and the percentage of workers female. Model 2b, shown in Table 4, includes additional firm-related characteristics only available in the ELMPS data sets, such as the size of the firm’s capital (in categories), net revenue per worker, the presence of non-household members among the workers, separately for relatives and non-relatives, and the type of workplace. Model 3, also shown in Table 4, adds to the firmrelated characteristics in Model 2b some characteristics related to the firm owner, specifically the owner’s sex, educational attainment, broad occupational group, and age group.

We present in Table 2 odds ratios from a logit regression for the probability of being formal using the common set of variables across all four data sets (Model 1). In the first column we show results for the pooled data, with dummy variables indicating the data source. Separate results by data set are shown in the four subsequent columns. As expected, firm size is a strong predictor of formality in all four data sets. According to the pooled model, compared to one-person firms, the odds of being formal are 66% higher for two-worker firms, more than twice as high for 3-4 worker firms, and more than five times as high for 5-24 worker firms.

Older firms are significantly more likely to be formal. Compared to firms aged one year old, those zero are less likely to be formal, usually significantly so. The surveys vary in terms of which ages start to predict significantly higher formality, with the ELMPS 2018 showing a later and weaker relationship. In the pooled model, formality largely rises steadily with age, and the “don’t know” firms tend to follow the pattern of older firms (likely don’t know means a firm is old enough that the owner cannot remember when, exactly, it started).

All industry groups except “various professional activities” have lower odds of formality compared to wholesale and retail trade, usually significantly so in the pooled model. The industry that is least likely to have formal firms, on average, is construction, followed by transport and storage. However, these results appear to differ across data set. Construction is least likely to be formal in the ELMPS 2012 and highly likely to be formal in EcC, but that effect is measured with a great deal of imprecision. Similarly, transport and storage is less likely to be formal in the two ELMPS waves than in the EcC. It should be kept in mind that few construction and transport firms are actually within establishments, so the group showing up here may be highly and differently selected.

As suggested by the descriptive statistics, firms in the Alexandria and Suez Canal region have higher odds of being formal than those in the Greater Cairo Region. Surprisingly, according to two of the four data sets (EcC and EsC), firms in Lower Egypt and Upper Egypt, two provincial regions, also have higher odds of being formal than those in Greater Cairo. This strongly suggests that being in a metropolitan region does not necessarily raise the odds of formality.

The pooled data results confirm the pattern observed in the descriptives that the two ELMPS waves produce higher measures of formality than the EcC and the EsC. After correcting for size, industry and region, the EcC produces odds of formality that are three-fifths lower and the EsC produces odds that are about half of what they are in ELMPS 2018.

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