where _yi,t_ is the outcome variable. In our analysis this is a dummy taking value 1 if firm _i_ at time _t_ has a loan or credit-line and zero otherwise ( _Loan availability_ ) or a dummy indicating if the firm has made an application for bank funding ( _Loan application_ ). _Constrained by informal_ is our dummy of interest, as defined in Section 2, and _Xi,t−_ 1 is a vector of structural controls for firms’ size, age, exporting behavior, number of competitors, belonging industry, and form of proprietorship. _λt_ and _μa_ ( _i_ ) are, respectively, time fixed effects and a set of granular indicators for the geographical area of the company (41 in total).<sup>7</sup> These are meant to capture time varying common factors and persistent heterogeneities linked with the operating environment of the firm. In our analysis, all regressors are lagged to rule out simultaneity bias. When _yi,t_ is a binary measure (Loan availability, Loan application) equation 2 is estimated via a logistic model while when it is a continuous measure (Expected sale growth) it is estimated using a linear probability model. In all tables, we report White’s heteroscedasticity-consistent standard errors but our results are largely robust to alternative clusterings.

**Discussion of the empirical strategy** In assessing the effect of informal competition on firms’ connectedness to the banking sector we face two main empirical challenges.

The first one has to do with _omitted variable bias_ , whereby relevant characteristics excluded from the model may drive the relationship between firms’ availability of bank finance and informal competition. For instance, if we missed to properly account for firms’ fundamentals that characterize the perception about informal pressure, and if this affects demand and/or supply of credit, our estimates may be biased. In order to assuage such concern, we augment our baseline specification with an extensive set of additional regressors that virtually cover any dimension available in the WBES survey. In our robustness checks, we account for past sales and productivity growth, capacity utilization, as well as granular information on main destination markets (local, national, or international), managerial characteristics (experience of the managers, female managers) and ownership (government ownership and female owners). All of which capture different dimensions of firms’ fundamentals. Moreover, we control for relevant characteristics of the local environment that may be spuriously associated with informality: the size of the city, measures of corruption (bribery depth or gifts to officials) and crime (loss from thefts and vandalism), as well as other shocks linked with the localization of the company (number and length of electric outages), together with geographic area fixed effects interacted with more granular sector controls (2-digit ISIC Rev. 3.1 level). Furthermore, in some specifications we enrich the vector _X_ with controls for the issuance of the loan, whether the company was originally informal, and the number of years since formal registration occurred (see below). Finally,

> 7Geographical areas are defined as the localization group used as a stratum in the WBES sampling scheme. From the sampling methodology note of the WBES: “Geographical distribution is defined to reflect the distribution of the non-agricultural economic activity of the country; for most countries this implies including the main urban centers or regions of the country.”

9