do not hire unrelated workers. The owners of these low-probability firms have below secondary or secondary education. These kinds of informal firms have more than a 50% chance of being in the lowest quartile of the probability of formality and almost no chance of being in the top quartile. These relatively young small firms with no hired workers constitute about 19% of all informal firms inside establishments in the ELMPS. Older one-person firms with relatively low revenue per worker come next in terms of representation in the lowest quartile, irrespective of owner’s education. These constitute an additional 20% of all informal firms. Adding these two groups together suggests that approximately 40% of all informal firms operating in establishments have a low chance of formalization.

Results based on Model 2 using EcC data, shown in Table 7, confirm this pattern. Here, young one and two-person firms with no wage workers are also the most highly represented category in the lowest quartile of the probability of formality, irrespective of the amount of capital they have and irrespective of their value added per worker. These young small firms represent 34% of informal firms in the EcC. It appears that being young and not hiring wage workers are the primary predictors here, since larger firms with these characteristics are also highly represented in the bottom quartile although they only constitute another 1% of informal firms.

The cluster analysis highlights additional variables that characterize the informal firms that have the lowest probabilities of formality. As shown in Figure 14, the analysis using ELMPS data highlights low probability of formalization clusters than can be characterized as “women-owned retail firms” (8% of informal firms in ELMPS, 62% of which are in the lowest quartile of predicted formality) and “youth-owned young firms” (10% of informal firms, 42% of which are in the lowest quartile of predicted formality). These are followed by a cluster we term “smaller professional firms” (4% of informal firms, 39% of which are in the lowest quartile of predicted formality).<sup>23</sup> All three of these clusters skew toward young firms, with the proportion of zero-year-old firms being twice the overall average in the first and third clusters and the proportion of one and two year-old firms being 2.5 times higher than average in the second cluster.

The analysis using EcC data, shown in Figure 15, highlights clusters that we term “smaller professional firms” (5% of informal firms in EcC), “smaller service firms” (7%), “smaller manufacturing and food service firms” (6%) and “one-person retail firms” (27%). From 65% to 95% of these firms are in the lowest quartile of predicted formality. Firm age does not appear to be as much of a distinguishing feature of a low probability of formality in the EcC cluster results as it was in the grouping analysis and the ELMPS cluster results.

We now move to the other end of spectrum to characterize the profiles of informal firms that have a relatively high probability of formality and can presumably be targeted in formalization efforts. According to the ELMPS grouping analysis, these include older firms with above median revenue per worker, irrespective of size or owner education (21% of informal firms in ELMPS), older firms with more educated

> 23 We assigned names to the clusters after examining the predominant characteristics that define them. These characteristics are summarized in Table 13 and Table 14 in the Appendix for the 10 clusters we identify in the data from each data set.

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