct connection to decision makers to influence the procurement process, rather than their connection to a third party. 9 Multiple imputation, however, requires that the mechanism that produces missing values is at least missing at random (MAR) in that the missing values are not completely random but that other observed variables can be used to predict the value of the missing ones. MAR moreover requires the ignorability assumption in that the probability of missing data does not depend on the value of the missing information itself. In our case, missing observations are distributed in a non-systematic way among both small and big firms winning both small and big contracts, as well as those that have other information reported.

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