question, we restrict the sample to Middle East and North Africa (MENA) regions, for which we have establishment-level data in the Arab Republic of Egypt, Jordan, Lebanon, Morocco, Tunisia, West Bank and Gaza. The survey is representative of the non-agricultural private sector of each country and provides information on all size classes, including small firms with less than 20 employees. This feature is of high importance for our analysis, as smaller companies are also more likely to suffer the competition from the informal sector.

The analysis takes advantage of the longitudinal dimension of the WBES to deal with simultaneity bias or unobserved factors in the empirical strategy. From the total sample of 13,000 company-year observations we focus on about 2,000 firms, for which we are able to match at least two consecutive waves of the survey.<sup>3</sup> We discuss possible selection issues in Section 2.

Our main measure of interest, _Constrained by informal_ , is a binary variable identifying firms that perceive the competition from the informal sector as a major constraint. The original WBES survey explicitly asks to what degree practices of competitors in the informal sector are an obstacle to the current operations of the firm. We classify a company to be constrained by the informal sector if it declares such practices to be a major or very severe obstacle (the top two categories).<sup>4</sup> Our measure has the great advantage of capturing an idiosyncratic component that goes beyond the mere diffusion of informal practices within the operating sector of a country. Instead, it represents a specific proxy for how much the company perceives informal competition as a jepardizing factor for its own business. As such, it is likely to be accounted for when firms make their decisions and form their own expectations about the future.<sup>5</sup> In this regard, the WBES dataset represents the only available survey providing forward-looking expectations on firms’ future sales for MENA regions. This is a critical piece of information that made our study possible and allows to shed light on the mechanism at stake. We employ a continuous measure for firms expected sales growth in the next year (E( _Sales growth_ )), as well as an ordinal measure capturing increasing, decreasing, or stable expected earnings (respectively, E( _Sales growth_ ): _Positive_ , _Negative_ , or _Stable_ ).

We match all this information with a wide set of financial characteristics of the company. First of all, we exploit data on the availability of outstanding loans or credit lines ( _Loan availability_ ) at the time of the survey, which is a synthetic measure for firms’ access to bank finance. Because we are interested in the

> 3The original number of waves for countries in the MENA region ranges between two and five, from 2007 to 2020. However, only in more recent years (i.e., the global dataset) firms are attached to a consistent panel identifier across waves that allows for longitudinal analyses. For most countries, two waves with panel identifiers are available: 2013–2019 for Jordan, Lebanon, Morocco, West Bank and Gaza, and 2013–2020 for Tunisia. The only exception is Egypt, for which three waves are available: 2013, 2016, and 2020. We account for differences across countries and timing with the inclusion of country-specific time fixed effects that purge the model from economy-wide factors that vary over time.

> 4We collapsed the two answers because there is no clear ranking between the available options and they are both identifying a significant perceived obstacle for the company. As a robustness check, we employed a categorical measure with unchanged results. The exact formulation of the questionnaires is provided in Table B1 of the Data Appendix.

> 5We also control for a dummy identifying those that started their activity as unregistered firms ( _Originally informal_ ) or a continuous measure for the number of years since the firm was formally registered ( _Years of formality_ ).

4