# **5. Discussion and Conclusions**

Egypt has struggled to create good, formal jobs for its young and growing labor force (World Bank, 2013; Gatti, Angel-Urdinola, Silva, & Bodor, 2014; Assaad, AlSharawy, & Salemi, 2019). Informal firms are a challenge not only for Egypt’s workers, but also the macroeconomy and tax base (AfDB, 2016; Ali & Najman, 2016). For some time, there have been recommendations to formalize the largely informal sector of micro and small firms in Egypt in order to expand the tax base and increase the availability of good jobs (Egyptian Center for Economic Studies, 2005; World Bank, 2014). Yet such calls assume that firms that are currently informal are formalizable, that they could afford and survive formalization, without negatively impacting job creation. This assumption has not been tested or substantiated in Egypt.

In an attempt to answer whether informal firms may be formalizable, in this paper we compared formal and informal non-agricultural firms with 1-24 workers. We used multiple data sources on micro and small firms in Egypt and analyzed which firm and owner characteristics predict formality. We used the predicted probability of formality to characterize informal firms that are like formal firms and may be amenable to formalization efforts, in contrast to those firms that are quite different and are thus highly unlikely to formalize. For the most part we used an expanded definition of formality that counts a firm as formal if it has a commercial registration or if it pays social insurance premiums or taxes. In the dynamic analyses, we used a more basic definition that just depends on the commercial registration status of the firm.

Although the different data sources – the 2017 Establishment Census (EsC), the 2012-13 Economic Census (EcC), and the various waves of the Egypt Labor Market Panel Survey (ELMPS) – produce different rates of formality for our universe, they demonstrated similar relationships between formality and observable characteristics. Results on the relationship between formality and characteristics were comparable across firm size (number of workers), firm age, economic activity and region. In a subset of the data sets, measures of firm productivity and whether or not they hired outside workers also showed comparable results. Only the ELMPS data include firms that operate outside fixed establishments, which are predominantly informal. After establishing the highly informal nature of firms outside establishments, we restricted most of our analyses to firms operating within fixed establishments, which can be found across all our data sources.

We generally found that firm size and firm age are strong predictors of formality, with larger and older firms being more likely to be formal. Industry and region were, at best, inconsistent if not unreliable predictors of formality. Firms that hired wage workers, especially non-relatives, were more likely to be formal, as were the ones that had higher labor productivity or net revenue per worker. When owner characteristics were available, we found that owners with secondary education, but especially higher education were more likely to have formal firms and so were prime age and older owners compared to owners under the age of 30.

When we compared the distributions of formal and informal firms in terms of their predicted probabilities of being formal, we found that there was some overlap. This result suggests that some informal firms are similar to formal firms and thus may be amenable to formalization. The overlap was larger when only firms that operate in fixed establishments were compared and when there was a limited set of 72