observables to predict the probability of formality. As the set of predictors expanded, the overlap between the two distributions declined. This reduced overlap means that, while we are better able to identify informal firms that look like some of their formal counterparts, there are fewer of them. With more detailed characteristics, we can see that fewer informal firms are similar to formal firms and fewer will be potentially formalizable. Informal firms operating outside establishments do not seem to have many counterparts that are formal and therefore are particularly unlikely to be formalized.

We used two different methods to group or cluster informal firms based on their observable characteristics and then classify these groupings according to their predicted probability of being formal. The first method was to group informal firms based on a combination of the most important predictors of formality, broken down into discrete categories. We made sure to aggregate these groups in such a way that no groups contained fewer than 10 informal firms. The second method was to use cluster analysis to create 10 clusters with maximum similarity between the observables in each cluster and dissimilarity across clusters. Using these methods separately on the EcC data and pooled ELMPS data, we identified the profiles of firms that were least likely to be amenable to formalization and those of firms that were most likely to have formal counterparts and be potentially formalizable.

The grouping analysis suggests that firms not amenable to formalization include young one- or two-person firms with no hired labor, irrespective of productivity. These firms make up 22% of informal firms in the ELMPS and 34% of informal firms in the EcC. Groups with low probability of formality also include older one-person firms with relatively low revenue per worker, which constitute another 20% of informal firms in the ELMPS. Cluster analysis on ELMPS data identified three clusters as least amenable to formalization, which we named “women-owned retail firms,” “youth-owned young firms,” and “smaller professional firms.” These clusters together make up 22% of informal firms and tend to skew young. Cluster analysis on the EcC data identified small professional, service and manufacturing firms and one-person retail firms as least amenable to formalization, which together made up 45% of informal firms in the EcC. On the other end of the spectrum, we identified the profiles of firms that have a relatively high probability of formality. The ELMPS grouping analysis identified older, more productive firms, older firms with more educated owners, and older larger firms. The grouping analysis on EcC identifies older larger firms that either hire wage workers or that are more productive as having a higher probability of formality.

What do our results imply about the determinants of formality and why they might matter? The cluster analysis on both data sets puts the emphasis on older and larger firms that hire wage workers as more amenable to formalization. These patterns are consistent with the fact that formalization imposes certain fixed costs and administrative hurdles (AfDB, 2016; World Bank, 2020) that younger and smaller firms are not able to afford.

Higher productivity is associated with greater formality, but the direction of causality is not clear. It seems more likely that more productive firms are better able to afford the costs of formality than it is that formality leads to higher productivity. Moreover, the grouping and cluster analyses demonstrated that firms’ productivity level was subordinate to firm size and age in terms of predicting formality. While productivity is a factor in decisions to formalize, it does not appear to be the driving factor in Egypt.

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