we account for the introduction of firm-specific fixed effects (in our benchmark results) through conditional logistic and linear probability (within estimators) models. This allows us for purging all firms’ characteristics (observable and unobservable) that are stable over time.<sup>8</sup> The second interrelated issue has to do with _endogeneity_ and, in particular, with possible reverse causality (i.e., whereby it is access to credit that drives the perception of informal competition and not the other way round; Friesen and Wacker, 2019). For instance, firms that are constrained by banks may be unable to fund potentially profitable projects, increase their production scale, or upgrade toward higher levels of productivity. While this is unlikely to impact the actual operating environment of the company (i.e., access to credit of a specific firm does not affect its real competition), we cannot _a priori_ exclude an effect on firms’ _perception_ about the magnitude and relevance of the informal competition faced.

Notice that, if this were the case, we should observe significantly-different patterns in a firm’s probability of rationing. However, as shown in Table 2, firms that suffer informal competition are linked neither with a differential likelihood of credit constraints (full or partial rationing), nor with actual or expected rejection rates on loan applications. Thus, our descriptive evidence seems to suggest that, at least in our sample of MENA countries, reverse causality should not be a relevant concern.

Nevertheless, we adopt a number of alternative approaches to deal with this potential endogeneity issue. First of all, we always employ lagged regressors so as to rule out simultaneity bias. This, however, does not address endogeneity if both the dependent variable and our measure of interest displays high degrees of persistence. Hence, we further shed light on this issue by restricting our estimating sample to companies with no credit access in _t −_ 1. Even focusing on “switchers” only (i.e., firms with new loans that were disconnected in the previous waves), our results prove to be extremely robust (see Table A2). Moreover, unreported regressions linking past loan availability with current perception of informal competition show no correlation between the two, further suggesting that reverse causality is unlikely to drive our findings.<sup>9</sup> Most importantly, we employ an IV approach to further take care of endogeneity. We rely on a cellaverage method wherein we instrument informal competition with the proportion of all other firms that are constrained by informality and operate within the same 2-digit sector and geographical area of each company (at a given time). This approach, widely used in the literature (see, among many others, Distinguin et al., 2016; Dollar et al., 2006; Fisman and Svensson, 2007; Amin and Soh, 2021; Amin, 2021), allows us to capture an environmental component of the informal competition faced by a company that is, however, unrelated to

> 8Because of the structure of the dataset, we can only introduce firms’ fixed effects when dealing with loan availability and application. Notice that, in such case, identification is achieved exclusively exploiting data from Egypt, which is the only country for which we have three waves (as discussed in footnote 3). For expectations, the absence of a panel structure does not allow for such analysis.

> 9Notice that, while being largely insignificant (p value of 0.210), the coefficient of past loan availability on informal competition is even of opposite sign: positive rather than negative.

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