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www.ccsenet.org/ijef International Journal of Economics and Finance Vol. 3, No. 2; May 2011
ISSN 1916-971X E-ISSN 1916-9728
176
Informal Self-Employment and Poverty Alleviation: Empirical Evidence
from Motorcycle Taxi Riders in Nigeria
Ogunrinola, I. Oluranti
Associate Professor of Economics
Department of Economics and Development Studies, Covenant University, Ota, Nigeria
Tel: 234-803-332-2048 E-mail: rantiogunrinola@yahoo.co.uk
Received: July 15, 2010 Accepted: August 5, 2010 doi:10.5539/ijef.v3n2p176
Abstract
This study examines the role of an urban informal transport sub-sector; the motorcycle taxis (popularly called ‘okada’),
towards the provision of self-employment and income-generating opportunities for many of the urban unemployed in
South West Nigeria. The data for the study was generated from a survey of 777 randomly selected auto cycle riders in
two cities in Nigeria and the SPSS software aided data analyses. In addition to the descriptive analyses, two
econometric models were specified and estimated using the OLS technique. The study revealed that the subsector is a
high employer of young school leavers in the accident-prone job of okada riding. Earnings analyses show that 86% of
the operators earn above the minimum wage level while human capital variables explain earnings distribution.
Implications of the findings of the study point to the need for a more rigorous regulation of the sector to promote safety
of operators and passengers.
Keywords: Okada, Self-employment, Earnings, Human capital, Motorcycle, Taxi
1. Introduction
Unemployment and poverty are two basic problems plaguing many developing nations of the world (ILO, 2007).
High rate of open unemployment especially among the youths in developing nations is as prevalent as the rate of
underemployed which are the major factors responsible for low standard of living in developing nations. Poverty is
therefore widespread both in the cities and urban areas of Nigeria and other developing nations of the world
(Amaghionyeodiwe, 2009). For instance, Nigeria was known to have been ranked as one of the middle-income,
oil-producing countries in the late 1970s, but her ranking plummeted to that of one of the lowest-income countries in
the early 1990s. Thus, in the early 2000’s, Ukwu (2002) had to describe Nigeria as ‘one of the poorest of the poor
among the nations of the world, confronted not just with pockets of poverty, disadvantaged or marginalized areas,
groups and individuals but with a situation in which most of the population exists at standards of living below those
required for full development and enjoyment of individual and societal well-being.’ In her effort to create jobs for
the unemployed and promote economic growth, the Government of Nigeria put in place a number of economic
reform programmes which have very minimal impacts on employment creation, poverty reduction and growth of the
national economy (Abiola and Oladeji, 1998; Akinbobola and Saibu, 2004; Amaghionyeodiwe, 2009).
In order to provide themselves with means of support, many of the unemployed have no choice but to exploit the
income and self or casual employment opportunities in the informal sector of the economy. The informal sector has
thus become a major provider of employment especially in developing and transitional economies (Khotkina, 2007).
The types of work available in the informal economy are diverse and multifarious. It stretches from casual and
unstable employment like garbage picking, street trading, domestic help, and so on; to self-employment as
master-craftsman in any given trade. While many researchers have studied the employment generation potentials of
the informal sector in Nigeria (Ogunrinola, 1991; Folawewo, 2006); not much study is known to have been carried
out on the income and employment generation as well as the poverty reduction implications of commercial
motorcycle taxi (popularly called ‘okada’) operations. The study by Olufayo (2006) in this direction attends more to
the issue of safety on commercial motorcycle taxis. The purpose of this study therefore is to examine how informal
okada transport mode has contributed to income, employment generation and poverty reduction in selected states of
South Western Nigeria. This study is organised as follows: Following this introductory part; Section 2 reviews some
empirical literature, Section 3 examines the Methodology of the Study, Section 4 gives the interpretation of data
while Section 5 concludes the paper.
2. Brief Survey of Literature
2.1 Conceptual Framework
Poverty is a multidimensional concept. While the World Bank (2000) defines it as ‘pronounced deprivation in
well-being’, Haughton and Khandker (2009) maintains that poverty describes a state of ‘lack of key capabilities
www.ccsenet.org/ijef International Journal of Economics and Finance Vol. 3, No. 2; May 2011
Published by Canadian Center of Science and Education 177
which may be income or education, or poor health, or insecurity or low self-confidence or a sense of powerlessness,
or the absence of rights such as freedom of speech.’ In empirical literature, poverty has been measured in terms of
consumption or level of income. A person is therefore regarded poor if she/he lives below a certain level of
consumption or income. The World Development Report (2008) reported that 71% of Nigerians are living below the
international poverty line of $1 per day while 92% are below the $2 a day poverty line. This can be contrasted with
Ghana that has 45% and 78% of her population living below the $1 and $2 poverty lines respectively.
One important contributory factor to poverty is the dearth of formal employment to absorb the rural-urban migrants
as well as graduates from the educational system that are unemployed and seeking wage work in the urban informal
sector (Eglama and Bamidele, 1997). For most of the developing nations, the rate of unemployment has been on a
rise while various policy measures aimed at restructuring the economy has destroyed more jobs than it created. The
global economic crisis has also worsened the employment situation as massive layoff became inevitable as
aggregate demand plummeted. An important implication of high unemployment rate in the formal sector is the rapid
growth of the informal labour market that is characterised by earnings flexibility and hence high absorptive capacity
for labour. In other words, both earnings and employment level in the informal sector behave in the neo-classical
tradition and this makes the sector an employer of last resort for most workers who would have otherwise remained
unemployed in the formal sector. Thus, the informal sector has become a source of employment generation, and
hence a means of fighting poverty by many innovative micro-entrepreneurs in developing countries (Debrah, 2007,
Faridi, et.al. 2011).
2.2 Brief Survey of Empirical Literature
The importance of the urban informal sector in the labour absorption process, on one hand and in poverty reduction
on the other hand was the main focus of the study of the Russian informal economy by Khotkina (2007). The study
found out that the informal economy is much larger than the formal; female employment growth doubles that of the
male; and that the relatively ‘low level of official wages ... compel people to seek salvation from poverty in the
informal sphere of the (Russian) economy’ (p.52). Contrary to the situation in an economy-in-transition like Russia,
the Ghanaian informal economy, like any other informal sector in developing economies, was found to be very large
and growing (Debrah, 2007). To combat unemployment and poverty, the Ghanaian government attempted an
interventionist programme through the Skill Training and Employment Programme (STEP) that was targeted at the
unemployed to enable them settle down as independent entrepreneurs in the informal sector after training. The
programme achieved limited success as most of the trainees desired formal sector jobs, rather than settling down to
informal entrepreneurship, among other mitigating factors. Like the case of Ghana, many interventionist policy
targeted at the informal sector employment creation (e.g. the National Directorate of Employment in Nigeria) rarely
achieve their goals due to official corruption and poor program design and implementation, among other factors
(Debrah 2007).
An important factor for the success of informal sector entrepreneurial activities is the ‘spontaneous entrepreneurial
response to free market conditions ....’ and a sense of felt need arising from a desire to profitably utilise an
unemployed resource/talent for the purpose of utility maximisation. This was the case for the development of urban
motorcycle taxi services in Kampala, Uganda (Kisaalita and Sentongo-Kibalama, 2007). The combined factors of
worsening urban traffic, the deregulation of the energy industry which led to the high fossil fuel cost in East Africa
and the sheer abundance of motorcycles and bicycles, among others, led to the development and unprecedented
growth of bicycle and motorcycle taxi (called boda-boda) in Kampala and other Ugandan cities. The study, which
interviewed thirty-seven boda-boda operators, found out that the operation helped to facilitate trade, improve urban
transportation, and created jobs (Kamuchanda and Schmidt, 2009).
In Nigeria, studies on motorcycle taxis dwell more on the issue of safety rather than that of income and employment
generation from the business. The earlier study was by Akinlade and Brieger (2003). The study relied on the hospital
records of 81 motorcycle taxis accidents, as well as a survey of 480 motorcycle taxi drivers in South Western
Nigeria. Other studies in Nigeria include Solagberu et. al (2006), and Olufayo, (2006). While the former examines
the issue of general safety for okada users, the latter investigates the particular exposure of youths to motorcycle
accidents in Nigeria. The present study investigates the impact of motorcycle taxis operation on income and
employment generation as well as its effect on poverty reduction in Nigeria.
3. Methodology of Study
3.1 Research Design and Sampling Procedure
This study covered two states from the six states within the South Western Region of Nigeria. To obtain reasonably
large and representative sample of respondents, a multi-stage sampling technique was adopted. In the first stage we
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selected two states (Lagos and Ogun) from the region; the second stage was the choice of Local Government Areas
within the state, while the final stage was the choice of enumeration areas (EAs) within the chosen local government
areas. In Lagos, Ojo Local Government Area was chosen while Ado-Odo/Ota was chosen in Ogun State. To draw
up the sampling frame, the take-off points of commercial motorcycle taxis were located. Many of these were found
on major roads, road junctions and local market areas.
Most of the locations have in place some form of rudimentary organisational structure with elected officials to direct
the affairs of each take-off point. In such locations, each okada operator is registered and pays the regular
association dues. The sampling frame therefore is made up of all registered and active members in the selected
take-off points. From the sampling frame, we randomly selected 10% of listed members, subject to a maximum of
10 respondents from each location. If the selected person was not available for interview, the next person in the list
was selected as replacement after a maximum of three call-backs. In all, we covered fifty-five garages in Lagos, and
forty-five in Ogun State, which was expected to give us a maximum of one thousand respondents from the two
states. However, at the end of the data collection exercise we had seven hundred and seventy-seven usable responses,
translating to a response rate of 78 percent.
3.2 Research Instrument and Data Collection Exercise
The data gathering exercise was made possible through the use of structured questionnaire specifically designed for
the purpose. Since okada riders are almost always on the move, (except when waiting on queue to take their turn in
passenger picking) we have made the questionnaire to be very brief. The one-paged questionnaire was in three
sections. Section A, was concerned with general questions relating to the main reasons why the respondents chose to
be involved in this risky venture, what he would have done if alternative opportunities were available and what he
intended doing later in life. Section B related to income analysis while the third section asked for the bio-data of the
respondents. The data collection exercise took place in the months of September and October, 2008. Previously
trained enumerators were sent to the field to administer the questionnaires after the initial visit to the officials of each
selected okada garage to secure approval for the conduct of the study in that location. The reliability statistic of our
data was tested using the Cronbach alpha which gave a result of 64%.
3.3 The Models and Analytical Technique
The methods of analyses adopted for the study are two. The first is basically descriptive using frequency counts and
cross-tabulations; while the second is the use of regression.
3.3.1 Earnings Distribution Function
Earnings analysis was carried out in order to determine the pattern as well as the influences of several explanatory
variables on earnings distribution of okada riders in the study locations. The earnings analysis was based on human
capital variables; migration characteristics, status of respondents (whether hired operators or owner-operators) and
other relevant variables. In general, following Becker (1975) and Mincer (1974), we postulate that:
Yf,………………………………………………1
Stating (1) explicitly, we have: Log Y δαβε……………………2
Where:Y is the weekly earnings of the respondents; H is the vector of human capital variables; while X is a vector
of personal and enterprise characteristics. For this study, the H variables are age of the entrepreneurs, experience and
its square, the level of formal educational attainment measured as number of years spent in school, the number of
years spent learning how to ride okada, and a dummy variable specifying if respondent previously learnt a
trade/craft or not. The vector of X variables include marital status of respondents, religion, Migration status of
respondents whether hired or owner-rider, accident experience of respondents and the number of such accidents
since riding okada, number of hours worked per day as well as the number of days worked in the previous week.
3.3.2 Employment Determinant Model
The supply of efforts into the informal transport sector depends on a variety of factors. Given the level of economic
activity, it is postulated that the amount of effort supplied into the okada riding business depends on the level of
earnings, the amount of capital available for investment purposes as well as the personal and household
characteristics of respondents. The effects of such factors were measured empirically using an employment supply
function specified in line with the earlier works of Ndebbio 1987; Ogunrinola, 1991, as:
Ei = f (W, K, P, Z) …………………………… (3)
Where –
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Published by Canadian Center of Science and Education 179
Ei is the number of hours worked per week by the ith respondent.
W is the earnings per week
K is the level of present capital; measured as dummy variable 1, if respondent owns a motor-bike and zero
otherwise.
P is the vector of personal characteristics of riders, whether continuous (P1i) or dummy (P2i).
Z is the vector of household and other variables affecting the level employment.
Two models were tried: one assuming linear relationship, and the other assuming a non- linear relationship as
shown in equation (2). That is:
∑∑ ….. …… (4)
The non-linear form takes the Cobb-Douglas form specified as:
∏
∏
∑
∑
….. ….. …. ….. (5)
Taking Logarithms on both sides of the equation to achieve linearity we have:
log
log
∑log
∑log ∑
∑ … (6)
Where: log , = log
P1 = Vector of Personal characteristics that are continuous e.g. age, experience, among others.
P2 = Vector of personal and other characteristics that are in binary/dummy forms, e.g. gender.
Z1 and Z2 are household characteristics similarly defined as P1 and P2.
Equations (4) and (6) are estimated and the results are as shown in Table 4.
4. RESULTS AND DISCUSSIONS
4.1 Some Characteristics of the Respondents
Two classes of riders were encountered in the survey: the hired operators as well as the owner-riders. As shown in
Table 1, the result of the survey revealed that the owner operators predominated with a figure of 430 representing 55%
of total respondents while the paid-operators are 347 or 45% of the total sample. In terms of the state of origin of
respondents, they were found to represent all the geopolitical zones of the country while a few were foreigners. In
specific terms, 494 respondents representing 64% are from South Western Nigeria which is made up of six states of
Lagos, Ekiti, Oyo, Oshun, Ondo and Ogun States; 52 and 54 respondents representing 6.7% and 6.9% of the total
sample are from the South-East and South-South respectively. Respondents from the entire Northern Nigeria are 65
representing about 8% of the sample; while ten representing 1.3% of the total are non-Nigerians.
In terms of age, the okada riding business is dominated by youths. Out of the 446 persons that responded to the
question on age, 264 of them representing 59% are below the age of 30; one hundred and seventy-seven of them or 40%
are aged 30-49, while the remaining 1% (just 5 of them) are over 50 years of age. Thus, the informal okada
transportation business provides job mainly for the youths who would have otherwise remained unemployed in the
urban labour market. In spite of their youthfulness, many of the okada riders appear to have started riding auto-cycles
early in life and perhaps this accounts for why it was easy for them to take up okada riding occupation when there was
no desired alternative formal sector job. About 14% of the respondents had been riding okada before age 20; while a
total of over 500 of them representing 81% of the respondents indicated that they had been riding motorcycle before
age 30. The distribution of riders by status shows that 60% of those riding before age 20 are the hired operators while
the remaining 40% are owner-riders. With respect to marital status of the respondents, many of them are already
married. While as many as over 400 representing over 60% of respondents are already married, only 32% are single
while the rest are either divorced or widowed. The married are predominant among the owners (60%) while those that
are single are almost evenly distributed between the hired operators and owners. Out of the 374 respondents that
reported having children, 49% of them have 1-2 children 39% have 3 to 4 children while the remaining 11% have at
least five children. On the average however, the sampled population have about three children.
4.2 Educational Attainment, Skill Development and Future Career Aspirations of ‘okada’ operators
One important issue that this study investigated is the previous education and skill development experience of these
operators prior to embarking on the informal business of motorcycle taxi riding. As shown in Table 1, only 8% of the
respondents belong to the ‘no schooling’ category; 25% have up to primary education; 55% have completed secondary
education while 12% are graduates of tertiary institutions. In terms of previous skill development, Table 2 shows that
573 of the respondents have either received training in a craft or are still in school. A further analysis of the type of
skill development and training undertaken by the respondents revealed that 25% (142 persons), have received training
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in Building and allied trades; another 182 representing 32% have been trained in Vehicle Maintenance and allied
disciplines; 16% in small-scale Trading; 14% have acquired skills in Garment and Shoe manufacturing trades, while 2%
have acquired skills in various aspects of Farming and agro-processing. The hired riders constitute 41% of those with
previous skills acquisition while the remaining 59% are owner-riders. The study also enquired as to why these riders
have decided to neglect the main skills learnt in favour of okada riding in the informal economy. The reason given by
67% of the respondents relates to lack of access to start-up capital. In terms of future career expectation, 488
respondents (63%) desire to start own enterprises, 12% wants to return to school, 7% desire formal sector jobs while
the rest either belong to the unclassified categories or did not respond to the question. In summary, self-employment in
the informal sector is the main future career goal of the majority of the respondents while their engagement in okada
riding is merely a means to that end.
4.3 Earnings of Respondents
This study shows that the mean monthly earning for all respondents is N38,211. When this is disaggregated by status,
the mean monthly earnings for the hired operators and owner-riders are N33,334 and N42,174 per month respectively.
In contrast, the modal monthly earnings is N20,000 – N29,999 income range. The owner-riders dominate this group
with 63% while the hired-riders account for the remaining 37%. This result suggests that the owners are better off on
the average than the hired operators. This result is not surprising as the owners are expected to be earning returns both
from their labour as well as the capital invested in the purchase of auto-cycles. Only 14% of the respondents earn less
than N10,000 per month, while the remaining 86% of the okada operators in our survey area are earning at least
N10,000 per month at the time when the monthly minimum wage in Nigeria was N7,500. Of the total number of 553
respondents earning N10,000 and above, 60% of them are the owner-riders while the remaining 40% are hired
operators.
Table 3 shows the regression result of the earnings function expressed in equation (2). Four regression estimates are
carried out and labelled regressions 1, 2, 3 and 4 respectively. Eight of the variables are used in regression 1, nine in
regression 2, seven in regression 3 and ten in regression 4. In regression 1, five of the variables are statistically
significant at the levels indicated while the coefficient of determination measured by R2 is 12.7% and the adjusted R2
(for the degrees of freedom) is 11%. In regression 2, the number of explanatory variables used was increased by an
important human capital variable (Migration Status) and this increases the R2 marginally to 13.2% but the said
variable is found to be insignificant statistically on its impact on earnings. In other words, both migrants and natives
are not different with respect to earnings level in the okada riding business in the study areas. In spite of this, however,
the two regressions thus reveal that the status of riders, educational level, okada riding experience and its square
(which captures the usual non-linearity assumption in the age-earning profiles) as well as marital status are significant
in their impacts on earnings. Thus, the regression estimates show that the owner-operators earn more than the
hired-riders; and that education and labour market experience contribute positively to earnings, while those that are
married earn less than those in the non-married category. Given the estimates of EXP2 which has the expected sign in
both regressions, the results show that earnings of okada riders peak at about 19 years (0.075÷0.004 = 18.75) of labour
market experience (Chiang 1984).
In regression 3, we dropped those variables that did not significantly affect earnings in regressions 1 and 2 and
introduced the alternate variables of age and education not used in the first two regression estimates. The impact of
these modifications to the model gave an improved fit with higher levels of R2 of 22%; adjusted R2 of 20% and
F-statistic of 10.4. Regression 4 added three variables; (i) ACCIDENT?, which is a dummy variable indicating
accident experience of riders; (ii) N_ACCIDENT, the number of accidents ever experienced; and (iii) Hours worked
in the previous week. The three variables are significant in their impacts on earnings and have the expected signs.
Accident variables have negative impact while the number of hours supplied have a positive impact on the level of
weekly earnings. Both the R2 and the Adjusted R2 also increased to 26% and 22% respectively.
4.4 Determinants of Labour Supply in Okada Riding Business
Factors determining the quantity of man-hours supplied and the estimates of the coefficients of each of them are as
stated in Table 4. The dependent variable is the number of hours worked in the week prior to the survey exercise
(Regression 1) and the natural logarithm of the same variable (Regression 2). Out of the nine variables used in the
labour supply function, it is only one in regression 1 and two in regression 2 that are statistically different from zero at
the critical levels indicated. The statistically significant variables in the two regressions are earnings (both at level and
in logarithm form) and the dummy variable “Learnt a Trade”. Regression 1 thus shows that earnings positively
influence the number of hours supplied, while those with skilled trade background supply less hours of okada riding
than the others. In quantitative terms, a unit change in the level of earnings brings about a 0.001 change in weekly
hours supplied. In elasticity terms (Regression 2), the degree of responsiveness of hours supplied to changes in
earnings is 0.191; signifying an inelastic relationship.
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The negative differential coefficient of ‘Learnt a Trade’ variable appears plausible for the respondents concerned.
Many among this class of riders (69% among the hired riders and 80% among the owners) are in okada riding
occupation to raise start-up capital. They may therefore be giving some part-time attention to the practice of skills they
have previously acquired. Furthermore, some of the skilled workers among the riders take okada riding as a gap-filling
occupation during the idle hours of their occupation which coincides, in most cases, with peak hours in okada riding
business. Such times include the early and late hours of each day when there is a heavy human traffic of those going to
and returning from work places. This brings about complementarity between the two informal sector jobs being held.
In sum, while the skilled respondents among the riders supply less hours than the other category; the generality of the
riders perceive the job as a mere staging posts to enable them acquire the necessary start-up capital for either their
business establishment along the line of their technical skill acquisition or to enable them return to school for further
studies.
5. Summary of Findings, Policy Implications of the Study and Conclusion
This study has brought to light several important findings. One, the commercial taxi operations is a source of
employment and income for many Nigerian youths and this has shown the importance of the informal sector in the
labour absorption process in the urban informal sector of South West Nigeria. The study has therefore confirmed other
studies in this respect (Debra, 2007; Khotikna, 2007). Second, the earnings analysis carried out confirms that the
majority of operators are earning more than the (then) minimum wage, and that, perhaps has made the sector to be
attractive to many educated youths (even up to tertiary level) who would have remained openly unemployed. Third,
the study confirmed that both human capital variables as well as personal and household characteristics are important
statistically in the determination of earnings. The third finding is that many of the operators have acquired some
technical skills prior to their engagement in the okada riding business while some of them are graduates of tertiary
institutions who had to get involved in auto-cycle riding due to lack of desired formal sector employment. For those
with previous skill training, the lack of financial resources to set up own enterprises is the main reason for their
involvement in okada riding. Many of them are however, planning to return to the occupation of their dream as soon as
they could save enough funds for start-up capital. Fourth, the study also brought out in quantitative terms the level of
risk (Table 2) involved in okada operation and such level of risk affects the distribution of earnings (Table 3,
Regression 4). Last, but nevertheless the least, the study has shown that earnings and the skill background of riders
significantly affect the number of hours supplied into the okada riding business (Table 4).
The findings of this exploratory study have brought about some implications for policy and further research. Okada
operation is one of the major providers of employment for youths who would have otherwise remained unemployed.
However, the nature of job provided has been shown to be risky given the rate of accidents reported. Therefore,
effective safety education and improvement in the enforcement of all safety measures by the relevant authorities are
important to reduce the already high accident and fatality rates arising from okada operations. Since over 90%
(Table 2) of the operators are literate, the communication of safety education is expected to be relatively easy. In
addition to safety education is the need for a more rigorous enforcement of licensing requirement for the operators to
ensure that only those that are qualified are certified as commercial riders. Though the commercial motorcycle
operation provides a fast means of transportation in the usual urban traffic hold-up as well as provide motorable
access to areas linked with bad and often flooded motorways (especially during the long rainy season in SouthWest
Nigeria), yet the importance of safety cannot be overemphasised.
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Published by Canadian Center of Science and Education 183
Table 1. Distribution of Respondents by Status and by Personal Characteristics
Main Variable Derived Variables
Status of Operators TOTAL
Hired Operators Owner Operators
No. Percent No. Percent No. Percent
Age of Respondents in years
Less than 20 5 50 5 50 10 2.2
20-29 Years 133 52 122 48 254 57.04
30-39 Years 57 43 76 57 133 29.75
40-49 Years 15 34 29 66 44 9.84
50 Years and over 4 80 1 20 5 1.11
TOTAL 214 48 233 52 447 100
Age since riding Okada
<20 Years 51 60 34 40 85 13.6
20-29 years 184 43 240 57 424 67.7
30-39 years 30 34 59 66 89 14.2
40-49 years 10 38 16 62 26 4.2
50yrs & over 2 100 - - 2 0.3
TOTAL 277 44.2 349 55.6 626 100
Highest Formal Educational Attainment
No formal education 34 55 28 45 62 8.0
Primary 69 35 126 65 195 25.1
Secondary 200 47 224 53 424 54.6
Post-Secondary 43 45 52 55 95 12.3
TOTAL 346 44.6 430 55.4 776 100
Region of Origin
South West 236 48 257 52 494 63.6
South East 18 35 34 65 52 6.7
South South 24 44 30 56 54 6.9
Northern Nigeria 28 43 37 57 65 8.4
Non-Nigerians 3 30 7 70 10 1.3
Non-Response 37 36 65 64 102 13.1
TOTAL 347 45 430 55 674 100
Marital Status
Married 174 40 263 60 437 61.4
Single 115 50 113 50 228 32
Divorced/Widowed 30 64 17 36 47 6.6
TOTAL 319 44.8 393 55.2 712 100
No. Of Children
1-2 74 40 109 60 183 49.3
3-4 64 44 81 56 145 39.1
5 and over 22 51 21 49 43 11.6
TOTAL 160 42.8 211 57.2 371 100
Source: Author’s computation from survey data.
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Table 2. Distribution of Respondents by Some Business Characteristics
Main Variable Derived Variables
Status of Operators
TOTAL
Hired Operators Owner
Operators
No. Percent No. Percent No. Percent
Types of Craft Learnt Before Engaging in
`okada Riding
Building and Allied trades 50 35.2 92 64.8 142 24.8
Vehicle Repairs 77 42.3 105 57.7 182 31.8
Trading 39 41.5 55 58.5 94 16.4
Garment and Shoe Manufacturing 39 50 39 50 78 13.6
Farming & Agro Processing 5 46 6 54 11 1.9
Miscellaneous 9 32 19 68 28 4.9
Schooling 16 42 22 58 38 6.6
ALL 235 41 338 59 573 100
Years spent Learning Trade/Craft
Between 1-2 Years 50 48 54 52 104 34
Between 3-4 Years 73 47 81 53 154 51
Over 4 Years 24 53 21 47 45 15
ALL 147 49 156 51 303 100
Major Reason for Engaging in okada riding
business
To raise money for business
start-up 160 40 244 60 404 67
Love for Self-Employment 47 57 36 43 83 14
Post-formal sector employment 34 49 36 51 70 12
No formal sector Job 15 38 25 62 40 7
TOTAL 315 43 371 57 597 100
Average Monthly Earnings from Okada
Riding Business
Up to N9,999 68 73 25 27 93 14.4
N10,000 - N19,999 82 55.8 65 44.2 147 22.8
N20,000-29,999 60 37 102 63 162 25.1
N30,000-39,999 21 33 42 67 63 9.8
N40,000-49,999 13 20 51 80 64 9.9
N50,000-59,999 6 30 14 70 20 3.1
N60,000 and over 39 40 58 60 97 15
ALL 289 45 357 55 646 100
Mean
N33,334
(n=290) N42,174
(n=357) N38,211
Any Accident? No 160 46.5 184 53.5 344 44
Yes 139 46.6 159 53.4 298 56
ALL 299 46.6 343 53.4 642 100
If Yes, how many times (N_Accident)
Once, only 49 42 67 58 116 43.4
Twice, only 39 50 39 50 78 29.2
Three times and over 27 37 46 63 73 27.4
ALL 115 43 152 57 267 100
Source: Author’s computation from survey data.
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Published by Canadian Center of Science and Education 185
Table 3. Regression Results of Earnings Distribution Function Among Okada Operators in Nigeria.
Dependent Variable: Natural Log of Earnings of Okada Riders in Naira
Variables Regression 1 Regression 2 Regression 3 Regression 4
Coeffic-ient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-Statistic
Status: Owner =1; zero otherwise 0.472 5.737* 0.470 5.722* 0.367 4.271* 0.407 3.997*
Educ Before Riding 0.018 3.105** 0.018 3.094**
Present Educ. Level 0.025 2.396** 0.037 2.721*
Experience (EXP) 0.075 2.526** 0.076 2.543** 0.033 1.202 0.032 0.728
Square of Experience (EXP2) -0.004 -2.276** -0.004 -2.325** -0.003 -1.936*** -0.003 -0.975
Period Spent Learning to ride
(RIDELEARN) 0.001 0.49 -0.002 -0.174
Age at Riding Okada (AGE_R) 0.001 0.112 0.002 0.346 0.012 1.625***
Present Age (AGE_P) 0.006 5.172*
Learn a Trade? Yes=1 0.090 0.899 0.126 1.232 0.260 2.071**
Marital Status -0.185 -2.108** -0.192 -2.198** -0.013 -0.161 -0.105 -0.914
MIGSTAT -0.131 -1.625 0.108 1.349
Accident? -0.465 2.671*
No. of Accidents -0.006 -3.413*
Hours worked 0.013 3.890*
CONSTANT 5.964 28.588* 5.974 28.678* 5.482 28.287* 4.021 10.848*
R
2 0.127 0.132 0.221 0.259
Adj. R2 0.11 0.114 0.200 0.224
F-statistic 7.618 7.092 10.4 7.382
Significance of F 0.000 0.000 0.000 0.000
Note: * = Significant at 1% level or better
** = Significant at 5% level or better
*** = Significant at 10% level or better
Table 4. Regression Results of Labour Supply Function
Variables
Regression 1 Regression 2
Dependent Var: Hours of labour supplied per
week
Dependent Var.: Natural Log of Hours
supplied per week
Coefficient t-statistic Coefficient t-statistic
Age -0.069 -0.349
Ln Age -0.179 -1.118
Education -0.288 -0.896
Ln Education -0.166 -1.582
Weekly Earnings 0.001 3.915*
Ln Wkly Earnings 0.191 4.638*
Marital Status -0.179 -0.058 -0.039 -0.501
Migrant 2.347 0.901 0.056 0.849
Status (Owner) -0.489 -0.188 -0.034 -0.519
Region of Origin (S.W.) 0.656 0.227 -0.007 -0.102
Learnt a Trade -2.989 -0.990 -0.136 -1.795***
Had Accident -0.018 -0.295 -0.001 -0.580
CONSTANT 46.704 5.945* 3.264 4.916*
R2 0.115 0.19
Adj. R2 0.55 0.13
F-statistic 1.931 3.236
Significance of F 0.053*** 0.001**
Note: * = Significant at 1% level or better **=Significant at 5% level or better
***=Significant at 10% level or better
Source: Computed from survey data
