DP2024-13

Multi-Dimensional Informality andDynamism of Microenterprises in Africa
N o b u a k i H A M AG U C H I
Hiroyuki HINOCharles PIOT
Jiahan YIN
March 28, 2024

Multi-dimensional informality and dynamism of microenterprises in Africa *Nobuaki Hamaguchi i
Hiroyuki Hino ii
Charles Piotiii
Jiahan Yin iv
Abstract
The informal economy in Africa has been considered both the cause and result of underdevelopment,
and that dichotomous understanding of the formal-informal divide, simply based on a single criterion
such as whether an enterprise is registered or not, is not satisfactory. In this paper, we propose
Composite Informality Index (CII) as a continuous measure of informality enclosing multiple
indicators of individual enterprise. We applied multiple correspondence analysis to quantify CII. We
examined whether CII has explanatory power for firm size, sales and growth, and the degree ofresiliency seen in shocks received from COVID-19 and recovery from them. We found that informality
is associated with a smaller size of employment and sales, consistent with stylized facts of informality.
However, CII is not directly related to sales growth, rejecting the general perception of linking
informality to slower business growth. A business owner-operator mindset, rather than being more orless informal, determines business growth. The informality is associated with smaller revenue loss
from COVID-19 shock, suggesting informal enterprises’ higher absorptive capacity. We also foundthat business perspectives that appreciate the full control of their own business and the flexibility toinnovate, which are also linked to higher sales growth, are associated with lower informality. Our
method would be particularly useful for devising policies to boost owner-operators’ motivation thatwould help African countries to enhance the dynamism of the microenterprise sector.
Keywords: MCA, sales growth, resiliency
JEL O17, O55, Z13

This study is supported by Kobe University Strategic International Collaborative Research Grant (TypeC Creating Joint Research). We thank Andrew Dabalen, Yemi Dipeolu, Misa Hirano, Murray Leibrandt,
Aika Matsumoto, Yoichi Mine, Diana Nyachieo, Sayaka Ogawa, Abdoul-Karim Soubeiga, Motoki
Takahashi, and other participants in Kobe-Duke Symposium on Africa Economic Development (July 20,2023) and to the support of ASA-I, especially Lilly Brouwer and Mohammad Iftekhar Jamal. The usual
disclaimer applies.
i
RIEB, Kobe University
ii
Office of Global Affairs, Duke University and RIEB, Kobe Universityiii
Department of Cultural Anthropology, Duke University
iv
Graduate School of Economics, Duke University*

1. Introduction
The informal economy in Africa has been considered both the cause and result of underdevelopment.
Firms are mostly self-employed, operating on a minuscule scale and at a low level of organization andtechnology. They are survivalists who avoid taxation and other costs of complying with the establishedjudicial, regulatory, and institutional requirements, taking the risk of being fragile without having
institutional protection (Fayomi et al., 2018). The dualistic nature of the labor market predominatedby the informal sector has shaped a stark income inequality (David et al., 2023).
The dominant view in economics literature recognizes the informal sector as a by-product ofunderdevelopment and poor regulatory framework and enforcement. Due to barriers to entry, workers'
low level of education prevents them from competing for higher wages in formal employment (Amaral
and Quintin, 2006). Malta et al. (2021) found that women are overrepresented in the informal economy
because existing gender gaps reduce their access to formal jobs. La Porta and Shleifer (2014) pointout that incentives for informal firms to become formal in poorer economies are weak, and as thecountry develops, informality becomes less important. Devine (2021) and Ohnsorge and Yu (2022)
associated informality with institutional settings that burden business activities, such as heavy taxes,
heavy-handed regulations, inability to enforce contracts and ownership, corrupt political environment,
and the lack of government accountability. These authors argue that streamlining regulatory and tax
frameworks and improving enforcement mechanisms should lead to the formalization of the informal
economy.
While economists viewed the informal economy as a peripheral entity and a transitional one thatwould dissolve as the economy and its institutions developed, anthropologists saw it as a lively andfree entity and an important complement to the institutional economy for a notable role in development,
especially in the provision of employment opportunities among women and youth. It is widely
acknowledged that the concept of the informal economy was first proposed by the anthropologicalframing by Hart (1973) based on his field research in Acra. He communicated with development
economists at the time, such as Todaro (1969) and his followers, to abandon theemployment/unemployment dichotomy because he noted most city dwellers being occupied in a street
economy outside the formal institutional protection of state bureaucracy. In his view, those who enter
informal occupations do not do so due to failure to obtain a wage job. He understood that cities attract
workers not by the chance of getting a job at higher wages but by the multiplicity of informal income
opportunities, which allows migrants to look to the prospect of a good life with or without a formal
job. He also noted that personal relationships, such as ethnic and kin-group membership, play a keyrole in distributing informal jobs and providing shelter for newcomers and those in an emergency.
Meagher (2010) argues that all markets are regulated, and an enterprise not being registered does notnecessarily mean that it is unregulated. She contends that informality should be conceptualized as an1alternative terrain of regulation operating under social networks outside the state. In this view, people
can actively explore the balance between formal and informal regulations. If there is no other choicethan to be informal, one would face the regulation based on social networks, which could be moretedious than formal ones in some respects.
Anthropologists also observed that informal traders play a critical role in the market economy byproviding multiple benefits that formal retailers do not offer, such as closeness, convenience, flexibility,
and selling at lower prices to consumers. Wegerif (2020) noted that unregistered street traders, hawkers,
spaza1 shops, and bakkie traders in South Africa contribute to integrating local businesses, fromproducers to local shops, bars, and restaurants, and they are the eyes on the street that create safer andmore hospitable public spaces. He reports that the government ignored these traders without giving
due support under the restriction during the COVID-19 sanitary crisis, and the closure of these traders'businesses significantly reduced social welfare.
If we define resiliency as one’s capability to absorb shocks, adapt, and restore to shifting
circumstances, informal businesses would seem to be less resilient and more fragile to shock because
they lack protection from formal institutions. Fragility, it is imagined, might also be associated withthe lack of necessary resources to cope with a crisis, such as the deployment of the Internet and online
tools under the lockdown.
However, the recent experience from the COVID-19 pandemic presents various examples to challenge
the idea that informal businesses were vulnerable. We witnessed numerous informal businesses
blossoming during the crisis, such as delivering restaurant food and daily necessities and e-commerceon online platforms. Some replaced or complemented disrupted businesses, and others emerged ascompletely new businesses from a structural change in consumer demand. Informal businesses areoften more resilient because they escape government regulations and respond flexibly to change.
Wrigley-Asante and Frimpong (2023) report that the border closure during the pandemic affectedGhanaian women in informal cross-border trading. However, they used a variety of strategies to getthemselves through those difficult times, including switching suppliers, using illegal routes to get theirproducts into the country, and utilizing various social networks created to facilitate their businesses.
Analyzing the West African value chain of African print textiles centered in Togo, Ebia (2023) foundthat the formal traders sourcing from a lead firm in the Netherlands broke the lead firm’s rigid sales
control and started to rely on the international sales network of informal traders who commercialize
non-branded imitations from Chinese manufacturers, thus competing with formal traders. The latter
continued its business by changing from the previous method of going to China to buy products,
ordering via the Internet, paying by credit card, and receiving goods by delivery service. The
negotiations with buyers were also switched to an online platform. The author suggests the flexibilityof informal traders had an advantage over the rigidly controlled formal sales system in adapting to theturbulence during COVID-19. Sampson (2020) reported that microenterprises in Aba, a Nigerian2commercial center which is known for the uncommon ability to fabricate or put together anything
under the sun, rose together to the lack of personal protection equipment amid the sanitary crisis tomass produce fabric face masks to be sold at affordable prices.
These narratives show that informal businesses can cope flexibly with adversity. However, theability of individual informal firms to respond to shocks and the extent to which government safety
nets are in place might vary from case to case. We cannot easily generalize about whether the informal
economy is more (or less) resilient. (Aditya and Amri 2023). The ability to resist also may be shortlived because of the limited resources to sustain them and the lack of government support.
2. Understanding multiple dimensions of informality in the African economy
ILO’s 15th International Conference on Labour Statistics (ICLS) resolution in 1993 broadly defines
the informal sector as units that “operate at a low level of organization, with little or no division
between labor and capital as factors of production and on a small scale. Labor relations – where they
exist – are based mostly on casual employment, kinship, or personal and social relations rather thancontractual arrangements with formal guarantees.” To operationalize this concept for the compilationof internationally comparable statistics on informality, ILO (2013) specifies informal businesses ashousehold unincorporated enterprises comprising own-account enterprises and enterprises of
employees without formal contracts 1. The 15th ICLS also specified the size criterion for employment
for informal enterprises of employees, which can be varied by country. It does not specify the location
criterion, whether enterprises are carried out in owners’ dwellings or other fixed premises or ambulates
without fixed workplaces.
ILO (2013) considers that the informal sector comprises enterprises that do not conform to legal
and regulatory frameworks. In this sense, the criterion of not being registered is generally applied. The
reference for registration includes laws and acts governing business conduct, labor contracts, tax, andsocial security, and regulations enacted by local authorities for a license or business permit. Based ona set of similar definitions, Schneider (2005) found that the informal economy in African nations
increased remarkably in the 1990s. However, in a follow-up research, Medina and Schneider (2018)
report that Sab-Saharan Africa’s informal sector size as a percent of GDP declined from 2000-2015,
although it stands at the highest level compared to other regions worldwide.
Many characteristics have already been given here, but this may not be a complete list yet. Meadand Morrisson (1996) likened the informal economy to an elephant because we know it when we see

1

The concept of the informal economy excludes any illicit activities and production wholly for its

own final use.

3

it, but we may not be able to define it precisely 2. De Castro et al. (2014) proposed that the binaryclassification of formal versus informal firms needs to evolve into a multi-dimensional continuum
along which the path to formalization unfolds. The uniform view that informal enterprises meet all the
ILO elements is also problematic. They are registered with the authorities and pay taxes in some parts.
However, they are unregistered in others, deviate from the regulations, and evade taxes. Bruno Lautier
viewed the informal economy as a unicorn. The literature abounds with definitions, but we could nevermeet one with all those characteristics because it does not exist (reported by Charmes 2016).
Following the foregone discussion, we consider adopting a binary definition of informality, suchas whether it is registered with the government or not, is inappropriate. Even if we use a pluralistic
definition of informal, it is still a binary definition that defines informal by whether it meets all of themany criteria as set forth by the ILO, and everything else is considered formal. An informal index
expressed in real numbers that can be sequentially ordered as more informal when more of the more
important criteria are met under the pluralistic criteria would have greater utility for the purpose of
empirically analyzing the relationship between informality and business outcomes. In order to use a
multi-dimensional definition in a way that is also operational, we propose to weight each criterion as
non-arbitrary as possible and aggregate them into a real number score for the informality of each
respondent.
In light of the above argument, our task is to construct the concept of informality composed ofmulti-dimensional information and translate a bundle of multivariate measurements into a composite
index in real numbers. We find a similar analytical challenge in social science to enumerate complex
ideas such as poverty, social value, social capital, well-being, and consumers’ tastes. We follow theapproach shown by Asselin (2009) for constructing a multi-dimensional composite index adopting themultiple component analysis (MCA) technique, which we will discuss in some detail in section 4.
3. DataThis study exploits a unique database obtained from a survey of a large pool of microcredit recipients.
In June 2021, ASA International (ASA-I) and Duke University Africa Initiative surveyed 1500 ASAI clients in Kenya, Ghana, and Nigeria 3. ASA-I is a major microfinance institution listed on the London
Stock Exchange, with more than 2.5 million clients in South Asia and sub-Saharan Africa.
The informal economy is predominant in the three countries. ILOSTAT reports that the proportion2

The parable of an elephant originates in an Indian Buddhist tale of blind men and an elephant, which

cautions the tendency of people to take as fact only their own limited subjective experiences without looking
at the whole.
3

See Hino et al (2024b) for a comprehensive information of the ASA-I survey.

4

of informal employment in total employment reaches 78.1% in Ghana (2015), 86.5% in Kenya (2019),
and 93.9% in Nigeria (2022). Onyebueke and Geyer (2011) reported that Nigeria has the largest
informal sector in Africa, notably among the young working age, due to the largest population in Africa
and decades of poor economic performance. According to the Federation of Kenya Employers (2021),
Kenya’s informal economy accounted for 33.8% of GDP in 2015. William et al. (2020) reports asimilar dominance of the informal economy in Ghana, accounting for 28.6% of GDP in 2017.
Five hundred interviewees were chosen from each country using the ASA-I’s corporate databaseof loan recipients. With a stratified random sampling frame, the survey divided the population inregional groups and randomly chose interviewees to be proportionate to the regional distribution of
microenterprises in each country. ASA-I’s trained loan officers rigorously controlled the interview
process. The collected information was put into a database at ASA-I’s country head offices, whichfurther underwent data cleaning at ASA-I’s Global Headquarters and Duke. The cleaned dataset
contains 1371 respondents, although it still includes unreported missing values.
The survey covered a wide range of issues, including clients’ social attributes and othercharacteristics, features of their business such as its location, and indicators of dynamism. Together,
the dataset contains information, at the firm level, on (a) personal, social, and personal attributes ofbusiness owners (gender, age, education, religion, ethnicity, languages, location, living situation,social activities, etc.); (b) characteristics of their firms (nature of business, ownership structure, thenumber of employees if any, contractual or other relationship with employees, cash or other methodsof payment and receipt, bookkeeping, reliance on family and other personal relations, business venue,
years in current business, use of ICT, etc); and (c) indicators of dynamism, i.e., the change in the loanbalance, business revenue and services offered. The information is anonymized and recorded with aunique identification code.
Some caveats are in order. First, our sample are all female owner-operators because ASA-I’s loan
recipients are all female. Second, the survey did not cover the regions where ASA-I is not present.
Third, the consistency with the actual distribution of microenterprises by activities was not considered
in random sampling. As a result, services and trade are over-represented in our samples for Ghana andNigeria because the survey covered very small shares of microenterprises in agriculture andmanufacturing.
4. Methodology
4.1 Multiple correspondence analysis
The scope of this work is to deploy the multiple correspondence analysis (MCA) technique to construct
a composite index of the informality consisting of primary indicators of informality that capture
distinct features of informality. As discussed fully by Asselin (2009), MCA is a technique like the5principal component analysis (PCA) for increasing the interpretability of data containing many
features per observation in reduced dimensions 4. This task is accomplished by projecting observationsin the data in a coordinate system of fewer dimensions through linear combinations of original primary
indicators. The axes of this transformed coordinate system are called dimensions, and each dimension
is interpreted as a scale with a distinctive meaning.
Let X(𝑛𝑛, 𝐽𝐽) be the indicator matrix of n observations on K indicators decomposed into 𝐽𝐽

categories, where each indicator 𝑘𝑘 ∈ 𝐾𝐾 consists of 𝐽𝐽𝑘𝑘 response choices totaling 𝐽𝐽 = ∑𝐾𝐾
𝑘𝑘=1 𝐽𝐽𝑘𝑘 .

Indicator 𝑘𝑘

represents a question in a questionnaire survey, for example: do you keep a record ofbusiness transactions? Categories are response choices, such as: keep detailed and systematic record;

detailed but not systematic, somewhat detailed, not detailed, or does not keep record at all (there are

five categories in this case). Each element in X(𝑛𝑛, 𝐽𝐽) is a binary code, to which we assign 1 if a person
would choose a category 𝐽𝐽𝑘𝑘 for the question 𝑘𝑘, and otherwise 0. We have 𝑛𝑛 = 825 individuals who

answered all 6 questions.

We associate individual i with its profile of chosen responses {𝑥𝑥𝑖𝑖𝑖𝑖 : 𝑘𝑘 = 1, ⋯ , 𝐾𝐾} . Point 𝑀𝑀𝑖𝑖

corresponds to this profile in individual space ℝ𝐾𝐾 (Pagès, 2015). The mass of 𝑀𝑀𝑖𝑖 in the individual

space constitutes a cloud N. Each point 𝑀𝑀𝑖𝑖 in N is referred to the center of gravity of the cloud, andcompute 𝜒𝜒 2, the total chi-square distance of 𝑀𝑀𝑖𝑖 from the center of the gravity of the cloud 5. It is

known that the inertia, 𝜒𝜒 2⁄𝑛𝑛, of the indicator matrix X(𝑛𝑛, 𝐽𝐽) takes on a simple form, depending only

on the number of indicators and number of responses (Greenacre, 2010) 6:
𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖(𝑋𝑋) =𝐽𝐽 − 𝐾𝐾
𝐾𝐾(1)Equation (1) implies that 𝐽𝐽 − 𝐾𝐾 is the dimensionality of the data and 1⁄𝐾𝐾 is the average inertia perdimension.

We transform the indicator matrix into the Burt matrix by B= X T X, The Burt matrix is 𝐽𝐽 × 𝐽𝐽

symmetric covariance matrix whose entries are frequencies of the co-occurrence of each pair of
4

PCA is suitable for the data in continuous numbers. MCA can be viewed as an extension of PCA to

categorical and binary data. MCA has a broad range of applications in social science to express complex
multi-dimensional concept in operational metrics, such as poverty (Asselin 2009), social capital (Teney and
Hanquinet 2012), well-being (Neff 2007), consumers’ taste (Le Roux and Rouanet 2010), social perceptions
such as meritocracy (Hjellbrekke 2019), and depth and breadth of small and medium enterprises’ network
content (Lin and Lin 2016).
5

We can calculate the deviation of a cloud with respect to any point on the space supporting the cloud. It

attains the minimum when the point is at the center of gravity of the cloud.
6

See also Le Roux and Rouanet (2010).

6

categories 7. The total inertia (variance) of the Burt matrix, denoted by 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖(𝐵𝐵), quantifies the total

variability or information on the association or dissimilarities among the categories in the original data.
It is known that the Burt matrix inflate the inertia because of large values in its diagonal elements. The
following equation makes adjustment to this issue of the total adjusted inertia of the Burt matrix.
𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑎𝑎𝑎𝑎𝑎𝑎 (𝐵𝐵) =𝐾𝐾
𝐽𝐽 − 𝐾𝐾
�𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖(𝐵𝐵) −�
𝐾𝐾 − 1
𝐾𝐾 2(2)

Next, we search principal axes to project cloud B passing through the center of gravity of thecloud on which the cloud is projected with the maximum inertia on the line. For this, we conduct aneigenvalue decomposition of the Burt matrix and obtain eigenvalues 8 𝜆𝜆𝛼𝛼 where 𝛼𝛼 = 1, ⋯ , 𝐽𝐽 .
Adjusted principal inertia of the 𝛼𝛼-th dimension denoted by 𝐹𝐹𝛼𝛼 is given as𝐾𝐾1
��𝜆𝜆𝛼𝛼 − � .
𝐾𝐾 − 1
𝐾𝐾(3)

𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑎𝑎𝑎𝑎𝑎𝑎 (𝐵𝐵) = � 𝐹𝐹𝛼𝛼 .

(4)𝐹𝐹𝛼𝛼 =The total adjusted inertia of the Burt matrix is the sum of the adjusted principal inertias obtained byequation (3):𝛼𝛼MCA projects the J dimensioned cloud on a space in smaller dimensions, with the least loss of
information. The smaller the number of dimensions is, the easier interpretation becomes. However,the less dimensions are included, the greater the loss of information is. Equation (3) implies that it is1

meaningful to consider dimensions only for �𝜆𝜆𝛼𝛼 > 𝐾𝐾. However, researchers would prefer adopting�the criterion that ∑𝛼𝛼𝛼𝛼=1𝐹𝐹𝛼𝛼 ⁄𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑎𝑎𝑎𝑎𝑎𝑎 (𝐵𝐵) is sufficiently large and the contribution of adding

dimension 𝐹𝐹𝛼𝛼�+1⁄𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑎𝑎𝑎𝑎𝑎𝑎 (𝐵𝐵)) is significantly small for determining the number of dimensions to
be 𝛼𝛼�.

4.2 Category weights from MCA to quantify multi-dimensionally defined informality
We chose six primary indicators of informality from the ASA-I data as listed in Table1. The indicators
incorporated here are intended to describe the characteristics of informal firms and not about the causes

7

The Burt matrix is also called the cloud of categories.

8

As with the principal component analysis, MCA is referred as eigen decomposition of the data covariancematrix.

7

or consequences of informality. Admittedly, the list is not exhaustive 9, but it includes characteristics
often cited in the literature.
Table 1
The concepts of whether an enterprise is registered in the public system and pays due taxes andwhether it complies with the regulations of the respective competent authorities are used in the ILO's
statistical standards as a definition of informal. Thus, pay_salestax, registered, and govregul arenatural choices for primary indicators of informality. Note that pay_salestax and registered are binary
choices and replying “no” to them is informal. The reply to govregul has six categories (not sure/not
at all/rarely/partially/mostly/fully). We consider these replies to be ordered from “not sure” and “notat all” as the most informal to “fully” the least.
Furthermore, we included three indicators related to business behavior. Abdul-Rahamon andAdejare (2014) pointed out that although the availability of accounting information facilitates thesolution of business planning, many small business owners have not given much attention to proper
bookkeeping because of the lack of sound knowledge and skilled accounting staff. The same authors
found that there is a strong positive relationship between accounting records keeping and theperformance of small-scale enterprises. Formal firms have a greater capacity for amortizing the fixed
costs involved in demanding the services provided by civil society institutions, such as formal
accounting procedures (Fajnzylber et al., 2009). The bookkeeping variable (account) allows fiveordered categories from “no record” as the most informal choice to “detailed and systematic” as theleast.
Regarding the business venue, working on the street or in open public spaces is a primary option
for informal enterprises (Chen, 2012). They engaged in small shops and workshops. They can beformally set up in a building for rent, or they will be settled in a shared space. The informal sector
claims temporary rights for the collective use of public land. Both civil and common law traditions in
sub-Saharan Africa can accommodate the broad definition of rights, obligations, and legal persons
entailed (Brown, 2015). Thus, among the six choices for the business venue variable (location), we
presume that shared space, open public space, street, and public rural land are ordered as forms withmore informal characteristics, and buildings and private farmland are less so.
Regarding the mode of receiving payment, Medina (2017) states that informal transactions take9

For example, informal business is often characterized by being own-account and the reliance on unpaid

family labor, although not considered here. The small size of business is also linked to informality, but weconsider it as an outcome rather than an attribute because small firms are not necessarily informal. In thesame vein, we consider that low productivity and slow growth are consequences but not characteristics.

8the form of cash payments to not leave an observable trace for the authorities. In a mistrusted
relationship, one would expect transactions to be self-liquidating, with immediate cash payment andno delayed obligations (Fafchamps, 1996). Thus, we expect that firms only or usually take immediate
cash payment and rarely or never later-on credit, which is associated with a higher degree of
informality.
We ran MCA using ©STATA based on the Burt matrix The result is shown in Table 2. We selected
the first three dimensions because their cumulative contribution reached 78.79% and adding the 4th
dimension adds little information. The total adjusted inertia is 0.100942, of which dimension 1
contributes 53.2%.

Table 2
Table 3 presents the MCA output of ©STATA. It reports the coordinates of each category on thespace of three factorial axes denominated as 𝑊𝑊𝑗𝑗1,𝑘𝑘, 𝑊𝑊𝑗𝑗2,𝑘𝑘, 𝑊𝑊𝑗𝑗3,𝑘𝑘
for each of three dimensions
𝑘𝑘
𝑘𝑘
𝑘𝑘

corresponding to indicators 𝑘𝑘 = 1, … , 6, and categories of each indicator 𝑗𝑗1 = 6, 𝑗𝑗2 = 5, 𝑗𝑗3 = 6, 𝑗𝑗4 =2, 𝑗𝑗5 = 2, 𝑗𝑗6 = 2. It also shows statistics for column categories in principal normalization. “Sqcorr” isthe squared correlation of the category with the dimension. It represents the amount of inertia of thepoint of category given by the coordinates �𝑊𝑊𝑗𝑗1,𝑘𝑘, 𝑊𝑊𝑗𝑗2,𝑘𝑘, 𝑊𝑊𝑗𝑗3,𝑘𝑘
�. The squared correlation is also called
𝑘𝑘
𝑘𝑘
𝑘𝑘

the squared cosine obtained as the quotient of the squared χ2 distance from the center of the cloud toeach categorical point in the space divided by the squared distance to its projected point onto each axis.
The greater the "Sqcorr.”, the better the respective dimension represents each category. The “Overall
quality” of a category is the sum of “Sqcorr” from the three dimensions. The higher the quality, thebetter the category is represented by the extracted dimensions. Following Le Roux and Rouanet (2010,𝑗𝑗2p.40), values of "Contrib.” denoted as 𝐶𝐶𝐶𝐶𝐶𝐶𝑗𝑗𝛼𝛼𝑘𝑘 in equation (5) present the contribution of �𝑓𝑓𝑗𝑗𝑘𝑘 ⁄𝐽𝐽��𝑦𝑦𝛼𝛼𝑘𝑘 � ,the variance due to category 𝑗𝑗𝑘𝑘 , to overall variance of the dimension 𝛼𝛼, as they sum up to 1 for eachdimension.

𝐶𝐶𝐶𝐶𝐶𝐶𝑗𝑗𝛼𝛼𝑘𝑘 =𝑗𝑗

�𝑓𝑓𝑗𝑗𝑘𝑘 ⁄𝐽𝐽��𝑦𝑦𝛼𝛼𝑘𝑘 �
𝜆𝜆𝛼𝛼

2

(5)

where 𝑓𝑓𝑗𝑗𝑘𝑘 is the share of individuals who chose category 𝑗𝑗𝑘𝑘 in the total population and 𝑓𝑓𝑗𝑗𝑘𝑘 ⁄𝐽𝐽denotes the “Mass” in Table 3. The “%inertia” presents overall contribution of each category to thetotal inertia, whose row-sum adds up to 1.

Table 39

Suppose we find a dimension that encompasses the characteristics of informality that have been
pointed out in previous studies. Asselin (2009) proposes that factorial scores generated by MCA onthat dimension can be used as category weights to individual i’s choices for selected indicators chosen
categories to compute a composite index of informality if it satisfies two properties: monotonicallyincreasing in each of the primary indicators considered in its construction (monotonicity axiom), andthe population ordering of the primary indicators is preserved with the composite indicator (ordering
consistency).
For illustration, it is convenient to plot the coordinates of the first two dimensions, �𝑊𝑊𝑗𝑗𝑘𝑘1,𝑘𝑘 , 𝑊𝑊𝑗𝑗𝑘𝑘2,𝑘𝑘 � ,in the two-dimensional plane of Figure 1, taking respectively dimension 1 and dimension 2 as thehorizontal and vertical axis. These plots show distinct positions of each response for each question.
For example, regarding the question on the compliance of government regulation (govregul), theresponses “fully” and “none” are positioned farthest apart on the horizontal measure. Likewise, about
the question of keeping a business record (account), the response “systematic and detailed” is the most
distant from “no record.”

Figure 1
In Figure 1, the direction to the right of dimension 1 on the horizontal axis corresponds tocompliance with the government regulation – “none,” business bookkeeping – “no record,” businesslocation – “street/public space/shared space,” receive payment only with cash – “yes,” pay sales tax –“no”, and whether a business is registered with the government – “no.” On the other hand, in the
direction to the left, dimension 1 captures categories such as “fully complying with government
regulation”, “detailed and systematic bookkeeping,” “business venue in building and private
farmland”, receiving payment only with cash – “no,” pay sales tax – “yes,” and whether the businessis registered – “yes.”
The values of squared correlation and contribution guide us to identify which categories should
be used to interpret each dimension. For example, regarding compliance with government regulations,“fully” and “none” have distinctively higher values of squared correlation with dimension 1, followedby “not sure,” “mostly,” and “partially,” while “rarely” is barely correlated. “Fully” and “none” also
contribute significantly to the total inertia of dimension 1. The contribution of “not sure” is as high as“none” because of the large population who chose this category. “Partially,” “mostly,” and “rarely”
contribute little to the variance of dimension 1. Thus, we select “fully,” “none,” and “not sure” withcompliance with government regulations for the interpretation of dimension 1.
In a similar way, “no record” and “detailed and systematic” from the bookkeeping variable and10

“shared space,” “public space,” and “building” from the business venue variable are more correlatedwith dimension1 and contribute more to dimension1’s variance. The “yes/no” response to binary
variables of cash payment, registration, and tax payment are also highly correlated with dimension 1.
Figure 2 presents a synthesis of the above analysis on dimension 1. Overall, there is ordinal
consistency in each category’s correspondence to definitions of informality as specified in previous
for each indicator 𝑘𝑘 = 1, … , 6 (first axis orderingresearch and dimension 1 coordinate 𝑊𝑊𝑗𝑗1,𝑘𝑘
𝑘𝑘

consistency, FAOC-I). It also satisfies the global first axis ordering consistency (FAOC-G) because
FAOC-I is fulfilled with the same orientation for all indicators. Thus, Figure 2 shows that dimension
1 aligns with FAOC-I and FAOC-G, which are necessary for the monotonicity axiom (Asselin, 2009)10.

Figure 2
MCA also allows the inclusion of supplemental variables. They do not contribute to calculating
MCA statistics, but they serve for interpretation and comparison purposes. In this analysis, we include
variables that designate the type of business (services, trade, manufacturing, and agriculture) andcountry (Nigeria, Kenya, and Ghana). Using the dimension 1 coordinates as a composite index of
informality, we find that services are more informal, and manufacturing and agriculture are lessinformal regarding the types of business. Concerning country characteristics, the data shows a higher
degree of informality in Gahana, while lower in Kenya and Nigeria.

Table 4

5. Composite informality index for individual

Having established the correspondence of dimension 1 to the six indicators included in the MCA
as the weighting factors of the composite index of informality for eachanalysis, we deploy 𝑊𝑊𝑗𝑗1,𝑘𝑘
𝑘𝑘

individual i. This approach follows Asselin (2009), who studies the multi-dimensionality of poverty
and proposes to take the first-dimension coordinates of MCA as weighting factors of the composite
indicator of poverty. He showed that the composite indicator is monotonically increasing in eachprimary indicator (monotonicity axiom), which implies a desirable property of the indicator of

10

Selected categories with high correlations and contributions to variance on dimensions 2 and 3 are notrelated to informality as we define it. However, we were not able to find consistent patterns to interpretdimensions 2 and 3 in any meaningful way.

11

ordering consistency. The latter asserts that the population ordering for a primary variable is preservedwith the composite indicator. This can be checked by inspecting that the order of values of 𝑊𝑊𝑗𝑗1,𝑘𝑘
in𝑘𝑘

Table 3 and Figure 1 is consistent with the assertion of the multi-dimensionality of informality that we
reviewed in Section 1. Other indicators being equal, if one firm chose a category with a higher 𝑊𝑊𝑗𝑗1,𝑘𝑘,
𝑘𝑘
it will get a higher informality index.

Following Asselin (2009), we use the duality property (or transition property) between the
category matrix and the indicator matrix, given by the following equation.

𝐹𝐹1𝑖𝑖 =

𝐽𝐽𝑘𝑘
∑𝐾𝐾
𝑘𝑘=1 ∑𝑗𝑗 =1
𝑘𝑘

𝐾𝐾

𝑊𝑊𝑗𝑗1,𝑘𝑘𝑘𝑘
𝑘𝑘𝐼𝐼𝑖𝑖,𝑗𝑗�𝜆𝜆1 𝑘𝑘(4)

𝑘𝑘where 𝐼𝐼𝑖𝑖,𝑗𝑗=1 if the category 𝑗𝑗𝑘𝑘 applies to individual 𝑖𝑖 and otherwise zero. This property implies
𝑘𝑘

that each person is characterized by the per-category average of the normalized category scores overall categories, i.e., 𝑊𝑊𝑗𝑗1,𝑘𝑘��𝜆𝜆1.
𝑘𝑘

𝑘𝑘
Using equation (4), we calculate 𝐹𝐹1𝑖𝑖 of each respondent based on the response pattern 𝐼𝐼𝑖𝑖,𝑗𝑗
. The
𝑘𝑘

raw scores on 𝐹𝐹1𝑖𝑖 vary with 𝑚𝑚𝑚𝑚𝑚𝑚 = 0.4488 and 𝑚𝑚𝑚𝑚𝑚𝑚 = −0.4405 with mean 0 and the standard
deviation 0.2319. For the easiness of interpretation, we rescale 𝐹𝐹1𝑖𝑖 to be measured in the interval [0,1].

Following Asselin (2009), we adjust the factorial row scores with the gap between those of the least
informal individuals 𝑊𝑊1𝛼𝛼,𝑘𝑘 as shown by the following formula:𝑎𝑎𝑎𝑎𝑎𝑎,𝑖𝑖

𝐹𝐹1

=

𝐽𝐽𝑘𝑘
∑𝐾𝐾
𝑘𝑘=1 ∑𝑗𝑗 =1
𝑘𝑘− 𝑊𝑊1𝛼𝛼,𝑘𝑘 � 𝑘𝑘
�𝑊𝑊𝑗𝑗1,𝑘𝑘
𝑘𝑘𝐼𝐼𝑖𝑖,𝑗𝑗𝑘𝑘
�𝜆𝜆𝛼𝛼
𝐾𝐾

(5)

where 𝑊𝑊1𝛼𝛼,𝑘𝑘 denotes the lowest scores in Table 3 from dimension α for each variable k. The score for������
𝑎𝑎𝑎𝑎𝑎𝑎the most informal individuals 𝐹𝐹𝛼𝛼is given by������
𝑎𝑎𝑎𝑎𝑎𝑎𝐹𝐹𝛼𝛼 =

𝐽𝐽𝑘𝑘∑𝐾𝐾
𝑘𝑘=1 ∑𝑗𝑗 =1𝑘𝑘

�𝑊𝑊2𝛼𝛼,𝑘𝑘 − 𝑊𝑊1𝛼𝛼,𝑘𝑘 � 𝑘𝑘
𝐼𝐼𝑖𝑖,𝑗𝑗𝑘𝑘
�𝜆𝜆𝛼𝛼
𝐾𝐾

(6)

where 𝑊𝑊2𝛼𝛼,𝑘𝑘 denotes the highest scores in Table 3 from dimension α for each variable k. Using

equations (5) and (6), we obtain the individual informality index as follows:
𝑎𝑎𝑎𝑎𝑎𝑎,𝑖𝑖

𝐹𝐹𝛼𝛼𝐶𝐶𝑖𝑖 = ������
𝑎𝑎𝑎𝑎𝑎𝑎𝐹𝐹𝛼𝛼(7)

We denote 𝐶𝐶𝑖𝑖 as the composite informality index (CII) for business operator i. Having been

rescaled in the interval of [0,1], CII has the mean 0.495 and standard deviation 0.261. The distribution
is shown in Figure 3.

12Figure 3
6. Analysis on the relationship between informality and business dynamics
6.1 Informality and employment size
In previous sections, we developed a novel approach for measuring the multi-dimensionally defined
informality of business operators. In this section, we deploy the composite informality index (CII)
given by equation (7) for the ASA-I data to compute the extent of the informality of each business
operator. The CII is meant to synthesize the contributions of six indicators listed in Table 1.
In the first analysis, we analyze the relationship between employment size and CII. It is taken as astylized fact that the majority of informal enterprises, defined as unincorporated small and/or
unregistered business entities, run businesses on their own account (Chen, 2012; Haussmanns, 2004;
OECD/ILO 2019). It should be difficult to establish a reverse causality because specialized freelancers
abound in formal service contracts.
We employ the number of workers as a dependent variable and set independent variables,
including CII, whether the business operator also owns her business (dummy), type of business (trade,manufacturing, and agriculture with service as the base case), and country (Nigeria as the base casefor Kenya and Ghana). We estimate an ordinary least square (OLS) regression.
The estimated result reported in Table 5 shows a strong negative correlation between CII and
employment size while controlling for owner-operator structure, as well as the heterogeneity by sector
and country. It supports the stylized fact regarding the small size of informal enterprises under the
proposed multi-dimensional definition of informality. This simple regression serves as a check for the
CII to be promising as a measure of informality.
Table 5
6.2 Informality and sales and growth
The second analysis inquiries about the relationship between informality and sales. It is widely
accepted in empirical studies that informal enterprises have lower sales and lower growth, other things
being equal. Regression (1), reported in Table 6, takes the log of sales value converted in US dollars
as the dependent variable, and regression (2) takes the ordered categorical variables on sales growth.
The latter derives from the replies to the question “How much has your business revenue
increased/decreased in the last five years?” in seven categories: “decreased by more than 50%,”“decreased by 30-50%,” “decreased by 10-30%,” “about the same,” “grown by 10-30%,” “grown by
30-50%,” and “grown more than 50%.” Other than CII, we included independent variables such as:
13

"working hours per week" for labor input intensity, "ICT use" for capital intensity specifying the useof telephone, smartphone, and tablet or personal computer, against the base case of "none" as the basecase. We also added dummy variables regarding respondents’ perception of the advantages ofoperating their own business, to which we assign one if the respondent affirms "you are in full controlof your business," "you are free to innovate," and "you are your own boss". We also controlled fortypes of businesses and countries.
We estimated regression (1) by OLS. Regression (2) was estimated by ordered logit because thedependent variable is categorical. The regression (1) showed a negative correlation between CII andsales, suggesting that more informal enterprises have lower sales. This result is consistent with theanalysis of employment size in Table 5. It seems counterintuitive that work hours per week arenegatively linked to sales in regression (1). This result suggests that poorly remunerating operators
tend to work longer hours. The magnitude of estimated coefficients of ICT use are positive andstatistically significant against the base case of using none, and they are in order of equipment that canhandle greater amounts of information. The results showing the correlation of “being her own boss”
perception with sales indicate that the sense of freedom is linked to greater sales.
Regression (2) examines the relationship between the same explanatory variables and sales
growth. It should be emphasized that there is no statistical relationship between business growth andthe degree of informality. Thus, unlike the widely held perception, informal business cannot be linked
to lower growth. There is a strong association between high business growth and the use of tablets andpersonal computers, which suggests the more intensive use of information. Regarding the perception
variables, other than ownboss, “full control of business” and “free to innovate”, translating
respectively to autonomy and entrepreneurship, correlate to higher sales growth. A business owneroperator mindset, rather than being more or less informal, determines business growth.
Table 6
6.3 Informality and resiliency to shock
The third analysis addresses the question of resiliency. Table 7 reports the results of estimating a modelwith two variables on the change in revenue after COVID-19 as dependent variables. The first column
reports the regression results for the reply to the question, “How much revenue did you lose or gain asa result of the coronavirus pandemic?” The second column is on the question, “How well has yourbusiness recovered from the coronavirus?” Respondents replied to both questions in 9 categories
ordered as “lost all revenues,” “lost 50-90%,” “lost 30-50%,” “lost 10-30%,” “about the same,” gainedby 10-30%,” “gained by 30-50%,” “gained by 50-90%,” or “gained by more than 100%.” With thisordered categorical dependent variable, we estimated the model by ordered logit. Other than the
independent variables included in the previous analysis, we added variables describing buy/sell
14

relationships. This information derives from the questions “From whom do you buy materials?” and“To whom do you sell your products?” Respondents were expected to choose from “formal/establishedcompanies,” “traders,” “family, friends, or others you know personally,” and “others (for buying)” or“random customers (for selling).” We set dummy variables, taking “others” and “random customers”
as base cases.
Column (1) shows that more informal business operators were able to reduce income loss,
suggesting a higher capability to absorb unexpected shocks. From buy/sell relationships, revenue loss
was greater for those who bought from traders and sold to formal companies. These results suggestthat informal businesses had “nothing to sell” because of the disruption of supply chains from tradersand “nowhere to sell” because of the temporary suspension of the formal sector during the lockdown.
Regarding the recovery after the shock, column (2) reports that the degree of informality did nothave an effect. This observation is similar to the relationship with the business growth in the normal
time reported in Table 6. It also reports that those who enjoy autonomous control and entrepreneurial
freedom could recover faster. Regarding the buy/sell relationship, those buying inputs from formalcompanies and traders and selling to traders could recover faster. In contrast, those whose customers
are formal companies were slower constrained by the slow recovery of formal companies.
Taken together, the results of the above analysis indicate that more informal firms were able tomitigate the decline in income during the shocks received from COVID. While informality does nothelp the recovery process, it does not impede recovery either. They are positioned in the supply chain,and their income decline was linked to the disruption of purchases from traders and the loss of sales
to formal firms that closed their doors as a result of the lockdown, border blockades, and other drastic
measures taken. After the resumption of economic activity, the relationship with traders wasparticularly important for recovering informal businesses' sales as a supplier and sales destination.
Table 7
6.4 Personal characteristics and informality
The fourth analysis examines how the degree of informality is linked to operators’ personal
characteristics. In previous studies, informality has been discussed as the result of the exclusion fromthe formal labor market due to low levels of human capital and the lack of legal and socioeconomic
institutions in force making people trust human relationships based on common personal factors suchas family, friends, race, language, religion, and neighborhood. Table 8 presents the results of theanalysis, taking CII as the dependent variable. Recall that CII is a composite of the following businessbehaviors: not keeping business records systematically, ambulating without fixed workplaces, notpaying taxes, receiving payment only in cash, not being registered, and not complying with regulations.
Firms that more often and more clearly fit these characteristics have a higher CII in [0,1]. Independent
15

variables capture personal characteristics, including the perception of the advantage of operating theirown business, which we employed above, years of schooling, and perspectives on who they trust morein running their business (to be chosen from family/relatives/neighbors/religious group/ethnic
group/government/strangers).
Table 8 provides some insights into the personal characteristics of informal business operators.
Regarding the advantage of operating a business oneself, “being her own boss” is not correlated. Hence,
it is a common perception for operators of any degree of informality. Those who feel advantageous in
the freedom to innovate (i.e., more entrepreneurial) and enjoy full control of business tend to be lessinformal. In Tables 6 and 7, we found that these perceptions are related to higher sales growth andfaster recovery from the downturn with the COVID-19 shock 11. The direct effect of CII on sales
growth and recovery was found to be negative but statistically insignificant. These results might havebeen caused by the collinearity between CII and innovate and control found in Table 8
Next, we analyzed the relationship between years of schooling and informality, using 9 to 11
years (corresponding to the completion of primary education) as the reference case. We found that thecoefficient of “1-5 years” (lower primary education) has a positive sign, “6-8 years” (higher primaryeducation) insignificantly differs from the reference, and “12-14 years” (middle education) has anegative sign. Thus, consistently, with a general perception of informality linked to a lower level of
education, CII decreases as one acquires a higher level of education. It is notable to find that the caseof “no schooling” has a negative sign, which means less informality than the base case. We can inferthat to be informal, as specified by the variables composing CII, requires some basic level of literacy,
numeracy, and communication skills.
Regarding the reliance on personal relationships, taking the reliance on strangers in the market
as the base case, Table 8 reports that CII is positively correlated with the reliance on family andneighbors. Working with family workers or apprenticeships from the same community without paying
a regular salary is a natural extension of informal own-account workers. As observed by Meagher(2009) religion has also been a critical factor in the organization of reliable economic networks outside
the framework of the state in Nigeria. She points out that religion tends to act as a modernizing force
encouraging educational attainment, facilitating relations of trust and solidarity, and fostering ethicsof accumulation through skills. Our result suggests that a stronger reliance on religious groups is linked
to a lower degree of informality, as for the reliance on the government.
Table 8

11

This result corroborates Hino et al. (2024a) which found that owner-operated small businesses tend toraise growth of business revenue if owner’s informal motivations are strong, e.g., valuing "freedom" highly.

16

7. Conclusion
There is a growing understanding of the multi-dimensionality of the informal economy, and that
dichotomous understanding of the formal-informal divide, simply based on a single criterion such aswhether an enterprise is registered or not, is not satisfactory. The ILO has provided a variety ofindicators to define informality, but defining informality as something to which they all apply is
extremely limited. It is not clear how they can be applied in practice to synthesize the complex
multi-dimensional concept into an operational indicator, which enables us to order enterprises
according to their level of informality. This situation is inconvenient because the informal sector isdominant in developing countries, and policymakers and international aid communities are eager toknow the evidence-based relationships between informality and economic development.
In this paper, we propose constructing a composite informality index (CII) using qualitative andquantitative information collected from respondents through a questionnaire. We applied multiple
correspondence analysis, which has already been used in previous research in the social sciences, toquantify complex concepts. To the best of the author's knowledge, this is the first time that thismethod has been applied to the analysis of informality.
We computed CII using data from a survey of microcredit clients in Ghana, Kenya, and Nigeria
conducted in cooperation with ASA-I and Duke University. We adopted six features of informality inbusiness practice: not keeping business records systematically, ambulating without fixed workplaces,
not paying taxes, receiving payment only in cash, not being registered, and not complying withregulations. From the statistical viewpoint of first axis ordering consistency and the global first axis
ordering consistency (Asselin, 2009), we concluded that the first factorial scores on the firstdimension axis serve as CII.
We tested whether CII has explanatory power for informality regarding firm size, sales andgrowth, and the degree of resiliency seen in shocks received from COVID-19 and recovery fromthem. Regression results show that CII is linked to a smaller size of employment and sales,consistent with the stylized facts of informality. However, we also found that CII is not directly
related to sales growth, rejecting the general perception of linking informality to slower production
growth. Regarding resiliency, we found that the informality measured with CII is associated withsmaller revenue loss, suggesting informal enterprises’ higher absorptive capacity of a shock.
We also examined the relationship between entrepreneurial characteristics and CII. We foundthat business perspectives that appreciate the full control of their own business and the flexibility toinnovate, which are also linked to higher sales growth, are associated with a lower informality. CII iscorrelated with business operators’ lower education level and reliance on personal relationships withfamily and neighbors.

17

Overall, we consider that CII works well as an operational measure of the degree of informalitythat could be used in empirical studies. Our method has the advantage of synthesizing diverse
informal factors into a composite index with statistically determined weights that are not based on apriori arbitrary judgments. However, this conclusion is based on a specific data set and needs to beconsidered more often.
From a policy standpoint, the paper confirms that informal enterprises are generally poor andsmall. This is not surprising, and there could be many reasons for this. However, the key takeaway
from the paper is that this does not mean informal enterprises are “inferior,” as often alluded to in thedevelopment economics literature. Rather, informality tends to contribute to raising business growthand enhance resilience to withstand negative shocks. The CII is an effective tool for identifying
which aspects of informality have such positive qualities and which ones do not. Therefore, the CII
would be particularly useful for devising policies that would help African countries to enhance thedynamism of the microenterprise sector. We should remember that all microenterprises have someelements of informality – it is not a matter of either formal or informal.

18

Tables and figures
Table 1. List of indicators from ASA-I data
Variable nameIndicators

Numbercategories

1. Extent of keeping a record of business transactions (detailed

account

5location6

3. Whether or not the respondent pays sales tax (yes/no)

pay_salestax

2

4. Mode of receiving payment (only or usually immediate cash

reconlycash

2registered2

govregul

6

andsystematic/detailed

but

not

systematic/somewhat

detailed/not detailed/no record)
2. Location of business (building, shared space, open public
space, street, private farmland, public rural land)

payment and rarely or never later on credit) (0/1)
5. Whether or not business is registered at any level ofgovernment- country, state, or local (yes/no)
6. Extent of compliance with government regulations/registration(not sure/not at all/rarely/partially/mostly/fully)
Total number of categories

23

Table 2. MCA analysis of the ASA-I data
Dimension

Principal
inertia%

Dim 1

0.053703

53.20

53.20

Dim 2

0.013767

13.64

66.84

Dim 3

0.012063

11.95

78.79

Dim 4

0.001643

1.63

80.42

Dim 5

0.000965

0.96

81.38

Dim 6

0.000231

0.23

81.60

Total

0.100942

100

Cumulative %Method: Burt/adjusted inertias

19

ofTable 3. Statistics for column categories in principal normalization
Categories

Mass

Overallquality%inertia

Govregul

Dimension_1
1,𝑘𝑘

𝑊𝑊𝑗𝑗𝑘𝑘

SqcorrDimension_2

Contrib

2,𝑘𝑘

𝑊𝑊𝑗𝑗𝑘𝑘

SqcorrDimension_3

Contrib

3,𝑘𝑘

𝑊𝑊𝑗𝑗𝑘𝑘

SqcorrContrib

None

0.012

0.855

0.046

0.441

0.481

0.042

-0.169

0.071

0.024

-0.350

0.304

0.117

not sure

0.023

0.735

0.081

0.325

0.299

0.045

0.369

0.386

0.228

0.132

0.049

0.033

Partially

0.025

0.812

0.043

0.148

0.125

0.010

-0.252

0.366

0.116

0.236

0.321

0.116

Mostly

0.033

0.476

0.010

0.081

0.215

0.004

-0.019

0.011

0.001

-0.088

0.249

0.021

Rarely

0.004

0.250

0.014

-0.016

0.001

0.000

-0.242

0.158

0.016

-0.184

0.091

0.011

Fully

0.070

0.912

0.056

-0.269

0.904

0.094

0.019

0.004

0.002

-0.019

0.004

0.002

no record

0.034

0.818

0.078

0.363

0.564

0.082

-0.144

0.089

0.051

-0.197

0.165

0.107

not detail

0.023

0.145

0.009

0.027

0.018

0.000

-0.065

0.102

0.007

0.032

0.025

0.002

some detail

0.021

0.491

0.017

0.023

0.007

0.000

-0.116

0.168

0.021

0.158

0.316

0.045

detail nossys

0.024

0.680

0.027

-0.089

0.072

0.004

-0.156

0.218

0.043

0.209

0.390

0.088

detail sys

0.064

0.741

0.060

-0.172

0.316

0.036

0.196

0.407

0.179

-0.041

0.018

0.009

shared space

0.027

0.694

0.024

0.186

0.383

0.017

0.002

0.000

0.000

0.168

0.311

0.062

public space

0.032

0.577

0.017

0.164

0.507

0.016

0.054

0.055

0.007

0.028

0.015

0.002

Street

0.024

0.746

0.030

0.126

0.123

0.007

-0.158

0.193

0.043

-0.235

0.430

0.110

public land

0.024

0.677

0.021

-0.122

0.166

0.007

-0.157

0.274

0.042

-0.146

0.238

0.042

private land

0.007

0.790

0.033

-0.132

0.037

0.002

-0.377

0.300

0.073

0.463

0.452

0.126

Building

0.054

0.848

0.034

-0.173

0.469

0.030

0.155

0.379

0.095

0.008

0.001

0.000

Yes

0.117

0.848

0.049

0.188

0.842

0.077

0.005

0.001

0.000

0.014

0.005

0.002

No

0.050

0.848

0.114

-0.441

0.842

0.180

-0.012

0.001

0.001

-0.032

0.005

0.004

No

0.075

0.833

0.069

0.267

0.762

0.099

0.063

0.042

0.022

-0.052

0.029

0.017

Yes

0.092

0.833

0.056

-0.217

0.762

0.081

-0.051

0.042

0.018

0.042

0.029

0.014

No

0.108

0.879

0.039

0.170

0.788

0.058

0.024

0.016

0.005

0.053

0.076

0.025

Yes

0.059

0.879

0.073

-0.314

0.788

0.107

-0.044

0.016

0.008

-0.097

0.076

0.046

Account

Location

Reconlycash

Registered

pay_salestax

20

Table 4. Supplementary variables: type of business and country
Mass

Overall%inertquality

Dimension 1

Dimension 2

Dimension 3

Coord

SqcorrCoord

SqcorrCoord

Sqcorrtype_bis
Services

0.302

0.061

0.694

0.083

0.029

0.061

0.016

0.060

0.016

Trade

0.518

0.098

0.643

0.012

0.001

0.033

0.009

-0.105

0.088

Manufacturing

0.098

0.120

0.661

-0.1540.035-0.166

0.040

0.174

0.045

Agriculture

0.082

0.169

0.710

-0.1980.045-0.234

0.063

0.231

0.061

Ghana

0.286

0.734

0.658

0.195

0.163

-0.2270.222-0.2850.350

Nigeria

0.428

0.408

0.696

-0.069

0.029

0.204

0.255

0.143

0.125

Kenya

0.286

0.075

0.740

-0.0920.032-0.079

0.024

0.071

0.019

Country21

Table 5. CII and firm size
VARIABLES
own_bisCII (Composite Informality Index)
2.type_bis
Trade3.type_bis
Manufacturing
4.type_bis
Agriculture
2.countrycode
Kenya
3.countrycode
Ghana
Constant
Observations
R-squaredStandard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1(1)
num_worker-2.462***(0.477)-1.261***(0.469)-1.438***(0.276)-0.0274(0.458)-1.217**(0.489)-1.525***(0.293)-0.929***(0.304)7.086***(0.552)
721
0.121

22

Table 6. CII and business growth
VARIABLES
CII (Composite Informality Index)
workperweek
2.ictuse_telephone
3.ictuse_smartphone
4.ictuse_tablet/pc
2.countrycode_kenya
3.countrycode_ghanacontrol
ownboss
innovate
2.type_bis/trade
3.type_bis/manufacturing
4.type_bis/agriculture
/cut1

(1)
logsales_usd(2)increase_bis-0.346***(0.116)-0.0741*(0.0386)
0.174*(0.0966)
0.382***(0.0946)
0.851***(0.166)0.205***(0.0767)
0.141*(0.0786)
0.00895(0.0869)
0.134**(0.0666)-0.238***(0.0685)-0.110*(0.0665)0.00409(0.114)0.181(0.119)-0.153(0.256)0.106(0.0951)-0.806***(0.206)0.197(0.204)1.484***(0.360)-0.901***(0.175)0.00324(0.178)0.650***(0.200)0.330**(0.153)0.450***(0.149)-0.334**(0.149)-0.249(0.260)-0.671***(0.258)-3.089***(0.716)-1.634**(0.691)-0.607(0.687)0.712(0.686)2.300***(0.691)4.206***(0.709)

/cut2
/cut3
/cut4
/cut5
/cut6
Constant
Observations
R-squaredStandard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

6.260***(0.290)
750
0.106

779

23

Table 7. CII and resiliency to the COVID shock
VARIABLES
CII (Composite Informality Index)
2.ictuse_telephone
3.ictuse_smartphone
4.ictuse_tablet/pc
2.type_bis/trade
3.type_bis/manufacturing
4.type_bis/agriculture
2.countrycode_kenya
3.countrycode_ghanacontrol
ownboss
innovate
buy_formalco
buy_traders
buy_personal
sell_formalco
sell_traders
sell_personal
/cut1
/cut2
/cut3
/cut4
/cut5
/cut6

(1)
(2)
covid_loss covid_recover
1.615***(0.286)
0.511**(0.222)0.0441(0.223)1.621***(0.390)-0.116(0.148)-0.0449(0.241)-0.0585(0.256)-1.362***(0.185)-1.245***(0.188)-0.451**(0.204)-0.373**(0.156)-0.215(0.153)-0.286(0.198)-0.413**(0.197)-0.135(0.216)-0.536**(0.237)0.186(0.153)-0.162(0.158)-4.570***(0.437)-4.187***(0.423)-3.039***(0.402)-1.282***(0.388)0.554(0.385)
1.991***

24-0.305(0.276)0.112(0.217)-0.450**(0.224)-1.737***(0.366)
0.0376(0.146)-0.475*(0.245)-0.733***(0.256)-0.500***(0.176)-0.475***(0.175)
1.149***(0.192)-0.0346(0.151)0.279*(0.149)0.968***(0.197)1.422***(0.193)0.241(0.227)-0.685***(0.233)0.396***(0.152)0.244(0.157)-1.029***(0.386)-0.956**(0.385)-0.448(0.381)0.544(0.379)1.482***(0.382)
1.979***

/cut7/cut8
Observations
Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1(0.398)2.518***(0.409)3.961***(0.484)(0.385)3.537***(0.394)6.450***(0.480)

822

822

25

Table 8. CII explained by personal characteristics.
VARIABLES
ownboss
innovate
control
1.y_school_no schooling
2.y_school_1-5 years
3.y_school_6-8 years
4.y_school_9-11 years
5.y_school_12-14 years
6.y_school_over 14 years
rely on family in business
rely on relatives in business
rely on neighbors in business
rely on religious group in business
rely on ethnic group in business
rely on government in business2.type_bis/trade
3.type_bis/manufacturing
4.type_bis/agriculture
2.countrycode_kenya
3.countrycode_ghana
Constant
Observations
R-squaredStandard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

CII
0.0255(0.0228)-0.0700***(0.0223)-0.0763***(0.0289)-0.181***(0.0579)
0.0996**(0.0477)-0.0438(0.0290)(Reference)-0.0456*(0.0266)
0.00958(0.0341)0.0395***(0.0119)-0.0133(0.0122)0.0712***(0.0107)-0.0552***(0.0113)-0.00824(0.0117)-0.0496***(0.0133)-0.0450**(0.0228)-0.0796**(0.0388)-0.0804**(0.0370)-0.000960(0.0309)0.150***(0.0287)
0.594***(0.0509)
536
0.269

26

Figure 1. Individual MCA coordinate plots of dimension 1 and 2

MCA coordinate plot
account

govregul.4.4

not_sure

.2

.2

fully

0

0

mostlynone-.2rarelydetail_syste

not_detail
some_detaildetail_nosys-.2

no_record

partially

-.4

-.4
-.5

0

-.5

.5

0

.5

Dimension 1 (horizontal) explains 53.2% inertia
Dimension 2 (vertical) explains 13.6% inertia
Coordinates in principal normalization

MCA coordinate plot
location

.4

.2

.2

building
public_spaceshared_space0
public_land-.20street

-.5

0

Yes

No

-.2

private_land-.4reconlycash

.4

-.4
-.5

.5

Dimension 1 (horizontal) explains 53.2% inertia
Dimension 2 (vertical) explains 13.6% inertia
Coordinates in principal normalization

27

0

.5

MCA coordinate plot
registered.4

pay_salestax

.4

.2

.2
No

0

No

0

Yes

Yes

-.2

-.2

-.4

-.4
-.5

0

.5

-.5

0

.5

Dimension 1 (horizontal) explains 53.2% inertia
Dimension 2 (vertical) explains 13.6% inertia
Coordinates in principal normalization

Figure 2. Interpretation of dimension1 from the MCA results

Interpretation of Dimension 1: Summary

Compliance withgovernment
regulation

Fully

None

Bookkeeping

Detailed, systematically or not

No record

Business venue

Building/Private farmland

Pay sales tax

Street/Public space/
Shared space

Yes

No

Only receive immediate
cash payment

No

Yes

Registered (at any levelof gov office)

Yes

No

Low
Less informal

Dimension 1 coordinate

28

High
More informal

Figure 3 Histogram of the informality index with a Kernel density plot29

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