DP2024-14

Informality and Dynamism ofMicrobusinesses in Africa: Possible
Causalities
Hiroyuki HINO
N o b u a k i H A M AG U C H I
Charles PIOT
Jiahan YIN
March 29, 2024

Informality and Dynamism of Microbusinesses in Africa: Possible Causalities 1
Hiroyuki Hino, Nobuaki Hamaguchi, Charles Piot, and Jiahan Yin 2
Abstract
This paper attempts to gain greater clarity about an issue which has considerable bearing on
economic policy in Africa yet remains poorly diagnosed: the relation between informality anddynamism among low-income micro enterprises. We begin with the premise that micro
entrepreneurs are driven by non-pecuniary motives, to varying degree, and adopt informal
ways of doing business when informal motivations are strong. Building on this premise, weconstruct a regression model in which the dynamism of microenterprises is explained by the
informality of entrepreneurs’ motivations and of his/her ways of doing business as well asinteractions among these two informality variables. We apply the model tomicroentrepreneurs in Ghana, Kenya and Nigeria, with data derived from a survey conducted
for this research. The regression results show that the relation between informality anddynamism is intricate. Two of the four informal business practices tested - small business sizeand limited bookkeeping – negatively affect business growth while the other two have nosignificant influence either way. On the other hand, informal motivations – represented by acomposite index of non-pecuniary motivations – have a positive effect on business growthalthough the effect is not directly observable. When an enterprise is operated in an informal
setting by an entrepreneur with strong informal motivations, the synergy between the setting
and the motivation works to increase the chance of business growth. The resilience of
microenterprises too is linked positively to informality although interactions between
motivation and business practice tend to reduce business growth in this case. Informal
motivations and informal business practices are interwoven, an insight that could help indeveloping policy that could strengthen informal enterprises on the continent.
Key Words: Informality, Africa, Microenterprises, Growth, Resilience
JEL: O55, O17, Z13

This paper is the second of the Duke Africa Initiative Series on the African informal economy. The first paper,“Rethinking the Informal Economy in Africa,” challenges many of the assumptions made in the literature about
African informality. A third paper applies Multiple Correspondence Analysis (MCA) to construct a composite index
of informal practices. It is being finalized concurrently. This study is supported by the Kobe University Strategic
International Collaborative Research Grant (Type C Creating Joint Research).
2
The authors are, respectively, Visiting Research Scholar, Office of Global Affairs, Duke University and Research
Fellow, Research Institute for Economics and Business Administration (RIEB), Kobe University; Professor of
Economics, RIEB, Kobe University; Professor of Cultural Anthropology and African & African American Studies,
Duke University; and Graduate Research Assistant, Department of Economics, Duke University. We acknowledge
with deep gratitude guidance and support we received from Praful Patel, former Vice President of the World Bank.
The authors are grateful to the other members of our research team—Lilly Brouwer, Iftekhar Jamal, Wanrong
Deng, Patrick Crotty, and Tian Chen, all of whom made significant contributions at earlier stages of this research.
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1. Introduction
The informal economy in Africa has been the subject of extensive studies by development
economists and cultural anthropologists, among others. Development economists generally see
the informal as that which does not conform to the institutional norms of economies in theWest. By contrast, anthropologists tend to see the informal in precarious African economies asnormative, and often highlight the dynamism and resilience of informal entrepreneurs.
This paper attempts to generalize the findings of anthropologists through quantitative analysesof a large number of owner-operators of low-income microenterprises in Ghana, Kenya andNigeria. A survey was conducted specifically to collect data for this analysis.
To model the informal economy for our analysis, we postulate the following:1) Informal entrepreneurs, as anthropologists suggest, are motivated by the desire to: (a)remain free of institutional constraints and obligations and pursue their own goals; (b)prepare for adversity and crisis, whether natural or economic; (c) rely on the trust offamily and friends in operating their businesses; and (d) acquire recognition for theirwork within their communities.
2) Informal business practices commonly cited in the literature are the result of choicesthat informal business owners make, importantly derived from the above non-pecuniary
motives. For modeling purposes, we select the following practices: bookkeeping,compliance/non-compliance with government regulations, owner-operated businesswith one or two workers, and location/venue of business, all commonly cited as
definitions of informal business in the literature.
3) Key outcomes of operating businesses, such as growth of revenue, are determined not
only by education, availability of ICT and other factors commonly cited in growth
regressions but also by the non-pecuniary motives mentioned above. In our model, we
examine two outcomes that are often hailed by anthropologists as hallmarks of informal
business: growth and the resilience of their business.
We will test these postulates by OLS and ordinal logit regressions, with both independent anddependent variables derived from the African Informal Entrepreneurs Survey (AIE). The AIE
survey was conducted by ASA-International, a micro finance institution listed in the London
Stock Exchange, in partnership with Duke University’s Africa Initiative (Duke-AI). Of the morethan 500,000 recipients of ASA-I’s micro-loans in Nigeria, Ghana and Kenya, 1,500 wereselected randomly for this study, 500 in each country. The questionnaire was designed jointly
by the local offices of ASA-I and Duke-AI for the purposes of this research, and responses werecollected by in-person interviews conducted by ASA-I loan officers.
The significance of non-pecuniary motivations in explaining informal business practices andbusiness outcomes will be tested with each motive as an independent variable and also withthe four non-pecuniary motives bundled into a single variable, what we call a composite
informal motivation index (CIMI). As explained below, a microentrepreneur’s four-dimensional2

motivation is captured graphically as a quadrilateral with each axis representing the degree of
informality of each non-pecuniary motive and CIMI is derived from it. We will see that CIMI andeach of the four non-pecuniary motives are partially effective in explaining aspects of informal
business practices and business outcomes.
Similarly, each of the four business practices—bookkeeping, compliance with government
regulation, business size, and location/venue of business—are categorized by the degree ofassumed informality (fully compliant, usually compliant, sometimes compliant, rarely comply,
never comply). The correlation with growth and resilience is tested with each business practiceas an independent variable.
The rest of this paper is organized as follows: Literature review (Section 2); Data (Section 3);
Modeling the informal economy (Section 4); Informality and resilience of the microenterprise
sector (Section 5); Research questions (Section 6); Regression models and results (Section 7);
and Conclusion (Section 8).
2. Literature Review
There are two broad threads in the literature on the relationship between informality anddynamism. One comes from cultural anthropology and focuses on cultural values andmotivations, and the other from development economics, which generally approaches theinformal economy with the framework of neoclassical economics.
In cultural anthropology, based on close observation of African communities over many
decades, researchers have found that the norms with which microentrepreneurs operate in
sub-Saharan Africa are rooted in societal and cultural values, in vernacular economic practices,
and in histories of resistance to colonial and postcolonial rule. Informal norms instill and valueautonomous decision-making and flexibility in responding to domestic and commercial needs,
while drawing on social and kin networks to further business aims and access liquidity.
Anthropologists suggest that informal norms of African microentrepreneurs are not antitheticalto formal business norms found in the West, and that they can produce dynamism andresilience.
Japanese anthropologist Sayaka Ogawa (2016) studied large samples of small-scale merchants
in urban centers in Tanzania over many years and found that actors in those sectors
value/experience the same three principles, namely: (i) uncertainty, which leads to livelihood
diversification and gifting; (ii) social recognition more than monetary gain; and (iii) trust in
personal relations. Another anthropologist, Misa Hirano (2021) collected 120 life histories of
informal enterprises in Yaoundé, Cameroon, and suggests that actors in the informal sector
easily move between the informal and the formal economy; often start businesses that fail,before beginning to recover; share money and work among relatives and peers; and,
importantly, prefer independence to working in a formal environment. Both scholars conclude
that informal enterprises are dynamic.
3

In development economics, views about the dynamism of informal enterprises have generally
been more subdued and less enthusiastic. The following quote from La Porta and Schleifer(2014, p. 109) may be representative: “We argue that the evidence is most consistent with dual
models, in which informality arises out of poverty and the informal and formal sectors are very
different. It seems that informal firms have low productivity and produce low-quality products…
Economic growth comes from the formal sector, that is, from firms run by educatedentrepreneurs and exhibiting much higher levels of productivity … A few informal firms convert
to formality, but more generally they disappear because they cannot compete with the much
more-productive formal firms.” In La Porta and Schleifer (2014) and others in the field ofdevelopment economics, the informal economy is defined by variants of the original definition
offered by economic anthropologist Keith Hart, viz, “income-generating activities outside theregulatory framework of the state” Hart (1973 [1971]: 61). 3
Since La Porta and Schleifer (2014), there has been some credible quantitative work that
supports a more positive view of informal enterprises. For example, Fourie (2019) analyzed
own-account or self-employed workers in South Africa as enterprises rather than employment.
With regression analyses relying on data from the Survey of Employers and the Self-Employed(SESE), he showed significant employment expansion by one-person and multi-person informal
enterprises as well as mobility from the informal to the formal sector. In addition, the analysis
showed that industry type, years in business, location of business, accounting, gender of owner,and educational attainment are correlated with business performance. According to the author,
this was the first quantitative, enterprise-based study focusing on the employment dynamics ofinformal enterprises in sub-Saharan Africa. 4 Aim and Islam (2015) found robust evidence that
small informal firms have higher labor productivity that large formal firms.
Nevertheless, negative assessment of informality remains prevalent in the development
economics literature. In a major publication on the informal economy (Ohnsorge, F. and Yu, S.,2022), the World Bank maintains that the more informal a country, the lower the country’s
development indicators are across the board. “Widespread informality has long been
associated with a whole host of development challenges … Most prominently, morewidespread informality has been associated with significantly poorer governance and greaterlags in achieving every dimension of the Sustainable Development Goals. Countries with larger
informal sectors tend to have less access to finance for the private sector, lower laborproductivity, slower physical and human capital accumulation, and smaller fiscal resources”

3

For definitions of informality used in development economics, see Maloney (2004), Chen et al (2012), Meagherand Lindell (2013), Amin and Islam (2015), Charmes (2016), Losby, et al (2002), OECD and ILO (2019), among
others.
4

A positive assessment of the informal is offered in Amin and Islam (2015) and David, Diallo and Nilsson (2023)

4

(Ohnsorge, F. and Yu, S., 2022, p.25). 5 These negative views are shared at least in part by anumber of other development economists, including Elgin et al (2021) and others. 6
It is difficult to validate the sweeping assessment of the informal by the World Bank’s report aspart of this literature review because of the methodological differences as well as the Bank’s
focus on economic performance at the country level. Nevertheless, if we narrow the formal vsinformal comparisons to the performance indicators of informal enterprises, our data largely
collaborates the claims in the Bank’s report but also challenges a few critical propositions.
Specifically, if the micro enterprises in our survey are divided into formal and informal groups
using a comparable attribute (compliance with government regulations), our data show thatlabor productivity of the informal group is significantly higher than that of the formal group.
See Appendix Table I. Moreover, as shown in Appendix Table II, if frequency of tax payment isused to define informal enterprise, labor productivity of the informal group falls as that thegroup is broadened to include less informal (more formal) enterprises.7
Finally, as discussed below, our regression analysis finds that lesser compliance withgovernment regulations (greater informality) is linked to lower revenue growth, consistent withthe claim in the report by the World Bank and other papers. However, if interactions between
informal motivations and ways of business are introduced in the regression equation, thesignificance of the coefficient on compliance with government regulations disappears. Instead,small business size and limited bookkeeping emerge as significant explanatory variables withnegative correlation. We should be careful about this negative correlation though because
apparently the synergy between business size and informal motivations and also between
regulatory compliance and informal motivations influence revenue growth positively (i.e., theinteractions terms have significant and positive coefficients.) These findings suggests that thenegative view of informality is sensitive to how informal enterprises are defined and selected
for analyses.
The above summary of the literature clarifies the relation between this paper and the existing
literature. First, this paper attempts to bridge the research in cultural anthropology and that ofdevelopment economics. We do so by integrating their respective theses in a model of theinformal economy. Second, no quantitative studies we know of have approached informal
business as an outcome of business decisions that reflect personal preferences (attributes) ofowner-operators of microbusinesses. 8 We have the data required for such analysis, thanks to

However, in its World Development Report 2013, the World Bank (2013) embraced informal activities.
Others paper with negative views are Fayomi et al (2018) and Medina et al (2017).
7
While a majority of researchers adopt a binary approach in defining informality, it is increasingly accepted in theliterature that informality is a matter of degree and is better thought of as lying on a continuum, ranging fromformal to informal. (See for example, Chen and Meagher (2012))
8
For a more conventional business decision model of informal enterprises, see Amaral and Quintin (2006).
5
6

5

the AIE survey. Finally, this paper offers a careful reassessment of the dominant view indevelopment economics that informal enterprises are inferior to formal enterprises.
3. Data
Data required for our analysis are derived primarily from a survey of owner-operators of lowincome micro businesses (AIE Survey) that was conducted by the ASA-International inpartnership with the Duke Africa Initiative (DAI). The survey was carried out in Kenya, Ghana
and Nigeria in May and June 2021. ASA-I’s corporate database was used as a secondary source
to supplement the primary data.
Five hundred (500) respondents were selected in each country from a large pool of recipients ofloans from ASA-I (over 100,000 in Kenya, 150,000 in Ghana, and more than 250,000 in Nigeria).
Loan recipients were low-income owners of micro businesses and were all female.
A stratified random sampling methodology was employed. Data from existing surveys wereused to estimate the geographic distribution of micro enterprises in each of the countries. The
client base of ASA-I’s regional offices was used as the sampling frame, and the number of
respondents from each region was determined proportionate to the regional distribution of
micro-enterprises in the country.
For each country, 25 loan officers were selected from local offices to serve as enumerators and
20 of their clients were assigned to each loan officer. They were trained to conduct interviews
professionally.
The survey questions encompassed: (a) owner-operators’ personal and institutional attributes;(b) size, sector, revenue and other characteristics of their businesses; (c) growth, diversificationand dynamism of their enterprises; and (d) compliance with government regulations, trust, andother features that are conventionally associated with informality in the literature. The impactof the Corona-19 pandemic on these micro-enterprises, and their response, was also
addressed.
The collected responses underwent quality control at ASA-I’s country head offices and were
uploaded to an excel file. The files were further cleaned up at ASAI’s Global HQ. Missing values
in individual responses were identified at Duke, and consistency of answers across different
questions was checked. Duplicate responses were identified and removed. Finally, somequestions were reformatted for ease of reference in the final datafile. The cleaned dataset
consisted of 1372 respondents, although the number of valid responses varied acrossquestions, at times dropping to less than 1000.
Note that our data is not free of sampling bias. Our samples are all female. Moreover, while
ASA-I operates in many regions and sub-regions in all three countries, some areas are notcovered in their operation, and hence are not in the survey. In addition, services and trade areover-represented in our samples for Ghana and Nigeria. In both countries, our samples show
6

very small shares of micro enterprises in agriculture and manufacturing while the share of bothare substantial in national samples. 9
4. Modeling the informal economy
The data described above allows us to construct and test a model where a low-income
entrepreneur owns her firm and operates it according to her own motives. She chooses thevenue and their own way of doing business (e.g., whether to keep written records or not),taking into account the infrastructure, locations of customers, access to materials and so on. In
this model, owner-operators’ personal attributes (motives) affect business outcomes directly
and also indirectly through the choice of the ways of doing business (business practice).
For our regression model, we adopt:(a) Four non-pecuniary motivation variables, namely, the desire to: (i) be free of
institutional constraints and obligations; (ii) prepare oneself for adversity; (iii) rely on kin
and friends; and (iv) be socially recognized. We assume that these motives constitutethe core of personal attributes of owner-operators of low-income microenterprises inAfrica. The stronger these motivations, the greater the deviation of the behaviors of
microentrepreneurs would be from the norm in a developed economy in the West. We
construct these motivation variables as ordinal categorical variables from responses inour survey, each category representing an ascending degree of informality.
(b) Four business practice variables, namely, (i) location/venue (e.g., a workshop in ashared space), (ii) organizational structure (e.g., the owner plus one worker), (iii)compliance with government regulations (e.g., rarely comply), and (iv) bookkeeping(e.g., partial and irregular). While a number of business practices are used in theliterature to define informality, we select these four to keep our regressions
manageable and also because they are the most commonly used. We construct the fourbusiness practices as ordinal categorical variables, from least informal to most informal.
(c) Two business outcome variables, namely, (i) the rate of growth of sales revenue in thelast five years, and (ii) resilience to major negative shocks. We adapt the concept of a
“Resilience Triangle,” an established model in civil engineering, to our case and draw
“Resilience Quadrilateral (RQ)” for each entrepreneur from the speed with which thebusiness revenue declined during the Covid-19 pandemic and the pace at which it
recovered.10 From RQ, we construct an “Informality Resilience Index (IRI),” that ranges
from -1, being least resilient (complete loss of functionality), to 0, being fully resilient(no loss of functionality). We use IRI as a dependent variable, together with growth ofrevenue, to capture the dynamism of microentrepreneurs.
For a more detailed presentation of the survey methodology, see Brouwer and Iftekhar (forthcoming).
The impact of Covid-19 pandemic has of course been covered very extensively. See, for example, Aditya1 andAmri (2023), which includes a discussion of coping mechanisms by informal enterprises.
9

10

7

Our model is represented in the chart below.

The above variables are derived from responses to the corresponding questions in the AIE
survey as follows: The survey questions and responses are presented in Table 1 and 2 on thisand the next page.
Table 1: Motivation of low-income micro-entrepreneurs•

8

Table 2: Informal Business Practices•
Behavioral motivation variables•

Desire to be free: The AIE survey included the question, “What are the main advantagesof having your own enterprise?” and respondents were asked to check-mark answers
that applied to them. For the freedom variable, we selected two from the list of
responses, i.e., “You are in full control of your business” and “the fact that you are yourown boss.” The entrepreneurs who check-marked either or both of these answers
apparently prefer to be free to do what they wish rather than being constrained in aformal work environment; they are thus considered informal. Accordingly, we assign 1
to the entrepreneurs who check-marked either or both of these answers and 0 to those
who did not. Indeed, almost of all of the respondents want to own businesses and do soin order to have full control – in 93% of our samples in Nigeria, 83% in Kenya and 76% in
Ghana.

•

Desire to rely on kinship and personal trust: The survey includes a question, “How muchdo you rely on the following people in conducting your business?” A number ofrelationship categories were listed, ranging from “immediate family” to “strangers,” andfor each category, respondents were asked to check-mark one from “not at all; “notvery much,” “somewhat,” and “very much.” From the list of categories, we selected9“immediate family,” “close relatives,” and “neighborhood community,” because we
presumed that the relationship with people in these groups was informal and involved
personal trust. We constructed the trust variable by assigning 0 for “not at all,” 3.33 for“not very much,” 6.67 for “somewhat rely on,” and 10 for “rely on very much,” for eachcategory and took a simple average of the three scores. Large proportions of Kenyan
micro entrepreneurs rely either very much or somewhat on immediately family andneighborhood communities while the reliance on these personal relationships is muchless in Ghana and Nigeria, though still significant.
•Desire to be prepared to deal with severe adversity: The question about the advantageof owning one’s own business also includes, “You are free to innovate and introduce
new products,” and “Earn extra income,” as possible responses. We use these answers
as a proxy to gauge an entrepreneur’s awareness of disaster risks and construct thereadiness to disaster variable by assigning 1 to those who check-marked either or bothof the above answers, and 0 to those who did not. This measure is far from ideal.
However, we cautiously accept it partly because the proposed measure indicates that
Kenyan micro- entrepreneurs are more conscious of disaster risks than their Nigeriancounterparts and this is consistent with the frequency of drought and other natural
disasters in each country. A large majority of the samples in Kenya identified, “free toinnovate and introduce new products,” and “earn extra income,” as advantages ofowning one’s own business, while in Nigeria, it was a minority that did the same; Ghana
was in between.

•

Desire to be socially recognized: The AIE survey does not ask directly how strongly the
respondents desire to be socially recognized. There is, however, a question about how
active respondents were in various social and political groups. The survey shows that
our sample entrepreneurs were, as a whole, fairly active in neighborhood communities
and religious groups in Kenya (score of 3.2 – 3.4 on a scale of 1 to 4) and in religious
groups in Nigeria and Ghana (score of 3.1 – 3.2). The entrepreneurs who are active inneighborhood communities and religious groups may not necessarily be motivated by adesire to gain social recognition for their business activity. Nevertheless, if anentrepreneur is active in those groups, she may well be influenced in her businessdecisions by considerations beneficial to those group activities and thus deviate fromthe neoclassical dictum of profit maximization or other European business norms. Those
entrepreneurs may be considered informal in that sense. On this basis, we constructed
the social recognition variable by assigning to a respondent 0 for “not at all,” 3.33 for“not very much,” 6.66 for “somewhat active,” and 10 for “very active,” in each question
and taking a simple average of both.

Business practice variables
10•

Location of business: In the literature, the location or venue of business is widely used
to define informal business. The AIE survey includes a question, “Where do you operate
your business?” and respondents were given seven locations/venues to choose from.
We group these locations into three categories in order of presumed informality (fromleast to most) - 1: commercial building and private farm; 2: workshop in a shared area
and own home; and 3: street, public market with mobile unit and public farm. This
grouping is used to make a business location variable for our regression. About one halfof the micro enterprises operate in those areas or venues that are most informal while,surprisingly, an almost equally large number of micro enterprises operate in locations
that are considered formal.

•

Sole owner-operator: In the literature, it is widely presumed that an informal
entrepreneur prefers to own her own business and operate it alone or with one or two
apprentices. The AIE survey includes a question on ownership structure as well as thenumber of workers, including the owner. For our model, we construct the owneroperator variable by assigning 1 if the number of workers of an enterprise is less than orequal to 2, including the owner, and 0 if the number of workers is greater than 2.
According to our survey, the median of the number of workers is 1 (i.e., the owneralone) in Kenya and Ghana, and 2 in Nigeria (i.e., an owner plus one worker) although
the number of larger enterprises is not insignificant.

•Recordkeeping: The extent of bookkeeping is widely cited as an indicator of informality.
The AIE survey includes a question, “To what extent do you record your business
transactions in writing?” and gives respondents five answers to choose from. For ourmodel, we constructed the record keeping variable by grouping these choices into three
categories in order of presumed informality (from least to most) - 1: Detailed andsystematically + detailed but not so systematically; 2: Somewhat detailed + keep records
but not detailed; and 3: No written records. The AIE survey shows that bookkeeping is
far more extensive than commonly assumed in the literature. In Kenya and Nigeria, amajority of micro enterprises keep detailed accounts in writing and only about one
quarter keep no or minimal accounts. The micro-enterprise sector in Ghana is more
informal than in the other countries according to this indicator, with a clear majority of
respondents keeping no or minimal records.

•

Compliance with government regulations: The AIE survey includes a question, “Doesyour business comply with government regulations?” and gives respondents six answers
to choose from. See Table 2. For our model, we construct the compliance variable bygrouping these choices into three groups in order of presumed informality (from least tomost) - 1: Fully comply + mostly comply; 2: Partially comply; and 3: Rarely comply +don't comply + Not sure. According to the AIE survey, about one half of microenterprises in Ghana and Nigeria fully or mostly comply with government regulations
while the remainder are distributed among partially complying, or rarely/none, or not11

sure. In Kenya, however, about 90% of samples fully or almost fully comply, suggestingthat according to this indicator, the micro-enterprise sector is hardly informal.
Business outcome variables•

Revenue Growth: In the AIE survey, respondents were asked, “How much has yourbusiness revenue increased/decreased in the last five years?” They were asked tochoose from 5 ranges. The revenue growth variable was derived directly from theanswers as discrete numeric variables as follows - 0: decreased >30%; 2.5: decreased 1030%; 5: about the same; 7.5: grown 10-30%; and 10: grown >30%. About 20% of microenterprises in the AIE survey increased their revenue by more than 30% over the last 5
years while fewer than 10% lost revenue by more than 30%. See Table 3 below. Thiscertainly is not consistent with the pessimistic view advanced in the literature. The
question is whether this positive revenue performance is correlated with the informalityof owner-operators.

•

Resilience: The AEI Survey also asked respondents, “How much income have you lost or
gained, either directly or indirectly, as a result of the Coronavirus outbreak?” and “Howwell has your business recovered from the Coronavirus?” Table 3 shows that more than
25% of micro-enterprises did not lose revenue during the Covid-driven economic
downturn; about 10% of microenterprises even increased their revenue by at least 10%.
We also see that about 20% of microenterprises surpassed their pre-Covid revenue
levels by at least 30% during the recovery phase. We capture this apparent resilience of
low-income microenterprises by an Informality Resilience Index (IRI) and use this index
to represent resilience in our model as a continuous numeric variable ranging fromminus 1 (no resiliency) to 0 (full resilience). See Appendix III for a note on the derivationof IRI.

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Table 3: Dynamism of Microenterprises

5. Informality Motivation and Resilience of Microenterprise Sector
Before we proceed to an analysis of the dynamism of microentrepreneurs and their enterprises
and its relations with informality, we look at the four non-pecuniary motives collectively andconstruct a Composite Informal Motivation index (CIMI) for each owner-operator; CIMI
measures the strength of informal motivations of that entrepreneur. We examine the
distribution of microenterprises by CIMI in Kenya, Ghana and Nigeria and the three countries asa whole. Similarly, we look at the response to the negative shock, the onset of the Coronavirus
pandemic, and the recovery from it together and construct an Informality Resilience Index (IRI)of a microenterprise. We assume that how much sales revenue declined from the pre-shocklevel, and how far it recovered from it, measure the loss of functionality due to the shock,namely, resilience.
Informality Motivations Quadrilateral and Composite Index
To visualize the strength and features of informal motivations of an owner-operator, we
present the four non-pecuniary motives as a quadrilateral, each axis representing each of four
non-pecuniary motivations (on a scale of 0 to 10). The degree of informal motivation of each
non-pecuniary motive is plotted along each axis, as derived from the non-pecuniary motive
variables outlined in Section 4, and an owner-operator’s quadrilateral is obtained by connecting
the four dots. See Figure 1 below.
13Figure 1: Informality Quadrilateral - Example

In this example, the entrepreneur, a Nigerian respondent, is strongly informal with respect toher desire to be free of institutional constraints and to be ready for disasters. However, she is
not at all informal with respect to her reliance on personal trust in running business. She ismotivated to be recognized in her community or religious groups for her work as a
microentrepreneur but not overwhelmingly so.
Informality Quadrilaterals for Kenya, Ghana and Nigeria are shown below. In Figure 2, theKenyan microentrepreneurs are strongly motivated to be socially recognized while relying
moderately on trust and personal relationship. Their Nigerian counterparts are most strongly
motivated to be free and not so keen to be prepared for adversity. In Ghana, our sample micro
entrepreneurs do not reply much on trust and personal relationships and are strong on risk
aversion. 11
Figure 2. Informality Quadrilateral for Kenya, Ghana and Nigeria
Average of Social
Recognition
10
5
Average ofFreedom Y/N0

Average of Trust

Average of
ReadinessToRisk

The degree of four-dimensional informal motivations of an owner-operator is measured by thearea under the quadrilateral. We convert this measure to an index by taking the ratio of the11

The high score for readiness to risks in Ghana may reflect entrepreneurs’ strong desire to innovate.

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area of the quadrilateral to the area of the quadrilateral that corresponds to complete
informality, in which each informality motivation is fully informal (score of 10). We normalize
this ratio by taking the square root, so the index ranges from 0 (zero informality) to 1 (complete
informality). We call this the “Composite Informality Motivation Index” (CIMI).
The distribution of micro entrepreneurs by the strength of their informality motivation, theCIMI, is presented in Figure 3 for the three countries as a whole. Note that the distribution isheavily skewed to stronger informality, with two thirds of the sample entrepreneurs scoring
CIMI of 0.6 or above. This suggests that while a significant portion of microentrepreneurs is
not very informal in terms of their motivations - almost 5 percent of our sample are hardly
informal – the microenterprise sector in the three countries is highly informal in terms of the
motivations of entrepreneurs.
The distributions for Ghana, Kenya and Nigeria are summarized by the key statistics of each
distribution in Table 4 below. The Kenyan microenterprise sector is more informal than Ghana’sor Nigeria’s with the bulk of micro entrepreneurs clustered around the higher end (0.7 – 0.8).
Ghana is less informal with a mean CIMI of 0.55.

Figure 3. Distribution of Micro Entrepreneurs by Degree of Informality Motivation
Ghana, Kenya and Nigeria

Table 4: Summary Statistics of CIMI distribution
CountryKENYA
GHANA
NIGERIA
All

Obs
321
383
215
942

Mean
0.7093987
0.5550045
0.6673759
0.6349322

Std.dev
0.1942049
0.269082
0.234001
0.2452919

Median0.7757911
0.6454972
0.6454972
0.6770032

Min
0.1924501
0
0
0

Max
1
1
1
1

15

Resilience Quadrilateral and Index
A Resilience Triangle is a well-established tool to measure resilience against shocks in the fieldof civil engineering. “It represents a measure of both the loss of functionality of a system after adisaster and the amount of time it takes for the system to return to normal performance levels”(Bevilacqua, M. et al., 2017).

We adapt this concept to our case, using two time periods: the first is from the onset of Covid
to the time when it was most severely felt (the shock period); the second, from the time when
Covid was most severely felt, T1, to the time when the survey was conducted, T2 (the recovery
period). We set the level of revenue prior to the onset of Covid-19 at 100 and assume that theextent of decline of revenue from 100 measures the loss of functionality as a microenterprise.
The AIE survey calculates for each enterprise the percentage of revenue lost from the pre-Covid
level to the time when Covid was most severely felt, and the percentage of revenue recoveredat the time when the survey was taken. Covid started in January 2021 (T0 in the chart below); it
was at its worst in February 2022, measured by the number of hospitalizations globally (T1).
And the survey was taken in May 2022 (T2). See the chart below. Note that many enterprises
did not recover their pre-Covid revenue levels by the time the survey was undertaken. Hence,
we use a quadrilateral, rather than the triangle cited in the literature.

16

Note also that there are many enterprises which increased revenue during the shock period
and a number of others whose revenue more than fully recovered to the pre-Covid level. For
those enterprises, we lower z and u to reset the revenue level to 100 at T1 and T2, to be able todraw a quadrilateral This means that we underestimate the resilience of somemicroenterprises. (See Appendix 3 for the assumptions and computations.)
We define the Informality Resilience Index, IRI (z, u), as the ratio of the area of quadrilateral for(z, u) to the area of quadrilateral of the case of maximum loss of revenue. That is, the revenue
fell by 100 during the shock period (revenue = 0 at T1) and did not recover at all during therecovery period (revenue=0 at T2).
IRI (z,u) = -1 < - IRM (z, u)/IRM(zero)< 0
where z is the percentage of pre-Covid revenue left at T1, and u is the percentage ofrevenue at T1 recovered by T2.
IRI (full) = 0,
where there was no loss of revenue, that is, z=1, and u = equal to or greater than 0
IRI (zero) = -1
where the functionality was totally lost, that is, z=0 and u=0, no resilience.
The distribution of microenterprises by the degree of resilience, as well as its key statistics, isshown in Figure 4 and Table 5. The distribution reveals that on the whole, microenterprises
are quite resilient. More than 20% of the sample of microenterprises are highly resilient, that17

is, IRI of -0.1 to 0. About one half of the micro enterprises are quite resilient, that is, IRI of -0.3
to 0. And almost none of them are highly vulnerable, that is, of IRI -1 to -0.6.
Figure 4 Distribution of microenterprises by degree of resilience, IRI.

Table 5: Summary Statistics of IRI Distributions
1%
5%
10%
25%

Percentiles-0.815407-0.7674419-0.7151163-0.4418605

50%-0.255814

75%
90%
95%
99%

-0.1302326
000Smallest 4 Obs-1-0.815407-0.815407-0.815407
Largest 4 Obs
0
0
0
0

Obs
Sum of wgt.

960
960

Mean
Std. dev.

-0.2897208
0.2353139

Variance
Skewness
Kurtosis0.0553726-0.6536569
2.689536

6. Research Questions
We have shown in the previous sections that the low-income microenterprises are on thewhole dynamic in the sense of having relatively high revenue growth and being fairly resilient to
the Covid shock. Given this, a key question is whether informality is a contributing factor in theobserved dynamism. In other words, the question is whether informality enhances dynamismof low-income micro entrepreneurs, and if so, through what channels?
Thus, our research questions are:
1. Do micro entrepreneurs who have strong informal motivations likely choose informal
business practices? If so, which pairs of non-pecuniary motive and business practice are
significantly correlated (pair-wise correlation)? It is generally assumed in the literature that18

informal entrepreneurs choose informal ways of doing business. This research question asks if
this conventional wisdom holds.
2. Do the informal motivations of a microentrepreneur likely raise revenue growth and/or
enhance resilience of his/her business, collectively or individually? If so, is it due to the direct
impact of those motives on productivity of the microenterprise or is it because the impact of
informal motivations is enhanced when microentrepreneurs work in a more informal businessenvironment? Similarly, do the informal business practices of a microenterprise raise revenue
growth and/or enhance resilience, either directly or indirectly?
7. Regression models and results
We tested the above hypotheses with two sets of regression models. The first set incorporates
the informal business practices as the dependent variables and the informal motivations as theindependent variables, with a few control variables. This relates to Research Question 1. The
second set has revenue growth and resilience as the dependent variables and the informal
motivations, CIMI, and the four business practices as the independent variables, with a few
control variables. Correlation was tested by OLS and ordinal logit regression, with country,
education, use of ICT, and a few other control variables. This relates to Research Question 2.
We experimented with a number of variations in deriving dependent and independent
variables for each regression model. None of them worked perfectly, as expected. However, thevariable specifications described above worked reasonably well, with several significant andinsightful correlations, some in line with, and a few others contrary to, our prior expectations.
Our main findings are presented below.
7.1 Research Question 1: non-pecuniary motives and business practices
Our answer to this question is two-fold.
First, CIMI, that measures the strength of informal motivations collectively, is not significantly
correlated to three of the four business practices, specifically, to compliance with government
regulations, location of business, or extent of book-keeping. It is significantly correlated to only
one of the business practices—the size of business, that is, the owner operating alone or at
most with one worker. Thus, the conventional view that informal entrepreneurs prefer towork in informal business settings does not hold as a general proposition. See Table 6.

19

Table 6. The Correlation Between CIMI and Business Practice Variables

CIMI
Own Business by Yourself Y/N
Latest loan amount ($100 USD)
ICT
Telephone
Smartphone
TabletEducation
6-8 years
9-11 years
12-14 years> 14 years
Business in Service/Trade Y/NBusiness in Manufacturing Y/NCountry
Kenya(1)
Location ofBusiness-0.317(0.3207)0.619*(0.2584)
0.0687(0.0382)(2)
Small Firm
Size
-0.768*(0.3695)
0.891***(0.2688)-0.00191(0.0432)(3)Compliance0.545(0.3355)
0.270(0.2655)
0.0550(0.0394)(4)
RecordKeeping
0.556(0.3401)
0.152(0.2647)-0.0453(0.0388)0.387(0.2714)-0.160(0.2534)-0.494(0.6627)

0.416(0.2903)
0.392(0.2593)-0.313(0.7353)-0.104(0.2775)-0.260(0.2509)-0.0174(0.7331)-0.426(0.2860)-0.463(0.2523)-1.365(0.7058)-0.436(0.3485)-0.0503(0.3503)-0.270(0.3413)-0.421(0.3778)
0.215(0.3242)0.663(0.3655)-0.298(0.3958)-0.259(0.3999)-0.121(0.3859)
0.156(0.4413)0.658*(0.3350)-0.569(0.3548)-0.255(0.3666)-0.0336(0.3670)-0.139(0.3525)-0.293(0.3965)-1.340***(0.3706)
0.702*(0.3377)-1.534***(0.3770)-1.302***(0.3788)-1.747***(0.3670)-1.376***(0.4073)-1.199***(0.3512)0.496(0.3040)0.257
0.874***-0.1590.707**(0.2209)(0.2519)(0.2272)(0.2361)
Ghana
0.437
0.659*
0.133
2.107***(0.2761)(0.3133)(0.2847)(0.2969)
Observations
681
707
693
662
Note: The baseline for ICT is no ICT usage, and for education, no completed primary schooling.

20

Second, clearly, the non-pecuniary motives and the business practices are individually interlinked, not in all cases but in many, and also, not necessarily in a direction one may have
hypothesized. Significant pair-wise correlations are summarized below. See Table 7, next page,for full regression results.
Compliance--

Small Firm
Size--

Positive--

Negative
Negative

Positive--

Positive
--

4
1

-1

Positive
3-2

Negative
2

2

Business
location

SocialRecognition
Trust
Readiness to
Risk
Freedom

No of significant
correlations

Negative

Recordkeeping--# of significant
correlations

1

Much of the above correlations are intuitively understandable but some are notstraightforward:(i)
Those who value personal trust in operating business tend to operate in a more
informal location, be less compliant with government regulations, and maintainfewer records in writing (no strong incentives to keep accounts in writing).
However, they tend to have a larger firm (where it might be easier to find workthrough personal connections?), contrary to our prior understanding.
(ii)
Those who value freedom tend to maintain small business size (to minimize
interventions from others). They also keep business records more fully (to keep
their business under tighter control).
(iii)
Those who value social recognition tend to comply more with government
regulations (having more formal relations enhance social recognition?). Finally,(iv)
Those who value to be prepared for risk tend to have a larger business size. We
hypothesized that informal entrepreneurs wanted to remain small, so as toremain flexible in case of major adversity. What this means is probably not thatthe hypothesis was invalid but that the readiness to risk variable needs to bereconstituted.
Micro entrepreneurs who reply on family and personal trust are more likely to choose informal
business locations. This is what a number of observers noted. More surprising is that those whowant to be their own boss (to be free) are apparently indifferent whether to work in a formal
location like a commercial building or an informal location like a public market. Similarly, it issurprising that those who are active socially and hence would appreciate social recognition are
not likely to have more detailed or open accounts.
To sum up, one half of par-wise correlations are insignificant; one quarter have significant
correlations in line with our prior suspicion; and the correlation with the remaining quarterare insignificant and are in an opposite direction. This suggests that informal motivations
21

could help explain the choice of informal business practices, but it depends crucially on whichpair of pecuniary motive and business practice one is trying to understand.
Table 7: Pair-wise Correlations Between Non-Pecuniary Motivations and Business Practices
Ordinal Logistic
Regression
SocialRecog
Trust
ReadinessToRisk Y/NFreedom Y/NBusiness in
Manufacturing Y/NCountry
Kenya
Ghana
cut1
cut2
Observations(1)
Location ofBusiness-0.0459(0.0253)0.0818***(0.0234)-0.186(0.150)-0.201(0.177)(2)
Small Firm
Size
-0.0375(0.0285)-0.145***(0.0268)-0.803***(0.1835)
0.758***(0.1930)(3)(4)

Compliance-0.0695**(0.0261)
0.0817***(0.0246)0.0848(0.1560)
0.133(0.1817)

Record Keeping-0.00469(0.0263)0.0889***(0.0247)-0.0297(0.1553)-0.537**(0.1829)0.00228(0.240)-0.640*(0.2530)-0.915***(0.2358)-0.928***(0.2431)0.450**(0.172)
1.233***(0.171)-0.437(0.271)0.686***(0.2008)
0.0468(0.1890)-1.252***(0.3016)-0.263(0.1797)
0.341*(0.1711)-0.237(0.2729)0.0840(0.1791)
1.503***(0.1817)-0.000738(0.2822)

919

0.121
(0.2728)903

1.606***(0.2872)863

0.792**(0.272)892

Note: Location of business, compliance and recordkeeping are ordered categorical variables.
They are ordered from less informal to more informal. Small Firm Size=1 if number of
employees <=2, and 0 if the number greater than 2.
7.2 Research Question 2a: Informality Variables and Revenue Growth
The regression results are presented in Table 8 in the next page. Table 8 shows:First, revenue growth is positively correlated to CIMI in a model where revenue growth isregressed against CIMI but not against business practices (Model 1, Table 8). The positive
correlation persists even when the business practices are added to the independent variables.

22

Table 8: CIMI and Revenue Growth

Location of Business
Workshop in a sharedarea/own home
Public market
unit/street/public farm
Small Firm Size Y/NCompliance to Gov't
Regulation
Partially
Rarely/Don't at all
Record Keeping
Written record somewhat
detailed or minimal
No written record
CIMI
CIMI * Location of Business
Workshop in a sharedarea/own home # CMII
Public market
unit/street/public
farm#CIMI
CIMI *Small Firm Size
CIMI * Compliance
Partially # CIMI
Rarely/None/not sure #CIMI
CIMI * Record Keeping
Written record somewhat
detailed or minimal #CIMI
No written record #CIMI
ICT use Y/NBusiness type
Manufacturing
Country
Kenya
Ghana
Observations（1）
Revenue Growth0.742** (0.2574)（2）
Revenue Growth（3）
Revenue Growth-0.0579 (0.1725)

1.055 (0.5935)-0.0978 (0.1640)-0.133 (0.1353)

0.655 (0.5312)-1.340** (0.4087)-0.623* (0.2484)-0.314* (0.1436)-0.780 (0.8507)
0.231 (0.4330)-0.187 (0.1544)-0.0970 (0.1702)
0.720* (0.2809)-1.716** (0.5251)-0.0734 (0.4926)
0.533 (0.6989)-1.720* (0.8078)-1.175 (0.7485)
1.824** (0.5746)
0.227 (1.1916)-0.861 (0.6102)-0.241 (0.1705)-0.0168 (0.1904)

2.248** (0.7288)-0.154 (0.7012)
0.0866 (0.1951)

0.635** (0.2438)

0.558* (0.2555)

0.496 (0.2599)-1.232*** (0.1624)-0.898*** (0.1595)
900-1.301** (0.1840)-0.670** (0.1792)806-1.458*** (0.1921)-0.692*** (0.1886)
806

Note: the reference group for location of business is commercial building/private farm; for compliance,
fully/mostly comply; and for recordkeeping, detailed and almost all systematically/detail but not sosystematically.

23

Second, when revenue growth is regressed on the four business practices (Model 2, Table 8),revenue growth correlates to only one of the four practices. Location of business, size ofbusiness and extent of bookkeeping are not significantly correlated to revenue growth, while itis negatively correlated to non-compliance with government regulations. This means that it islikely that growth is lower for a business whose compliance with government regulation islimited or none.
Third, in a model that incorporates the interaction terms as additional independent variables(Model 3), namely, when the interactions between CIMI and business practices are taken into
account:•••

The direct correlation between CIMI and revenue growth is insignificant.
The correlation between two of the four business variables and revenue growth arestrongly negative. However, the significance of one of the two, bookkeeping, isambiguous. This is because while the category “keep somewhat detailed or minimal
record” is significant, the category “keep no record” is insignificant. This means that the
regression cannot distinguish those who keep no records from those who keep fullrecords (the reference category for the compliance variable.)
The interaction between CIMI and business size (operating alone or with at most one
worker) and bookkeeping (written records somewhat detailed or minimal) is positiveand significant while the interaction between CIMI and the location of business(workshop in a shared area or at own home) is significantly negative. In other words,
CIMI likely lowers revenue growth where business is located in an informal location but
raises it where business is operated by the owner alone or where it keeps limited
records in writing.

The above findings suggest that the positive impact of informality on revenue growth - whichappears in models (1) and (2) - is derived not directly from informal motivations but throughtheir interactions with business practices. In other words, informal motivations enhancerevenue when those motivations are combined with advantages of owners working alone
and maintaining limited bookkeeping. Being an own-account business (working alone), itself
tends to reduce growth. However, own-account business owners with a high CIMI tend to growfaster. In Table 7, we learned that those who value "freedom" tend to have own-accountbusiness. This suggests the possibility that valuing freedom may indirectly explain the observed
revenue growth.
Interestingly, being in Kenya or Ghana is likely to reduce revenue growth relative to Nigeria.
Being in manufacturing is a significant and positive factor for revenue growth, relative totrade/services/agriculture, but this correlation disappears when the interaction terms areincorporated. Sophistication in the use of ICTs is not significant in any of the models.

24

a. Research Question 2b: informal variables and resilience:
The regression results are presented in Table 9. They show that correlation between informality
and resilience is notable if interactions between CIMI and business practice is accounted for,but it is rather weak when the interaction terms are absent. More specifically,•••In the regression model where CIMI and resilience are correlated without businesspractice variables (Model 1, Table 9), CIMI is not significantly correlated to the resiliencevariable, IRI.
When business practices are added as explanatory variables (Model 2, Table 9),correlation with CIMI remains insignificant. Resilience, IRI, is positively correlated tobusiness location and negatively correlated to compliance with government regulation.
Correlation with business size (owner-operator plus one) or the extent of bookkeeping is
insignificant. In other words, a microenterprise that is located in a more informal
location is likely to have a smaller loss of revenue and/or a faster recovery while anenterprise with limited or no compliance with government regulation is likely toexperience a larger loss of revenue and/or a more limited recovery.
In a model where interactions between CIMI and business practices are incorporated(Model 3, Table 9), the direct correlation between CIMI and IRI is significant andpositive. The direct correlation with business location and limited or no compliance ispositive and significant while the other two business variables are insignificant.
Interaction between CIMI and limited or no compliance is negative and significant but
interaction with the other business variables is insignificant.

The above results suggest that the CIMI has a positive effect on resiliency, but thepositive effect does not appear directly (Models (1) and (2)) because it is offset byindirect effects through non-compliance with regulation (Model (3)). Both CIMI andlimited or no compliance have positive direct effects on resiliency but the interaction
term of the two has a negative effect. From Table 7, we learned that greater reliance on
trust in operating business tends to reduce compliance with government regulation.
Thus, through its effect on compliance, trust likely enhances resilience.
Looking at this the other way around, microenterprises that rely more strongly on moreformal relations in operating business are likely to be more vulnerable to natural
disaster and other shocks.
Interactions between CIMI and business practices apparently play a pivotal role indetermining the relation between informality and resilience. Informal motivations
collectively enhance resilience. In addition, having limited compliance with government
regulation and operating in an informal location also likely increases resilience.

25

Table 9. CIMI and Resilience Index

Location of Business
Workshop in a sharedarea/own home
Public market
unit/street/public farm
Small Firm Size Y/NCompliance to Gov't
Regulation
Partially
Rarely/Not at all
Record Keeping
Written record somewhat
detailed or minimal
No written record
CIMI
CIMI * Location ofBusiness
Workshop in a sharedarea/own home # CIMI
Public market
unit/street/public
farm#CIMI
CIMI *Small Firm Size
CIMI * Compliance
Partially # CIMI
Rarely/None/not sure #CIMI
CIMI * Record Keeping
Written record somewhat
detailed or minimal #CIMI
No written record #CIMI
ICT use Y/NBusiness type
Manufacturing
Country
Kenya
Ghana
Constant
Observations（1）
Resilience Index-0.0333 (0.0383)（2）
Resilience Index（3）
Resilience Index

0.0902*** (0.0249)

0.156 (0.0840)

0.0489* (0.0233)
0.00271 (0.0192)

0.161* (0.0770)-0.0109 (0.0558)-0.0597 (0.0385)-0.0471* (0.0203)-0.0836 (0.1160)
0.194*** (0.0576)

0.00800 (0.0219)
0.0170 (0.0247)-0.0344 (0.0408)-0.0264 (0.0754)
0.00606 (0.0687)
0.273** (0.1021)-0.107 (0.1147)-0.175 (0.1068)
0.0200 (0.0790)
0.0437 (0.1711)-0.371*** (0.0820)

0.00293 (0.0257)-0.0117 (0.0281)

0.0427 (0.1038)
0.00650 (0.0977)
0.00469 (0.0281)-0.126*** (0.0335)-0.127*** (0.0355)-0.150*** (0.0351)-0.0986*** (0.0239)-0.0158 (0.0237)-0.237*** (0.0371)
680-0.0961***(0.0263)-0.0248 (0.0260)-0.244*** (0.0451)
621-0.111*** (0.0263)-0.0296 (0.0264)-0.448*** (0.0720)
621

Note: the reference group is: commercial building/private farm for location of business; fully/mostly comply
for compliance; and detailed and almost all systematically/detail but not so systematically for record keeping.

26

7. Conclusion - Moving Forward
The findings presented above are still provisional. Questions in the AIE survey did not exactly
match the non-pecuniary motives that we intended to measure, and our data are not free ofsampling bias. Nevertheless, our findings have important implications on both theories of theinformal economy and policies that African governments and development agencies pursue todevelop the informal economy.
First, microentrepreneurs and the enterprises they own and operate are, as a group, dynamic.
Their sales revenue rose by 5.3% per year on average over the last five years. These enterprises
are also resilient. About 70% of the enterprises in our sample are highly resilient, with thescore of the Informal Resiliency Index of -0.3 to 0.0 (on a scale of -1.0 to 0.0).
Second, low-income micro entrepreneurs in our survey are not uniform in terms of the degreeof their informality. According to the Composite Informality Motivation Index, the informalityof a large majority of micro entrepreneurs is in a medium to high range but somemicroentrepreneurs show few signs of informality.
Third, the nature of informality is diverse. The Informality Motivation Quadrilateral shows thatthe desire to be free – to be “one’s own boss” – is by far the most common motivation in
becoming informal while the desire to prepare oneself for adversity is not as strong as might bethought. In terms of ways of doing business, non-compliance with government regulation is nota defining characteristic for most microentrepreneurs.
Fourth, non-pecuniary motives of microentrepreneurs and their ways of doing business areinterconnected but not necessarily in the sense that our expectations predicted. For example,
the greater the reliance on personal relationship and trust, the more likely that micro
entrepreneurs operate in more informal business locations, but they are less likely to operatebusinesses alone or with a single apprentice.
Fifth, the informal business practices tested in this paper, all of which are commonly used todefine informality in development economics, are unlikely to raise growth of businessrevenue or resilience. In fact, limited compliance with government regulation is likely to lead tosmaller businesses size and slower growth of business revenue as often presumed in theliterature. Similarly, an owner-operated business (small business) is linked negatively to growthof business revenue. However, owner-operated small businesses tend to raise growth ofbusiness revenue if owner’s informal motivations are strong, e.g., valuing "freedom" highly.
Finally, informal motivations are collectively linked positively to the dynamism of
microenterprises, either directly or through interaction with certain informal businesspractices. Informal motivations enhance revenue growth when those motivations are
combined with advantages gained by owners working alone and maintaining limited
bookkeeping. Informal motivations directly strengthen resilience although the positive effect27

appears to be offset by negative impact of its interaction with limited compliance withgovernment regulations.
Thus, a pervasive view that informality leads to low productivity and little growth needs to bequalified. The smallness of microenterprises may be by choice and not necessarily aconsequence of underdevelopment.
Going Forward, it is imperative to perform a refined and improved iteration of conceptualizing
informality. It is equally important to improve the database so that non-pecuniary motives canbe better represented. Moreover, informality in doing business should be carefully defined andunderpinned by theoretical understanding. Particular attention should be paid to the synergy
between informal motivations and informal ways of doing business, which, we have amply
shown, plays a pivotal role in determining the relation between informality and dynamism.
While there is apparent merit in the hypotheses advanced by anthropologists, quantitative
work at scale is needed if those hypotheses are to be generalized. Therefore, a promisingavenue for advancing this research lies in a more comprehensive interdisciplinary approach.,combining large-scale surveys backed by ethnography, multi-year monitoring by professional
anthropologists, and advanced quantitative methods for analysis. Such multifaceted strategy
will unveil further nuanced dimensions of the informal economy, not only enriching ourunderstanding but also contributing to more informed policy.

28

References:
Aditya, B. and Amri, I. (2023) Rethinking informal economy resilience during crisis: Experiencefrom COVID‑19 pandemic. The Indian Journal of Labour Economics 2023 66:711–737
https://doi.org/10.1007/s41027-023-00458-1
Amin, M. and Islam, A (2015) “Are Large Firms More Produc�ve than Small Informal Firms?
Evidence from Firm-level Surveys in Africa. World Development, Vol 74, Oct 2015, p 37438\
Amaral, P. S. and Quintin, E. (2006). A competitive model of the informal sector. Journal ofmonetary Economics, 53(7), 1541-1553. https://doi.org/10.1016/j.jmoneco.2005.07.016
Bevilacqua, M., F. E. Ciarapica, G. Marcucci (2017) “Supply Chain Resilience Triangle: The Studyand Development of a Framework” World Academy of Science, Engineering and Technology
International Journal of Economics and Management Engineering Vol:11, No:8, p. 1995
Benjamin, Nancy and Ahmadou Mbaye (2012) The Informal Sector in Francophone Africa: FirmSize, Productivity, Institutions. International Bank for Reconstruction and Development &The World Bank. https://openknowledge.worldbank.org/handle/10986/9364 (accessed
November 1, 2020).
Blunch, Niels-Hugo, Sudarshan Canagarajah and Dhushyanth Raju. (2001) The Informal Sector
Revisited: A Synthesis Across Space and Time. World Bank Social Protection Discussion
Paper No. 0119. https://documents.worldbank.org/en/publication/documentsreports/documentdetail/496711468766544695/the-informal-sector-revisited-a-synthesisacross-space-and-time
Brouwer, lily, Iftekhar (2024), “The ASA-I – Duke Survey of Informal Enterprises in Ghana, Kenya
and Nigeria” forthcoming
Elgin, C., Kose, M., Ohnsorge, F. and Yu, S. (2021) "Understanding informality," CAMA Working
Papers 2021-76, Centre for Applied Macroeconomic Analysis, Crawford School of Public
Policy, The Australian National University.
Charmes, J. (2016) The informal economy: Definitions, size, contribution and main
characteristics. in Kraemer-Mbula, E. and Wunsch-Vincent, S. eds. The Informal Economy in
Developing Nations: Hidden Engine of Innovation? Cambridge, Cambridge University Press.
Chen, Martha Alter (2012) The Informal Economy: Definitions, Theories and Policies. Women inInformal Employment: Globalizing and Organizing (WIEGO) Working Papers No. 1
David, A., Diallo, Y., and Nilsson, Y. (2023) Informality and Inequality: The African Case, Journalof African Economies 32(2), ii273–ii295.
Devine, H. (2021) How institutions shape the informal economy, in C.C. Deléchat and L. Medina
eds. The Global Informal Workforce: Priorities for Inclusive Growth, International Monetary
Fund, Washington, D.C. 223-252.
29

Fayomi, O. O., Adebayo, G. and Okorie, U. (2018) “The resilient informal economy in the milieu
of African development.” in S.O. Oloruntoba, T. Falola (eds.), The Palgrave Handbook of
African Politics, Governance and Development. Palgrave Macmillan New York: 661-676.
Fourie, F. (2018). Informal-sector employment in South Africa: An enterprise analysis using the
SESE survey. The South African Informal Sector: Creating jobs, reducing poverty, 103-150.
Hart, K. (1973). Informal Income Opportunities and Urban Employment in Ghana. The Journalof Modern African Studies, 11(1), 61–89. https://doi.org/10.1017/S0022278X00008089
ILO (2013) Measuring Informality: A Statistical Manual on the Informal Sector and Informal
Employment, Geneva, International Labour Organization.
La Porta, R and A. Schleifer (2014), “Informality and Development”, Journal of Economic
Perspectives, Vol. 28, No. 3, Summer 2014 (pp. 109 -26)
Losby, J.L, et al (2002), Informal Economy Literature Review, ISED and the Aspen Institute
Maloney, William F.(2004) "Informality Revisited," World Development, Elsevier, vol. 32(7),pages 1159-1178, July.
Meagher, Kate, and Ilda Lindell, eds. (2013). “Introduction. ASR Forum: Engaging with African
Informal Economies: Social Inclusion or Adverse Incorporation? “ African Studies Review 56(3): 57-76.
Meagher, K. (2010) Identity Economics – Social Networks & The Informal Economy in Nigeria.
Rochester, NY. James Currey.
Medina, L., Jonelis, A, and Cangul.M (2017).The Informal Economy in Sub-Saharan Africa: Sizeand Determinants. IMF Working Paper.
https://www.imf.org/en/Publications/WP/Issues/2017/07/10/The-Informal-Economy-inSub-Saharan-Africa-Size-and-Determinants-45017.
Ochiai T, Hirano-Nomoto, M and Agbiboa DE (eds.) (2021) People, predicaments
and potentials in Africa. Langaa Research and Publishing Common Initiative Group(Bamenda, Cameroon)
OECD/International Labour Organization (2019), “Definitions of informal economy, informal
sector and informal employment”, in Tackling Vulnerability in the Informal Economy, OECD
Publishing, Paris.
Ogawa, Sayaka (2016) Toshi wo ikinuku tame no kochi – Tanzania no reisai shounin no
minzokushi (Shrewd for survival in urban centers: ethnography of Machinga, precarious
merchants in Tanzania) Sekai Shisou Sha
Ohnsorge, F. and Yu, S. (2022) The Long Shadow of Informality: Challenges and Policies, The
World Bank, Washington, D.C.
https://thedocs.worldbank.org/en/doc/37511318c092e6fd4ca3c60f0af0bea30350012021/related/Informal-economy-full-report.pdf
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Shahid, M.S., Williams, C.C., and Martinez-Perez, A. (2020) “Beyond the formal/informal
enterprise dualism: explaining the level of (in)formality of entrepreneurs” The International
Journal of Entrepreneurship and Innovation 21(3): 191-205.
World Bank (2016) Informal Enterprises in Kenya. Washington D.C.: The World Bank.
https://openknowledge.worldbank.org/handle/10986/24973
World Bank (2013), World Development Report 2013,https://documents1.worldbank.org/curated/en/263351468330025810/pdf/73068-v1english-revised-PUBLIC-PUBDATE-10-15-12.pdf
World Bank (2022), The Informal Sector Enterprise Survey,
https://www.enterprisesurveys.org/en/informal-businesses

31Appendix I Formal vs Informal: Comparison of Business Outcomes

Outcomeindicator
Monthly revenue(US Dollar)
Labor
productivity
No. of workers
Revenue Growth

Notes:

Informality Definition Criteria:
Compliance with Gov’t Regulation
Tax Payment
FormalInformal
FormalEnterpriseEnterprises
Enterprises

InformalEnterprises

545.63450.84518.52350.39258.422.7
0.06

298.052.0
0.04

286.432.5
0.07

263.891.7
0.04(1) Monthly revenue in US dollar converted from local currency; labor productivity equals monthly
revenue divided by number of workers; revenue growth was originally reported in percent, forexample, -0.3 = decreased >30 %, -0.2= decreased 10-30%, 0 = about the same, 0.2 = grown 1030%, 0.3 = grown >30%.
(2) Formal enterprises are microenterprises that fully, mostly, or partially comply with government
regulation. Informal enterprises are microenterprises that rarely or not at all & don’t knowcomply regulation.
(3) Formal enterprises are microenterprises that fully, mostly, or partially comply with government
regulation. Informal enterprises rarely or not at all & don’t know comply regulation.

Appendix II Sensitivity of business outcomes of microenterprises to definition of informality
Informality Definition Criteria:

OutcomeIndicators
Monthly revenue(US Dollar)
Labor
productivity
No. of workers
Revenue Growth

Notes:

Microenterprise groups by
bookkeeping categories
5&4& 5&4&3
5
5&4
3
&2

Microenterprise groups by tax
payments categories
5&4& 5&4&3
5
5&4
3
&2

333.80380.41410.03478.43355.76350.39455.10448.65215.232.1
0.06

253.972.1
0.06

258.372.2
0.06

271.962.3
0.05

268.181.7
0.04

263.891.7
0.04

243.142.4
0.04

253.342.5
0.07(1) Categories for bookkeeping: 5 - no written record; 4 – minimal; 3 - keep some record; 2 detailed record but not systematic; and 1 – keep detailed record systematically.
(2) Categories for tax payment: 5 – never; 4 – rarely; 3 – sometimes; 2 – usually; and 1 -always.

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Appendix III Construction of the Informal Resilience Index (IRI) 12
According to the original formulation, Resilience Triangle represents “the amount of time ittakes for the system to return to normal performance levels.” (Bevilacqua M. et al, 2017). We
measure the loss of functionality due to the shock by the decline in revenue from the pre-Covid
levels, following the original formulation. However, in or case, in many cases, the level ofrevenue did not reach the pre Covid level when the Survey was taken. In those cases, we obtain aquadrilateral, rather than a triangle as shown below.
Figure 3.1 Resilience Quadrilateral

In Figure 3.1, the performance level at T1, y1, equals 100*z, where z is the rate of change inrevenue during shock period, T0 to T1. The performance level at T2, y2, equals y1*(1+u), where
u is the rate of change in revenue during recovery period, T1 to T2. The area surrounded bypoints ABCDA measures the loss of functionality from T0 to T2. We call this “the Informality
Resilience Measure (IRM)”.
In a typical case, revenue would fall during the shock period, i.e., z is negative, and it would riseduring the recovery period, i.e., u is positive. IRM in this case is depicted in the left panel of
Figure 3.2. There are also cases where revenue keeps falling during the recovery period, i.e., u isnegative. IRM in those cases are depicted in the right panel of Figure 3.2

12Prepared by Jiahan Yin

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Figure 3.2 Informality Resilience Measure (IRM)

We derive the rate of change in revenue during the shock period, z, from the following questionin the Survey: “How much revenue did you lose or gain as a result of the coronavirus pandemic?
Please compare your revenue before the outbreak of coronavirus and your revenue when theimpact was most severely felt.” Respondents were asked to select an answer from: lost all
revenue; lost 50-90%; lost 30-50%; lost 10-30%; about the same; gained by 10- 30%; gained by
30-50%; gained by 50-100%; gained by more than 100%. For simplification, we grouprespondents who gained revenue during the shock period (i.e., z greater than zero) together withthose whose revenue was about the same (i.e., z equals zero). In other words, for the purpose of
calculating the degree of resilience, we assume that no microenterprise increased revenue during
the shock period. Then, we take a midpoint of the range in each answer to derive z:
z= -1.00 (i.e., 100%) for lost all revenue;z = - 0.75 (i.e., 75%) for lost 50-90%;z = - 0.4 (40%) for lost 30-50%;
z = -0.2 (20%) for lost 10-30%;
z = 0 (0%) for about the same; gained by 10-30%; gained by 30-50%; gained by 50-100%;
gained by more than 100%.
We derive the rate of change of revenue during the recovery period, u, from the following
Survey question: “How well has your business recovered from the coronavirus? Please compare
your revenue at the time when the impact of coronavirus was felt most severely to your revenue
now. How much revenue did you lose or gain over this period?” Respondents were asked to
select an answer from: lost all revenue; lost 50-90%; lost 30-50%; lost 10-30%; about the same;
gained by 10-30%; gained by 30-50%; gained by 50-100%; gained by more than 100%. Taking
mid-points, u are approximated by:
u = -1 (100%) for lost all revenue;34u = -0.75 (75%) for lost 50-90%;
u = -0.4 (40%) for lost 30-50%;
u = -0.2 (20%) for lost 10-30%;
u = 0 (0%) for about the same;u = 0.2 (20%) for gained by 10-30%;
u = 0.4 (40%) for gained by 30-50%;
u= 0.75 (75%) for gained by 50-100%;
u= 1.00 (100%) for gained by more than 100%.
Note that the performance level at T2 could exceed 100 if u is very large, specifically if u >1/(1+z)-1. This case is illustrated in the left panel of Figure 3.3. In this case, the loss offunctionality cannot be calculated as the distance between the pre-Covid performance level andthe performance level during T0 and T2. Thus, to measure the loss of functionality, we reset u to1/(1+z)-1, as shown in the right panel of Figure 3.3. In this case, IRM is measured by area
∆ABC.
Figure 3.3 Adjustments in the rate of recovery, uNow, we convert IRM to an index, Informal Resilience Index, IRI. We set IRI of a
microenterprise to 0 if the microenterprise is completely resilient, i.e., no revenue was lost during
the shock period and also the recovery perio. This case is shown in the right panel of Figure 3.4
below. We set IRI to -1 if the enterprise has no resliency, i.e., the pre-Covid revenue was totally
lost during the shock period and revenue did not recover at all during the recovery period. Thiscase is depicted in the left panel of Figure 3.4.
We derive IRI for a combination of z and u, IRI(z,u), by deviding IRM (z,u) by IRM (zero), i.e.,the case of maximum loss of functionality, i.e., revenue totally lost during the shcok period anddid not at all recover during the recovery period, IRM (zero) = IRM(-1, 0). See Figure 3.4.
Figure 3.4 Cases of zero resilience and full resilience35

In Figure 3.4,
IRM(zero) = ABCDA (-1, 0) =(15+28)*100*0.5=2150
IRM (full) = IRM (0,0) = 0
Then, the Informality Resilience Index, IRI, is derived as follows:
IRI (zero) = - IRM (zero)/IRM (zero) = -1
IRI (z, u) = IRM (z, u)/IRM (zero),for z>0 and u>0, and z<0 and u <0 and |z|>|u|,IRI (full) = IRM (full)/IRM (zero) = 0/IRM (zero) = 0.

36