metadata: authors: 0: author: Krafft,Caroline Gould
1: author: Assaad,Ragui A.
2: author: Rahman,Khandker Wahedur
3: author: Cumanzala,Maakwe
count: Egypt, Arab Republic of
docna: 0: docna: How Do Small Formal and Informal Firms in
            the Arab Republic of Egypt Compare ?
docty: Policy Research Working Paper
subtopic: Social Development & Poverty,Transport Services,Labor Markets,Educational Sciences,Employment and Unemployment
teratopic: Social Protections and Labor,Education,Poverty Reduction,Transport
abstracts: cdata!: Formalizing firms can potentially
            increase the tax base, expand safety and social protections
            for workers, create good jobs, and grow the economy.
            However, the costs and processes of formality may be too
            challenging for firms, particularly the smallest firms, to
            bear. Thus, informal firms may not be able to survive the
            transition to formality and attempts to expand formality may
            be harmful and counterproductive to job creation and growth.
            This paper investigates the potential for currently informal
            firms to formalize in the Arab Republic of Egypt. The paper
            compares the characteristics and dynamics of micro and small
            non-agricultural firms by formality and identifies the
            extent of overlap and potential for formalization. The
            analysis finds that, beyond firm size, the basic and easily
            observable characteristics of firms are not closely linked
            to formality. Firm age, productivity, and owner
            characteristics such as education are strongly predictive of
            formality. There is some overlap in the predicted
            probability of formality between formal and informal firms,
            suggesting some potential for formalization. The paper
            develops profiles (groups and clusters) of similar firms to
            identify those with a higher potential for formalization. In
            terms of dynamics, new firms tend to be informal and
            informal firms are more likely to exit (close), but
            conditional on firm survival, employment growth is similar
            across formal and informal firms.
geo_reg_key: 517202,517191
repnme: repnme: How Do Small Formal and Informal Firms in
            the Arab Republic of Egypt Compare ?
display_title: How Do Small Formal and Informal
            Firms in the Arab Republic of Egypt Compare
listing_relative_url: /research/2020/10/32463246/small-formal-informal-firms-arab-republic-egypt-compare
topicv3: Transport m1326661 135,Social Insurance m1326950 751,Education m1327048 470,Jobs m1503931 2670,Self-Employment m1504000 2698
publishtoextweb_dt: 2020-10-05T00:00:00Z
historic_topic: Social Protections and Labor,Education,Poverty Reduction,Transport
origu: EFI-MNA-POV-Poverty and Equity (EMNPV)
no_of_pages: 100
author_keys: 0: author_key: 1224471
1: author_key: 606784
2: author_key: 1277597
model_extractions: Policy Research Working Paper 9423 How Do Small Formal and Informal Firms in the Arab Republic of Egypt Compare?

_Caroline Krafft Ragui Assaad Khandker Wahedur Rahman Maakwe Cumanzala_ Equitable Growth, Finance and Institutions Practice Group October 2020
Policy Research Working Paper 9423

# **Abstract**

Formalizing firms can potentially increase the tax base, expand safety and social protections for workers, create good jobs, and grow the economy. However, the costs and processes of formality may be too challenging for firms, particularly the smallest firms, to bear. Thus, informal firms may not be able to survive the transition to formality and attempts to expand formality may be harmful and counterproductive to job creation and growth. This paper investigates the potential for currently informal firms to formalize in the Arab Republic of Egypt. The paper compares the characteristics and dynamics of micro and small non-agricultural firms by formality and identifies the extent of overlap and potential for formalization. The analysis finds that, beyond firm size, the basic and easily observable characteristics of firms are not closely linked to formality. Firm age, productivity, and owner characteristics such as education are strongly predictive of formality. There is some overlap in the predicted probability of formality between formal and informal firms, suggesting some potential for formalization. The paper develops profiles (groups and clusters) of similar firms to identify those with a higher potential for formalization. In terms of dynamics, new firms tend to be informal and informal firms are more likely to exit (close), but conditional on firm survival, employment growth is similar across formal and informal firms.

This paper is a product of the Equitable Growth, Finance and Institutions Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at nsinha@worldbank.org, agonzalez4@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**How Do Small Formal and Informal Firms in the Arab Republic of Egypt Compare? An Analysis of Firm Characteristics and Implications for Formalization Efforts**<sup>**1**</sup>

Caroline Krafft,<sup>2</sup> Ragui Assaad,<sup>3</sup> Khandker Wahedur Rahman,<sup>4</sup> Maakwe Cumanzala<sup>5</sup>

**Keywords:** Firms, microenterprises, formality, Egypt

**JEL codes:** D22, L11, L26

1 This paper was prepared as a background paper for the second Egypt Systematic Country Diagnostic under the task management of Alvaro Gonzalez (Principal Economist, Social Protection and Jobs) and Nistha Sinha (Senior Economist, Poverty and Equity). We appreciate the team's comments on various drafts and the questions and comments of participants in the Egypt Country Office Strategic Discussion Series seminar.

2 Corresponding author. Department of Economics and Political Science, St. Catherine University, cgkrafft@stkate.edu 3 Humphrey School of Public Affairs, University of Minnesota.

4 Department of Applied Economics, University of Minnesota.

5 Department of Economics and Political Science, St. Catherine University.
# **1. Introduction**

The Egyptian labor market, since structural adjustment in the 1990s, has been characterized by a sharp decline in formal employment and growth of the informal economy. The public sector shrank from 39% of employment in 1998 to 26% in 2018 (Assaad, AlSharawy, & Salemi, 2019). The private sector was unable to replace the shrinking public sector with good, formal jobs (jobs with social insurance or contracts). From 1998 to 2018 the share of formal private wage employment in total employment grew from 8% to just 12%, while the share of informal private employment grew from 53% to 62% (Assaad, AlSharawy, & Salemi, 2019).<sup>6</sup> Lack of formality and its associated benefits, such as protections afforded through the labor code, paid leave and retirement benefits, are a key impediment to women’s participation in the labor force. Despite rising education, women in the Arab Republic of Egypt have increasingly withdrawn from the labor market (Assaad, Hendy, Lassassi, & Yassin, 2018). Women particularly exit at marriage, in the face of informal employment opportunities in the private sector that are irreconcilable with their domestic responsibilities (Assaad, Krafft, & Selwaness, 2017; Selwaness & Krafft, 2018). Access to formal employment and its associated benefits has come to increasingly rely on class and connections for the younger generation (Assaad, Krafft, & Salemi, 2019; Assaad & Krafft, 2020). Frustrations with the lack of good jobs among young people and the middle class were a key component of the Arab Spring protests (World Bank, 2013; Gatti, Angel-Urdinola, Silva, & Bodor, 2014).

On top of the challenges informal employment presents to Egyptian workers, informal firms are a challenge to the macroeconomy. Informal firms do not pay taxes, which creates a difficult fiscal situation for Egypt (AfDB, 2016). The ongoing growth of informal firms in the economy compounds this issue (Elshamy, 2015). Informal firms, facing lower costs, may out-compete formal firms, creating a particular challenge for growing the formal sector (Ali & Najman, 2016). Informal firms may also stay small and not create jobs in order to avoid the attention of authorities (AfDB, 2016). There have therefore been longstanding calls in policy circles in Egypt to formalize micro and small firms to expand the tax base, increase access to formal employment with its various benefits and protections, and facilitate firm access to finance and international trade opportunities (Egyptian Center for Economic Studies, 2005; World Bank, 2014).

Yet formality may create challenges for job creation overall, if the process of formalizing or the requisites of formality include burdensome regulations and substantially higher costs (Sparks & Barnett, 2010). Micro and small enterprises play a critical role in employment creation and dynamics (Li & Rama, 2015). Individuals may start firms more out of necessity or survival (primarily self-employment) than entrepreneurial opportunity (Naudé, 2008, 2010; Brixiova, 2010; Krafft & Rizk, 2018). If the costs of formality are sizeable and fixed in nature, they may prove particularly burdensome for the smallest firms. There are heterogenous results across countries and studies in terms of whether informal firms are

> 6 This percentage may include a small fraction of formal self-employment, but it is unlikely to exceed a few percentage points.

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similarly productive to formal firms (Gelb, Mengistae, Ramachandran, & Shah, 2009; Benjamin & Mbaye, 2012). In some contexts, informal firms were more like formal firms, suggesting potential for formalization.

The reasons firms in Egypt are informal are tied up with costs, although it is not clear if the costs would preclude firm survival and growth. Employers who are informal in Egypt state that their most common reason for remaining informal is that it “saves time and effort” followed closely by “formalization does not involve advantages” (25%-30%). Financial reasons – “no need to pay employees’ social insurance” and “no need to pay taxes” are the next most common reasons (around 15%), followed by no need to apply labor laws (around 6%-7%) (AfDB, 2016). Since firms are presumably engaged in a cost-benefit analysis when making their decisions to remain informal, that calculus will likely have to change to induce them to formalize (World Bank, 2014).

There have been substantial recent improvements in the procedures to start a new business (or to formalize an existing business) in Egypt. Although the cost of formally starting a business declined from 69% of income per capita in 2007 to 20% in 2020, it may still be prohibitive for very small firms (World Bank, 2006, 2020). Similarly, the time required to start a business fell from 19 days in 2007 to 12 days in 2020. Egypt’s rank in starting a business has accordingly improved from 165<sup>th</sup> out of 175 economies in 2007 to 90<sup>th</sup> out of 190 economies in 2020 (World Bank, 2006, 2020). However, other aspects of formality, such as paying taxes, enforcing contracts, and registering property remain extremely burdensome, with Egypt ranking 156<sup>th</sup> out of 190 economies in paying taxes, 166<sup>th</sup> in enforcing contracts, and 130<sup>th</sup> in registering property in 2020 (World Bank, 2020).

The assumption underlying the calls to formalize informal firms is that small informal firms, or at least subsets of them, can be formalized. This implies that informal and formal firms are similar to each other in terms of productivity, employment, types of activities, location, etc. This assumption implies that formalization would be feasible and requiring formality would not result in the firm instead disappearing altogether. This paper sets out to test whether currently informal firms in Egypt are “formalizable” and, if so, which firms, in particular, might be susceptible to formalization.

We use multiple data sources from Egypt to assess which firms are more likely to be formal. We then quantify the amount of overlap between formal and informal firms in terms of their predicted probability of formality, based on observable characteristics. While we find some overlap in the probability to be formal, particularly among firms in fixed establishments, we also find that many informal firms have a low probability of formality and may not be formalizable. We create profiles (clusters, groups) of firms with similar characteristics to explore which informal firms are more, or less, likely to formalize. We also explore the relationship between firm dynamics and formality. We conclude that, in the current landscape in Egypt, while enforcement and encouragement may yield some progress in expanding the formal sector, more substantive reforms to the process and practice of formality are likely needed to substantially expand the formal sector.

3
# **2. Data**

# **2.1. Data sources**

Since we are interested in the phenomenon of firm informality, for this study we focus on a universe of private non-agricultural firms with fewer than 25 workers, but situate this universe within employment in Egypt. This focus is because public enterprises and government establishments are not at risk of being informal.<sup>7</sup> Formality is typically not relevant for most agricultural activities, and informality among firms of 25 workers or more is rare (Assaad, AlSharawy, & Salemi, 2019). Given this universe, we use data sets from household surveys with a household enterprise module and establishment surveys or censuses that represent both formal and informal firms of all sizes.

The first data source we use is the Egypt Labor Market Panel Survey (ELMPS) carried out by the Economic Research Forum in collaboration with Egypt’s Central Agency for Public Mobilization and Statistics (CAPMAS). Four waves of this survey were carried out in 1998, 2006, 2012, and 2018.<sup>8</sup> We primarily use the 2012 and 2018 waves for contemporaneity with our other data sources, but use the 1998 and 2006 waves when exploring firm dynamics. The second data source is the 2012/13 Economic Census (EcC), which is an in-depth survey of establishments carried out by CAPMAS every five to six years. The third is the Egypt 2017 Establishment Census (EsC), which is a full census of establishments, also carried out by CAPMAS in conjunction with the decennial population and housing census. These data sources have different coverage and depth of detail, as we discuss below.

# **2.1.1. Egypt Labor Market Panel Survey**

The ELMPS is a nationally-representative household survey that has tracked a panel of households (continually adding refresher samples) since 1998.<sup>9</sup> The ELMPS has, since its inception, included a nonagricultural household enterprise module. Information on agricultural activities is collected in other modules that are not relevant for our purpose. Any household which reports a member who is selfemployed, an employer, or unpaid family worker in either their primary or secondary jobs must respond to either the non-agricultural or agricultural enterprise module.<sup>10</sup> Households can report on multiple enterprises. There were 2,315 enterprises in 2,195 households in the 2012 wave of the ELMPS and 2,228 7 The public sector does not have informal firms, but it does, increasingly, have some informal employment (Selwaness & Ehab, 2019).

8 For more information on the data, see Krafft, Assaad, and Rahman (2019). The data are publicly available at <u>www.erfdataportal.com.</u> 9 The ELMPS does not cover the Frontier governorates of Matrouh, New Valley, Red Sea and North and South Sinai, which together comprise about 2% of Egypt’s population.

10 Households that did not initially report members as employed in these statuses may also report non-agricultural enterprises; indeed, allowing this structure identifies substantially more employment among women (Krafft, Keo, & Fedi, 2019).

4
enterprises in 2,131 households in the 2018 wave.<sup>11</sup> For each enterprise, there are questions about enterprise characteristics, household members’ participation, employment of non-household members, net revenues and expenditures.

# **2.1.2. Economic Census and Establishment Census**

We use the public release 50% sub-sample of Egypt’s 2012/2013 EcC. Despite being called a census, the EcC is a survey of fixed establishments, with the largest establishments having a sampling rate of 100%. The 50% sub-sample we have access to includes 62,108 establishments. Although the 2017 EsC is truly a census of fixed establishments, we only have access to a 20% random sample of these establishments. Thus, our sample is made up of 772,432 establishments. The EcC and EsC cover all non-governmental establishments and therefore exclude any firms that are not in fixed establishments, i.e. ones that involve mobile workers or ones whose activities take place in a field or open worksite, such as the vast majority of firms engaged in construction, agriculture, and transportation activities.<sup>12</sup> The EcC and EsC include public enterprises, but we exclude those from our analyses since they are by definition formal. We also exclude the very small number of agricultural firms that show up in both data sources for comparability with the ELMPS. Since the ELMPS excludes the Frontier governorates, we likewise exclude these governorates from the EsC and EcC data. Since the EcC and EsC only include firms that operate in fixed establishments but the ELMPS includes firms that operate both in and out of establishments, most of our analysis is limited to firms operating within fixed establishments.

# **2.2. Measuring Formality**

Firm formality can be defined and operationalized in a variety of ways. We test two increasingly expansive measures of firm formality. First, our basic measure of formality is commercial registration. However, in the EcC, the registration question was not asked of firms in a number of industries.<sup>13</sup> In our extended definition, we expand this measure to include firms that paid social insurance premiums (available in EcC and EsC) or taxes (available in the ELMPS).

# **2.3. Firm characteristics**

We have a limited set of harmonizable, common characteristics across all the data sets we use. We compare firms across these characteristics in terms of whether they are describing firms in our universe in the same fashion and whether the different data sources find similar patterns of formality across characteristics. We then conduct some analyses on a broader set of characteristics present only in the EcC or only in the ELMPS.

> 11 For the dynamic analyses we also use the 1998 wave (1,059 households and 1,134 enterprises) and 2006 wave (1,899 households and 2,044 enterprises).

> 12 Firms operating in fixed establishments include firms that operate in private homes.

> 13 The EcC uses 11 different survey instruments for firms in different industries. Some of these did not include a question on commercial registration.

5
# **2.3.1. Common characteristics**

The firm’s size is one of our common characteristics. We categorize firms into one worker (primarily selfemployment), two workers, 3-4 workers, or 5-24 workers. Workers may be paid or unpaid. Industry, at the one-digit level (or aggregated further to present meaningful descriptives), is available in all three data sets. The 2012 and 2018 ELMPS and EcC have the calendar year the firm started, while the EsC has the calendar year if 2010 or later and then switches to decades (e.g. 2000-2009). We therefore create a harmonized firm age variable that is single years of age from 0-7 years old, and then 8-17 years old, 18-27 years old, 28+, or do not know.<sup>14</sup> We further have information on the governorate, which we present in terms of regions: Greater Cairo, Alexandria and Suez Canal, Lower Egypt, and Upper Egypt.<sup>15</sup>

# **2.3.2. Characteristics in the Economic Census**

In the EcC, we have particularly rich detail on firm characteristics. In addition to the common characteristics described above, we include indicators for having unpaid workers and wage workers. We also measure the share (percentage) of workers who are female. Since we have detailed information on value added, invested capital, and labor, we analyze the relationship between formality and the log of capital per worker and the log of value added per worker. We also include information on whether the firm is a sole proprietorship or some type of collective ownership arrangement.

# **2.3.3. Characteristics in the ELMPS**

In the ELMPS, we can identify additional detail about firm characteristics and owners. We consider the owner to be the person who worked the most on the enterprise.<sup>16</sup> We include the owner’s sex, age group (<30, 30-49, 50+), education (illiterate, less than secondary, secondary, higher education), and occupation (professional/technical, other white collar, blue collar craft, blue collar non-craft<sup>17</sup> ).

In addition to owner characteristics, we use firm characteristics from the ELMPS enterprise module. Specifically, we have current capital (categorically), the type of the workplace (e.g. office, workshop, etc.), and sole proprietorship (household) vs. collective ownership (partnership). Using the number of workers and the net revenues of the enterprise going to the household, we calculate net revenue/worker as a 14 Since the ELMPS 1998 and 2006 waves have start years categorically, when analyzing dynamics (which we can only do using the ELMPS) we switch to harmonized categorical start years.

15 Greater Cairo includes the governorates of Cairo, Giza, and Qalyoubia. Alexandria and the Suez Canal includes Alexandria, Port-Said, Suez, and Ismailia. Lower Egypt includes Damietta, Dakahliya, Sharkiya, Kafr El-Sheikh, Gharbiya, Menofiya, and Behira. Upper Egypt includes Beni Sueif, Fayyoum, Menia, Assuit, Souhag, Qena, Aswan, and Luxor.

> 16 This generally overlaps with who makes the most decisions, a variable added in 2018 (Krafft, Keo, & Fedi, 2019).

> 17 Individuals who did not report being employed in the preceding three months were classified as blue-collar noncraft.

6
crude measure of productivity. We also use indicators for whether the firm hires relatives from outside the household. A similar measure indicates whether the firm hires any non-relatives.<sup>18</sup>

# **2.4. Firm Dynamics**

Since the ELMPS follows households in four waves, 1998, 2006, 2012 and 2018, we can trace the dynamics of firms in our data set. Dynamics can occur over three pairs of waves: 1998-2006, 2006-2012, or 20122018. An observation for our dynamic model thus has data from a base wave and a subsequent wave, e.g. for 1998-2006 the base wave is 1998 and the subsequent wave is 2006.

Creating a panel of firms from a household survey presents a number of complexities. We restrict all analyses to cases where the household was observed in both the base and subsequent wave of a pair, so we do not classify a firm as being created simply because a household joined our data set, nor a firm exited because the household attrited. We do track splits, so cases such as where an unmarried son may have had a household enterprise and then took it with him to his new household when he married and moved out (split) are captured as a firm surviving, so long as we successfully located the split household. We define an enterprise that existed in the base wave as having survived over a pair of waves if at least one household member still has an enterprise of the same economic activity (identified on the one-digit level using ISIC 4 coding). We examine the outcome of exit (closure) as the complement to an enterprise surviving. We also consider the formation of new household enterprises, that is, among households that were not engaging in an economic activity in the base wave of a pair, new enterprises in the subsequent wave. We further consider – among enterprises that survived across a pair of waves – the dynamics of (in)formalization, that is, changing formality status between the base and subsequent wave. We also consider, again among surviving enterprises across a pair of waves, employment growth, specifically whether the total number of workers in the enterprise is larger in the subsequent period than it was in the base period.

# **3. Methods**

# **3.1. Situating our universe of firms and employment**

We begin our analyses by situating our focus – private non-agricultural establishments with 1-24 workers – within the landscape of employment in Egypt. We describe the percentage of employment and the number of workers in this segment of the economy, in comparison to other sectors. We also describe employment formality by segment; note that this is distinct from firm formality, both in terms of the denominator, and in that, while a firm must be formal to have formal employees, not all employees within a formal firm are necessarily formal.

> 18 The distinction between relatives and non-relatives is not available in 1998 or 2006, so when analyzing dynamics we simply use an indicator for employment outside the household, which is available in all waves.

7
# **3.2. Comparisons of establishment characteristics and formality across data sets**

We then focus on our universe of interest, comparing the formality of private non-agricultural establishments with 1-24 workers across the three data sets, using various definitions. We start by examining the distribution of firms along various characteristics across data sets to ascertain if they represent similar firms. Subsequently, we examine the patterns of formality across the common characteristics and data sets. We present our results descriptively and also test whether there are significant differences using regressions. Specifically, we pool the three data sets and run a logit model for probability of being formal, with all the common characteristics fully interacted with a categorical variable indicating the data source, to understand whether there are significant differences in formality across data sources.

# **3.3. Predictors of formality**

A key focus of our research is investigating what factors predict formality and whether formal and informal firms are comparable or different. We run logit models to predict formality. In “Model 1” we include only the few characteristics that are common across all three data sources. “Model 2” includes additional firm characteristics and can only be run on the EcC and ELMPS (with a somewhat different set of variables in each survey; see above). “Model 3,” which adds the owner’s characteristics, is only possible for the ELMPS.

# **3.4. Comparing Distributions of Predicted Probability of Formality by Formality Status**

After running the various models mentioned above, we predict the probability of being formal using each model and data source. We compare the distribution of the predicted probability of formality by whether or not firms are actually formal. We take the size of the overlap in the distribution functions of predicted probability across formal and informal firms as a test and metric of whether informal firms are similar to formal firms and thus might potentially formalize. We acknowledge this overlap is, in part, driven by the number of covariates included in the model. We also look at the proportion of informal firms in different quartiles of the predicted probability to ascertain the proportion of informal firms that are most eligible for formalization.

# **3.5. Grouping Analysis**

We undertake grouping of firms, by characteristics that were particularly important in our regression models, as one approach to creating profiles of firms that vary in their potential to formalize. We specifically group together informal firms with common characteristics and explore the proportion in the different quartiles of the predicted probability of formality. We undertake this work only for the ELMPS (model 3) and EcC (model 2), since the EsC has a very limited set of covariates.

For the ELMPS, we pool 2012 and 2018. We group on firm size, firm age, current capital, whether the firm hired a non-relative outside of the household, quartile of the log of revenue per worker, and owner’s education level. We use a collapsed set of categories for these covariates to have a manageable number 8
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.

9
estimate logit models of firm exit, changing formality, and employment growth using base wave characteristics. Since entering firms do not exist in the base wave, we do not estimate models for new firms.

# **4. Results**

# **4.1. Situating our universe of firms and employment**

We focus on non-agricultural, private sector employment, in firms in establishments with 1-24 workers. This is about a fifth of employment in Egypt’s economy (22%, Table 1), a quarter of employment is in the public sector (24%), another quarter outside of establishments (24%) and a further fifth (19%) in agriculture. Lastly, 11% of employment is in non-agricultural private firms in establishments that have 25+ employees. The segment we focus on represents 5.3 million workers, out of the 24.4 million workers in Egypt.

The table also measures employment formality in terms of social insurance. Not even in the public sector do all employees have social insurance (only 82% do). Among non-agricultural private firms in establishments with 25+ workers, half (52%) of workers have social insurance. The share is much lower, only 12%, among those working in non-agricultural private sector firms with 1-24 workers, and lower still outside establishments (8%) and in agriculture (2%). It is important to note that many of the workers who lack social insurance may still be in firms that are registered or otherwise formal, so this is an underestimate of workers who are working in formal firms. Hereafter we focus on non-agricultural, private sector employment in establishments with 1-24 workers and turn to the firm as the unit of analysis.

**Table 1. Employment and employment formality by firm type**

||**Percentage**<br>**of emp.**|**No. of emp.**<br>**in millions**|**Percentage**<br>**w/ soc. ins.**|
|---|---|---|---|
|**Public sector**|24|5.9|82|
|**Private agriculture**|19|4.6|2|
|**Non-ag. private outside est.**|24|5.9|8|
|**Non-ag private in est. 1-24**|22|5.3|12|
|**Non-ag private in est. 25+**|11|2.8|52|
|**Total**|100|24.4|31|

Source: Authors’ calculations based on ELMPS 2018 10
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Focusing now on firms in establishments, the proportion of firms that are formal according to the basic definition is comparable across the two ELMPS waves (49%-55%), somewhat lower in the EsC (39%), and much lower in the EcC (14%), likely because of the way that some EcC survey instruments excluded the question about registration. Moving to the extended definition more than doubles the share of firms that are formal in the EcC (34%), does not change the estimate of 39% for EsC, and raises the estimate of formality by a few percentage points (to 54-61%) in the two ELMPS waves. Thus, the extended estimates are broadly consistent with each other and suggest that somewhere from over a third to half of all private non-agricultural firms of fewer than 25 workers operating in establishments are formal, depending on the data source. Hereafter, we use the extended definition of formality as our primary measure and focus on firms that operate inside fixed establishments in our analysis. We note any exceptions to this approach.

Although Figure 1 shows the rate of formality for firms, since we have the number of employees in each firm, we can also calculate the number of employees in each data source within the universe of nonagricultural firms with 1-24 workers in establishments. Likewise, among these workers, we can calculate the number within formal firms versus informal firms. In the ELMPS 2012, using the extended definition, there are 3.5 million workers overall and 2.4 million within formal firms – although not necessarily formal employment. This compares to 5.8 million workers in the contemporaneous Economic Census, of whom 2.6 million were within formal firms. In the ELMPS 2018, there were 3.0 million workers, of whom 1.9 million would be within formal firms. The Establishment Census generates a much larger number of workers, 9.2 million, of whom 4.5 million were within formal firms.

# **4.2.2. Distribution of firm characteristics across data sources**

We now examine firm characteristics across the four data sets to determine whether we are observing roughly the same firms. As shown in Figure 2, the two waves of the ELMPS have a somewhat higher proportion of one-person firms and are conversely somewhat under-represented in the three larger size categories. This may be due to branches of firms, which tend to be larger, being included as separate establishments in the EcC and EsC. Despite this tilt toward smaller firm sizes, the ELMPS data produces higher estimates of formality than the other two sources, as we have seen above. Besides the fact that EcC has a higher representation of two-person firms (35%) and the EsC a higher representation of threeor four-person firms (25%), these two data sources produce fairly comparable estimates in the three other size categories. Overall, firms in our universe skew toward the smaller size categories, with one or twoperson firms making up 66% to 86% of all firms in the universe and firms of 5-24 workers making up at most 10%.

12
<!-- Start of picture text --> 70<br>64 go<br>60<br>50<br>n<br>E<br>re}= 40 35 35 35<br>5 31<br>® 30<br>© 24 25<br>a 20 20<br>20<br>10 10 10<br>10 9 6 5<br>()<br>1 2 3-4 5-24<br>Number of workers in the firm<br>ME ELS 2012 inest. MM ELMPS 2018 in est.<br>YY) Economic Census Establishment Census<br><!-- End of picture text -->
<!-- Start of picture text --> 31<br>30 28<br>23<br>o<br>E<br>= 20 19<br>2<br>15 14<br>5 16<br>= 13 12<br>a "44 1<br>7 7 7<br>5) 5 5,4) 8 7 6 a7 se 6 s 6<br>3 344 3 3<br>2<br>0<br>0<br>0 1 2 3 4 5 6 7 8-17 18-27 28+ Don't<br>Know<br>Firm age (years)<br>MMB ELMPS 2012inest. MEM ELMPS 2018 in est.<br>IN Economic Census Establishment Census<br><!-- End of picture text -->
<!-- Start of picture text --> 27<br>Greater . 22<br> Cairo 27<br>30<br>15<br>Alexandria and Suez Canal 9 41<br>11<br>37<br>Lower 42<br>Egypt 38<br>38<br>20<br>Upper 27<br>Egypt 24<br>21<br>0 10 20 30 40 50<br>Percent of firms<br>ME ELS 2012 inest. MM ELMPS 2018 in est.<br>YY) Economic Census Establishment Census<br><!-- End of picture text -->
<!-- Start of picture text --> 15<br>Manufacturing and related trades 196<br>13<br>2<br>Construction fo!<br>3<br>8<br>Wholesale and retail Boe<br>53<br>9<br>Transportation and storage | 8 6<br>6<br>Accommodation and food service ts<br>5<br>12<br>Various professional acts. 19<br>11<br>Z<br>Other service {9<br>10<br>0 10 20 30 40 50 60 70<br>Percent of firms<br>MS ELMPS 2012 inest. MMMM ELMPS 2018 in est.<br>Economic Census Establishment Census<br><!-- End of picture text -->
<!-- Start of picture text --> 100<br>90<br>86<br>80<br>72<br>3E ot 63 61 62 65 61<br>2[e) 60 56 54<br>E 48 50<br>be 41 39<br>8 40 34 37 34<br>oO<br>a. 26<br>22<br>20<br>0<br>1 2 3-4 5-24 All firms<br>Number of workers in the firm<br>MMMM ELIVPS 2012 inest. MEM ELMPS 2018 in est.<br>YY) Economic Census Establishment Census<br><!-- End of picture text -->
<!-- Start of picture text --> 100<br>80 75 73.9 70<br>— 67 68<br>oO 65 64<br>£ 62 61 61 61 61<br>S 58 5g °°<br>= 60 5 54° aS 3<br>E 49 pO<br>-_ 44<br>2 eo o UF S| cd | ie 4 39<br>8 40 3 pa | Mere | coe J 4<br>{0} 30 30<br>a 26 27 26<br>21 23<br>20 16 lea<br>0<br>0 1 2 3 4 5 6 7 817 18-27 28+ Don't All<br>Know Firms<br>Firm age (years)<br>MM ELMPS 2012 inest. MMMM ELMPS 2018 in est.<br>YY) Economic Census Establishment Census<br><!-- End of picture text -->
<!-- Start of picture text --> 62<br>Greater Cairo 26 87<br>36<br>67<br>Alexandria and Suez Canal 39 88<br>50<br>58<br>Lower Egypt 33 34<br>39<br>61<br>Upper Egypt a1 $3<br>40<br>61<br>All firms mn 4<br>39<br>0) 20 40 60 80<br>Percent of firms formal<br>MME ELMPS 2012 inest. MEM ELMPS 2018 in est.<br>YY) Economic Census Establishment Census<br><!-- End of picture text -->
|Manufacturing andrelatedtrades<br>3f,<br>be|
|---|
|Construction<br>_<br>93<br>|
|;<br>65<br>Wholesaleand retail<br>35 a,<br>S2|
|Transportation and storage<br>y<br>65<br> <br><br>|
|Accommodationandfoodservice<br>Bi<br>&<br> <br><br>|
|Variousprofessional acts.<br>o7<br>56 °Bs|
|Otherservice<br>as<br>247<br>|
|61<br>All firms<br>4|
|0<br>20<br>40<br>60<br>80<br>100|
|Percent of firms formal|
|MM ELMPS 2012 inest.<br>MMMM ELMPS 2018 in est.|
|YY)EconomicCensus<br>EstablishmentCensus|
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.

21
**Table 2. Odds ratios from logit “Model 1” of extended formality by data source**

||**Pooled**|**ELMPS**<br>**2012 in est.**|**ELMPS**<br>**2018 in est.**|**Economic**<br>**Census**|**Establishm**<br>**ent Census**|
|---|---|---|---|---|---|
|**Data (ELMPS 2018 in est. omit)**||||||
|ELMPS 2012 in est.|1.349**|||||
||(0.147)|||||
|Economic Census|0.400***|||||
||(0.035)|||||
|Establishment Census|0.532***|||||
|**Ent. size (one worker omit.)**|(0.045)|||||
|2|1.655***|1.462|1.887***|1.814***|1.628***|
||(0.094)|(0.292)|(0.358)|(0.130)|(0.011)|
|3-4|2.414***|2.096**|1.394|2.651***|2.750***|
||(0.141)|(0.506)|(0.368)|(0.275)|(0.019)|
|5-24|5.661***|7.228***|9.753***|7.441***|5.108***|
||(0.402)|(3.428)|(5.122)|(0.622)|(0.051)|
|**Ent. age (one year old omit.)**||||||
|0|0.728**|0.685|0.339*|0.825|0.831***|
||(0.078)|(0.442)|(0.167)|(0.094)|(0.013)|
|2|1.165|0.995|0.668|1.622***|1.181***|
||(0.108)|(0.420)|(0.308)|(0.162)|(0.015)|
|3|1.461***|1.903|0.959|1.601***|1.353***|
||(0.168)|(0.730)|(0.456)|(0.172)|(0.019)|
|4|1.619***|2.400*|0.937|1.830***|1.410***|
||(0.150)|(0.958)|(0.439)|(0.199)|(0.018)|
|5|1.946***|3.073*|2.116|2.255***|1.547***|
||(0.190)|(1.451)|(1.127)|(0.280)|(0.020)|
|6|2.063***|1.678|2.681|2.507***|1.706***|

22
||**Pooled**|**ELMPS**<br>**2012 in est.**|**ELMPS**<br>**2018 in est.**|**Economic**<br>**Census**|**Establishm**<br>**ent Census**|
|---|---|---|---|---|---|
||(0.239)|(0.805)|(1.360)|(0.321)|(0.026)|
|7|1.907***|2.162|2.875|1.976***|1.725***|
||(0.162)|(0.870)|(1.590)|(0.341)|(0.020)|
|8-17|2.426***|2.401**|1.931|2.780***|2.449***|
||(0.196)|(0.734)|(0.712)|(0.314)|(0.028)|
|18-27|3.190***|4.597***|1.299|4.899***|3.223***|
||(0.326)|(1.601)|(0.514)|(0.490)|(0.044)|
|28+|4.302***|4.414***|2.869*|5.045***|4.795***|
||(0.495)|(1.677)|(1.222)|(0.542)|(0.074)|
|Don't know|3.313***|2.166|3.590**|1.859***||
||(0.842)|(1.021)|(1.685)|(0.287)||
|**Industry (wholesale/retail omit.)**||||||
|Manufacturing and related trades|0.661***|0.442***|0.972|0.757***|0.639***|
||(0.049)|(0.096)|(0.256)|(0.054)|(0.005)|
|Construction|0.362***|0.147***|0.491|7.319**|0.523***|
||(0.085)|(0.080)|(0.480)|(4.675)|(0.009)|
|Transportation and storage|0.526***|0.464|0.563|1.903|0.514***|
||(0.020)|(0.469)|(0.430)|(0.714)|(0.006)|
|Accommodation and food service|0.842|0.816|1.574|0.794**|0.759***|
||(0.075)|(0.249)|(0.532)|(0.068)|(0.009)|
|Various professional acts.|1.074|0.756|1.552|0.530***|1.562***|
||(0.075)|(0.183)|(0.402)|(0.041)|(0.013)|
|Other service|0.620***|0.488**|0.922|0.715***|0.537***|
||(0.046)|(0.130)|(0.251)|(0.051)|(0.005)|
|**Region (Greater Cairo omit.)**||||||
|Alexandria and Suez Canal|1.667***|1.227|1.086|2.139***|2.048***|
||(0.130)|(0.291)|(0.356)|(0.162)|(0.019)|

23
|||**ELMPS**|**ELMPS**|**Economic**|**Establishm**|
|---|---|---|---|---|---|
||**Pooled**|**2012 in est.**|**2018 in est.**|**Census**|**ent Census**|
|Lower Egypt|1.331***|0.948|0.937|1.956***|1.438***|
||(0.081)|(0.187)|(0.233)|(0.123)|(0.009)|
|Upper Egypt|1.431***|0.932|1.022|2.738***|1.414***|
||(0.096)|(0.202)|(0.263)|(0.282)|(0.011)|
|**N**|766840|1200|1155|52287|712198|
|**Pseudo R-squared**|.113|.0821|.0953|.119|.0989|

Source: Authors’ calculations based on ELMPS 2012, ELMPS 2018, EcC 2012/13, EsC 2017 Notes: *p<0.05; **p<0.01; ***p<0.001.

We now move to the results from Model 2a estimated in the EcC data and shown in Table 3. The results on size are in the same direction as before, but are somewhat attenuated because of the inclusion of other regressors associated with firm size. The results on firm age, industry, and region are little changed when compared to the results of Model 1 on the EcC data. With regard to the new variables added, we see that collective ownership as opposed to sole proprietorship slightly and significantly raises the odds of formality. Higher labor productivity is associated with substantially higher odds of formality, as is higher capital intensity. As expected, having unpaid family workers reduces the odds of formality, but having wage workers increases it. The proportion of female workers in the firm’s workforce is not associated with the odds of formality.

24
**Table 3. Odds ratios from logit “Model 2a” of extended formality in EcC 2012/13**

|**Ent. size (one worker omit.)**||
|---|---|
|2|1.640***|
||(0.157)|
|3-4|2.005***|
||(0.254)|
|5-24|4.945***|
||(0.617)|
|**Industry (wholesale/retail omit.)**||
|Manufacturing and related trades|0.697***|
||(0.056)|
|Construction|4.965**|
||(3.018)|
|Transportation and storage|1.579|
||(0.572)|
|Accommodation and food service|0.764**|
||(0.070)|
|Various professional acts.|0.492***|
||(0.038)|
|Other service|0.748***|
||(0.054)|
|**Region (Greater Cairo omit.)**||
|Alexandria and Suez Canal|2.146***|
||(0.168)|
|Lower Egypt|2.052***|
||(0.139)|
|Upper Egypt|3.085***|
||(0.341)|

25
# **Partnership (sole proprietorship omit.)**

- Collective Ownership 1.185* (0.101)

# **Ent. age (one year old omit.)**

|0|0.917|
|---|---|
||(0.106)|
|2|1.629***|
||(0.163)|
|3|1.577***|
||(0.172)|
|4|1.773***|
||(0.196)|
|5|2.235***|
||(0.279)|
|6|2.441***|
||(0.316)|
|7|1.853**|
||(0.358)|
|8-17|2.635***|
||(0.298)|
|18-27|4.727***|
||(0.480)|
|28+|4.915***|
||(0.523)|
|Don't know|2.274***|
||(0.390)|
|**Ln capital to labor ratio**|1.078***|
||(0.023)|

26
|**Ln value added per worker**|1.188***|
|---|---|
||(0.044)|
|**Has unpaid workers (no omit.)**||
|Yes|0.516***|
||(0.083)|
|**Has wage workers (no omit.)**||
|Yes|1.460***|
||(0.119)|
|**Percentage of workers female**|0.999|
||(0.001)|
|**N**|51323|
|**Pseudo R-squared**|.133|

Source: Authors’ calculations based on EcC 2012/13 Notes: *p<0.05; **p<0.01; ***p<0.001.

We now turn to the results of Model 2b and Model 3 estimated on the two waves of the ELMPS data (Table 4). The results on the effect of the firm characteristics do not change appreciably once we add the owner characteristics in Model 3, so for these characteristics we discuss both models for both waves together and then move to the results on owner characteristics in Model 3.

First, we note that firm size plays the same steadily increasing and highly significant role we observed in Model 1, but, as in the case of Model 2a, adding additional regressors attenuates the effect of size. The effects of industry become more mixed and lose significance as more regressors are added to the model. We conclude from this that industry is not a reliable predictor of formality once other characteristics have been taken into account. The effect of region on formality also becomes somewhat mixed when additional regressors are added. Firms in the Alexandria and Suez Canal region are more likely to be formal only in 2012 but not in 2018. Thus, region is also not a reliable predictor of formality. Firm age is more likely to be significant in 2012 than 2018 and effects are attenuated as more regressors are added.

As we found with the EcC data, firms with collective ownership are more likely to be formal relative to sole proprietorships across years and across the two models, but the effect is larger in 2018 than in 2012. Having more capital increases the odds of being formal, but the effect appears to kick in only when the capital exceeds EGP 1000. As in the case of labor productivity in Model 2a above, higher log net revenue per worker is associated with a higher odds of being formal.

27
Hiring relatives from outside the household does not seem to be associated with higher formality rates, but hiring non-relatives does, as we found to be the case with wage workers in Model 2a. The type of workplace is also associated with formality. Shops and workshop/factories are most likely to be formal, whereas being located in a flat, room or building are associated with lower odds of formality. Being in a hut or kiosk is associated with very low levels of formality in 2012, but not in 2018.

We now examine results on the characteristics of the firm owner included in Model 3. Having a female as opposed to a male owner is associated with a lower odds of being formal in 2018 but not in 2012 and in both cases is insignificant. Firms with a more educated owner have a substantially higher odds of being formal, with owners with higher education having between 3.5- and 4.0-times higher odds of formalizing their firms than illiterate owners. Beyond education, occupation does not seem to have an additional effect, expect for possibly a lower odds of formality for craft blue collar occupations, and only in 2018. Finally, older owners are more likely to have formal firms than young owners, significantly so in 2012.

28
**Table 4. Odds ratios from logit “Model 2b” and “Model 3” of extended formality in ELMPS 2012 and 2018**

||**M**|**odel 2**|**M**|**odel 3**|
|---|---|---|---|---|
||**2012**|**2018**|**2012**|**2018**|
|**Ent. size (one worker omit.)**|||||
|2|1.224|1.510|1.215|1.572*|
||(0.271)|(0.324)|(0.270)|(0.332)|
|3-4|1.683|0.793|1.801|0.758|
||(0.500)|(0.236)|(0.584)|(0.241)|
|5-24|3.823*|4.572**|3.329*|4.460**|
||(2.066)|(2.623)|(1.744)|(2.566)|
|**Industry (wholesale/retail omit.)**|||||
|Manufacturing and related trades|0.501*|1.093|0.570|1.382|
||(0.136)|(0.338)|(0.169)|(0.462)|
|Construction|0.183**|0.874|0.174*|1.185|
||(0.114)|(0.777)|(0.126)|(1.064)|
|Transportation and storage|1.533|0.428|2.666|0.415|
||(1.489)|(0.362)|(2.600)|(0.372)|
|Accommodation and food service|1.041|1.416|1.337|1.236|
||(0.341)|(0.497)|(0.434)|(0.519)|
|Various professional acts.|1.002|1.991*|0.796|1.381|
||(0.324)|(0.626)|(0.265)|(0.493)|
|Other service|0.756|0.996|0.891|1.124|
||(0.213)|(0.279)|(0.282)|(0.336)|
|**Region (Greater Cairo omit.)**|||||
|Alexandria and Suez Canal|1.635|0.973|1.925*|1.072|
||(0.423)|(0.340)|(0.527)|(0.381)|
|Lower Egypt|1.257|1.049|1.327|1.078|
||(0.278)|(0.268)|(0.303)|(0.297)|

29
||**M**|**odel 2**|**M**|**odel 3**|
|---|---|---|---|---|
||**2012**|**2018**|**2012**|**2018**|
|Upper Egypt|1.170|1.171|1.374|1.269|
||(0.280)|(0.306)|(0.333)|(0.362)|
|**Partnership (sole proprietorship omit.)**|||||
|Collective Ownership|1.439|2.087**|1.591|2.408***|
||(0.339)|(0.512)|(0.381)|(0.624)|
|**Capital (none omit.)**|||||
|less than LE 100|1.166|0.580|1.529|0.511|
||(0.606)|(0.292)|(0.893)|(0.265)|
|LE 100-499|1.637|0.683|2.545|0.634|
||(0.810)|(0.342)|(1.381)|(0.346)|
|LE 500-999|1.428|0.898|1.872|0.627|
||(0.658)|(0.389)|(0.940)|(0.285)|
|LE 1000-4999|2.812*|1.017|4.014**|0.822|
||(1.296)|(0.441)|(2.039)|(0.379)|
|LE5000-9999|7.440***|1.469|9.265***|0.999|
||(3.406)|(0.587)|(4.620)|(0.425)|
|LE10 000 or more|4.076*|2.431|6.451**|1.893|
||(2.392)|(1.331)|(4.291)|(1.072)|
|**Ln net revenue per worker**|1.205*|1.114**|1.187*|1.125**|
||(0.092)|(0.040)|(0.092)|(0.043)|
|**Ent. age (one year old omit.)**|||||
|0|0.704|0.246**|0.724|0.274*|
||(0.389)|(0.128)|(0.458)|(0.149)|
|2|0.991|0.493|0.881|0.419|
||(0.449)|(0.237)|(0.406)|(0.211)|
|3|2.129|0.706|2.095|0.666|

30
||**M**|**odel 2**|**M**|**odel 3**|
|---|---|---|---|---|
||**2012**|**2018**|**2012**|**2018**|
||(0.847)|(0.365)|(0.889)|(0.361)|
|4|2.872*|0.702|3.094**|0.629|
||(1.201)|(0.356)|(1.314)|(0.334)|
|5|3.511**|1.530|3.965**|1.397|
||(1.662)|(0.941)|(1.855)|(0.772)|
|6|1.853|2.332|2.016|2.959|
||(0.882)|(1.301)|(1.012)|(1.916)|
|7|2.016|1.817|1.967|2.338|
||(0.850)|(1.049)|(0.850)|(1.429)|
|8-17|2.388**|1.404|2.117*|1.414|
||(0.748)|(0.550)|(0.730)|(0.588)|
|18-27|5.658***|1.049|4.837***|1.125|
||(2.078)|(0.437)|(1.979)|(0.499)|
|28+|5.694***|2.346|3.441**|2.349|
||(2.307)|(1.060)|(1.507)|(1.166)|
|Don't know|2.146|2.366|1.695|2.204|
||(1.084)|(1.209)|(0.921)|(1.267)|
|**Relatives outside the HH employed (no omit.)**|||||
|Yes|0.753|1.045|0.760|0.905|
||(0.190)|(0.318)|(0.193)|(0.270)|
|**Non-relatives outside the HH employed (no omit.)**|||||
|Yes|1.344|2.293***|1.309|1.796**|
||(0.269)|(0.488)|(0.275)|(0.402)|
|**Location (shop omit.)**|||||
|Office/flat/building/rooms|0.583|0.433***|0.331***|0.447**|
||(0.186)|(0.105)|(0.110)|(0.116)|

31
||**Model 2**|**M**|**odel 3**|
|---|---|---|---|
||**2012**<br>**2018**|**2012**|**2018**|
|Workshop/factory|0.911<br>0.858|1.043|1.087|
||(0.279)<br>(0.247)|(0.328)|(0.331)|
|Kiosk/hut|0.015***<br>0.757|0.012***|0.964|
||(0.016)<br>(0.380)|(0.013)|(0.545)|
|**Sex (male omit.)**||||
|Female||1.141|0.594|
|||(0.301)|(0.159)|
|**Education (illit. omit.)**||||
|Less than sec.||1.180|1.672|
|||(0.306)|(0.492)|
|Secondary||1.730*|2.201**|
|||(0.454)|(0.617)|
|Higher education||3.965***|3.521***|
|||(1.176)|(1.169)|
|**Occupation (prof./tech. omit.)**||||
|Other white collar||0.660|0.864|
|||(0.157)|(0.234)|
|Blue collar craft||0.967|0.441*|
|||(0.279)|(0.148)|
|Blue collar non-craft||0.868|0.730|
|||(0.305)|(0.264)|
|**Age group (<30 omit.)**||||
|30-49||1.892**|1.105|
|||(0.429)|(0.295)|
|50+||4.112***|1.385|
|||(1.135)|(0.426)|

32
|||**Model 2**||**Model 3**|
|---|---|---|---|---|
||**2012**|**2018**|**2012**|**2018**|
|**N**|1222|1149|1221|1107|
|**Pseudo R-squared**|0.222|0.168|0.262|0.209|

Source: Authors’ calculations based on ELMPS 2012 and ELMPS 2018 Notes: *p<0.05; **p<0.01; ***p<0.001.

# **4.3.2. Are informal firms like formal firms?**

Using the preceding models, we now turn to considering whether the predicted probabilities of being formal overlap between formal and informal firms. We take the extent of this overlap to be a metric of the potential to formalize. However, we recognize that this is entirely driven by the predictive power of the models, which is in turn driven by data availability. The pseudo-R-squareds of the models are another metric of this; consistent with our finding that location and industry were poor predictors of formality, Model 1, which includes these, firm size, and firm age, has pseudo-R-squareds that range from 0.0820.119. Model 2a for EcC is 0.133, and the ELMPS model 2b 0.221 (2012) and 0.168 (2018). Owner characteristics in Model 3 raise this to 0.262 (2012) and 0.209 (2018).

Consistent with the low R-squareds from Model 1, Figure 10 shows high overlap between predicted probabilities, ranging from a 67% overlap in the EcC to an 76% overlap in ELMPS 2012. Since these are the easily observable characteristics, this bodes poorly for the potential of policymakers to target firms that are likely to formalize based on readily observed characteristics, whether for incentives or enforcement. Model 2a (Figure 11) for the EcC with firm characteristics shows 65% overlap. There is a distinct mode at 0.2 probability for informal firms and a mode at 0.4 for formal firms.

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<!-- Start of picture text --> ELMPS 2012 in est. ELMPS 2018 in est.<br>vt +<br>Q iQ<br>o o<br>oO oO<br>iQ iQ<br>a8 a<br>a 0.76 overlap a 73 overlap<br>Q Q<br>Oo Oo<br>0 2 4 6 8 1 0 2 4 6 8 1<br>Probability formal Probability formal<br>Economic Census Establishment Census<br>: :<br>nto nt)oO<br>o oO<br>tt tt<br>ge ge<br>- 0.67 overlap a 0.70 overlap<br>re 2<br>Oo Oo<br>0 2 4 6 8 1 0 2 4 6 8 1<br>Probability formal Probability formal<br>Informal ©——— Formal<br><!-- End of picture text -->
<!-- Start of picture text --> EC<br>op)<br>ike)<br>ni<br>WN<br>Pa<br>Ow<br>Q 19<br>rab) r<br>a<br>iQ .65 overlap<br>oO<br>0 2 4 6 8 1<br>Probability formal<br>Informal _——— Formal<br><!-- End of picture text -->
<!-- Start of picture text --> 2012 in est. 2018 in est.<br>++<br>99<br>o o<br>oO oO<br>a8 a<br>2 “64 overlap iQ .71 overlap<br>Oo Oo<br>0 2 4 6 8 1 0 2 4 6 8 1<br>Probability formal Probability formal<br>2012 all 2018 all<br>vt +<br>wno wQoO<br>o \ oO<br>c nN \ aN<br>faa \ fal 2<br>a .40 overlap a ee oN<br>Oo Oo<br>0 2 4 6 8 1 0 2 4 6 8 1<br>Probability formal Probability formal<br>Informal ——— Formal<br><!-- End of picture text -->
<!-- Start of picture text --> 2012 in est. 2018 in est.<br>++<br>QiQ<br>o o<br>oO oO<br>2 n c a<br>a8 a<br>2 56 overlap 2 .62 overlap :<br>Oo Oo<br>0 2 4 6 8 1 0 2 4 6 8 1<br>Probability formal Probability formal<br>2012 all 2018 all<br>vt +<br>nto nt)oO<br>o \ oO<br>faa é =<br>2) -38 overlap re) ire SN<br>Oo Oo<br>0 2 4 6 8 1 0 2 4 6 8 1<br>Probability formal Probability formal<br>Informal ——— Formal<br><!-- End of picture text -->
**Table 5. Quartiles of predicted probability of formality by model and data source, informal firms**

|**Predicted probability of formality:**|**<25%**|**25% to <50%**|**50% to <75%**|**75%+**|
|---|---|---|---|---|
||**Model**|**1**|||
|**ELMPS 2012 in est.**|5.8|26.2|62.4|5.5|
|**ELMPS 2018 in est.**|6.8|42.6|46.7|3.8|
|**Economic Census**|48.4|39.8|11.0|0.8|
|**Establishment Census**|30.9|51.7|16.1|1.4|
||**Model**|**2**|||
|**Economic Census**|50.2|39.0|9.9|1.0|
|**ELMPS 2012 in est.**|19.7|37.0|30.6|12.7|
|**ELMPS 2018 in est.**|17.3|44.1|30.1|8.5|
||**Model**|**3**|||
|**ELMPS 2012 in est.**|27.2|36.5|23.2|13.0|
|**ELMPS 2018 in est.**|30.1|37.8|23.3|8.9|

Source: predictions based on models in Table 2, Table 3, and Table 4

# **4.4. Profiles: Grouping and Clustering Analysis**

In what follows we attempt to characterize which informal firms operating inside establishments are least likely to be formalizable and which have a higher potential for formalization.<sup>22</sup> We do this first by grouping informal firms into different profiles (collections of characteristics that explain formality) based on Model 2 for the EcC data and Model 3 for the pooled ELMPS data. We also use cluster analysis to classify informal firms into 10 clusters based on the variables in EcC Model 2 and ELMPS Model 3. After examining how the mean characteristics of these clusters compare to those of all informal firms within establishments (shown in Table 13 and Table 14 in the appendix), we attempt to assign these clusters names that best describe the firms that fall within them. Finally, we identify, separately for each data set, which groups or clusters of informal firms are least likely and most likely to be formalizable based on the proportion that falls into each quartile of the probability of being formal.

As shown in Table 6, according to the ELMPS, the firms most represented in the bottom quartile of the probability of formality are one or two-person firms that are relatively young (<5 years of age) and that

> 22 The very high informality rates for firms operating outside fixed establishments precludes this kind of analysis for these firms since few of them are likely to be appropriate candidates for formalization.

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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.

39
owners but below median revenue per worker, irrespective of size (6%), and older firms with three or more workers, irrespective of revenue per worker or owner’s education (5%).

The EcC results for informal firms with high predicted probability of formality also highlight older 3-24 person firms that hire wage workers, irrespective of value added per worker (10% of informal firms), and those 3-24 person firms that do not hire wage workers but have high value added per worker (0.3%) as having the highest probability of formality. It further highlights older, two-person firms with value added per worker in the top quartile (3%). Their counterparts in the second and third quartile of value added per worker come next in priority (5%).

As before, cluster analysis provides a different lens to characterize the informal firms that are most amenable to formalization. According to the analysis using ELMPS data, shown in Figure 14, these include two clusters we describe as “larger professional” (8% of informal firms in ELMPS) and “larger nonprofessional” firms” (7% of informal firms). The latter cluster in particular is also disproportionately made up of firms with collective forms of ownership and relatively high capital. Both of these clusters disproportionately hire workers from outside the household. The first hires mostly non-relatives and the second hires relatives. These are followed by clusters we describe as “shops with older owners” (14%), and “workshops” (11%). These two clusters tend to skew older in terms of firm age and the latter is more likely to hire wage workers than average. The four clusters that have the lowest share of the lowest quartile of predicted probability of formality (5%-25%) still have only 14-26% of their firms in the top quartile of predicted probability.

Cluster analysis using the EcC data, shown in Figure 15, also highlights the larger professional (2% of informal firms in the EcC) and non-professional firm clusters (14%) as well as a clusters we call “larger retail” (8%) and “non-retail with 3+ workers” (10%). Like before, firms in these clusters tend to skew older than average and to be more likely to hire wage workers. Although these clusters have the lowest shares of the lowest quartile of predicted probability of formality (8-31%) between less than 1% and 13% of firms have predicted probabilities of formality in the top quartile, although 13-38% are in quartile three.

40
# **Table 6. Percentage of firms in each quartile of predicted probability of formality by group, ordered by quartile one, pooled ELMPS 2012 and 2018**

|**Profil**<br>**Group**|**e**<br> <br>**Firm**<br>**Size**|<br>**Firm Age**|**Capital**|**Non-rel. out of**<br>**HH hired**|<br>**Quartile of Ln(Reven**<br>**per Worker)**|**ue**<br>**Owner's Education**|**N**<br>**(Obs**|**.)**<br>**% of**<br>**Informal**<br>**Firms in**<br>**Group**|**% of Firms in Each**<br>**Quartile of**<br>**Predicted**<br>**Probability**|
|---|---|---|---|---|---|---|---|---|---|
||||||||||Q1 Q2 Q3 Q4|
|1|1|Less than 5<br>years|All capital (Aggregate)|No|1 & 2 (Aggregate)|Below secondary|72|6.7|80.9 13.9 3.6 1.7|
|2|1|Less than 5<br>years|<5K|No|1|Secondary|28|2.6|67.6 32.4 0.0 0.0|
|3|2|Less than 5<br>years|All capital (Aggregate)|No|1 & 2 (Aggregate)|Below secondary|20|2.6|62.6 26.5 10.8 0.0|
|4|1|Less than 5<br>years|All capital (Aggregate)|No|3 & 4 (Aggregate)|Below secondary|34|3.4|57.4 36.2 6.4 0.0|
|5|1|Less than 5<br>years|<5K|No|2|Secondary|16|1.4|51.2 31.6 17.3 0.0|
|6|2|Less than 5<br>years|All capital (Aggregate)|No|1 & 2 (Aggregate)|Secondary+ (Aggregate)|25|2.6|50.7 40.3 9.0 0.0|
|7|1|5 years or abo|ve <5K|No|2|Below secondary|53|5.5|47.4 39.8 8.7 4.2|
|8|1|Less than 5<br>years|<5K|No|1 & 2 (Aggregate)|Above secondary|23|2.2|47.2 41.5 11.3 0.0|
|9|1|5 years or abo|ve <5K|No|1|Below secondary|60|6.0|44.9 38.8 16.3 0.0|
|10|1|5 years or abo|ve All capital (Aggregate)|Yes|1 & 2 (Aggregate)|Below secondary|10|1.0|43.7 32.4 18.1 5.7|
|11|1|5 years or abo|ve <5K|No|1|Secondary|30|2.9|42.2 42.1 12.3 3.3|
|12|1|5 years or abo|ve <5K|No|3|Below secondary|22|1.9|36.6 28.3 35.1 0.0|

41
|**Profil**|**e**<br>**Firm**|<br>**Firm Age**|**Capital**<br>**Non-rel. out of**<br>**Quartile of Ln(Revenue**|<br>**Owner's Education**|**N**|**% of**|**% of Firms in Each**|
|---|---|---|---|---|---|---|---|
|**Grou**|**p**<br>**Size**||**HH hired**<br>**per Worker)**||**(Obs**|**.)**<br>**Informal**<br>**Firms in**<br>**Group**|**Quartile of**<br>**Predicted**<br>**Probability**|
||||||||Q1 Q2 Q3 Q4|
|13|1|5 years or abo|ve 5K+<br>No<br>1 & 2 (Aggregate)|Below secondary|11|1.3|30.1 30.4 22.9 16.6|
|14|1|5 years or abo|ve <5K<br>No<br>1|Above secondary|14|1.9|26.9 29.7 43.4 0.0|
|15|1|Less than 5<br>years|All capital (Aggregate) Yes<br>3 & 4 (Aggregate)|All education (Aggregate|) 17|1.7|25.6 31.9 39.3 3.2|
|16|3-24|Less than 5<br>years|All capital (Aggregate) All (Aggregate) All quartile (Aggregate)|All education (Aggregate|) 26|3.1|25.4 42.9 27.8 3.8|
|17|2|Less than 5<br>years|All capital (Aggregate) No<br>3 & 4 (Aggregate)|All education (Aggregate|) 13|1.4|25.1 37.1 22.4 15.4|
|18|3-24|5 years or abo|ve All capital (Aggregate) No<br>All quartile (Aggregate)|All education (Aggregate|) 22|2.2|24.4 48.9 9.1 17.6|
|19|2|Less than 5<br>years|All capital (Aggregate) Yes<br>All quartile (Aggregate)|All education (Aggregate|) 11|1.3|23.9 49.8 22.9 3.4|
|20|1|Less than 5<br>years|All capital (Aggregate) No<br>1 & 2 (Aggregate)|Secondary+ (Aggregate)|15|1.4|23.6 38.4 29.3 8.7|
|21|1|5 years or abo|ve <5K<br>No<br>2|Above secondary|12|1.4|22.7 60.2 7.8 9.2|
|22|1|Less than 5<br>years|All capital (Aggregate) No<br>3 & 4 (Aggregate)|Secondary+ (Aggregate)|53|5.4|19.4 52.1 21.4 7.1|
|23|2|5 years or abo|ve All capital (Aggregate) No<br>1 & 2 (Aggregate)|Secondary+ (Aggregate)|12|1.0|19.0 20.8 60.1 0.0|
|24|1|5 years or abo|ve 5K+<br>No<br>3 & 4 (Aggregate)|Below secondary|27|2.6|18.5 33.8 27.2 20.4|
|25|2|5 years or abo|ve All capital (Aggregate) No<br>1 & 2 (Aggregate)|Below secondary|29|3.1|17.9 49.2 25.0 7.9|
|26|1|5 years or abo|ve <5K<br>No<br>3|Secondary|19|2.0|13.7 68.5 17.8 0.0|

42
|**Profil**<br>**Grou**|**e**<br>**p**<br>**Firm**<br>**Size**|<br> <br>**Firm Age**|**Capital**|**Non-rel. out**<br>**HH hired**|**of**<br>**Quartile of Ln(Reven**<br>**per Worker)**|**ue**<br>**Owner's Education**|**N**<br>**(Obs**|**.)**<br>**% of**<br>**Informal**<br>**Firms in**<br>**Group**|**% of Firms in Each**<br>**Quartile of**<br>**Predicted**<br>**Probability**<br>Q1 Q2 Q3 Q4|
|---|---|---|---|---|---|---|---|---|---|
|27|2|5 years or abo|ve All capital (Aggregate)|Yes|1 & 2 (Aggregate)|Secondary+ (Aggregate)|14|1.5|13.2 24.4 24.5 37.9|
|28|1|Less than 5<br>years|All capital (Aggregate)|Yes|1 & 2 (Aggregate)|All education (Aggregate|) 13|1.3|11.6 48.2 32.5 7.7|
|29|3-24|5 years or abo|ve All capital (Aggregate)|Yes|1 & 2 (Aggregate)|All education (Aggregate|) 19|2.4|8.4 35.7 10.1 45.7|
|30|1|5 years or abo|ve <5K|No|4|Below secondary|29|2.4|7.2 56.5 35.0 1.3|
|31|2|5 years or abo|ve All capital (Aggregate)|Yes|1 & 2 (Aggregate)|Below secondary|18|2.1|6.7 41.7 26.0 25.6|
|32|2|5 years or abo|ve All capital (Aggregate)|No|3 & 4 (Aggregate)|All education (Aggregate|) 18|2.4|6.6 34.3 38.4 20.8|
|33|1|5 years or abo|ve All capital (Aggregate)|Yes|3 & 4 (Aggregate)|Secondary+ (Aggregate)|20|1.9|4.9 20.3 38.1 36.7|
|34|1|5 years or abo|ve <5K|No|2|Secondary|33|3.3|3.4 69.4 27.2 0.0|
|35|1|5 years or abo|ve <5K|No|3 & 4 (Aggregate)|Above secondary|18|2.0|2.3 19.6 37.1 40.9|
|36|1|5 years or abo|ve All capital (Aggregate)|Yes|3 & 4 (Aggregate)|Below secondary|10|1.1|0.0 59.8 18.9 21.3|
|37|1|5 years or abo|ve 5K+|No|1 & 2 (Aggregate)|Secondary+ (Aggregate)|21|1.7|0.0 22.3 60.0 17.6|
|38|1|5 years or abo|ve <5K|No|4|Secondary|18|1.9|0.0 19.0 51.5 29.5|
|39|3-24|5 years or abo|ve All capital (Aggregate)|Yes|3 & 4 (Aggregate)|All education (Aggregate|) 20|1.7|0.0 18.4 34.8 46.8|
|40|1|5 years or abo|ve 5K+|No|3 & 4 (Aggregate)|Secondary|23|2.0|0.0 17.4 58.4 24.2|
|41|1|5 years or abo|ve 5K+|No|3 & 4 (Aggregate)|Above secondary|15|1.4|0.0 16.1 25.9 58.0|
|42|2|5 years or abo|ve All capital (Aggregate)|Yes|3 & 4 (Aggregate)|All education (Aggregate|) 13|1.1|0.0 7.7 30.9 61.4|
|43|1|5 years or abo|ve All capital (Aggregate)|Yes|1 & 2 (Aggregate)|Secondary+ (Aggregate)|13|1.1|0.0 7.7 55.7 36.6|

43
Source: Authors’ calculations based on predicted probability from model 3 for ELMPS 2012 and ELMPS 2018, see Table 4 Notes: Firm age category “don’t know” was combined with 5 years or above.

44
**Table 7. Percentage of firms in each quartile of predicted probability of formality by group, ordered by quartile one, EcC 2012/13**

|**Profile**<br>**Group**|<br> <br>**Firm**<br>**Size**|<br>**Firm Age**<br>**Capita**|**l Wage**<br>**Worker**|<br>**Quartile of Ln(Labor**<br>**Value Added)**|**N (Obs.)**|**% of**<br>**Informal**<br>**Firms in**<br>**Group**|**% of Firm**<br>**Quart**<br>**Predi**<br>**Proba**|**s in Each**<br>**ile of**<br>**cted**<br>**bility**|
|---|---|---|---|---|---|---|---|---|
||||||||**Q1 Q2**|**Q3 Q4**|
|1|1|Less than 5 years 5K+|No|1|677|3.7|96.3 3.7|0.0 0.0|
|2|1|Less than 5 years <5K|No|1|963|5.1|96.2 3.8|0.0 0.0|
|3|1|Less than 5 years 5K+|No|2|721|3.9|94.9 5.1|0.0 0.0|
|4|1|Less than 5 years <5K|No|2|722|3.3|94.7 5.3|0.0 0.0|
|5|1|Less than 5 years 5K+|No|3|702|3.3|93.8 6.2|0.0 0.0|
|6|1|Less than 5 years <5K|No|3|523|2.3|88.9 11.1|0.0 0.0|
|7|1|Less than 5 years <5K|No|4|231|1.0|84.1 15.9|0.0 0.0|
|8|2|Less than 5 years 5K+|No|1|544|3.5|82.1 17.9|0.0 0.0|
|9|1|Less than 5 years 5K+|No|4|548|2.7|81.2 18.8|0.0 0.0|
|10|2|Less than 5 years <5K|No|1|442|2.6|74.7 25.3|0.0 0.0|
|11|2|Less than 5 years <5K|No|2|135|0.8|73.6 26.4|0.0 0.0|
|12|3-24|Less than 5 years <5K|No|All quartile<br>(Aggregate)|82|0.5|73.5 26.5|0.0 0.0|
|13|3-24|Less than 5 years 5K+|No|3|29|0.1|70.4 29.6|0.0 0.0|
|14|2|Less than 5 years 5K+|No|2|300|1.9|68.5 31.5|0.0 0.0|
|15|3-24|Less than 5 years <5K|Yes|1|180|0.5|68.4 30.9|0.6 0.0|
|16|1|5 years or above 5K+|No|1|335|1.9|67.8 31.6|0.6 0.0|
|17|2|Less than 5 years <5K|No|4|15|0.1|64.1 35.9|0.0 0.0|
|18|3-24|Less than 5 years 5K+|No|1|126|0.6|63.5 36.5|0.0 0.0|
|19|3-24|Less than 5 years 5K+|No|2|40|0.2|62.8 36.9|0.3 0.0|
|20|2|Less than 5 years 5K+|Yes|1|451|1.6|61.7 38.1|0.2 0.0|
|21|1|5 years or above <5K|No|1|666|2.7|61.0 37.6|1.4 0.0|
|22|2|Less than 5 years 5K+|No|3|146|0.7|58.4 41.6|0.0 0.0|
|23|2|Less than 5 years <5K|Yes|1|244|0.9|57.4 42.5|0.1 0.0|
|24|3-24|Less than 5 years <5K|Yes|3|64|0.1|57.3 29.5|13.2 0.0|

45
|**Profile**<br>**Group**|<br> <br>**Firm**<br>**Size**|<br>**Firm Age**<br>**Capita**|**l Wage**<br>**Worker**|<br>**Quartile of Ln(Labor**<br>**Value Added)**|**N (Obs.)**|**% of**<br>**Informal**<br>**Firms in**<br>**Group**|**% of Firms in Each**<br>**Quartile of**<br>**Predicted**<br>**Probability**|
|---|---|---|---|---|---|---|---|
||||||||**Q1 Q2 Q3 Q4**|
|25|2|Less than 5 years <5K|Yes|2|198|0.5|56.5 43.5 0.0 0.0|
|26|2|Less than 5 years 5K+|No|4|81|0.4|54.9 44.8 0.3 0.0|
|27|2|Less than 5 years <5K|No|3|38|0.2|54.6 45.4 0.0 0.0|
|28|3-24|Less than 5 years <5K|Yes|2|107|0.3|53.5 45.4 1.1 0.0|
|29|1|5 years or above 5K+|No|2|436|1.9|52.2 45.5 2.3 0.0|
|30|3-24|Less than 5 years 5K+|Yes|1|733|2.0|49.9 44.6 5.4 0.1|
|31|2|Less than 5 years 5K+|Yes|3|539|1.8|49.0 50.1 0.9 0.0|
|32|2|Less than 5 years 5K+|Yes|2|609|2.1|46.3 52.9 0.8 0.0|
|33|1|5 years or above <5K|No|2|655|2.2|45.6 52.6 1.8 0.0|
|34|2|Less than 5 years 5K+|Yes|4|399|1.1|45.3 48.9 5.8 0.0|
|35|1|5 years or above 5K+|No|3|559|1.9|43.9 56.1 0.0 0.0|
|36|2|Less than 5 years <5K|Yes|4|53|0.1|41.6 58.4 0.0 0.0|
|37|1|5 years or above <5K|No|3|605|2.0|40.0 58.7 1.3 0.0|
|38|2|Less than 5 years <5K|Yes|3|137|0.6|37.7 61.7 0.6 0.0|
|39|3-24|Less than 5 years 5K+|Yes|2|786|2.3|36.9 54.3 8.4 0.4|
|40|3-24|Less than 5 years 5K+|Yes|3|903|3.0|33.9 60.2 5.7 0.1|
|41|3-24|5 years or above 5K+|No|1|209|0.5|33.7 54.3 11.9 0.0|
|42|3-24|Less than 5 years <5K|Yes|4|31|0.1|29.9 68.2 1.7 0.2|
|43|1|5 years or above <5K|No|4|365|1.3|25.8 60.4 13.8 0.0|
|44|2|5 years or above 5K+|Yes|1|335|1.1|21.8 59.7 18.4 0.1|
|45|2|5 years or above <5K|Yes|1|193|0.4|20.8 58.6 20.6 0.0|
|46|2|5 years or above 5K+|No|1|302|2.8|19.2 77.8 3.0 0.0|
|47|3-24|Less than 5 years 5K+|Yes|4|725|2.1|18.0 69.4 12.2 0.4|
|48|1|5 years or above 5K+|No|4|437|2.8|17.7 74.6 7.7 0.0|
|49|3-24|Less than 5 years 5K+|No|4|17|0.2|17.1 82.9 0.0 0.0|
|50|2|5 years or above <5K|No|1|373|1.9|16.7 72.0 11.3 0.0|

46
|**Profile**<br>**Group**|<br> <br>**Firm**<br>**Size**|<br>**Firm Age**<br>**Capita**|**l Wage**<br>**Worker**|<br>**Quartile of Ln(Labor**<br>**Value Added)**|**N (Obs.)**|**% of**<br>**Informal**<br>**Firms in**<br>**Group**|**% of Firms in Each**<br>**Quartile of**<br>**Predicted**<br>**Probability**|
|---|---|---|---|---|---|---|---|
||||||||**Q1 Q2 Q3 Q4**|
|51|2|5 years or above 5K+|Yes|2|619|1.6|14.3 62.6 23.1 0.0|
|52|3-24|5 years or above <5K|No|1|156|0.5|14.3 56.3 29.3 0.0|
|53|2|5 years or above 5K+|No|2|214|0.9|14.1 67.9 18.0 0.0|
|54|2|5 years or above 5K+|Yes|3|761|1.8|13.3 51.4 35.2 0.0|
|55|3-24|5 years or above 5K+|No|2|64|0.2|13.0 70.7 16.3 0.0|
|56|3-24|5 years or above <5K|No|2|49|0.2|12.1 58.7 26.4 2.7|
|57|2|5 years or above <5K|Yes|2|359|0.9|11.8 68.6 19.4 0.2|
|58|2|5 years or above 5K+|No|3|120|0.4|10.2 65.1 24.7 0.0|
|59|2|5 years or above <5K|No|3|83|0.3|8.9 55.1 36.0 0.0|
|60|2|5 years or above <5K|No|2|197|0.9|8.7 63.9 27.3 0.0|
|61|3-24|5 years or above 5K+|Yes|1|885|1.3|7.1 45.9 38.7 8.3|
|62|2|5 years or above 5K+|Yes|4|912|1.6|5.9 43.1 50.9 0.1|
|63|2|5 years or above 5K+|No|4|75|0.4|5.9 61.9 24.9 7.3|
|64|2|5 years or above <5K|Yes|3|261|0.5|5.1 69.5 25.4 0.0|
|65|3-24|5 years or above 5K+|Yes|2|915|1.9|4.9 58.1 31.9 5.1|
|66|3-24|5 years or above <5K|Yes|1|346|0.5|4.4 54.6 38.0 3.0|
|67|3-24|5 years or above 5K+|No|3|34|0.1|3.6 76.8 19.6 0.0|
|68|3-24|5 years or above 5K+|Yes|3|1101|2.3|3.3 52.1 36.8 7.8|
|69|2|5 years or above <5K|No|4|26|0.1|2.9 20.8 72.3 4.1|
|70|2|5 years or above <5K|Yes|4|200|0.2|2.8 46.6 49.3 1.4|
|71|3-24|5 years or above <5K|Yes|2|218|0.4|1.7 62.0 33.1 3.1|
|72|3-24|5 years or above <5K|No|3 & 4 (Aggregate)|29|0.1|1.5 37.2 52.2 9.1|
|73|3-24|5 years or above 5K+|Yes|4|1292|2.2|0.5 30.9 49.0 19.6|
|74|3-24|5 years or above <5K|Yes|3|178|0.4|0.5 34.0 60.6 5.0|
|75|3-24|5 years or above 5K+|No|4|30|0.1|0.0 40.6 52.3 7.1|
|76|3-24|5 years or above <5K|Yes|4|115|0.9|0.0 5.9 92.4 1.6|

47
Source: Authors’ calculations based on predicted probability from model 2 using EcC 2012/13, see Table 3.

Notes: Firm age category “don’t know” was combined with 5 years or above. Firms that reported firm size of one and that hire wage workers were recoded to firm size two.

48
Quartile 1 MM Quartile 2 MEME Quartie3 MEE Quartile 4

# Women-owned retail

38 18 Youthrowned new fms se Smaller prof. fms se44 17 Low. prod firms with employees and older owners Blue collar firms 0 20 40 60 80 100 Percent of informal firms
|I) Quartie1|MM Quartie2<br>HEME Quartile3<br>HEIN|Quartile 4|
|---|---|---|
|Smaller prof. firms|85|15|
|Smaller service firms|84|15|
|Smaller manuf. and rest.|68<br>27|5|
|One-person retail|65<br>32|3|
|Two person prof. firms<br>47|45|8|
|Two-worker retail<br>47|46|7|
|3+ worker non-retail<br>31|55|13|
|Larger non-prof.<br>26|53|20|
|Larger retail<br>21|47<br>28|4|
|Larger prof. firms |<br>—<br>|41<br>38|13|
|0<br>20|40<br>60<br>80|100|
||Percentofinformalfirms||
<!-- Start of picture text --> 80 74<br>71<br>69<br>62 62<br>~ 60 59<br>o 53<br>7) 49<br>a<br>E<br>5<br>®40<br>Do<br>&<br>Cc<br>oO<br>o<br>a<br>20<br>0 Informal Formal Total<br>Base wave formality<br>1998-2006 [NNN 2006-2012 MEE 2012-2018<br><!-- End of picture text -->
in 2006-2012. There are significant differences over time in the probability of exit, but not significant differences in the relationship between formality and exit.

52
**Table 8. Odds ratios from logit model of firm exit, by pair of waves, ELMPS 1998-2018**

||**1998-2006**|**2006-2012**|**2012-2018**|**2012-2018**|**Pool**|
|---|---|---|---|---|---|
|**Formality (informal omit.)**||||||
|Formal|0.811|0.625*|0.919|0.915|0.957|
||(0.243)|(0.117)|(0.159)|(0.158)|(0.193)|
|**Wave (1998-2006 omit.)**||||||
|2006-2012|||||1.481*|
||||||(0.232)|
|2012-2018|||||1.745***|
||||||(0.286)|
|**Interactions between formality**<br>**and wave**||||||
|Formality*2006-2012|||||0.710|
||||||(0.161)|
|Formality*2012-2018|||||0.873|
||||||(0.196)|
|**Ent. size (one worker omit.)**||||||
|2|0.950|0.885|0.714|0.722|0.863|
||(0.259)|(0.159)|(0.131)|(0.134)|(0.099)|
|3-4|1.090|0.830|0.677|0.687|0.853|
||(0.351)|(0.198)|(0.180)|(0.185)|(0.129)|
|5-24|1.417|0.743|2.043|2.096|1.275|
||(0.699)|(0.289)|(0.817)|(0.845)|(0.309)|
|**Industry (wholesale/retail omit.)**||||||
|Manufacturing and related||||||
|<br>trades|0.477|1.313|1.661*|1.668*|1.202|
||(0.197)|(0.294)|(0.427)|(0.430)|(0.180)|
|Construction|0.563|2.099*|4.624***|4.624***|2.107***|
||(0.353)|(0.656)|(1.492)|(1.490)|(0.436)|

53
||**1998-2006**|**2006-2012**|**2012-2018**|**2012-2018**|**Pool**|
|---|---|---|---|---|---|
|Transportation and storage|2.346|1.271|3.852*|3.847*|2.030|
||(1.913)|(0.646)|(2.288)|(2.291)|(0.803)|
|Accommodation and food||||||
|service|0.790|2.241**|1.252|1.256|1.428|
||(0.358)|(0.693)|(0.394)|(0.395)|(0.280)|
|Various professional acts.|1.559|0.562|2.158**|2.158**|1.233|
||(0.854)|(0.189)|(0.621)|(0.622)|(0.254)|
|Other service|2.106|1.434|1.642|1.651|1.993***|
||(0.919)|(0.531)|(0.532)|(0.534)|(0.413)|
|**Region (Greater Cairo omit.)**||||||
|Alexandria and Suez Canal|0.999|1.113|1.035|1.035|1.018|
||(0.299)|(0.265)|(0.255)|(0.255)|(0.151)|
|Lower Egypt|1.131|0.905|0.595**|0.597*|0.823|
||(0.291)|(0.157)|(0.119)|(0.120)|(0.092)|
|Upper Egypt|0.815|0.988|0.653*|0.657*|0.816|
||(0.226)|(0.178)|(0.137)|(0.138)|(0.096)|
|**Partnership (sole proprietorship**<br>**omit.)**||||||
|Collective Ownership|1.775|0.761|1.045|1.044|1.023|
||(0.549)|(0.186)|(0.252)|(0.252)|(0.150)|
|**Capital (none omit.)**||||||
|less than LE 100|1.001|0.621|0.623|0.628|0.705|
||(0.377)|(0.172)|(0.205)|(0.207)|(0.129)|
|LE 100-499|0.341*|0.545*|0.788|0.791|0.562**|
||(0.146)|(0.158)|(0.267)|(0.268)|(0.107)|
|LE 500-999|0.837|0.647|0.707|0.708|0.712|
||(0.332)|(0.189)|(0.220)|(0.221)|(0.134)|
|LE 1000-4999|0.518|0.490*|0.880|0.875|0.645*|

54
||**1998-2006**|**2006-2012**|**2012-2018**|**2012-2018**|**Pool**|
|---|---|---|---|---|---|
||(0.246)|(0.144)|(0.293)|(0.291)|(0.125)|
|LE5000-9999|0.736|0.575|0.630|0.624|0.609*|
||(0.344)|(0.167)|(0.202)|(0.201)|(0.118)|
|LE10 000 or more|0.654|0.780|0.660|0.655|0.792|
||(0.433)|(0.296)|(0.314)|(0.311)|(0.216)|
|**Ln net revenue per worker**||||1.022||
|||||(0.060)||
|**Firm start year (1980-1989 omit.)**||||||
|Don't know|1.143|0.530|1.088|1.090|1.114|
||(0.812)|(0.436)|(0.451)|(0.451)|(0.350)|
|Before 1950|0.512|0.809|0.803|0.797|0.677|
||(0.351)|(0.531)|(0.959)|(0.955)|(0.284)|
|1950-1959|1.424|0.820|0.466|0.462|0.957|
||(0.721)|(0.429)|(0.407)|(0.407)|(0.317)|
|1960-1969|1.208|1.050|0.558|0.558|0.981|
||(0.535)|(0.436)|(0.400)|(0.398)|(0.272)|
|1970-1979|1.364|0.864|1.076|1.080|1.041|
||(0.447)|(0.221)|(0.397)|(0.398)|(0.178)|
|1990-1999|1.304|1.382|1.228|1.233|1.326*|
||(0.310)|(0.241)|(0.284)|(0.285)|(0.158)|
|2000-2009||1.789**|1.352|1.359|1.621***|
|||(0.357)|(0.309)|(0.311)|(0.222)|
|2010-2018|||2.124**|2.150**|2.321***|
||||(0.605)|(0.612)|(0.521)|
|**People outside the HH employed**||||||
|**(no omit.)**||||||
|Yes|0.509**|1.022|0.771|0.767|0.799*|
||(0.128)|(0.195)|(0.133)|(0.133)|(0.089)|

55
||**1998-2006**|**2006-2012**|**2012-2018**|**2012-2018**|**Pool**|
|---|---|---|---|---|---|
|**Location (shop omit.)**||||||
|Own home|1.738|1.418|1.084|1.090|1.378*|
||(0.665)|(0.362)|(0.258)|(0.262)|(0.221)|
|Office/flat/building/rooms|0.685|1.487|0.589|0.586|0.901|
||(0.352)|(0.499)|(0.198)|(0.197)|(0.188)|
|Workshop/factory|3.551**|1.456|1.323|1.329|1.651**|
||(1.549)|(0.438)|(0.384)|(0.385)|(0.312)|
|Mobile worker|2.100|1.052|1.399|1.405|1.341|
||(1.177)|(0.290)|(0.358)|(0.361)|(0.242)|
|Kiosk/hut|1.741|0.812|1.693|1.708|1.376|
||(0.928)|(0.457)|(0.770)|(0.779)|(0.387)|
|Transport based|0.512|0.742|0.323|0.324|0.574|
||(0.534)|(0.421)|(0.191)|(0.191)|(0.251)|
|Street/field/farm|0.943|0.958|1.194|1.193|1.085|
||(0.451)|(0.278)|(0.341)|(0.342)|(0.200)|
|**Sex (male omit.)**||||||
|Female|1.192|1.452|1.598*|1.613*|1.449**|
||(0.385)|(0.326)|(0.349)|(0.353)|(0.204)|
|**Education (illit. omit.)**||||||
|Less than sec.|1.443|1.285|0.914|0.914|1.183|
||(0.381)|(0.223)|(0.173)|(0.173)|(0.133)|
|Secondary|3.235***|1.521*|0.960|0.956|1.460**|
||(1.114)|(0.290)|(0.185)|(0.185)|(0.184)|
|Higher education|2.398*|1.272|1.336|1.333|1.546**|
||(0.890)|(0.312)|(0.295)|(0.295)|(0.229)|
|**Occupation (prof./tech. omit.)**||||||
|Other white collar|1.709|0.980|1.263|1.269|1.194|

56
||**1998-2006**|**2006-2012**|**2012-2018**|**2012-2018**|**Pool**|
|---|---|---|---|---|---|
||(0.510)|(0.191)|(0.258)|(0.258)|(0.152)|
|Blue collar craft|2.229*|1.000|0.960|0.955|1.184|
||(0.828)|(0.208)|(0.212)|(0.212)|(0.162)|
|Blue collar non-craft|1.029|1.317|1.667*|1.669*|1.377*|
||(0.397)|(0.331)|(0.382)|(0.382)|(0.206)|
|**Age group (<30 omit.)**||||||
|30-49|0.738|0.662*|0.614*|0.613*|0.657***|
||(0.246)|(0.116)|(0.119)|(0.118)|(0.078)|
|50+|1.226|0.959|0.893|0.896|0.964|
||(0.467)|(0.219)|(0.214)|(0.215)|(0.145)|
|**N**|806|1503|1832|1832|4141|
|**Pseudo R-squared**|0.120|0.070|0.085|0.085|0.075|

Source: Author’s calculations based on ELMPS 1998-2018 Notes: *p<0.05; **p<0.01; ***p<0.001.

# **4.5.2. Entry of firms**

We turn now to entry of firms. We consider new firms to be those that started between waves. Figure 17 shows the percentage of firms that are new by subsequent wave formality. Informal firms are more likely to be new firms. In 2018, 73% of all informal firms were new compared to 55% for formal firms. Figure 18 shows the percentage of firms that are formal by whether they are new. The figure confirms that already established firms are more likely to be formal compared to new ones. Formality has fallen over time across both established and new firms. In 2018, 44% of established firms were formal compared to 26% of new firms. There is suggestive evidence that firms may start as informal (and later formalize). Taken with the higher rates of exit for informal firms, it may also indicate that this sector is more dynamic, has greater churn, or is a fallback form of employment (necessity entrepreneurship).

57
<!-- Start of picture text --> 80<br>73<br>70<br>66<br>64<br>+1<br>59<br>= 60 55<br>2 52 52<br>”<br>E<br>re}<br>© 40<br>(>)<br>&<br>i<br>®<br>go<br>oO<br>ao 20<br>0 Informal Formal Total<br>Subsequent wave<br>1998-2006 [NNN 2006-2012 MEE 2012-2018<br><!-- End of picture text -->
<!-- Start of picture text --> 44<br>ad 38<br>_ 40<br>E<br>33 32<br>2 31<br>2 28<br>E 30 96<br>We)<br>o><br>& 20<br>i<br>oO<br>Oo<br>{<>}<br>al<br>10<br>a)<br>Not new New Total<br>Newness of firm<br>1998-2006 [NNN 2006-2012 ME 2012-2018<br><!-- End of picture text -->
<!-- Start of picture text --> 40<br>g<br>oO<br>= 30<br>= 30 29<br>n oO2 27<br>ES 24<br>=2<br>22<br>3 5<br>es. 20 18<br>c=<br>OO<br>SE<br>as<br>2 10<br>Dd)<br>Cc<br>oO<br>on<br>[S)<br>0 Informal Formal<br>Base wave formality<br>1998-2006 [NNN 2006-2012 MEE 2012-2018<br><!-- End of picture text -->
**Table 9. Odds ratios from logit model of changing formality in subsequent wave by base wave formality, ELMPS 1998-2018**

||**Pooled**<br>**base**<br>**informal**|**Pooled**<br>**base formal**|**2012-2018**<br>**Base**<br>**informal**|**2012-2018**<br>**Base**<br>**formal**|
|---|---|---|---|---|
|**Wave (1998-2006 omit.)**|||||
|06-12|0.514*|1.762|||
||(0.156)|(0.545)|||
|12-18|0.541|1.734|||
||(0.187)|(0.635)|||
|**Ent. size (one worker omit.)**|||||
|2|0.793|1.271|1.453|1.508|
||(0.279)|(0.349)|(1.081)|(0.707)|
|3-4|0.650|0.948|0.178|2.696|
||(0.316)|(0.308)|(0.181)|(1.804)|
|5-24|2.677|0.363|||
||(2.489)|(0.228)|||
|**Industry (wholesale/retail omit.)**|||||
|Manufacturing and related trades|1.534|2.271*|1.768|0.674|
||(0.655)|(0.907)|(1.217)|(0.621)|
|Construction|0.871|304.036***|0.597||
||(0.685)|(354.038)|(0.832)||
|Transportation and storage|1.009|551.995***|0.864||
||(0.806)|(825.188)|(1.012)||
|Accommodation and food service|3.036|0.560|2.276|0.520|
||(2.022)|(0.276)|(4.233)|(0.334)|
|Various professional acts.|0.808|27.048***|1.118|14.497|
||(0.570)|(26.003)|(1.600)|(22.405)|
|Other service|1.520|2.460|0.935|2.534|

61
||**Pooled**||**2012-2018**|**2012-2018**|
|---|---|---|---|---|
||**base**<br>**informal**|**Pooled**<br>**base formal**|**Base**<br>**informal**|**Base**<br>**formal**|
||(0.793)|(1.476)|(0.845)|(2.456)|
|**Region (Greater Cairo omit.)**|||||
|Alexandria and Suez Canal|2.676*|1.046|5.429|1.324|
||(1.236)|(0.422)|(4.908)|(1.172)|
|Lower Egypt|2.394*|2.152*|3.390|6.303*|
||(0.846)|(0.688)|(2.227)|(4.584)|
|Upper Egypt|2.653*|1.564|4.122*|2.933|
||(1.035)|(0.536)|(2.949)|(2.093)|
|**Partnership (sole proprietorship omit.)**|||||
|Collective Ownership|2.087|0.646|2.832|1.239|
||(0.805)|(0.204)|(1.669)|(0.728)|
|**Capital (none omit.)**|||||
|less than LE 100|0.882|1.801|1.639|1.504|
||(0.404)|(2.017)|(1.333)|(2.146)|
|LE 100-499|0.649|2.744|0.606|1.495|
||(0.300)|(2.866)|(0.564)|(2.003)|
|LE 500-999|0.953|2.423|0.393|0.867|
||(0.418)|(2.474)|(0.301)|(1.130)|
|LE 1000-4999|0.590|4.010|0.649|2.869|
||(0.281)|(4.132)|(0.607)|(3.455)|
|LE5000-9999|1.416|2.472|1.325|0.722|
||(0.698)|(2.593)|(1.165)|(0.854)|
|LE10 000 or more|1.783|1.976|4.577||
||(1.298)|(2.367)|(4.983)||
|**Ln net revenue per worker**|||1.164|1.410|
||||(0.195)|(0.290)|

62
||**Pooled**||**2012-2018**|**2012-2018**|
|---|---|---|---|---|
||**base**<br>**informal**|**Pooled**<br>**base formal**|**Base**<br>**informal**|**Base**<br>**formal**|
|**Firm start year (1980-1989 omit.)**|||||
|Don't know|4.605*|0.290|4.288|0.318|
||(3.176)|(0.296)|(4.394)|(0.363)|
|1950-1959|0.664|0.695||1.367|
||(0.614)|(0.557)||(2.219)|
|1960-1969|2.126|0.410|||
||(1.397)|(0.287)|||
|1970-1979|2.875*|0.848|1.805|2.490|
||(1.226)|(0.330)|(2.677)|(2.949)|
|1990-1999|0.882|1.003|1.618|1.859|
||(0.269)|(0.313)|(1.010)|(1.370)|
|2000-2009|1.447|1.171|1.771|1.560|
||(0.510)|(0.443)|(1.002)|(1.085)|
|2010-2018|1.335|3.099|2.166|4.014|
||(0.833)|(2.013)|(1.864)|(3.605)|
|Before 1950||0.429|||
|||(0.494)|||
|**People outside the HH employed (no omit.)**|||||
|Yes|1.175|0.666|1.380|0.810|
||(0.384)|(0.182)|(0.733)|(0.434)|
|**Location (shop omit.)**|||||
|Own home|0.321**|0.223*|0.205*|0.249|
||(0.122)|(0.166)|(0.147)|(0.308)|
|Office/flat/building/rooms|0.571|0.056**|0.555|0.131|
||(0.378)|(0.057)|(0.670)|(0.211)|
|Workshop/factory|1.125|0.784|0.650|0.493|

63
||**Pooled**||**2012-2018**|**2012-2018**|
|---|---|---|---|---|
||**base**<br>**informal**|**Pooled**<br>**base formal**|**Base**<br>**informal**|**Base**<br>**formal**|
||(0.517)|(0.343)|(0.490)|(0.542)|
|Mobile worker|0.164***|0.031*|0.132*||
||(0.076)|(0.044)|(0.128)||
|Kiosk/hut|0.520|3.740|1.544||
||(0.390)|(3.275)|(1.630)||
|Transport based|0.185*||0.065**||
||(0.148)||(0.066)||
|Street/field/farm|0.276**|44.965**|0.503||
||(0.117)|(62.582)|(0.287)||
|**Sex (male omit.)**|||||
|Female|0.195***|0.801|0.183*|0.454|
||(0.095)|(0.403)|(0.132)|(0.423)|
|**Education (illit. omit.)**|||||
|Less than sec.|1.817*|0.489*|2.039|0.247|
||(0.523)|(0.161)|(1.194)|(0.196)|
|Secondary|1.556|0.286**|1.191|0.202*|
||(0.526)|(0.113)|(0.732)|(0.164)|
|Higher education|2.399*|0.268**|2.829|0.207|
||(1.064)|(0.115)|(2.562)|(0.180)|
|**Occupation (prof./tech. omit.)**|||||
|Other white collar|1.021|1.719|0.785|2.114|
||(0.371)|(0.707)|(0.472)|(1.401)|
|Blue collar craft|0.488|0.762|0.162**|0.554|
||(0.213)|(0.286)|(0.102)|(0.458)|
|Blue collar non-craft|0.974|0.778|2.094|0.254|
||(0.324)|(0.552)|(1.315)|(0.284)|

64
||**Pooled**<br>**base**|**Pooled**|**2012-2018**<br>**Base**|**2012-2018**<br>**Base**|
|---|---|---|---|---|
||**informal**|**base formal**|**informal**|**formal**|
|**Age group (<30 omit.)**|||||
|30-49|1.120|1.140|0.985|1.963|
||(0.368)|(0.446)|(0.505)|(1.756)|
|50+|1.106|0.775|2.063|2.646|
||(0.434)|(0.360)|(1.480)|(2.703)|
|**N**|788|675|295|190|
|**Pseudo R-squared**|0.193|0.153|0.279|0.192|

# Source: Author’s calculations based on ELMPS 1998-2018

Notes: *p<0.05; **p<0.01; ***p<0.001. Blank cells indicate dropped perfect predictors

# **4.5.4. Formality and growth**

We turn now to our last dynamic outcome, growth (an increase in the number of workers) among surviving firms (Figure 20). Formal firms are slightly more likely to grow than informal firms, although the gap in growth has narrowed over time. In 1998-2006, while 29% of informal firms grew, 35% of formal firms did so. However, in 2012-2018, while 19% of informal firms grew 21% of formal firms did so. Growth among surviving firms was clearly highest in 1998-2006 and lowest in 2006-2012; the increase in growth must be tempered by rising rates of exit (Figure 16).

65
<!-- Start of picture text --> 40<br>= 35<br>®<br>DoD 32<br>2 30 29<br>1?)<br>E<br>oD)<br>s 20 19 - 20<br>2<br>2 16<br>5 13<br>o 12<br>S<br>= 10<br>Oo<br>o<br>{«)<br>Oo.<br>0 Informal Formal Total<br>Base wave formality<br>98-06 06-12 MER 12-18<br><!-- End of picture text -->
**Table 10. Odds ratios from logit model of growth, by wave, ELMPS 1998-2018**

|||**Pool with**<br>**subsequent**||
|---|---|---|---|
||**Pool**|**formality**|**2012-2018**|
|**Base wave formality (informal omit.)**||||
|Formal|1.096|1.233|0.729|
||(0.339)|(0.414)|(0.243)|
|**Subsequent wave formality (informal omit.)**||||
|Formal||1.287||
|||(0.412)||
|**Wave (1998-2006 omit.)**||||
|06-12|0.371***|0.341***||
||(0.105)|(0.106)||
|12-18|0.560*|0.503*||
||(0.162)|(0.166)||
|**Interactions between base formality and wave**||||
|06-12|1.056|0.713||
||(0.407)|(0.329)||
|12-18|0.897|0.779||
||(0.337)|(0.332)||
|**Interactions between subsequent formality and wave**||||
|06-12||1.660||
|||(0.760)||
|12-18||1.350||
|||(0.580)||
|**Ent. size (one worker omit.)**||||
|2|0.308***|0.319***|0.332**|
||(0.073)|(0.076)|(0.142)|
|3-4|0.188***|0.174***|0.236*|

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|||**Pool with**<br>**subsequent**||
|---|---|---|---|
||**Pool**|**formality**|**2012-2018**|
||(0.063)|(0.059)|(0.152)|
|5-24|0.191***|0.170***|0.557|
||(0.082)|(0.073)|(0.510)|
|**Industry (wholesale/retail omit.)**||||
|Manufacturing and related trades|1.955*|2.200*|1.577|
||(0.602)|(0.694)|(0.712)|
|Construction|2.879*|3.355**|0.913|
||(1.316)|(1.564)|(1.022)|
|Transportation and storage|0.184***|0.189**|0.102*|
||(0.091)|(0.098)|(0.099)|
|Accommodation and food service|2.111*|2.207*|4.235*|
||(0.774)|(0.816)|(2.596)|
|Various professional acts.|0.950|0.756|0.184|
||(0.465)|(0.368)|(0.176)|
|Other service|0.561|0.580|1.856|
||(0.256)|(0.274)|(1.237)|
|**Region (Greater Cairo omit.)**||||
|Alexandria and Suez Canal|0.855|0.875|0.816|
||(0.250)|(0.262)|(0.476)|
|Lower Egypt|0.729|0.747|0.928|
||(0.154)|(0.164)|(0.398)|
|Upper Egypt|1.014|1.066|0.594|
||(0.233)|(0.250)|(0.276)|
|**Partnership (sole proprietorship omit.)**||||
|Collective Ownership|1.136|1.079|1.041|
||(0.292)|(0.276)|(0.415)|

68
|||**Pool with**<br>**subsequent**||
|---|---|---|---|
||**Pool**|**formality**|**2012-2018**|
|**Capital (none omit.)**||||
|less than LE 100|0.895|0.878|0.986|
||(0.340)|(0.354)|(0.869)|
|LE 100-499|1.489|1.261|2.343|
||(0.591)|(0.525)|(2.039)|
|LE 500-999|1.557|1.521|1.267|
||(0.569)|(0.583)|(1.051)|
|LE 1000-4999|1.993|1.874|1.623|
||(0.775)|(0.753)|(1.340)|
|LE5000-9999|2.316*|1.936|3.102|
||(0.934)|(0.806)|(2.616)|
|LE10 000 or more|3.956**|3.349*|2.552|
||(2.038)|(1.790)|(2.958)|
|**Ln net revenue per worker**|||1.136|
||||(0.137)|
|**Firm start year (1980-1989 omit.)**||||
|Don't know|0.440|0.367|0.483|
||(0.297)|(0.246)|(0.432)|
|Before 1950|1.937|2.017||
||(1.162)|(1.194)||
|1950-1959|1.873|1.837||
||(1.211)|(1.209)||
|1960-1969|1.138|1.016||
||(0.515)|(0.475)||
|1970-1979|1.975*|1.821*|2.668|
||(0.564)|(0.513)|(1.936)|

69
|||**Pool with**<br>**subsequent**||
|---|---|---|---|
||**Pool**|**formality**|**2012-2018**|
|1990-1999|1.211|1.208|1.641|
||(0.273)|(0.272)|(0.802)|
|2000-2009|1.130|1.126|1.177|
||(0.309)|(0.314)|(0.568)|
|2010-2018|2.327*|2.382*|2.428|
||(0.991)|(1.050)|(1.459)|
|**People outside the HH employed (no omit.)**||||
|Yes|1.799**|1.738*|0.947|
||(0.404)|(0.394)|(0.367)|
|**Location (shop omit.)**||||
|Own home|1.287|1.189|1.786|
||(0.420)|(0.398)|(0.901)|
|Office/flat/building/rooms|1.106|1.332|3.412|
||(0.524)|(0.632)|(3.262)|
|Workshop/factory|1.340|1.388|1.351|
||(0.480)|(0.516)|(0.768)|
|Mobile worker|1.399|1.608|0.871|
||(0.504)|(0.614)|(0.586)|
|Kiosk/hut|0.195|0.219|0.389|
||(0.181)|(0.209)|(0.757)|
|Transport based|1.271|1.683|0.152*|
||(0.630)|(0.907)|(0.129)|
|Street/field/farm|1.069|1.220|1.202|
||(0.381)|(0.449)|(0.702)|
|**Sex (male omit.)**||||
|Female|0.693|0.625|0.685|

70
|||**Pool with**<br>**subsequent**||
|---|---|---|---|
||**Pool**|**formality**|**2012-2018**|
||(0.208)|(0.201)|(0.390)|
|**Education (illit. omit.)**||||
|Less than sec.|1.075|0.909|1.344|
||(0.252)|(0.219)|(0.566)|
|Secondary|0.888|0.801|0.895|
||(0.242)|(0.225)|(0.424)|
|Higher education|0.723|0.648|1.201|
||(0.219)|(0.202)|(0.642)|
|**Occupation (prof./tech. omit.)**||||
|Other white collar|0.967|1.039|0.528|
||(0.257)|(0.288)|(0.249)|
|Blue collar craft|0.605|0.614|0.405|
||(0.184)|(0.191)|(0.205)|
|Blue collar non-craft|1.106|1.158|2.129|
||(0.373)|(0.393)|(1.137)|
|**Age group (<30 omit.)**||||
|30-49|0.821|0.783|0.602|
||(0.208)|(0.205)|(0.212)|
|50+|0.689|0.662|0.417|
||(0.210)|(0.209)|(0.204)|
|**N**|1533|1478|529|
|**Pseudo R-squared**|0.117|0.129|0.161|

Source: Author’s calculations based on ELMPS 1998-2018 Notes: *p<0.05; **p<0.01; ***p<0.001. Blank cells indicate dropped perfect predictors 71
# **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
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.

73
Globally, overlap in productivity between formal and informal firms varies substantially by country context (Gelb, Mengistae, Ramachandran, & Shah, 2009; Benjamin & Mbaye, 2012).

Having a more educated owner is more conducive to formalization presumably because it makes it easier to negotiate the complex bureaucracy of formality (Almaal, 2020; World Bank, 2020). The strong association of formality with firm age also has two possible explanations: firms formalize later in the life cycle or informal firms are less likely to survive to old age. Our analysis of firm dynamics and informality, which we turn to next, suggests that the second explanation has some truth to it, but it is not clear whether the difference in survival between formal and informal firms is sufficient to account for the strong association between firm age and formality.

Our analysis of firms’ dynamics used panel data from different waves of the ELMPS to examine the relationship between formality and new firm formation, firm survival, and firm growth and formalization among firms that survive. Our findings point to the fact that informal firms experience more churn, with higher rates of firm formation and destruction among informal firms. Informal firms also seem to be more susceptible to macroeconomic conditions, exhibiting a clearer pro-cyclical pattern than formal firms.

Our data show that formalization does occur during the life cycle of firms, but that informalization also occurs, which we interpret as a possible failure to renew firm registration when it expires. In any case, registration of new businesses cannot be interpreted as new firm formation, since formalization could come fairly late in the life cycle of existing informal firms. Moreover, requiring firms to renew their registration every five years and the costs that this imposes may be a factor that contributes to higher rates of informality. Conditional on survival across waves, employment growth at the firm level does not seem to be strongly associated with the formality status in the base year, but is associated with subsequent formality, suggesting that firms that grow are also more able to afford formality.

Our results have important implications for current debates about the need to extend the scope of formality to a broader range of firms. First, it is clear that such efforts must be accompanied by attempts to streamline the process and dramatically reduce its costs, while enhancing the immediate benefits of formality, such has access to credit and input and product markets. While the costs of formality declined dramatically in Egypt from 69% to 20% of per capita income from 2007 to 2020, they may still be prohibitive for newly formed small firms (World Bank, 2006, 2020). Other aspects of formality, such as paying taxes, enforcing contracts and registering property remain prohibitively onerous and expensive. Firms presumably compare the costs and benefits of formalization when making decisions and employers who are informal in Egypt identify high costs (including time and effort) and a lack of advantages as their reasons for remaining informal (World Bank, 2014; AfDB, 2016).

Any push toward formalization should be targeted to firms that may be able to afford it, namely somewhat larger and older informal firms that hire wage workers. Efforts should, at least initially and in the current policy environment, avoid firms for whom there are few formal counterparts, such as firms operating outside fixed establishments or young/small firms. This and other work suggests that such firms, along with those with younger or female owners, no hired labor, or older low-productivity firms may be more survival self-employment than growth-oriented entrepreneurship (Krafft, 2016; Krafft & Rizk, 2018).

74
It is important to keep in mind that employment in 1-24 worker non-agricultural firms in establishments is only 22% of Egypt’s employment. Informality of employment, even among formal firms, remains an issue in other economic sectors. Within the micro and small establishment segment of the economy, our results suggest that there are some currently informal firms that are potentially amenable to formalization. However, only some of those potentially formalizable firms are likely to actually formalize even if renewed efforts to increase formality are rolled out. There is also a large fraction of informal firms that are very unlikely to formalize given the current economic and policy environment. It is an important and unanswered question whether further reducing the costs and raising the benefits of formality would shift this calculus.

Reforms designed to lower the cost and complexity of formality for micro-enterprises in Brazil did increase formality there (Fajnzylber, Maloney, & Montes-Rojas, 2011). Particularly notably, reform-induced increases in formality also led to higher profitability and increased employment. A key channel for this effect was shifting production to a permanent location. Given the rapid growth of employment outside establishments in Egypt, employment with the worst working conditions (Assaad, AlSharawy, & Salemi, 2019), reforms that lowered costs in ways that shifted employment into establishments could be positive. Conversely, increased enforcement of existing regulations, without changing the costs and benefits of formality, might drive employment out of establishments and worsen working conditions as well as other economic outcomes. Efforts to expand formality must be carefully designed to maximize the benefits for the economy and workers and minimize the costs.

75