overall degree of connectedness of the company, our baseline specification does not impose any constraint on the original issuance of the loan. However, our results are broadly robust if we restrict the analysis to loans that are issued within shorter horizons (ten years, seven, five, two, or even one year before the interview). Second, we employ information on loan applications in the last year ( _Loan application_ ) to provide preliminary evidence on whether the heterogeneous availability of funds is due to firms’ credit demand or is, instead, driven by a differential probability of banks’ acceptance. Our extensive set of controls include information on firms’ belonging sector ( _Manufacturing, Retail, Other services_ ) and form of proprietorship ( _Listed company, LLC, Sole proprietorship, Partnership, Ltd Partnership_ ), structural characteristics ( _Size, Age_ ), exporting status ( _Export_ ), realized past performance ( _Sales growth_ ), and number of competitors ( _N competitors_ ). All variables are defined in Tables B1 and B2 of the Data Appendix.

**Selection and attrition** Since we rely on the longitudinal dimension of the WBES dataset, it is worth discussing possible selection issues affecting our estimating sample. This has clearly to do with the nonrandom probability of response and the self-selection of companies that kept answering the survey in following waves, vis-`a-vis firms that dropped out of the sample. Indeed, if such selection is somewhat simultaneously correlated with our main regressors of interest and dependent variables, it may create a bias driving our conclusions. In Table A1 of the Online Appendix, we tackle this issue by focusing on the full set of respondents in the original 2013-waves and testing the correlation between firms’ likelihood of being interviewed a second time and the variables employed in the analysis. Our estimates assuage concerns about systematic biases by showing no significance between firms’ probability of belonging to the panel and our main variables of interest (Constrained by informal, Loan availability, Loan application, and Investment).<sup>6</sup> Nevertheless, we also provide additional robustness to our results by presenting Heckman selection models that deal with endogenous sampling selection and propensity score matching techniques (see Section 3.3.2).

# **3 Empirical analysis**

## **3.1 Descriptive evidence**

Table 1 presents some descriptive statistics for the main variables in the sample. Loans and credit lines are available only for 20% of the firms in the sample, suggesting that access to finance is underdeveloped. Yet, the share of firms applying for a loan is only mildly larger. We interpret this as evidence of the degree of disconnectedness from the banking sector of firms in the MENA region. Most firms finance working capital

> 6Results are presented in Table A1 of the Online Appendix. The only exception is represented by Age, whereby older firms are more likely to belong to the panel than younger companies, possibly because of their different probability of survival. However, since we always control for such a characteristic in our estimating regression, our estimates should still be unbiased.

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