of resulting groups and reasonable cell size. We further collapse categories as needed.<sup>19</sup> After this exercise we have 43 groups all of which have 10 or more informal firms.

Similarly, we group firms in the EcC. We use firm size, firm age, invested capital, whether the firm hired a wage worker, and quartile of value added per worker. Similar to ELMPS, after obtaining the number of informal firms for each combination of categories from these variables, we impose the constraint that no category can have fewer than 10 informal firms.<sup>20</sup> After this exercise we have 76 groups all of which have 10 or more informal firms.

# **3.6. Cluster Analysis**

As an additional approach to identifying profiles of firms with varying potential for formalization, we undertake cluster analysis. Cluster analysis is a data reduction and classification method, designed to create a small number of clusters (groups) where we have maximized similarity within the groups and difference between the groups (Everitt, Landau, Leese, & Stahl, 2011). We use k-medians clustering and Gower’s dissimilarity coefficient (since it is amenable to a mix of binary and continuous data) to create 10 clusters in each of the ELMPS (pooling 2012 and 2018) and EcC. For ELMPS, we use the variables in model 3 for the cluster analysis. For EcC, we use the variables in model 2 for the cluster analysis.

After obtaining the clusters, we explore the characteristics of each of them. Based on the characteristics of these clusters, we identify the delineating features for each clusters and name clusters accordingly. We present descriptive statistics on categorical variables showing the ratio of each category within the cluster relative to the category overall in the sample.<sup>21</sup> In addition, we standardize the continuous variables in each of the clusters relative to the mean and standard deviation of that variable in the sample.

# **3.7. Dynamics**

We descriptively present the relationship between firm dynamics and formality over pairs of waves in terms of five measures: (1) firm exit, (2) the share of new vs. not new firms that are formal, (3) the share of firms that are new by formality, (4) changing formality status by base wave formality status (conditional on firm survival), and (5) employment growth (as a binary indicator) by base wave formality. We then 19 We impose the constraint that no category can have fewer than 10 informal firms and aggregate groups that have fewer than 10 informal firms with groups of similar characteristics (i.e., all variables have the same value except the one that is used for aggregation) using the following algorithm: i) group quartiles of the log of revenue per worker on the same side of the median together, i.e., quartiles 1 and 2 together, and quartiles 3 and 4 together; ii) aggregate secondary and above secondary education levels of the owner together; iii) aggregate all capital categories together; iv) aggregate all levels of owner’s education together; v) aggregate all quartiles of log of revenue per worker; and vi) aggregate all categories of hiring non-relatives from outside the household.

20 To aggregate groups that have fewer than 10 informal firms with groups of similar characteristics, i) we first group quartiles of the log of revenue per worker on the same side of the median together, i.e., quartiles 1 and 2 together, and quartiles 3 and 4 together; ii) and then aggregate all quartiles of log of revenue per worker.

> 21 For ELMPS, we use the sample mean of each wave.

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