INFORMAL ENTERPRISES
IN KENYA
workshops jua kali handicrafts autoparts furniture
furniture handicrafts jua kali workshops enterprises
INFORMAL ENTERPRISES
IN KENYA
January, 2016
TABLE OF CONTENTS
Acknowledgements ........................................................................................................................................................................................................... i
Introduction.............................................................................................................................................................................................................................. ii
Section 1. Background and Overview...................................................................................................................................................................... 1
Section 2. Financing Informality ................................................................................................................................................................................. 7
Section 3. Productivity ...................................................................................................................................................................................................... 11
Section 4. Firm Dynamics ................................................................................................................................................................................................ 17
Section 5. Remaining Informal...................................................................................................................................................................................... 19
Section 6. Summary and Policy Advice .................................................................................................................................................................. 25
LIST OF FIGURES
Figure 1: Access to finance is the top obstacle in all regions............................................................................................................... 5
Figure 2: Use of Bank finance for working capital is more common among firms with more educated
owners and among the more productive and large firms .............................................................................................. 8
Figure 3: Larger and more productive firms are less likely to be financially constrained .................................................. 9
Figure 4: Informal enterprises are less productive than formal enterprises................................................................................ 11
Figure 5: Labor productivity is lower among informal firms compared with formal micro firms, but the gap
varies by region............................................................................................................................................................................................ 12
Figure 6: Variations in labor productivity of informal firms ................................................................................................................... 13
Figure 7: Education level of the manager is positively correlated with labor productivity of the informal firms .... 15
Figure 8: Percentage of firms that increased number of employees, machines, or space used over the last
three years varies across regions....................................................................................................................................................... 18
Figure 9: Willingness to register is higher among firms that consider the various obstacles as severe for their
business operations .................................................................................................................................................................................. 20
Figure 10: Reasons for not registering vary across regions and by education level of the manager ............................ 20
Figure 11: Perceived benefits of registration vary by region and firms’ perceived severity of the obstacles............ 22
Figure 12: Ease of registering a business is associated with greater willingness among informal firms to register.. 23
Figure 13: Better contract enforcement in Mombasa is associated with more firms reporting being able to
issue receipts to customers and suppliers as a benefit of registration ..................................................................... 23
Figure 14: On average, labor productivity increases with greater ease of registering a business.................................... 24
LIST OF TABLES
Table 1: General firm characteristics......................................................................................................................................................................... 10
Table 2: Firm ownership characteristics................................................................................................................................................................. 11
Table 3: General management of the business ................................................................................................................................................ 11
Table 4: Key obstacles faced by informal firms.................................................................................................................................................. 12
LIST OF ANNEXES
Annex 1: Summary statistics and regressions .................................................................................................................................................... 29
Annex 2: Kenya – Survey of informal firms (2013)............................................................................................................................................ 38
Annex 3: Business environment and productivity............................................................................................................................................ 40
ACKNOWLEDGEMENTS
T his report is the outcome of collaborative efforts of the World Bank’s Trade & Competitiveness
and Finance & Markets Global Practices. It is a part of the Kenya Investment Climate
Assessment ESW (P151793), supported by the Kenya Investment Climate Program-II, which is
generously supported by DFID and the Netherlands. The team was led by Mehnaz Safavian (Lead
Financial Sector Specialist), Joshua Seth Wimpey (Private Sector Development Specialist) and
Mohammad Amin (Senior Economist).
i KENYA INFORMAL ENTERPRISES
INTRODUCTION
J ua Kali means fierce sun in Swahili. It is also
the name given to Kenya’s informal sector,
the thousands of workshops where people
part of the World Bank’s Enterprise Survey
initiative for Kenya. The purpose of the note is
to assess the main constraints facing informal
bang out pots, pans, autoparts, furniture, firms, identify patterns of productivity and
and handicrafts, literally under the hot sun, firm dynamics, and better understand drivers
day in and day out. In low and middle-income for formalization. Section one provides an
countries, informal firms make up the majority overview of key characteristics and main
of all enterprises. In Kenya, this is also true, investment climate constraints facing informal
with the Kenya National Bureau of Statistics firms. In section two, patterns of informal firm
estimating that, as of 2014, the informal sector finance are explored, while in sections three
represents 82.7 percent of employment. and four, labor productivity and drivers of firm
growth are analyzed. Section five examines
While the domination of the informal incentives to remain informal and policies that
sector is well known, its implications, costs, can catalyze formalization. This is followed by a
reforms, and impact are less well known, conclusion. Due to the sampling methodology
and questions abound. What are the main used, all results pertain to the sample of
constraints facing informal firms? Why do surveyed firms; hence, due caution is necessary
firms choose to remain informal, and what in extrapolating the results to the broader
are the benefits to formalization? How much informal sector in Kenya.
does informality ‘cost’ in terms of lost revenue
and lower productivity? As firms grow in size, Nevertheless, the assessment of the surveyed
do they stay informal? Do policies to boost firms could provide important information
formalization work and are they worth the cost on identifying policies as well as firm-level
to design and implement? support that could boost productivity and
catalyze formalization. This could have
This note draws from an emerging literature important implications for economic growth
on firm informality as well as data collected and job creation in Kenya.
on micro enterprises and informal firms as
KENYA INFORMAL ENTERPRISES ii
SECTION ONE
BACKGROUND AND OVERVIEW
T he informal sector across Africa is
ubiquitous, with a significant number
of people engaged in small and household
characteristics of firms and their owners,
their main investment climate challenges
and obstacles to growth, and firm dynamics.1
enterprises outside formal wage employment. The World Bank’s Informal Enterprise Surveys
A World Bank review of household enterprises (IFS) collect data on non-registered business
in Sub-Saharan Africa (Fox and Sohnesen activities in every region of the world, and an
2012) confirms that the informal nonfarm informal enterprise survey was conducted in
sector is an important contributor to economic Kenya in April and May of 2013. The Kenya
development in low-income Sub-Saharan IFS used a standardized survey instrument
Africa as a source of employment, earnings, designed to assess the business environment
and household livelihoods. Nearly 70 percent for non-registered businesses within a well-
of employment outside farming is in the defined universe of activities, which have been
informal sector. Improving the productivity identified using information from previous
of informal enterprises is therefore essential iterations of the studies. The IFS covered
for employment, income growth, and poverty business environment topics including general
reduction in the region. business characteristics, infrastructure, crime,
sales and supplies, finance, labor, registration,
Kenya’s informal sector is large and dynamic business environment, and assets. In Kenya, a
- 95 percent of the country’s businesses and total of 533 firms were interviewed. The urban
entrepreneurs are found here. According to centers identified were Nairobi (137 firms),
2015 Economic Survey, the total number of Mombasa (110), Central (103), Nyanza (93), and
persons enrolled in both formal and informal Nakuru (90).
sectors increased from 13.5 million in 2013 to
14.3 million in 2014, and of the 799,700 new jobs, The IFS in Kenya allows for comparison across
700,000 were created by the informal sector. different dimensions, including sector of
Men account for a majority of employment in activity (manufacturing vs. services), firmsize
the informal sector of Kenya and more than (number of employees in a regular month),
two-thirds of informal sector jobs are in trade, location (Nairobi, Mombasa, Central, Nyanza,
restaurants, and hotels. Employment in the and Nakuru), gender of the main decision
informal sector is associated with significantly maker/owner, whether the firm operates from
lower levels of poverty than those experienced inside or outside of household premises, and
in farming. education level of the primary owner. A full set
of summary statistics of all variables are provided
Data recently collected can fill some important
gaps in information on the informal sector
1
See Annex 2 for a detailed description of the data and
in Kenya and provide some insight into the methodology.
KENYA INFORMAL ENTERPRISES 1
1. Background and Overview
in Annex 1.2 As mentioned above, the lack of typically smaller than 50m2 in size and largely
a proper sampling frame for the universe of located outside of the household premises. Of
informal firms in Kenya implies that the sample these, 45 percent of the premises were fixed,
we use is not necessarily representative of the permanent structures and owners who did not
broader informal economy in Kenya or in the own the premises rented these in almost 82
cities covered. Hence, all our results apply to the percent of the cases.
sample of surveyed firms and extrapolation to the
broader informal economy requires due caution. For the full sample, 27.1 percent of firms had
expanded in the last three years (increase in
Excerpts from the summary data in Table 1 employees, machinery, or space occupied)
reveal that the average age of firms covered but higher growth was seen in companies
in the IFS survey was six years and almost half where owners had a secondary education
the businesses operated in the manufacturing (32 percent of firms) vs. owners who had no
sector. Only 1.3 percent of the sampled primary education (16.6 percent of firms).
firms were registered when they started and Similarly, firms managed by males expanded
over 40 percent of employees were family in more cases than those managed by females
members of the owners. Firm premises were (31.2 percent vs. 20.9 percent).
Table 1: General firm characteristics
Measure Result Measure Result
Average age of the firm 6.5 years Total area occupied by the business or activity 45m2
% of firms that were registered at start up 1.3% Firms located within household premises 13%
Firms that belong to the manufacturing sector 48% For firms located inside household premises, % 60%
reporting main reason is that it costs less to run
the business from home
Firms with increase in employees, machinery or space 27% For firms located outside of household premises, 45%
occupied during the last 3 years % of firms that have fixed premises and with
permanent structure
Business is located in an industrial zone or cluster 16% Among businesses whose owners do not own the 82%
space occupied by the business, % who pay rent
for the space occupied
Business is located in the city center 7% Number of family members of the owners working 44%
at the firm as a percentage of all workers during
the last month
As shown in Table 2, firms were typically
2
For all variables covered by IFS, regression analysis was owned by an individual who had an average
conducted by regressing each of the variables covered
by IFS on various cuts (sector, firmsize, education level of of eight years of experience in the sector.
primary owner, etc.) listed above. OLS regression is used The average age of the owner was 35 years
where the dependent variable is a continuous variable
and logit model is used for categorical (dummy) variables. of age and almost 40 percent of owners were
All regression results use Huber-White robust standard female. In 94.3 percent of the cases, the main
errors. As we find below, significant regional differences
are found in many IFS variables. This is not surprising since owner had started the business themselves (or
the informal sector often operates at the local rather than
the national level. Hence, all our regression results are run
with a partner), and in many instances (66.3
with region fixed effects (dummy variables indicating the percent), these owners came from homes in
region to which a firm belongs).
2 KENYA INFORMAL ENTERPRISES
1. Background and Overview
Table 2: Firm ownership characteristics
Measure Result Measure Result
Number of owners in the business 1.1 For firms with largest owner who has not spent 64.4%
his/her entire life in the city, % of firms where
owner migrated from a smaller city
Number of years of experience that the main decision 8.1 Number of people who live in the largest owner’s 3.8
maker has working in the sector household premises
Age of the largest owner 35.0 Firms with largest owner’s parents having no 66.3%
education or primary education
% of owners of the firm that are female 37.8% Firms with largest owner employed in the same 23.4%
activity prior to current business
Largest owner acquired ownership of the firm by 94.3% Prior to starting this business, % of firms with the 21.7%
starting the business alone or with partners largest owner being unemployed
Largest owner migrated to the city where the 78.8% Number of businesses or activities started by the 1.0
business is located from another city or from another largest owner in the last three years
country
which parents had no education or a primary average firm operates for approximately 65
education. Almost 80 percent of the largest hours per week and this remains constant across
owners migrated to the city in which the sector, region, type of ownership, and stage
business is located and, of these, the majority of maturity of the business. A large majority
(64.4 percent) migrated from smaller cities. of the businesses (86.8 percent) use their own
About a fifth of the owners of the surveyed funds to finance the day-to-day operations,
businesses were unemployed prior to starting with only 8.7 percent using banks. However,
their respective businesses. 16 percent of firms managed by individuals
with a vocational or university degree make
Table 3 provides further insight into the use of bank financing for this purpose vs. only
management of the day-to-day operations 3 percent of managers with no education or a
of the businesses surveyed. As shown, the primary education. On average, 34.4 percent of
Table 3: General management of the business
Measure Result Measure Result
Firms where the largest owner is also the main 96.8% % of firms that have a bank account to run the 34.4%
decision maker business
Hours of normal operation of the firm per week 64.8 For firms that have a bank account to run the 52.6%
business, % of them that use separate bank
account for their household
% of firms who use electricity 51.8% Total cost of workers for the last month Ksh 12,679
% of firms that use water for business purposes 36.9% % of firms who experienced losses due to crime 7.0%
during the last month
% of firms that used own funds to finance their day- 86.8% Losses due to crime during the month as % of 46.7%
to-day operations monthly sales among firms who had positive
losses due to crime in the last month
% of firms that used banks to finance their day-to- 8.7% Losses due to crime during the last month as a 2.9%
day operations percentage of sales in a regular month including
zero losses for firms that had no such losses
KENYA INFORMAL ENTERPRISES 3
1. Background and Overview
firms use a bank account to manage their funds education), by number of employees (22.1
and the use of bank accounts is doubled when percent for multiple employee businesses
comparing level of education (vocational or vs. 60.7 percent for single employee), and by
university degree vs. no/primary education). Of gender (29.1 percent for female managed
those that make use of bank accounts, just over businesses vs. 54.1 percent for male managed
half the firms separate business and household businesses).
bank accounts. Once again, level of education
is a large driver of separation (70 percent vs. Firms were provided with a list of eight
25.7 percent with no/primary education). obstacles in running their business and asked
to choose the most important one. The
Just over half the firms surveyed use electricity obstacles include access to finance, access to
to operate their businesses (51.8 percent) and land, corruption, power supply or electricity,
only 37 percent use water. The average cost crime, water supply, access to technology,
of workers per month is Ksh 12,679, although and inadequately educated workers. Access
there are substantial differences by sector to finance was the top obstacle, cited by 59
(Ksh 16,448 in manufacturing vs. Ksh 9,056 in percent of firms surveyed. This was followed by
services), by level of education of owners (Ksh electricity problems (10.3 percent), access to
16,178 with university degree vs. Ksh 8,937 land (9.3 percent), and corruption (9.3 percent).
with no/primary education), and by gender of
manager (Ksh 15,613 for males vs. Ksh 8,022 for As seen in table 4, 63.8 percent of firms cite
females). Seven percent of firms experienced access to finance as a severe obstacle, and
losses due to crime in the month prior to being limited access to land is also a severe stumbling
surveyed. Of those firms, the losses represented block for 41.3 percent of firms surveyed.
almost 47 percent of sales for the month. There Corruption appears to be widespread, with
were differences in this percentage by level 33 percent of the sampled firms reporting it
of education (31.0 percent with secondary as a severe obstacle, 60 percent reporting
education vs. 72.5 percent with no/primary harassment by government officials during the
Table 4: Key obstacles faced by informal firms
Measure Result Measure Result
% of firms that consider limited access to finance as a 63.8% Limited access to land is a severe obstacle to firm’s 41.3%
severe obstacle to their current operations operations (% of firms)
% of firm that rank limited access to finance as the 59.3% % of firms reporting electricity problems as a 38.5%
most important obstacle within the set of eight severe obstacle to their current operations
obstacles
% of firms who report crime as a severe obstacle for 28.0% For firms that use electricity, % of firms that 83.6%
their operations experienced power outages during the last month
% of firms who report corruption as a severe obstacle 33.0% % of firms reporting water problems as a severe 22.9%
for their operations obstacle to their current operations
Business experienced harassment by government 60.0% For firms that use water for business purposes, 43.0%
officials during the last month (% of firms) % of firms that experienced insufficient supply
during the last month
% of firms who believe that firms like themselves give 52.9% % of firms that would like their business to be 53.0%
informal payments or bribes or protection payments registered with the Registrar General
in order to stay in business
4 KENYA INFORMAL ENTERPRISES
1. Background and Overview
last month, and 53 percent reporting that they firms surveyed in Mombasa region (Figure
believe bribes are required to stay in business. 1). This is significantly higher than in Nakuru
This figure is significantly higher among (51 percent) at the low end. Nyanza and
surveyed firms in the manufacturing sector Mombasa stand out with a significantly higher
vs. the services sector (80.0 percent vs. 31.6 proportion of firms that rank access to land as
percent). Access to services is also a challenge the top obstacle (21 percent and 14 percent,
as almost 40 percent of firms surveyed face respectively) compared with each of the
electricity problems (over 80 percent of firms remaining regions. In contrast, no surveyed
using electricity experienced power outages in firm in Nyanza considers corruption as the top
the prior month), and almost a quarter of firms obstacle compared with 11 percent on average
face severe water problems (over 40 percent elsewhere, and no surveyed firm in Mombasa
of those using water experienced insufficient considers crime as the top obstacle compared
supply in the prior month).
with 8 percent of firms on average surveyed
elsewhere. The Central and Nakuru regions
Access to finance continues to be the top
stand out with a significantly larger proportion
obstacle even within sub-samples such as
of firms surveyed reporting poor power supply
sector of activity, region, gender of manager,
single vs. multiple employee firm, etc. as the most important obstacles (20 and 17
percent, respectively) than firms in any of
By region, at the high end, access to finance the other regions (average for the remaining
is the top obstacle for 65 percent of the regions is 5.6 percent).
Figure 1: Access to finance is the top obstacle in all regions
70 65
62 60 59
60 56
51
50
Percentage of rms
40
30
20 21
20 17
14 15 13 14 12 10
10 9 7 8 9 9
5 6 4 6
3 3 3 3
0 0 0
Central Nyanza Mombasa Nairobi Nakuru All rms
Access to nance Access to land Corruption Crime Electricity
Source: Kenya Informal Enterprise Survey, 2013
KENYA INFORMAL ENTERPRISES 5
SECTION TWO
FINANCING INFORMALITY
proportion of firms that find access to finance
C ommon in the literature on informality
is the consistent pattern that access
to finance (among other variables) is a
as the top obstacle and firmsize measured by
monthly sales of the firm.
key determinant of the rate of formality. The proportion of surveyed firms that use their
Furthermore, the greatest perceived obstacle own internal funds to finance operations does
for both informal and formal firms is access not vary much by firm-size, labor productivity,
to finance, although this could often be gender of the manager, education level of the
interpreted more fundamentally as an issue of manager, whether a firm operates from inside
limited human capital (LA Porta, Shliefer, 2014). or outside of household premises, industrial
sectors, or whether or not the firm expanded
In Kenya, an overwhelming majority of informal over the last three years. There are, however,
firms surveyed use their own funds to finance some significant differences in other categories.
working capital requirements; internal funds Younger firms are significantly more likely to
serve as a source of financing for working use their own funds than older firms. This result
capital for 87 percent of firms surveyed. seems to be largely driven by firms that are 10
This is followed by money from friends and years or older (about 20 percent of the sample).
relatives (used by 35 percent of firms), credit For instance, 81 percent of the firms surveyed
and advances from suppliers and customers that are 10 years or older use their own funds to
(19 percent), micro-finance institutions (16 finance operations compared with 89 percent
percent), moneylenders (9 percent), and banks of the firms surveyed that are younger.
(9 percent).
The sampled firms in the furniture industry
There is also a fair amount of literature showing are an anomaly as they are less likely to use
that financial constraints are particularly their own funds (75 percent) than the sampled
acute for relatively smaller firms. Data from firms in the rest of manufacturing (92 percent)
the informality survey in Kenya are consistent as well as services sector (85 percent). This
in this respect. That is, the proportion of may suggest that the furniture industry enjoys
firms that consider access to finance their top somewhat greater access to finance. Regional
obstacle is significantly higher as firmsize, differences for the full sample are noticeable.
measured by the number of employees, Specifically, firms surveyed in the Central and
decreases. For example, 62 percent of the Mombasa regions have a higher proportion
single employee firms rank access to finance of firms using their own funds (98 percent in
as the top obstacle, compared with only 55 Central region and 94 percent in Mombasa)
percent of multiple employee firms. However, than firms in Nyanza (77 percent), Nairobi (84
there is no noticeable relationship between the percent), and Nakuru (81 percent).
KENYA INFORMAL ENTERPRISES 7
2. Financing Informality
Although close to 20 percent of firms in the with firmsize (sales, employment), labor
full sample use advances and credit from productivity, and education level of the
suppliers and customers, the percentage manager (Figure 2). It is also higher for
increases significantly with firmsize (sales, manufacturing vs. services sector firms (11 and
employment), labor productivity, firm’s age, 6 percent, respectively), for firms that expanded
and the level of education of the manager. workers, machines, or space, used over the
For example, 26 percent of the firms surveyed last three years vs. others (13 percent vs. 7
with above median level of labor productivity percent, respectively). As might be expected,
use advances/credit compared with 12 percent surveyed firms that currently use bank finance
of the firms surveyed with below median labor are less likely to report that they would benefit
productivity. Use of this source of finance also from better access to finance as a result of
differs significantly between the sample of registration. Among firms that use bank finance,
firms in the furniture industry (38 percent), rest 63 percent report better access to finance as a
of manufacturing (23 percent) and services benefit from registering; the corresponding
(13 percent), and it is significantly higher for figure for firms that do not use bank finance is
dynamic firms that increased workers, machines, significantly higher at 78 percent.
or space used over the last three years vs. those
that did not (33 and 14 percent, respectively). The survey provides information on whether
Last, there is not much regional variation with or not a firm applied for a loan during the
the exception that firms surveyed in Mombasa last year, and if not, the main reason for not
use advances/credit from suppliers/customers doing so. We define a firm to be financially
less compared with each of the other regions constrained if it did not apply for a loan during
(8 percent vs. 22 percent). the last year and the main reason for not doing
so is either high interest rates, lack of required
While only 9 percent of firms in the sample guarantees, complex application procedures,
use banks to finance working capital, the it did not think it would be approved, and the
proportion of such firms increase significantly residual category of other reasons.
Figure 2: Use of Bank finance for working capital is more common among firms with more educated owners and among the more
productive and large firms
18
16 16
Percentage of rms that use banks to
14 13
nance working capital
12 11
10
8
6 6 6
6
4 3
2
0
Manager has no or Manager has secondary Manager has vocational Firm has single Firm has more than one Below median labor Above median labor
primary education education training or university employee employees productivity productivity
degree
Source: Kenya Informal Enterprise Survey, 2013
8 KENYA INFORMAL ENTERPRISES
2. Financing Informality
According to this measure, about 60 percent of labor productivity. Across regions, sampled
the sampled firms are financially constrained. firms that are financially constrained are more
This proportion does decline with increases common in the Mombasa region. Figure 3
in firmsize (sales), firm age, labor productivity, provides more detail with respect to regions,
and firm growth. firm age, productivity, and sales. As might be
expected, firms that consider access to finance
Surveyed firms with higher labor productivity as an obstacle are more likely to be financially
are less financially constrained (at 53 percent) constrained vs. those that are not (76 vs. 39
compared with 67 percent of firms with lower percent, respectively).
Figure 3: Larger and more productive firms are less likely to be financially constrained
90
82
80
70 67 65
61 63
60
56
Percentage of rms
60 53 53 53 55
50 47
40
30
20
10
0
Full sample Central Nyanza Mombasa Nairobi Nakuru Above median Below median Older than Median or Above median Below median
labor labor median age below median month sales monthly sales
productivity productivity (4 years) age (4 years)
Source: Kenya Informal Enterprise Survey, 2013
KENYA INFORMAL ENTERPRISES 9
SECTION THREE
PRODUCTIVITY
G iven that a large proportion of workers
in the informal sector belong to the low-
income category, increasing labor productivity
Consistent with the broader literature, in
Kenya, formal or registered micro firms show
a much higher level of labor productivity than
in the informal sector may be crucial for their informal firm counterparts surveyed,
reducing poverty, increasing income equality, but the gap varies by region. The mean value
and improving the living conditions of relatively of labor productivity for micro firms is about
poorer sections of society. 8.4 times that of informal firms surveyed. The
corresponding figure for median level of labor
In general, it is well understood that informal productivity is lower, but sill 3.8 times that of
firms are much less productive than formal informal firms surveyed.
firms, with productivity calculated as value
added per employee. La Porta and Shliefer Figure 4: Informal enterprises are less productive than formal
enterprises
(2014) present evidence that this is an
accurate representation and not just under- 200 190
180
reporting by informal firms. The low value-
Monthly sales per worker (KES, '000)
160
added per employee reflects the low quality 140
of products produced by informal firms, which 120
is also indicated by the concerns informal 100
entrepreneurs report about competition from 80
60 50
the formal sector. Low productivity is also
40
reflected in the growth rates of informal firms 22
20 13
(La Porta and Shiliefer, 2014). 0
Mean Median
Informal Micro
We define labor productivity as the (log of) ratio Source: Kenya Informal Enterprise Survey, 2013
of sales to employment in a regular month. Note: All the micro firms belong to the formal or registered sector.
Regression analysis was performed to analyze
the relationship between labor productivity The productivity gap between formal micro
and various firm-characteristics. Unless stated firms and informal firms surveyed grows at
otherwise, all the results for labor productivity higher levels of labor productivity. Focusing
continue to hold even after accounting for on the mean level of labor productivity,
differences in basic firm characteristics including there is no significant difference in the gap
firm-size (log of number of employees), age of between micro and informal firms surveyed
the firm, sector of activity, regional location, with respect to firm’s age, sector of activity
and the number of years of experience of the (manufacturing vs. services), and firm-size
main decision-maker. (number of employees). However, the gap
does vary significantly across regions (Figure 5).
KENYA INFORMAL ENTERPRISES 11
3. Productivity
Figure 5: Labor productivity is lower among informal firms compared with formal micro firms, but the gap varies by region
12.0 11.2 11.3
10.8 10.9
9.7 9.7 10.1
10.0 9.6
Monthly sales per worker (KES, logs)
9.2 9.2
8.0
6.0
4.0
2.0
0
Central Nyanza Mombasa Nairobi Nakuru
Informal Micro
Source: Kenya Informal Enterprise Survey, 2013
Labor productivity is significantly higher for lower in Mombasa and Nyanza compared with
formal micro firms compared with the sampled the other three regions (panel A, Figure 6).4
informal firms in all the regions, but the gap For instance, in Nairobi, labor productivity
is significantly smaller in Nakuru than in any of is almost twice the level in Mombasa. These
the other four regions. results are robust to some basic controls such
as firmsize (number of employees at the firm),
While there is substantial work on firm’s age, sector (manufacturing vs. services),
determinants of labor productivity for firms in gender of the manager, and the level of
the formal or registered sector, there is little education of the manager.
work in this area for informal sector firms. For
instance, studies of formal sector firms show There is a fair amount of research on the
that labor productivity and other measures of impact of firmsize on firm productivity. Large
firm-performance are much higher for older firms enjoy economies of scale while small
firms, firms that are larger, and firms managed firms tend to be more flexible and adapt more
by men rather than women. Regional or sub- quickly to new market opportunities. While the
national differences have also been found in a majority of the evidence in this area suggests
number of studies. that large firms have higher productivity than
small firms, the contrary evidence cannot be
For the informal firms surveyed in Kenya, the neglected. The issue of firmsize is of special
mean value of labor productivity equals KES interest to the informal sector. One view is that
22,481, and the median value is KES 13,000.3 informal firms are inefficiently small and hence
However, there are sharp differences in labor not capable of contributing to vibrant growth
productivity along a number of dimensions. of the private sector.
Across regions, labor productivity is significantly
Labor productivity is defined as value of sales per
3
employee in a regular month over the last one year. While
4
Unless stated otherwise, all the results discussed below
this is only one measure of firm performance, it provides are statistically significant at the 10 percent level or better
useful information on how productive labor is on average. and are robust to region fixed effects.
12 KENYA INFORMAL ENTERPRISES
3. Productivity
Can we expect firm productivity to improve than large informal firms where firmsize is
as informal firms get bigger? There is very measured by the number of employees at
little by way of formal work on this issue, and the firm. They conclude that even though
the studies that do exist show mixed results. poor performance of informal firms is typically
For example, Benjamin and Mbaye (2012) use attributed to their small size vis-à-vis registered
survey data of 900 formal and informal firms that or formal sector firms, incremental increases in
they collected in West Africa. They distinguish the size of informal firms do not necessarily
between the relatively large vs. small informal imply a narrowing of the formal-informal firm
firms and find that the large informal firms productivity gap.
have much higher productivity (labor and total
productivity) than the small informal firms. While a proper analysis of the firm-size and
The authors suggest that the large informal productivity relationship for Kenya would
firms are at the fringes of the formal-informal require a rigorous empirical analysis beyond
divide and therefore much closer to the formal the scope of this note, preliminary results
sector firms in terms of productivity and other for Kenya show that increasing firmsize may
characteristics than the small informal firms. A not necessarily translate to higher labor
similar result is found by McKenzie and Sakho productivity. That is, for the informal firms
(2010) who find that owners of large firms that surveyed in Kenya, labor productivity is lower
have managed to stay informal have higher for the relatively larger firms and significantly
entrepreneurial ability than owners of formal so, once region specific and sector specific
firms, potentially indicating higher productivity differences in labor productivity are taken into
of large informal firms over small informal account. For example, labor productivity for
firms. However, Amin and Islam (2015) use firms with a single employee averages KES
data for over 500 informal or unregistered firms 24,096 while labor productivity for firms with
in seven countries in Africa and find different more than one employee averages KES 21,628
results. They find robust evidence that small (panel B, figure 6).
informal firms have higher labor productivity
Figure 6: Variations in labor productivity of informal firms
Panel A Panel B Panel C
35 25 24 30 28
31
24
30 25
Montly sales per worker (KES '000)
Montly sales per worker (KES '000)
Montly sales per worker (KES '000)
26 24 22
25 21
23 20
20
20 18 23
17 15
15 22 22
22 10
10
21
5 5
21
0 20 0
More than one Single
asa
za
ru
al
bi
Services Rest of Furniture
ntr
employee
iro
employee rm
ku
an
mb
Na
Na
Ny
Ce
manufacturing
Mo
Source: Kenya Informal Enterprise Survey, 2013
KENYA INFORMAL ENTERPRISES 13
3. Productivity
One explanation here could be that a larger among relatively older firms.6 The importance
firmsize raises evasion costs associated with of human capital and the level of education
being informal and this evasion expenditure for overall economic development is now
affects firm performance. However, it is also well established. Some work is also beginning
possible that the most productive large firms to emerge explaining differences in labor
formalize, biasing labor productivity among productivity between formal and informal
the remaining large informal firms towards a firms based on the level of education of firm
lower level. managers.
Sector specific differences in labor For the case of informal firms surveyed in
productivity are also observed in the sample Kenya, as predicted above, labor productivity
of informal firms. Labor productivity is much increases with a firm’s age (panel A, figure
higher in the manufacturing sector compared 7). For example, labor productivity for
with the services sector. Further, sampled firms firms above the median age of four years
in the furniture industry stand out with a labor averages KES 25,505 compared with a much
productivity level that is significantly higher lower KES 19,649 for the remaining firms.
than for firms surveyed in the services sector There is no difference in the age-to-labor
and the rest of manufacturing (panel C, figure productivity relationship between firms in the
6). For example, labor productivity for firms in manufacturing and services sector, by firmsize
the furniture industry is about 1.3 times the (number of employees), and the gender of the
level in the rest of the sample. Differences manager. However, firms in the furniture sector
in location of firms, firmsize, age of the firm, again stand out with younger firms showing a
and the education level of the manager do much higher level of labor productivity than
not seem to the driving force behind these older firms (panel B, figure 7).
productivity differences.
The education level of manager is highly
Labor productivity for firms surveyed is higher correlated with the level of labor productivity
among relatively older firms and firms with of the surveyed firm (panel C, figure 7). For
more educated managers.5 example, labor productivity for firms with
managers that have no education or only
A fairly large literature exists on differences primary education is only 72 percent of that
in firm productivity depending on the age of of firms with managers that have vocational
the firm. Natural selection, whereby the less training or a university degree. Education
efficient firms are weeded out, and learning- matters for labor productivity for the sampled
by-doing effects that favor longer tenures firms in both the manufacturing and services
suggest that firm productivity should be higher sector.
6
Interestingly, for formal firms in the manufacturing sector in
Kenya, this does not hold true. In some subsectors—and
for the manufacturing sector as a whole—low-productivity
firms employ more workers than high-productivity firms.
This result contrasts with results for the European Union,
5
The positive relationship between labor productivity and a where low-productivity firms are always smaller than the
firm’s age becomes statistically weak and insignificant at the median-productivity firm and high productive firms are
10 percent level when we control for the number of years of 5–12 times larger than the median-productivity firm (see
experience of the main decision maker in the industry. Kenya Economic Update, December 2014, Issue 11).
14 KENYA INFORMAL ENTERPRISES
3. Productivity
Figure 7: Education level of the manager is positively correlated with labor productivity of the informal firms
Panel B: Furniture industry
4 2
Labor productiivty (KES, logs, residuals)
Labor productiivty (KES, logs, residuals)
2 1
0 0
-2 -1
-4 -2
-2 -1 0 1 2 -2 -1 0 1 2
coef = .13651961, (robust) se = .05337474, t = 2.56 -.37555935, (robust) se = .13841094,
coef = - t = 2.71
Panel C
30 Manufacturing Services 26
26
24
Monthly sales per worker (KES '000)
25 22
21
20 19
16
15
10
5
0
No or primary Secondary Vocational training
education education or university degree
Source: Kenya Informal Enterprise Survey, 2013
Note: Panel A and B contain partial scatter plots obtained after controlling for regional fixed effects
There is also evidence that gender disparity also holds when we look at median values
is less among informal firms surveyed than instead of the mean values (as above) of labor
among the formal sector firms. That is, while productivity. That is, for the sample of informal
labor productivity is significantly lower for firms firms, the median labor productivity for female
with a female manager among informal and vs. male managed firms is KES 12,250 vs. KES
formal micro firms surveyed, this gender-based 13,167, respectively. For the formal micro firms,
gap is significantly smaller for the informal median labor productivity for female managed
firms surveyed compared with firms in the firms equals KES 31,250 compared with KES
formal sector. Average labor productivity for 61,111 for male managed firms.
the surveyed informal firms managed by men
is higher by KES 6,881 (KES 25,290 vs. KES This note also provides some analysis to
18,409). The corresponding gap for the formal explore whether improvements in the business
micro firms is much larger at KES 125,456 (KES environment translate into higher levels of
219,675 vs. KES 94,219), which in relative terms productivity, replicating a similar analysis by
is roughly three times as large. This result for Gelb et al (2009). They speculate that when
the gender-based gap in labor productivity the business environment improves, gaps
for informal vs. formal micro firms surveyed in productivity between formal and informal
KENYA INFORMAL ENTERPRISES 15
3. Productivity
firms will emerge. In Kenya, evidence suggests very little distinction between the productivity
that between 2007 and 2013 the business of formal and the sample of informal firms
environment changed significantly, and over in 2007, formal firms’ productivity became
the same period productivity gaps between substantially higher than informal firms by 2013.
formal and informal firms surveyed emerged. (See Annex 3 for the methodological approach
In other words, the investment climate has and empirical findings).
changed in such a way that while there was
16 KENYA INFORMAL ENTERPRISES
SECTION FOUR
FIRM DYNAMICS
F irm dynamics, measured by an increase in
employees, machines, and space used by
the firm, suggests that firms in the furniture
with a firm’s age. Among firms that are older
than the median age (four years in our sample),
about 32 percent expanded compared with a
industry, older firms, firms with more educated much lower 22 percent of younger firms. As we
managers, and those located in the Central might expect, education level of the manager
and Nairobi regions are more dynamic. is significantly positively correlated with the
probability of firm expansion. Seventeen
In a survey question, firms were asked if over percent of firms surveyed with managers
the last three years they had expanded the that have no education or primary education
number of employees, machines, or space expanded over the last three years. The
used. In another question, firms were asked corresponding figure for the remaining firms
about the current number of workers at the that have managers with secondary education,
firm and when the firm started operations. vocational training, or university degrees is
Firms that answered in the affirmative to the significantly higher at 31 percent.
first question are defined as dynamic firms. A
second definition of dynamism is if the number Again, in terms of expansion, manufacturing
of employees at the firm increased since it firms outperform services firms with 31
began operations. The two measures overlap percent of the former vs. a significantly
but not entirely with correlation coefficient of lower 24 percent of the latter in our sample
0.37. The results discussed below hold for both experiencing expansion. It should be noted
measures in the qualitative sense and so we that this difference between manufacturing and
focus only on the first measure. It should be services firms is entirely driven by the furniture
noted that information on exiting firms or firms industry. Approximately 43 percent of firms in
that close down is not available in the survey. the furniture industry surveyed experienced
Since exiting firms have different dynamics expansion, compared with a significantly lower
than the surviving firms, our results below for 27 percent of firms in the rest of manufacturing
firm dynamics are potentially biased as far as and 24 percent of firms in the services sector.
the whole sample is concerned. The difference between firms in the services
sector and the rest of manufacturing discussed
About 27 percent of the informal firms surveyed here is not significant.
increased employees, machines, or space used
(henceforth, expanded or expansion) over the We also looked at the regional level and found
last three years. There is substantial literature firms surveyed in the Central and Nairobi
that suggests that younger firms are more regions to be significantly more dynamic
dynamic than older firms. We find no evidence than in Nyanza and Mombasa in terms of the
of this in our sample. In fact, the probability of percentage of firms that expanded (Figure 8).
expansion is significantly positively associated We examined a number of business climate
KENYA INFORMAL ENTERPRISES 17
4. Firm Dynamics
measures but found no consistent pattern to bribe payments. The percentage of firms
of any relationship with the likelihood of firm that expanded is significantly lower among
expansion in our sample. For example, the firms that report making informal payments
percentage of firms that expanded is only or bribes to remain unregistered (18 percent)
poorly correlated with whether or not the compared with the rest of the firms (31 percent).
firm faced power outages or water shortages. This finding regarding informal payments does
Expansion is also poorly correlated with not hold for our second definition of a dynamic
measures of crime and security, and with firm based on employment growth since the
various firm perceptions about factors such as firm started operating and may signal a weaker
land and access to finance being an obstacle for relationship between bribery and workforce.
their business. One exception we find relates
Figure 8: Percentage of firms that increased number of employees, machines, or space used over the last three years varies across regions
50
45 43
40 38
36
35
Percentage of rms
30 27 27
26 26
25
20 18
15
15
10
5
0
All rms Central Nyanza Mombasa Nairobi Nakuru Furniture Manufacturing Services
Increased workers, machines or space used in last three years
Source: Kenya Informal Enterprise Survey, 2013
18 KENYA INFORMAL ENTERPRISES
SECTION FIVE
REMAINING INFORMAL
F ormalization, or bringing the informal
firms within the fold of the formal sector,
is suggested as a possible solution to low
percent of small formal firms, 5 percent of
medium formal firms, and only 2 percent of
large formal firms were not registered at start-
income levels and lack of dynamism in the up. The median length of operations without
informal sector. Moving to the formal sector registration for these previously informal firms
is expected to improve access to physical is one or two years for all size categories. It
infrastructure, finance, and public services; the seems that the opportunity for becoming
move also benefits the government through formal may be associated with what takes place
better compliance with the laws and more in the earliest year or two of a startup.
tax revenue. However, the move to the formal
sector has been notoriously difficult to achieve In our sample of informal firms, larger firms (in
and slow in most countries. Hence, an important terms of sales, employment) are significantly
question here is whether informal firms want to more likely to report willingness to register
register, and what sorts of informal firms are than smaller firms. Sixty percent of firms with
more likely to do so. more than one employee report wanting to
register compared with just 49 percent of firms
The informality survey in Kenya asked firm with a single employee. Second, manufacturing
owners if they would like their firms to firms report wanting to register significantly
be registered. Close to 53 percent of the more than services firms, but again, this
firm owners surveyed responded ‘Yes’ to difference is entirely due to the furniture sector.
the question. The desire to register is more That is, 70 percent of the surveyed firms in the
common in our sample among firms that are furniture industry report wanting to register,
larger and more dynamic, firms in the furniture and this is significantly higher than the 53
industry, firms located in Nyanza region, and percent in the remaining manufacturing sector
firms that face water, electricity, crime, access and 49 percent in the services sector.
to land, access to finance, and corruption
constraints. Third, regional differences are noticeable
with the proportion of sampled firms wanting
Comparing the behavior of firms that remain to register being significantly higher in the
informal with formal firms that began in the Nyanza region (80 percent) than in any of
informal economy suggests that there may be the other regions. Firms in the Central region
little crossover between the groups. La Porta report a desire to register only 33 percent of
and Schleifer’s recent paper confirms that very the time, and this is significantly lower than the
few firms crossover from the informal to formal corresponding figures for Nyanza, Nairobi and
sector;7 21 percent of micro formal firms, 11 Nakuru regions.
7
Rafael La Porta, and Andrei Shleifer, (2014), “Informality
and Development,” Journal of Economic Perspectives,
28(3): 109-126.
KENYA INFORMAL ENTERPRISES 19
5. Remaining Informal
Figure 9: Willingness to register is higher among firms that consider the various obstacles as severe for their business operations
80
Percentage of rms who would like to register
72
70 64
57 60 58
60
49 49 46
50 43 41
40
30
20
10
0
Access to land Electricity Water supply Access to nance Crime
Severe obstacle Not severe obstacle
Source: Kenya Informal Enterprise Survey, 2013
Fourth, if informal firms expect formalization firms not registering, but there are sharp
to ease the difficulties they face in obtaining differences by region, firm productivity, and
finance, accessing electricity, water, and other education level of the manager. In the survey,
public services, and dealing with corruption firms were asked if the following were reasons
and harassment from public officials, the why they had not registered: cost of registering
willingness to register may be higher among (time, fees and paper work required), taxes that
firms that consider these problems to be registered business have to pay, inspections
more constraining relative to other firms. and meeting with government officials post
The survey for Kenya does not reject such a registration, bribes registered businesses need
possibility. That is, firms that consider these to pay, and no benefit from registering.
obstacles to be severe are significantly more
likely to show willingness to register than firms Figure 9 shows how surveyed firms view
that do not find these obstacles to be severe. these costs. In the full sample, taxes
Figure 8 provides more detail on this issue. following registration are cited as a reason
for not registering for 57 percent of the
The costs associated with registering and firms, followed by the cost of registering
taxes that registered businesses have to pay (56 percent), no benefit from registering (47
are the most common reasons for surveyed percent), inspections and meetings required
Figure 10: Reasons for not registering vary across regions and by education level of the manager
100
Reason for not registering (% of rms)
90 85 84 86
80
80
70 65 66 65 66
59 57 58 58
60 53 54 55 57
50 48 52 52
50 44 45 42
40 38 35 33 37 37 36 37
35 31 31 35
30 26 22
20
10 9 7 10
10
0
Central Nyanza Mombasa Nairobi Nakuru No or Primary Secondary education Vocational or
education University degree
Time, fees and paper work required for registering Taxes paid by registered businesses Inspections and meeting with public o cials
Bribes that registered rms need to pay No bene t from registering
Source: Kenya Informal Enterprise Survey, 2013
20 KENYA INFORMAL ENTERPRISES
5. Remaining Informal
(37 percent), and bribes paid (36 percent). from registering would help further the cause
Considered individually, these reasons for not of formalization.
registering show significant variation across
different firm types. For example, older firms In the survey, firms were asked if registering
are significantly more likely to report bribe would bring the following potential benefits:
payments and no need to register as reasons better access to finance; better access to raw
for not registering compared to younger firms. materials, infrastructure, and government
The cost of registering disincentivizes a higher services; less bribes to pay; and being able to
proportion of relatively larger firms (sales and issue receipts to customers. About 77 percent
employment wise), and controlling for region of firms surveyed consider better access to
specific effects, more dynamic firms are more finance as a benefit, followed by better access
likely to report taxes that registered businesses to raw materials, infrastructure and government
have to pay as a reason for not registering. services (61 percent), issue of receipts (42
Interestingly, 21 percent of firms reported percent) and less bribes to pay (40 percent).
having to pay a bribe in order to remain Regional differences abound. For example,
unregistered and continue operations. less bribes to pay is sees as a potential benefit
for over half of firms in Mombasa, Nairobi, and
The most glaring differences in reasons given Nakuru regions. This is significantly higher than
for not registering by the surveyed firms are what we find in Nyanza (21 percent) and the
seen across regions, labor productivity, and Central region (4 percent).
the education level of the manager. Higher
labor productivity is associated with a higher Panel A of figure 10 contains the full distribution
proportion of firms reporting each of the of regional differences. Controlling for region
above as reasons, with the exception of paying specific differences (region fixed effects),
taxes, for not registering. Figure 9 shows the firms that are larger in terms of monthly sales
distribution by region and the education level and firms that have higher labor productivity
of the manager. Many of the differences shown are significantly more likely to report each
in these figures are significant. For example, of the above factors as potential benefits of
no benefit from registering is a reason for only registering. For example, being able to issue
9.5 percent of the firms in Nyanza region, and receipts is a potential benefit of registering for
significantly lower than what we find in each of 41 percent of the firms that exhibit lower labor
the other regions. productivity compared with much higher 48
percent of firms with higher labor productivity.
The findings in the previous paragraph shed
light on the possible course of policy measures The benefits to registering also seem to be
to facilitate registration. That is, to the extent reported more frequently among firms that
that firm’s perceptions regarding the various feel constrained in their current operations.
obstacles discussed above are due to lack of Firms that report access to finance as a severe
proper information, policies aimed at providing obstacle for their business are more likely to
better information to the firms would be useful; consider better access to finance following
and where the perceptions mirror objective registration to be a potential benefit (panel B,
reality, policies aimed at reducing registration figure 10). The same holds for firms that report
costs, taxes, corruption and improving benefits corruption as a severe obstacle and perceive
KENYA INFORMAL ENTERPRISES 21
5. Remaining Informal
Figure 11: Perceived benefits ofregistration vary by region and firms’perceived severity of the obstacles
Panel A: Bene ts from registering by region
100
90
90 83
82
77 78 75
Bene t from registering (% of rms)
80
71
70 67
61 59
60 55 55
50 50 47
50 44 46
40 40 42 40
30
30
21
20 17
10 4
0
All rms Central Nyanza Mombasa Nairobi Nakuru
Better access to nance Better access to raw materials, infrastructure and government services Less bribes to pay Being able to issue receipts
Panel B: Better access to nance is a bene t from registering Panel C: Less bribes to pay is a bene t from registering
100 60
53
84
50
80
Percentage of rms
Percentage of rms
63 40 37
60
30
40
20
20 10
0 0
Less bribes to pay
Access to nance is a severe obstacle Access to nance is not a severe obstacle Corruption is a severe obstacle Corruption is not a severe obstacle
Source: Kenya Informal Enterprise Survey, 2013
less bribes to pay as a potential benefit of on firm’s perceptions. One problem with
registration (panel C, figure 10), and among such perceptions is that they may not always
firms that report access to land as a severe reflect the underlying objective reality of
obstacle and better access to raw materials, the costs and benefits of registering. For
physical infrastructure, and government instance, lack of proper information may bias
services as a potential benefit of registration. a firm’s perceptions. Fortunately, in the case
Interestingly, we do not find any significant of Kenya, the World Bank’s Sub-National
correlation between the potential benefit of Doing Business project provides information
better access to raw materials, infrastructure, on select business environment measures for
and government services and whether or Mombasa, Nairobi, and Nakuru regions. The
not electricity and water supply are severe Sub-National Doing Business measures cover
obstacles for firms’ current operations. areas including starting a business, registering
property, enforcing a contract, and dealing
with a construction permit. We find some
The discussion above as to whether or not evidence that, at least to some extent, firms’
firms would like to be registered, as well perceptions reflect objective reality. That is,
as the obstacles to registering are based the proportion of firms surveyed that would
22 KENYA INFORMAL ENTERPRISES
5. Remaining Informal
like to be registered is significantly higher in high cost of registering as to why they are not
regions where registering a business is less registered is 54 percent, 65 percent, and 84
cumbersome to the firms (overall composite percent in these three regions, respectively.
measure of registering based on the number
of procedures, time and cost of registering, In terms of the reported benefits from
and the minimum paid up capital required). registering, a better contract enforcement
Figure 11 provides the details. system, as measured by Sub-National Doing
Business (composite measure of procedures,
We also find that more cumbersome business time and cost of enforcing contract), is also
registration processes are associated with associated with a proportionately larger number
proportionately more firms on average that of the sampled firms that report being able to
report a high cost of registering as a reason issue receipts to customers and suppliers as
for not registering, although this result does a benefit of registration. However, this result
not hold for Nakuru and Mombasa (Figure does not hold for Nairobi and Nakuru; the
11). Looking separately at the time and the result is also statistically insignificant in the full
monetary cost of registering as measured by sample. Figure 12 provides the details.
Sub-National Doing Business project, the
proportion of surveyed firms that report high A more cumbersome business registration
costs (time, fees, etc.) as reasons why they are system, as measured by Sub-National Doing
not registered is significantly higher in regions Business, is associated with lower labor
with high time cost (as measured by Doing productivity and a smaller firmsize of informal
Business), but there is no such relationship for firms surveyed. While business registration
the Sub-National Doing Business’ monetary is not the only element of the business
cost of registering. For example, according to environment that may be important to informal
Sub-National Doing Business, it takes 32 days sector firms, it is perhaps the most important
to register a business in Nairobi, followed by proxy measure of broader institutional
37 days in Mombasa, and 38 days in Nakuru. environment faced by them. As above, we use
The percentage of firms surveyed that cite a the composite Sub-National Doing Business
Figure 12: Ease of registering a business is associated with greater Figure 13: Better contract enforcement in Mombasa is associated
willingness among informal firms to register with more firms reporting being able to issue receipts to customers
and suppliers as a benefit of registration
90 56 55
84
80 54
70 65
Percentage of rms
61 52
60 54
Percentage of rms
52 50
50
40 48
40 47
46
30 46
20
44
10
42
0
Nairobi Nakuru Mombasa
(Best ranked in ease of registering (Worst ranked in ease of registering 40
a business, Doing Business) a business, Doing Business) Mombasa Nakuru Nairobi
(best ranked by Doing Business (worst ranked by Doing Business
% of rms that would like to be registered in contract enforcement) in contract enforcement)
% of rms that for whom high registration cost is a reason for not registering Being able to issue to receipts is a bene t due to registration
Source: Enterprise Surveys Source: Kenya Informal Enterprise Survey, 2013
KENYA INFORMAL ENTERPRISES 23
5. Remaining Informal
ranking for starting a business in terms of the in Nairobi, the best ranked region; this is not
number of procedures required to register, too different from the mean of 3.9 employees in
the time it takes to complete the procedures, the next best region of Nakuru. However, firms
the cost of complying with the registration in Mombasa, the worst ranked region, hire only
procedures, and the minimum paid up 2.5 employees, significantly less than what we
capital required. For this composite measure find in Nairobi as well as in Nakuru. Figure 16
and for the firms surveyed, Nairobi is the provides the details for labor productivity.
best ranked region followed by Nakuru and Figure 14: On average, labor productivity increases with greater
then Mombasa. We looked at both firmsize ease of registering a business
(employment, sales) and labor productivity to 30,000
Labor productivity (KES, median values)
see how firm performance compares across 25,000
25,000
regions depending on the ease of registering
businesses. 20,000
15,000 15,000
15,000
Overall, in our sample of informal firms,
10,000
firmsize and labor productivity are both
5,000
significantly positively correlated with greater
ease of registering a business, although the 0
Nairobi Nakuru Mombasa
(Best ranked in ease of (Worst ranked in ease of
result does not hold for all bilateral regional registering a business, registering a business,
Doing Business) Doing Business)
comparisons. For example, the mean number
Source: Kenya Informal Enterprise Survey, 2013
of employees at the firm equals 3.4 employees
24 KENYA INFORMAL ENTERPRISES
SECTION SIX
SUMMARY AND POLICY ADVICE
T his note provided an overview of the
landscape of informal firms surveyed
by the World Bank’s Enterprise Surveys
from suppliers (19 percent) and microfinance
(16 percent). However, the overwhelming
majority of informal enterprises surveyed draw
in Kenya, with a particular focus on their on finance through internal sources (87 percent)
operating characteristics, key constraints, and family/friends (35 percent). Smaller firms
access to finance, labor productivity, and (as measured by the number of employees) in
constraints and incentives for registration. the survey are more likely to consider access to
Very interesting patterns emerged from the finance as a key constraint, while using supplier
data and analysis, some of which could inform credit or relying on banks is associated with
policy and investment choices of both public larger, more dynamic firms with higher labor
and private sector players. productivity, and better educated owners.
Firstly, in our sample, attributes of the Regional differences are pronounced.
principal owner are important. For example, Mombasa consistently stands out as the
a key finding of the analysis is the role played most challenging region for surveyed firms
by the education of the owner in almost all to access finance, whereas Nakuru is on the
elements of firm performance. More educated opposite end of the spectrum for financial
owners have more dynamic and productive access. Labor productivity is significantly lower
firms, are less financially constrained, more for firms surveyed in Mombasa and Nyanza,
likely to use banks and formal sources of the gap between productivity in the formal
finance for their businesses, and even less likely and informal sector is the highest, and firms
to experience theft and other security-related from these two regions are the least likely
losses. The gender of the owner also matters. to expand and grow. Mombasa and Nyanza
That is, in our sample, female owned firms are have the lowest percent of firms that want
less productive, less dynamic, and pay their to register. On the positive side, there is no
workers less compared to male owned firms. firm in Nyanza that perceives corruption an
obstacle, while crime and electricity are not
Secondly, access to finance is consistently major constraints in Mombasa, compared to
identified as the largest obstacle for informal other regions. Nairobi and Central regions
firms surveyed in Kenya, with over 60 percent consistently stand out with the sampled firms
ranking it as the number one obstacle. Other having highest labor productivity and most
key constraints include electricity, access to dynamic firms, and Nairobi is ranked top in
land, and corruption. Bank credit as a source ease of doing business; however, it is also
of working capital is low, with only 9 percent where corruption as a constraint stands out
of informal firms using banks to finance their relative to other regions.
operations, compared to firms using credit
KENYA INFORMAL ENTERPRISES 25
6. Summary and Policy Advice
Furniture also stands out in many respects when combined with reductions in labor taxes.
amongst all sectors. In terms of finance, From an informal firm’s perspective, there are
surveyed firms in the furniture sector are less also compelling reasons for both becoming
likely to use their own funds, and much more formal and remaining informal. Firms perceive
likely to use supplier credit and bank finance. formalization can lead to better access to credit
Surveyed firms in the furniture sector have,on and protection of property rights, while taxes,
average, the highest labor productivity, the corruption, and bureaucracy are disincentives
most dynamic firms, and are more likely to hire to formalize.
more employees. Firms in this sector are also
more inclined to register their businesses. The question then becomes about identifying
the most effective means to foster business
The majority of firms surveyed prefer to registration in Kenya. While there is evidence
remain informal because of taxes and the that simplifying the process and lowering
cost of registration, especially younger firms the costs to start a business are important
and those that are more dynamic. Conversely, predictors of firm registrations, overall, efforts
the main reason informal enterprises are at formalization through streamlining business
interested in formalizing is greater perceived registration processes are mixed (see Kaplan,
access to finance. The proportion of firms that Peiro and Siera (2007); Straub (2005); McKenzie
want to register in our sample is significantly and Sakho (2007) to name a few.
higher in regions where registering a business
is less cumbersome, and the converse holds Klapper and Love (2010) find that small reforms
true— firms are more likely to not want to (less than a 40 percent reduction in procedures
register in regions with more cumbersome or 60 percent reduction in costs) do not have
registration processes. In terms of impact, a significant effect on new registrations, and
a more cumbersome registration process is that there are important synergies in multiple
linked to lower labor productivity. reforms of two or more business environment
indicators.
A key issue for policy makers is then whether
there is a public rationale for attempting to Kaplan et al (2007) suggest that in cases
formalize small-scale firms. McKenzie and where the impact of reforms are modest
Bruhn (2013) make the case that there are or temporary, it is because of the burden
several compelling reasons to try and bring of complementary procedures and overall
larger and more profitable informal firms into institutional quality. More inclusive programs
the formal system, including increasing revenue could have a much bigger impact on start-
mobilization and widening the tax base, and ups. It should also be noted that burdensome
leveling the playing field between large informal registration regulations may not be the
firms and efficient formal firms which will only important barrier to firm creation or
foster growth and productivity. Sharma (2009) formalization. Instead, the cost of paying taxes
highlights potential gains in labor productivity may still outweigh the benefits of registering,
after business regulation reforms, especially especially when credit is scarce.
26 KENYA INFORMAL ENTERPRISES
6. Summary and Policy Advice
Given the experience globally, and the While bringing some of the larger, more
context in Kenya, this note suggests some productive firms in to the formal sector can
policy recommendations for consideration. benefit Kenya’s growth and employment
Firstly, attempts at business registration trajectory, the reality is that there will remain
and broader business environment reforms, a large cadre of informal firms for whom the
especially at the county level, appear to be costs of registration outweigh the benefits.
having an impact on informal firms’ incentives These small enterprises nonetheless provide
to register, and are linked to increases in labor income and employment to the vast majority
productivity. Therefore, these reforms should of the unemployed, and many of them may
be accelerated and broadened regionally. eventually grow into more dynamic enterprises.
Secondly, there is a compelling case to be Therefore they also merit support. Increasing
made for the impact of business environment the skills of the main owner appears to be the
reforms when they are broader, deeper, and most effective means to increasing productivity
include stronger institutional capacity and and growth, while lowering barriers to financial
stronger enforcement. Therefore, a reform access could further support microenterprises
agenda should entail substantial changes to to increase survival rates and maximize their
the modus operandi, and include support to opportunity to grow and expand.
build the capacity of enforcing institutions.
KENYA INFORMAL ENTERPRISES 27
REFERENCES
Amin, Mohammad, and Asif Islam. (2015). “Are Large Firms More Productive than Small Informal Firms?
Evidence from Firm-level Surveys in Africa.” Mimeograph.
Benjamin, Nancy, and Ahmadou Aly Mbaye. (2012). “The Informal Sector, Productivity, and Enforcement
in West Africa: A Firm-level Analysis.” Review of Development Economics 16(4): 664-680.
Gelb, Alan, Taye Mengistae, Vijaya Ramachandran, and Manju Kedia Shah. (2009). “To Formalize or
Not to Formalize? Comparisons of Microenterprise Data from Southern and Eastern Africa.” Working
Paper 175, Center for Global Development.
La Porta, Rafael, and Andrei Shleifer. (2014). “Informality and Development.” Journal of Economic
Perspectives, 28(3): 109-126.
Mckenzie, David, and Yaye Seynabou Sakho. (2010). “Does it Pay Firms to Register for Taxes? The
Impact of Formality on Firm Profitability.” Journal of Development Economics 91 (2010): 15-24.
World Bank. (2013).“Kenya Informal Enterprise Survey.”
World Bank. (2014). “Anchoring High Growth.” Kenya Economic Update Issue 11, December 2014.
28 KENYA INFORMAL ENTERPRISES
Annex 1: Summary statistics and regressions
TABLE 5: Summary Statistics for the Full Sample of Firms in Kenya, Informal Survey (2013)
Std. 95% confidence
Variable Observations Mean deviation Min. Max. interval
% of firms that belong to the manufacturing sector 533 48 50 0 100 44 53
(Log of) Number of workers at the firm during a normal 526 0.3 0.5 0.0 4 0.3 0.4
month
(log of) Total sales (LCUs) of the firm during a normal 483 10 1 7 14 10 10
month
(Log of) Sales per worker during a normal month 483 9 1 7 13 9 10
% of firms located within household premises 533 13 34 0 100 10 16
% of firms that have more than one business activity 427 18 38 0 100 14 21
% of owners of the firm that are female 530 38 47 0 100 34 42
% of firms that have at least one female owner 530 40 49 0 100 36 45
Largest owner acquired ownership of the firm by 529 94 23 0 100 92 96
starting the business alone or with partners (% of
firms)
% of firms with a female main decision maker 530 38 49 0 100 34 42
% of firms that have a married largest owner 528 76 43 0 100 73 80
Number of years the largest owner has lived in the city 505 18 13 1 56 17 20
where the business is located
Largest owner migrated to the city where the business 505 79 41 0 100 75 82
is located from another city in the country or from
another country (% of firms)
Largest owner currently has a job in the formal sector 524 15 35 0 100 11 18
or has been looking for one over the past two years (%
of firms)
For firms that use electricity, number of power outages 233 7 15 0 144 5 9
faced during the last month including no power
outages
For firms that use electricity, % of electricity from 245 0.2 2 0 25 0.0 0.4
generators including zero for firms that do not own/
share/use a generator
For firms that use water for business purposes, number 112 2 3 0 15 1 2
of incidents of water insufficiency during the last
month including zero for firms with no such incidents
Amount paid for security as a percentage of total sales 509 1 5 0 67 1 2
in a regular month including zero amount for firms that
did not pay for security
Losses due to crime during the last month as a 528 3 19 0 333 1 5
percentage of sales in a regular month including zero
losses for firms that had no such losses
Number of crime incidents experienced by the firm in 530 0.1 1 0 6 0.1 0.2
the last month including zero incidents for firms with
no such incidents
% of firms for whom own funds are the most 481 77 42 0 100 74 81
commonly used source of finance for their day-to-day
operations
KENYA INFORMAL ENTERPRISES 29
Annexes
Std. 95% confidence
Variable Observations Mean deviation Min. Max. interval
For firms that bought any machinery, vehicles or other 99 76 43 0 100 67 84
means of transport, equipment, land or buildings
during the last three months, % reporting own funds
as the most important source of finance for the
purchase
Number of family members of the owners working at 523 44 47 0 100 40 48
the firm as a percentage of all workers during the last
month
% of firms that have a physical location 533 78 41 0 100 75 82
Number of owners in the business 531 1 0 1 3 1.1 1.1
% of firms that have a female largest owner 530 39 49 0 100 35 43
Number of businesses or activities started by the 523 1 1 0 10 0.9 1.0
largest owner in the last three years
For the sample of firms whose largest owner started 414 1 0 0 5 1.0 1.1
a business during the last three years, number of
businesses still owned or managed by the largest
owner
% of firms that had an increase in the number of 528 27 44 0 100 23 31
employees, machinery used or the space occupied
during the last three years
% of firms where the largest owner is also the main 532 97 18 0 100 95 98
decision maker
% of firms with a female main decision maker 530 38 49 0 100 34 42
Number of years of experience that the main decision 523 8 7 0 50 7 9
maker has working in the sector
Age of the firm 522 6 6 0 43 6 7
Number of employees at the firm when the firm 520 1 1 1 8 1.3 1.5
started operations
% of firms that were registered at start up 528 1 11 0 100 0.3 2.3
Age of the largest owner 520 35 9 18 85 34 36
% of firms that have a married largest owner 528 76 43 0 100 73 80
For the sample of firms with a largest owner who 416 64 48 0 100 60 69
has not spent his/her entire life in the city, % of firms
where the largest owner migrated from a smaller city
For the sample of firms with a largest owner who 416 26 44 0 100 22 30
has not spent his/her entire life in the city, % of firms
where the largest owner migrated from a bigger or
same size city in the same country
For the sample of firms with a largest owner who 416 10 30 0 100 7 13
has not spent his/her entire life in the city, % of firms
where the largest owner migrated from a different
country
Number of people who live in the largest owner's 522 4 2 0 35 3.6 4.0
household premises
Number of people less than six years old who live in the 524 1 1 0 8 0.7 0.8
largest owner's household premises
Number of people in largest owner's household 526 0.3 1 0 2 0.2 0.3
premises who have employment under a contract
% of firms with largest owner having no education or 516 30 46 0 100 26 34
primary education (completed or not)
30 KENYA INFORMAL ENTERPRISES
Annexes
Std. 95% confidence
Variable Observations Mean deviation Min. Max. interval
% of firms with largest owner having secondary 516 34 47 0 100 30 38
education (completed or not)
% of firms with largest owner having vocational 516 36 48 0 100 32 40
training or university training (completed or not)
% of firms with ether of largest owner's parents having 454 66 47 0 100 62 71
no education or primary education (completed or not)
% of firms with ether of largest owner's parents having 454 16 37 0 100 13 19
secondary education (completed or not)
% of firms with ether of largest owner's parents having 454 18 38 0 100 14 21
vocational training or university training (completed
or not)
% of firms with largest owner's parents owning a 499 42 49 0 100 38 46
business in the past or currently
Prior to starting this business, % of firms with largest 521 23 42 0 100 20 27
owner employed in the same activity as the current
business
Prior to starting this business, % of firms with largest 521 22 41 0 100 18 25
owner employed in a different activity than the current
business
Prior to starting this business, % of firms with largest 521 15 35 0 100 12 18
owner self- employed in a different activity than the
current business
Prior to starting this business, % of firms with largest 521 14 35 0 100 11 17
owner self- employed in a same type of activity as the
current business
Prior to starting this business, % of firms with the 521 22 41 0 100 18 25
largest owner being unemployed
Prior to starting this business, % of firms with the 521 4 21 0 100 3 6
largest owner's employment status was different from
above
For firms with largest owner not being unemployed 205 53 50 0 100 46 60
and not being in the same activity as the current
business prior to starting this business, % who changed
activity because the change offered a more attractive
business activity
For firms with largest owner not being unemployed 205 13 33 0 100 8 17
and not being in the same activity as the current
business prior to starting this business, % who
changed activity because change offered better hours
or better location
For firms with largest owner not being unemployed 205 34 48 0 100 28 41
and not being in the same activity as the current
business prior to starting this business, % who changed
activity because the owner could not open a business
in the same activity or desired location or for other
(than above) reasons
% of firms with largest owner currently having a job in 523 4 20 0 100 2 6
a formal (registered) business
% of firms whose largest owner tried to get a job in the 503 11 31 0 100 8 14
formal sector during the past two years
Among firms whose largest owner tried to get a job 55 13 34 0 100 4 22
in the formal sector during the past two years, % of
largest owners who got the job
% of firms whose largest owner has insurance 513 10 30 0 100 8 13
KENYA INFORMAL ENTERPRISES 31
Annexes
Std. 95% confidence
Variable Observations Mean deviation Min. Max. interval
For firms located inside household premises, % 70 60 49 0 100 48 72
reporting the main reason to be located inside is that it
costs less to run the business from home
For firms located inside household premises, % 70 29 46 0 100 18 39
reporting the main reason to be located inside is that
it is easier to manage family responsibility along with
work
For firms located inside household premises, % 70 11 32 0 100 4 19
reporting the main reason to be located inside to
be other than above or that there is no benefit from
locating inside
For firms located outside of household premises, % of 462 45 50 0 100 41 50
firms that have fixed premises and with permanent
structure
For firms located outside of household premises, % 462 42 49 0 100 37 46
of firms that have fixed premises and with temporary
structure
For firms located outside of household premises, % of 462 13 34 0 100 10 16
firms that have no fixed premises
Total area occupied by the business or activity (square 449 45 183 2 3025 28 62
meters)
Owner or owners own the location or space occupied 472 13 34 0 100 10 16
by the business (% of firms)
Among businesses whose owners do not own the space 409 82 39 0 100 78 86
occupied by the business, % who pay rent for the space
occupied
For firms whose owners own the space occupied by the 313 18 38 0 100 14 22
business, % of firms whose owners have a title for the
space occupied at the land registry
Firm changed its main business location over the last 461 5 21 0 100 3 7
12 months due to lack of formal title for its land (% of
firms)
Business is located in an industrial zone or cluster (% 471 16 37 0 100 13 19
of firms)
Business is located in the city center (% of firms) 473 7 26 0 100 5 9
Limited access to land is a severe obstacle to firm's 467 41 49 0 100 37 46
operations (% of firms)
% of firms who use electricity 473 52 50 0 100 47 56
For firms that use electricity, % that are connected to 245 76 43 0 100 70 81
the electricity grid
For firms that use electricity, % of firms that 244 84 37 0 100 79 88
experienced power outages during the last month
For firms that use electricity and report having power 193 8 16 1 144 6 10
outages in the last month, number of power outages
faced by the business in the last month
For firms that use electricity and report having power 197 7 25 1 336 4 11
outages in the last month, average duration (hours) of
power outages in the last month
% of firms that own or share a generator 245 2 15 0 100 0.5 4
For firms that own or share a generator, % of electricity 6 8 9 2 25 -1.4 17
that comes from generators
% of firms that use water for business purposes 472 37 48 0 100 32 41
32 KENYA INFORMAL ENTERPRISES
Annexes
Std. 95% confidence
Variable Observations Mean deviation Min. Max. interval
For firms that use water for business purposes, % who 175 54 50 0 100 47 62
obtain water form public sources
For firms that use water for business purposes, % who 175 35 48 0 100 28 42
obtain water from private sources
For firms that use water for business purposes, % who 175 11 31 0 100 6 16
obtain water from both public and private sources
For firms that use water for business purposes, % of 114 43 50 0 100 34 52
firms that experienced insufficient water supply during
the last month
For firms that use water for business purposes and 47 4 3 1 15 3 5
those who report insufficient water supply during the
last month, number of incidents of water insufficiency
in the last month
Average duration of insufficient water supply during 45 15 32 1 160 6 25
the last among firms who use water for business
purposes and experienced insufficient water supply
incidents during the month
% of firms reporting electricity problems as a severe 468 38 49 0 100 34 43
obstacle to their current operations
% of firms reporting water problems as a severe 468 23 42 0 100 19 27
obstacle to their current operations
% of firms who paid for security during the last month 533 19 39 0 100 15 22
For firms who paid for security during the last month, 76 8 12 0 67 5 10
total spending on security during the last month as a
percentage of monthly sales
% of firms who experienced losses due to crime during 532 7 25 0 100 5 9
the last month
Losses due to crime during the month as a percentage 33 47 60 5 333 25 68
of monthly sales among firms who had positive losses
due to crime in the last month
Number of incidents of crime in the last month among 35 2 1 1 6 1 2
firms who experienced losses due to crime in the last
month
% of firms who believe that firms like themselves give 34 53 51 0 100 35 71
informal payments or bribes or protection payments in
order to stay in business
Business experienced harassment by government 35 60 50 0 100 43 77
officials during the last month (% of firms)
% of firms who report crime as a severe obstacle for 528 28 45 0 100 24 32
their operations
% of firms who report corruption as a severe obstacle 531 33 47 0 100 29 37
for their operations
% of firms who produce or sell under contract for 533 9 29 0 100 7 12
another business or person
Number of years the firm has worked with its primary 48 3 2 1 10 2 4
supplier of its main input or sales item
Hours of normal operation of the firm per week 532 65 20 3 126 63 67
% of firms that presently use cell phones for their 533 76 43 0 100 72 80
operations
% of firms that presently use internet for their 533 3 16 0 100 1 4
operations
KENYA INFORMAL ENTERPRISES 33
Annexes
Std. 95% confidence
Variable Observations Mean deviation Min. Max. interval
% of firms that presently use machinery, vehicles or 533 47 50 0 100 42 51
other means of transport or equipment
For firms that presently use machinery, vehicles, other 237 46 50 0 100 40 53
means of transport or equipment, % of firms reporting
these as less than 3 years old
For firms that presently use machinery, vehicles, other 237 26 44 0 100 20 31
means of transport or equipment, % of firms reporting
these as 3 to 5 years old
For firms that presently use machinery, vehicles, other 237 20 40 0 100 15 25
means of transport or equipment, % of firms reporting
these as 5 to 10 years old
For firms that presently use machinery, vehicles, other 237 8 27 0 100 5 11
means of transport or equipment, % of firms reporting
these as more than 10 years old
For firms that presently use machinery, vehicles, other 243 35 48 0 100 29 41
means of transport or equipment, % of firms reporting
difficulty with finding spare parts in the last year
For firms that presently use machinery, vehicles, other 242 42 49 0 100 35 48
means of transport or equipment, % of firms reporting
difficulty with repairing in the last year
For firms that presently use machinery, vehicles, other 242 32 47 0 100 26 38
means of transport or equipment, % of firms reporting
difficulty with maintenance in the last year
Business accounts kept separately from household 522 33 47 0 100 29 37
expenses (% of firms)
% of firms that used own funds to finance their day-to- 524 87 34 0 100 84 90
day operations
% of firms that used credit from suppliers or advances 526 19 40 0 100 16 23
from customers to finance their day-to-day operations
% of firms that used money lenders to finance their 517 9 28 0 100 6 11
day-to-day operations
% of firms that used microfinance institutions to 518 16 36 0 100 12 19
finance their day-to-day operations
% of firms that used banks to finance their day-to-day 520 9 28 0 100 6 11
operations
% of firms that used friends or relatives to finance their 517 35 48 0 100 31 39
day-to-day operations
% of firms that used other (than above) sources to 515 5 21 0 100 3 6
finance their day-to-day operations
% of firms that in the last three years bought any 528 20 40 0 100 17 24
machinery, vehicles or other means of transport,
equipment, land or buildings
For firms that spent on machinery, vehicles, 97 24147 31043 0 170000 17891 30404
equipment, land or buildings in the last three years,
amount spent in the last 3 years on purchase of new or
used machinery (LCUs)
For firms that spent on machinery, vehicles, 91 10688 14940 0 79000 7576 13799
equipment, land or buildings in the last three years,
amount spent in the last 3 years on purchase of new or
used equipment's and tools (LCUs)
34 KENYA INFORMAL ENTERPRISES
Annexes
Std. 95% confidence
Variable Observations Mean deviation Min. Max. interval
For firms that spent on machinery, vehicles, 83 19049 74962 0 500000 2681 35418
equipment, land or buildings in the last three years,
amount spent in the last 3 years on purchase of new or
used vehicles and other means of transport (LCUs)
For firms that spent on machinery, vehicles, 90 0.1 1 0 9 -0.1 0.3
equipment, land or buildings in the last three years,
amount spent in the last 3 years on the purchase of
land (LCUs)
For firms that spent on machinery, vehicles, 93 1376 8777 0 60000 -431 3184
equipment, land or buildings in the last three years,
amount spent in the last 3 years on the purchase or
construction of buildings (LCUs)
For firms that spent on machinery, vehicles, 104 88 33 0 100 81 94
equipment, land or buildings in the last three years, %
of them who financed the purchase through own funds
For firms that spent on machinery, vehicles, 105 10 29 0 100 4 15
equipment, land or buildings in the last three years,
% of them who financed the purchase through credit
from suppliers or advances from customers
For firms that spent on machinery, vehicles, 104 8 27 0 100 2 13
equipment, land or buildings in the last three years,
% of them who financed the purchase through
moneylenders
For firms that spent on machinery, vehicles, 102 12 32 0 100 5 18
equipment, land or buildings in the last three years,
% of them who financed the purchase through
microfinance institutions
For firms that spent on machinery, vehicles, 103 14 34 0 100 7 20
equipment, land or buildings in the last three years, %
of them who financed the purchase through banks
For firms that spent on machinery, vehicles, 104 23 42 0 100 15 31
equipment, land or buildings in the last three years, %
of them who financed the purchase through friends/
relatives
For firms that spent on machinery, vehicles, 103 4 19 0 100 0.1 8
equipment, land or buildings in the last three years, %
of them who financed the purchase through other than
above sources
% of firms that have a bank account to run the business 520 34 48 0 100 30 39
For firms that have a bank account to run the business, 175 53 50 0 100 45 60
% of them that use separate bank account for their
household
% of firms that have a loan against the firm or against 523 9 28 0 100 6 11
the largest owner for business purposes
% of firms that applied for a loan during the last year 518 10 31 0 100 8 13
For firms that did not apply for a loan during the last 479 33 47 0 100 29 37
year, % of firms reporting the main reason for not
applying is no need for a loan
For firms that did not apply for a loan during the last 479 14 35 0 100 11 17
year, % of firms reporting the main reason for not
applying is complex application procedures
For firms that did not apply for a loan during the last 479 25 43 0 100 21 29
year, % of firms reporting the main reason for not
applying is high interest rates
KENYA INFORMAL ENTERPRISES 35
Annexes
Std. 95% confidence
Variable Observations Mean deviation Min. Max. interval
For firms that did not apply for a loan during the last 479 10 30 0 100 7 13
year, % of firms reporting the main reason for not
applying is lack of required guarantees
For firms that did not apply for a loan during the last 479 2 15 0 100 1 4
year, % of firms reporting the main reason for not
applying is that the firm thought the loan would not
be approved
For firms that did not apply for a loan during the last 479 16 37 0 100 13 19
year, % of firms reporting the main reason for not
applying is other than above
% of firms that consider limited access to finance as a 470 64 48 0 100 59 68
severe obstacle to their current operations
% of firms that are financially constrained where a firm 518 60 49 0 100 56 64
is defined as financially constrained if it did not apply
for a loan during the last year for reasons other than
"no need for a loan"
Number of family members of the owner(s) who were 527 1 1 0 3 0.5 0.6
working in the business in the last month
Average monthly salary for an average worker at the 461 5405 3340 1 25000 5099 5710
firm (LCUs)
Average monthly salary for a female full-time worker 235 4850 2794 1 17000 4491 5209
at the firm (LCUs)
Number of men working at the firm who have social 526 0.1 0.4 0 4 0.1 0.1
security coverage
Number of women working at the firm who have social 529 0.1 0.3 0 4 0.1 0.1
security coverage
% of firms that would like their business to be 500 53 50 0 100 49 57
registered with the Registrar General
% of firms for whom time, fees, and paper work 500 56 50 0 100 52 61
required for registering is a reason for not registering
% of firms for whom taxes that registered businesses 494 57 50 0 100 53 61
have to pay is a reason for not registering
% of firms for whom inspections and meetings with 486 37 48 0 100 32 41
government officials that follow registration is a reason
for not registering
% of firms for whom bribes that registered businesses 481 36 48 0 100 32 40
need to pay is a reason for not registering
% of firms for whom no benefit from registering is a 493 46 50 0 100 42 51
reason for not registering
% of firms that report having to pay gifts, informal 507 19 39 0 100 15 22
payments or bribes to remain unregistered
% of firms for whom better access to financing is a 467 77 42 0 100 73 81
benefit from registering
% of firms for whom better access to raw materials, 459 61 49 0 100 56 65
infrastructure services and government services is a
benefit from registering
% of firms for whom less bribes to pay is a benefit from 449 40 49 0 100 36 45
registering
% of firms for whom being able to issue receipts to 473 42 49 0 100 38 47
attract customers is a benefit from registering
Amount of time (days) the firm thinks it will take to 307 18 51 1 365 12 24
register the business
36 KENYA INFORMAL ENTERPRISES
Annexes
Std. 95% confidence
Variable Observations Mean deviation Min. Max. interval
Maximum amount of time (days) the firm thinks it will 303 26 66 1 450 19 33
take to register the business
Minimum amount of time (days) the firm thinks it will 309 9 33 1 365 5 13
take to register the business
% of firm that rank limited access to finance as the 388 59 49 0 100 54 64
most important obstacle within the set of eight
obstacles
% of firm that rank limited access to land as the most 388 9 29 0 100 6 12
important obstacle within the set of eight obstacles
% of firm that rank corruption as the most important 388 9 29 0 100 6 12
obstacle within the set of eight obstacles
% of firm that rank crime as the most important 388 6 24 0 100 4 9
obstacle within the set of eight obstacles
% of firm that rank problems with electricity supply 388 10 30 0 100 7 13
as the most important obstacle within the set of eight
obstacles
% of firm that rank problems with water supply as 388 3 17 0 100 1 5
the most important obstacle within the set of eight
obstacles
% of firm that rank limited access to technology as 388 1 9 0 100 -0.1 2
the most important obstacle within the set of eight
obstacles
% of firm that rank inadequately educated workforce 388 2 13 0 100 0.5 3
as the most important obstacle within the set of eight
obstacles
Total cost of workers for the last month (LCUs) 451 12679 33268 0 600000 9600 15757
Total cost of electricity for the last month (LCUs) 421 796 1813 0 15000 622 970
Total cost of transportation in the last month (LCUs) 455 1064 2665 0 39000 819 1310
Total cost of raw materials for the last month (LCUs; 241 17101 26003 0 250000 13802 20401
only for manufacturing firms)
% of firms that use machinery (excluding tools, 533 44 50 0 100 40 49
equipment and computers) in their current operations
For firms that use currently use machinery, cost of 194 89163 182336 200 1000000 63344 114983
purchasing machinery and equipment (LCUs) used
by the firm in its current condition (excluding tools,
equipment and computers)
% of firms that use own vehicles or other means of 532 19 39 0 100 15 22
transport in their current operations
For firms that currently use own vehicles or other 56 158760 228775 100 1000000 97494 220026
means of transport, cost of purchasing them in their
current condition (LCUs)
Cost of purchasing all the tools, equipment and 343 48486 188031 100 2000000 28516 68455
computers (excluding machinery and vehicles) in their
current condition (LCUs)
KENYA INFORMAL ENTERPRISES 37
Annexes
Annex 2: Kenya – survey of informal firms (2013)
Description of the Informality Survey
The World Bank’s Informal Enterprise Surveys (IFS) collect data on non-registered business activities
in every region of the world. The IFS are implemented in parallel to the World Bank’s Enterprise
Surveys (ES), which interview formal, private, non-agricultural firms in countries around the world
(www.enterprisesurveys.org). The IFS use a standardized survey instrument designed to assess the
business environment for non-registered businesses within a well-defined universe of activities,
which have been identified using information from previous iterations of the studies. The IFS cover
business environment topics including: general business characteristics, infrastructure, crime,
sales & supplies, finance, labor, registration, business environment, and assets. The objective of
the IFS can be summarized as follows:
• To provide information about the state of the private sector for informal businesses in client
countries;
• To generate information about the reasons of said informality;
• To collect useful data for the research agenda on informality; and
• To provide information on the level of activity in the informal sector of selected urban centers
in each country
The IFS are conducted using a uniform sampling methodology in order to minimize measurement
error and yield data that are comparable across the world’s economies. The primary sampling units
of the IFS are non-registered business entities.For consistency, “registration” is defined according
to the established convention for the Enterprise Surveys in each country. In these surveys, the
requirements for registration are defined on a country-by-country basis consulting information
collected by Doing Business and information from the in-country contractors. For the case of
Kenya, informal firms were defined as those not registered with the Kenya Revenue Authority
(KRA). The survey was conducted between April,18th and May, 11th2013.
In each country, the IFS are conducted in selected urban centers, which are intended to coincide with
the locations for the implementation of the main Enterprise Surveys. Each urban center is divided
into an appropriate number of zones. The zones are identified using regional considerations and
the concentration of informal business activity through consulting local knowledge. The overall
number of interviews is pre-determined, and these interviews are distributed between the selected
urban centers, according to criteria such as the level of business activity and each urban center’s
population, etc.In Kenya, a total of 533 firms were interviewed. The urban centers identified were
Nairobi (137 firms), Mombasa (110), Central (103), Nyanza (93), and Nakuru (90). These urban
centers were divided into 122 zones and at least four interviews were completed for each zone. In
order to provide information on diverse aspects of the informal economy, the sample is designed
to have equal proportions of services and manufacturing (50:50). These business activity sectors
are defined by responses provided by each informal business to a question on the business’s main
activity included in the screener portion of the questionnaire.
38 KENYA INFORMAL ENTERPRISES
Annexes
Due to lack of proper sampling frame and the limited geographical coverage, the informality
survey for Kenya (and other countries) is not necessarily representative of the informal sector in the
country or even the informal sector in the urban centers covered. Hence, all the results presented
below using IFS for Kenya should be treated with due caution as pertaining to the sample of firms
surveyed and not necessarily the informal sector more broadly. Nevertheless, Enterprise Surveys
take appropriate measures to keep the IFS as truly random so that the results based on these
data are not systematically biased in one direction or the other. In the case of Kenya, the following
steps were taken to ensure randomness of the selection process:
• Each interviewer receives one or more maps of the geographic sectors he/she has to cover
with the indication of the starting points and the direction to follow.
• The interviewers were instructed to follow the direction of the street.
• Four interviews (two services and two manufacturing firms) were completed from each
starting point. The instruction was that interviews be conducted in every address (or stall)
passed until 4 completed interviews have been achieved.
• GPS coordinates of the interviewed business were recorded.
KENYA INFORMAL ENTERPRISES 39
Annexes
Annex 3: Business environment and productivity
To answer this question, we replicated some of the analysis that was produced by Gelb et al in 20099
which examines firm productivity by contrasting informal firms with their formal micro-enterprise
and SME counterparts. They speculate that growth and productivity within the informal market is
dependent on the quality of the business environment. Their hypothesis is that when the business
environment is poor, informal and formal firms will be less distinguishable, and conversely, in
a higher quality business environment, differences in growth and productivity between formal
and informal firms will emerge. Their hypothesis rests on a differential treatment of firms in a
higher quality business environment through “sticks” in the form of tougher enforcement limiting
informal activity and/or through “carrots” in the form of improved business service access for
formal firms.
In particular, they find (using this same Enterprise Survey data from the World Bank but from
2007-2009) that informal firms from four countries in East Africa (Kenya, Tanzania, Uganda, and
Rwanda)) exhibit productivity profiles that are indistinguishable from their formal counterparts
while informal firms in southern Africa (Botswana, Namibia, and South Africa) are considerably
poorer performers than their formal sector counterparts. This uni-modal vs bi-modal finding for
the probability density of productivity in each country drives their entire result. The Gelb et al 2009
paper shows Kenya Informal firms in 2007 to be indistinguishable from their formal counterparts
(Figure 3b). Using the new data, we find that the informal firms are now distinguishable from their
formal counterparts (Figure 3c). This suggests that the quality of the business environment—at
least as differentially experienced by formal and informal firms—in Kenya may have changed since
the last survey.
TABLE 6: Summary of Kenya’s Progress on Doing Business Indicators
Measure Result DB 2007 DB 2014 Difference
Procedures (number) 13 10 -3
Starting a Business Time (days) 54 32 -22
Cost (% of income per capita) 46.3 38.2 -8.1
Procedures (number) 6 8 +2
Dealing with Construction Permits Time (days) 158 125 -33
Cost (% of income per capita) 1 3.4 +2.4
Payments (number per year) 42 41 -1
Paying Taxes Time (hours per year) 432 307.5 -125
Total tax rate (% profit) 49.8 38.1 -11.7
9
Gelb, Alan and Mengistae, Taye and Ramachandran, Vijaya and Shah, Manju Kedia, To Formalize or Not to Formalize? Comparisons of Microenterprise
Data from Southern and East Africa (July 20, 2009 Available at SSRN: http://ssrn.com/abstract=1473273 or http://dx.doi.org/10.2139/ssrn.1473273
40 KENYA INFORMAL ENTERPRISES
Annexes
Figure 3b: In 2013, informal firms in Kenya exhibited productivity
profiles that are quite different from their formal counterparts.
Figure 3b: In 2007, informal firms in Kenya exhibited productivity This differential effect has been associated with stronger business
profiles that are indistinguishable from their formal counterparts environments in other research.
Source: Source:
KENYA INFORMAL ENTERPRISES 41
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