1

THE ROLE OF INFORMAL SECTOR IN ALLEVIATING

YOUTH UNEMPLOYMENT IN HAWASSA CITY, ETHIOPIA

Abstract

Informal sector plays an important role in reducing urban unemployment, crime and

violence, and serving as a breeding ground for new entrepreneurs. This study is aimed at

assessing the role of informal sector in reducing youth unemployment. Data are gathered

from a sample of 264 youth informal sector operators in Hawassa city. Ordinary logistic

regression is used to determine the factors that can contribute to the livelihood

improvement of the operators. Nearly, 90 percent of the operators have witnessed that their

livelihood has improved after they joined the sectors. Operators who are more educated,

natives to the city, more profitable, stayed longer in the activity, and have a culture of

saving, have depicted better livelihood improvement vis-à-vis their counterparts. However,

lack of working capital, working premises, adequate market and raw materials were

reported as the major impediments for the operators. Given the immense contribution that

the sector has, therefore, the government needs to consider the sector as one of the

fundamental pillar to combat youth unemployment. Thus, operators should be encouraged

to join the formal sector by lessening the bureaucracy to get license, minimizing entry cost

such as lowering registration or licensing cost, and providing tax-holidays for sometimes.

Key words: Informal sector, formal sector, livelihood, youth, unemployment.

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1. Introduction

There are more than 1.2 billion youth people (15 to 24 years of age) in the world. Of

which, about 90 percent are leaving in developing countries. The proportion of youth

population in Sub-Saharan Africa is 14 percent (UN 2011), in Ethiopia it was about 21

percent and 30 percent in the study area, Hawassa city (CSA 2008).

In 2011, nearly 75 million youth were unemployed around the world. Besides, youth were

three times more likely to be unemployed than adults. The youth unemployment rate in

Sub-Saharan Africa was 11.5 percent (ILO 2012); and this figure was about 28 percent in

Ethiopia, and 23 percent in Hawassa city (CSA 2011). Thus, youth unemployment

becomes a serious threat to the social, economic, and political stability of a nation (ILO

2012).

Several factors aggravated the high youth unemployment rate in Africa. Among the factors

high proportion of youths population (slightly more than 20 percent); underdevelopment of

the economies which leads to low job creation; small private sectors to employ the

growing youth population; low quality of education contributing mismatch of skills needed

by the labour market; lack of general and job-related skills; and limited formal work

experience are notable (Semboja 2007; UNECA 2005). Unemployment has been driving

many African youths to engage in violence and criminal activities, young women and girls

into sex work, sexual violence, substance and drug abuse, victims of HIV/AIDS etc.

Informal sector refers to a home based or an individual establishment or an activity

operated by the owner with a few or no employees. Informal sector operators have little or

no access to organized markets, credit institutions, modern technologies, formal trainings,

and public services. Besides, they do not have a fixed place to work; as a result they often

carry out their business in small shops, streets, outlets or home-based activities (CSA

2004).

Informal sector plays an important role in urban poverty alleviation through creating jobs

and reducing unemployment (Lal and Raj 2006; Reddy, Vijay and Manoranjan 2002). In

urban areas of Africa, for example, the employment in informal sector is estimated to be 60

3

percent (World Bank 2008); this figure is about 37 percent in Ethiopia, and 26 percent in

Hawassa city (CSA 2011). The sector also provides a wide range of services, and produces

a variety of basic goods that can be used by all classes of consumers, especially by the low

income groups (Asmamaw 2004). Besides, the sector can serve as a breeding ground for

new entrepreneurs, and absorbs the labour force that is left out from the formal sector

employment (UNCHS 2006). In addition, the sector contributes a lot in reducing urban

crime and violence (Reddy, Vijay and Manoranjan 2002).

Informal sector is a dynamic sector, and not a transitory phenomenon in the development

process. It is rather, to be absorbed soon by the formal sector (Ruffer and Knight 2007).

The 2006 United Nations Center for Human Settlement document has regarded informal

sector as a transitional stage in the move to formal sector (UNCHS 2006).

Poverty alleviation and its eventual elimination occupy an innermost position in the

development agenda of many developing countries, including Africa (Dhemba 1999).

Now-a-days, it seems that developing countries are giving more emphasis on improving

socio-economic status of underprivileged groups (including youth) of the society to open-

up better opportunities for employment and income generation (Asmamaw 2004).

Development could be negatively affected if high rate of youth unemployment persist.

Thus, many African countries are placing greater emphasis on youth development.

It is well documented that informal sector is the major provider of job for the youth in

Africa (ILO 2012). For instance, about 38 percent of youths were engaged in informal

sector businesses in Ethiopia (CSA 2011). Besides, the informal economy gives youth

opportunities to legal work by offering experiences and self-employment opportunities.

Thus, understanding the contribution of informal sector employment in reducing youth

unemployment is crucial for the success of economic development policies and poverty

reduction strategies.

However, studies in the area of informal sector nexus with youth unemployment are

limited in Ethiopia, particularly in the study area. This study is, thus, aimed at assessing the

role of informal sector in reducing youth unemployment in Hawassa city. Specifically, the

study intended to identify the problems facing the youth informal sector operators, assess

4

the improvements in livelihood of the operators after joining the sector, and suggest

appropriate policy recommendations.

2. Methodology

2.1 Sampling and Method of Data Collection

The study area, Hawassa city, is the capital of the SNNP region that is located about 275

kilometer South of Addis Ababa, the capital of Ethiopia. It is the main administrative,

commercial, industrial and tourist center in the Southern region. The city is one of the

fastest growing urban centers in the country. According to the 2007 Ethiopian Population

and Housing Census, the youth population of Hawassa city was 23,116; of which 47.2

percent were males and 52.8 percent were females (CSA 2008).

Multistage sampling method is employed to identify the informal sectors operated by

youths. Hawassa city is subdivided into eight sub-cities (kifle ketemas). In the first stage

eight clusters were formed using the eight sub-cities. Then using simple random sampling

method, four of the sub-cities were selected. The selected sub-cities are: Tabor, Meneharia,

Misrak and Haike-dar. The selected sub-cities have twelve kebeles (the smallest political

and administrative unit in Ethiopia). In the second stage, sample of nine kebeles were

selected from the sub-cities using simple random sampling technique. Then a central

position within a kebele is located and a random direction is then chosen by spinning a

pen. Following, every fifth informal sector activities operated by youths were selected in

that direction from the central position to the end of the kebele.

The Central Statistics Agency (CSA) of Ethiopia’s definition is used to identify whether

the enterprise is informal sector enterprise or not. Accordingly, the enterprise/business is

informal if it does not have a license, and full written book of account that shows monthly

income statement and balance sheet (CSA 2011).

The sample size is determined using the formula (Cochran 1977)

5

)1(

2

2 ppe

zn −

=

α

where e is the level of precision (= 6%); α is level of significance (= 5%); p is estimated

proportion of youths in the informal sector. According to urban employment

unemployment survey of Ethiopia, among currently employed youth population in the

urban areas about 38 percent are employed in the informal sector (CSA 2011). Hence, p =

0.38 is used to calculate the sample size. By substituting the values in the above formula

and adding 5 percent contingency, sample of size ( =n 264) informal sectors operated by

youths were selected. Following, the sample size is distributed among the selected kebeles

based on the proportion (size) of informal sectors in the kebeles. Finally, data is gathered

using structured questionnaire. The data collection was undertaken from April 11 – 24,

2011.

2.2 Variables in the study

The dependent variable of the study is the livelihood improvement of the youth informal

sector operators. It is categorized in to four ordered responses: no improvement,

satisfactory improvement, good improvement, and very good improvement in livelihood.

The explanatory variables included in the study are age, sex, educational level, migration

status, saving status, initial capital, monthly profit, marital status, religion, and length of

stay in the activity.

2.3 Methods of Data Analysis

The ordinal logistic regression method is used to model the relationship between ordinal

response variable and different explanatory variables. Models with terms that reflect

ordinal characteristics, such as monotone trend can improve model parsimony and power

(Agresti 2002). The ordinal regression analysis takes into account the ordinal nature of the

dependent variable (Green 2003). Thus, this study uses ordinal logistic regression to

determine factors that affect livelihood improvements of the operators.

6

The proportional odds model is the widely used logistic regression model for ordinal

response. The response variable of this study (improvement in livelihood) is ordinal, and

has four categories: no, satisfactory, good, and very good improvement in livelihood. The

proportional odds model compares the probability of unequal or smaller response, jY ≤ ,

to the probability of a large response, jY > , that is

1...,,2,1...

)(...)()(

)(...)()(log

)\(

)\(log]\([log

11110

21

21

−=+++=

+++

+++=

>

≤=≤

−−

++

Jjxx

xxx

xxx

xjYP

xjYPxjYPit

ppj

Jjj

j

βββ

πππ

πππ

This is called the proportional odds model. It is based on the assumption that the effects of

the covariates 121 ,...,, −pxxx are the same for all categories in a logarithmic scale, and each

cumulative logit has its own intercept (Agresti 2002; Dobson 2002). Besides, the

proportional odds assumption (parallel regression assumption) is checked by using

likelihood ratio test. The chi-square statistic is found to be insignificant (P-value > 0.05)

showing that the assumption is satisfied.

3. Results and Discussion

3.1 Sample Characteristics

Majority of the operators, about 70 percent, are young adults (20-24 years), while the

remaining 30 percent are teenagers (15-19 years). The mean and median ages of the

respondents are 20.52 and 20 years respectively with a standard deviation of 2.4 years.

Regarding the sex distribution, about 62 percent are males and the remaining are females.

Of the total sample respondents, slightly higher than half of them have primary education,

a quarter of them have secondary education, and a tenth of them have certificate and above

level of education. This finding suggests that overwhelming majorities (almost 92 percent)

of the respondents have basic literacy. This is due to the fact that youths with more

education are entering in to the sector. As to the marital status of the operators, about 79

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percent of them are never married and the remaining 21 percent are ever married.

Regarding the religious affiliation, about 48 percent are Protestant, 40 percent Orthodox,

10 percent Muslim, and 2 percent belong to other creed followers. The migration status of

respondents indicates that about 70 percent of the sampled operators are migrants to

Hawassa city. Concerning the length of stay in the activity, about 60 percent of the

operators stayed more than a year and the rest 40 percent stayed a year or less (see table

3.1).

Table 3.1: Background characteristics of sample of youth informal sector operators in

Hawassa city, April 2011.

Characteristics

Respondents (n = 264)

Number Percent

Sex

Male 164 62.1

Female 100 37.9

Age group

15-19 80 30.3

20-24 184 69.7

Educational level

No education 21 8.0

Primary (1-8) 149 56.4

Secondary (9-10) 69 26.1

Certificate and above 25 9.5

Marital status

Never married 209 79.2

Ever married 55 20.8

Religion

Protestant 127 48.1

Orthodox 104 39.4

Muslim 27 10.2

Others 6 2.3

Migration status

Migrant 185 70.1

Natives 79 29.9

Length of stay in the activity

A year or below 106 40.2

More than a year 158 59.8 Source: Own Computation 2011

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3.2 Informal Sectors Covered in the Survey

The informal sector covers a wide range of activities. Some of the activities includes

selling fruits and vegetables, clothes and shoes, various items in kiosk, food processing and

sale, small manufacturing, production, construction and repair of goods, coolies, money

changing, domestic works, prostitution, drug peddling, small-scale artisans, barberry and

shoeshine (UNESC 2006; Yuki 2007; Reddy, Vijay and Manoranjan 2002).

In this study, however, only seven informal sector activities are selected, namely: selling

cooked foods/drinks (23.5 percent), selling clothes/shoes (16.7 percent), bicycle/motor

bicycle repairing (16.3 percent), vegetable/fruit vending (12.5 percent), beauty work (12.1

percent), shoes polishing (11.0 percent), and the rest 8.0 percent engaged in selling various

items in kiosk (see table 3.2).

Table 3.2: Types of informal sector activities captured in the survey in Hawassa city, April

2011.

Activities Number of operators Percents

Selling cooked foods/drinks 62 23.5

Selling clothes/shoes 44 16.7

Bicycle/motor bicycle repair 43 16.3

Vegetable/fruit vending 33 12.5

Beauty work 32 12.1

Shoes polishing 29 11.0

Selling various items in kiosk 21 8.0

Total 264 100

3.3 Nature of the Informal Sector

3.3.1 Skills Acquired to Apply in the Job

Informal sector workers usually acquire necessary skills through either on job training or

traditional apparent-ship system (UNESC 2006). The survey result shows that majority of

the operators (82.5 percent) have learned the skills by self thought, 11.4 percent through

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apprentice or on job training, and 6.1 percent from their family. This clearly indicates that

the operators lack formal skills training that support their activity (see figure 3.1).

Figure 3.1: Skills acquired to apply in the informal sector activities in Hawassa city, April

2011

Source: Own Computation 2011

3.3.2 The Length of Working Days and Hours

The informal sector activity requires longer working days and hours. The result

demonstrates that, on average, the operators spent 6.34 days per week and 10.71 hours per

day. This is one of the indicators which show that the sector demands extensive labour

time investment, because – the marginal profit contribution of labour productivity is very

low. As a result, the operators are needed to invest longer hours to maximize their profit.

3.3.3 Initial Capital and Monthly Profit

Entry barriers into informal sector are quite negligible for most of the operators, because in

most cases the business requires low initial capital. This study has found that the average

initial capital required for the selected informal sectors is about 1938 Birr. Shoe-shining

business requires the minimum start-up capital of all sectors considered, that is, on average

Birr 208 whereas, to start a beauty salon, on average Birr 5721 is requires, which is the

maximum start-up capital of all sectors considered (see table 3.3). Sixty seven percent of

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the operators indicated that their source of start-up capital came from own rotary saving

mechanism called “Equb”. While 17 percent have borrowed from friends/relatives, 13

percent have obtained assistance from friends/ relatives, 2 percent have borrowed from

micro finance institutions, and the rest 1 percent have utilized inheritance or assistance

from governmental and non-governmental organizations to start their business. This

finding clearly indicates that only a slim portion of the operators are getting support from

formal financial institutions.

Profit level in the informal sector is generally low (UNESC 2006). However, in this study

the average monthly profit is found to be about Birr 1033, which is almost equal to the

monthly salary of a bachelor’s degree holder in Ethiopia’s civil service salary scale. Yet

the average profit ranges from Birr 500 for shoeshine workers to Birr 1821 per month for

vegetable/fruit vendors. There is large variation in the monthly profit which ranges from

Birr 100 to 7560 (see table 3.3).

Table 3.3: Initial capital and monthly profit of sample of youth informal sector operators in

Hawassa city, April 2011.

Variables Number Percent Mean

Initial capital (in Birr) (n=259) 1938.47

<=250 77 29.7

250 – 1000 111 42.9

1001 – 3000 37 14.3

> 3000 34 13.1

Profit per month (in Birr) (n=262) 1032.72

<350 48 18.3

350 – 1100 134 51.1

> 1100 80 30.5

Birr is Ethiopian currency

3.4 Constraints and Challenges Facing the Operators

Informal sector operators are constrained by a myriad of challenges which deter and limit

the potential for growth and productivity. The result shows that, about 72 percent of the

operators have been struggling with impediments which hinder their activity. These are

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shortage of working capital that is affecting 63.3 percent of the operators, followed by lack

of working places (55.3 percent), inadequate market (43.6 percent), and lack of raw

materials (39.4 percent). In addition, the operators indicated that bureaucratic bottlenecks

to obtain license, family responsibilities, problems with workers, social responsibilities,

inadequate skill, health problems and government regulations are among the challenges the

operators have been struggling with (see figure 3.2).

Figure 3.2: Constraints and challenges facing sample of youth informal sector operators in

Hawassa city, April 2011.

Source: Own Computation 2011

3.5 Improvements in Livelihood of Informal Sector Operators

The sampled operators were asked to rate their livelihood improvement after they joined

the sector. Accordingly, 35.4 percent of them rated their livelihood improvement as

satisfactory, 30.4 percent as good, 23.6 percent as very good, and the remaining 10.6

percent did not witness any change in their livelihood. In sum, about nine out of ten

operators have witnessed that their livelihood improved in one way or another after they

have joined the sector (see figure 3.3).

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Figure 3.3: Improvements in livelihood among sample of youth informal sector operators

in Hawassa city, April 2011.

Source: Own Computation 2011

In this connection, 79.9 percent of the operators have indicated that they have managed to

meet their basic needs, and 76.5 percent of them depicted that they have relieved from

unemployment problem after they joined the sector. On top of this, 38.9 percent of them

achieved supporting their family, 15.4 percent have started to participate in social life that

requires financial contribution, 14.1 percent have resumed their education, and 4.7 percent

created permanent asset (see figure 3.4).

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Figure 3.4: Types of improvements in livelihood among sample of youth informal sector

operators in Hawassa city, April 2011.

Source: Own Computation 2011

3.6 Determinants of Improvements in Livelihood

Ordinal logistic regression analysis is used to identify the determinant factors of livelihood

improvements of the youth informal sector operators. Among the variables included in the

model, educational level, migration status, monthly profit, saving status, and length of stay

in the activity are found to be statistically significant predictors of livelihood improvement

as indicated in Table 3.4.

The finding indicates that the odds of attaining better livelihood improvement is 74 percent

less for operators with no education, and 55 percent less for operators with primary

education, than operators with certificate and above level of education. This result suggests

that improvement in livelihood goes with the educational level of the operators. Education

is more relevant to the modern world of work in which problem-solving and flexibility are

required to go along with changing technologies, and to tackle the market competition

(Simon et.al. 1994). This finding also corroborates with Wamuthenya (2010) and Adams

(2008) conclusions that earning increase is proportional with level of education in the

informal sector.

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Migrant operators are 54 percent less odds of livelihood improvement compared to the

native operators of the city. This may be because migrants move to the city with high

expectation of livelihood improvement. However, they are obviously confronted with a

host of challenges and difficulties while struggling to adjust themselves with the city life

style. Partly, also migrants might be treated partially when looking for working premises,

and access to finance to expand their business.

The odds of gaining better improvement in livelihood is 79 percent and 59 percent lower

for operators who earn an average monthly profit of less than Birr 350, and Birr 350 –

1100, respectively than those operators who earn an average monthly profit of more than

1100 Birr. This means operators who are generating higher profit are enjoying better

improvement in their livelihood. It is obvious that improvement in livelihood is associated

with more profit generating capacity.

Saving can boost the odds of livelihood improvement by over 3 folds when the operators

have a saving culture than when they do not have the culture. Saving helps people to plan

for future expense, cope with stochastic crises and cover unanticipated expenses (Bamlaku

2006).

The longer the operators stayed in the business, the better the operators have improvement

in their livelihood compared to the operators who stayed short in the business.

Accordingly, those operators who stayed in the sector for a year or less have shown a

chance of improvement in livelihood by 42 percent less than those operators who stayed in

the sector for more than a year. This shows that longer stay in the business is associated

with better improvement. With longer stay in the business, the operators familiarize

themselves with the business environment and generate more profit which helps them to

improve their livelihood.

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Table 3.4: Ordinal logistic regression analysis of determinants of improvements in

livelihood of youth informal sector operators in Hawassa city, April 2011.

Improvements

Odds

Ratio

Standard

error

95% Confidence Interval

Lower limit Upper limit

Sex

Male 0.64 0.19 0.36 1.14

Female (ref)

Level of Education

No education 0.26** 0.17 0.07 0.93

Primary 0.45* 0.21 0.18 1.12

Secondary 0.52 0.25 0.20 1.33

Certificate and above (ref)

Age

10-19 0.99 0.27 0.58 1.70

20-24 (ref)

Migration status

Migrant 0.46*** 0.13 0.26 0.80

Native (ref)

Monthly profit (in Birr)

<350 0.21*** 0.08 0.09 0.47

350-1100 0.41*** 0.13 0.22 0.75

>1100 (ref)

Initial capital (in Birr)

<250 0.73 0.32 0.31 1.72

250-1000 1.04 0.41 0.48 2.25

1001-3000 1.57 0.72 0.63 3.88

>3000 (ref)

Saving status

Savers 3.82*** 1.16 2.11 6.92

Non savers (ref)

Length of stay in the activity

A year or less 0.58** 0.16 0.34 0.98

More than a year (ref)

LR chi2 (16) 97.48

Prob > chi2 0.0000

***Significant at 1%; **Significant at 5%; *Significant at 10%; ref- indicates reference category;

unmarked- indicates insignificant variables; the significant LR statistics (Prob>chi2=0.000) shows that

the overall model is significant. Source: Own Computation 2011

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4. Conclusion and Recommendations

Informal sector plays a crucial role in urban poverty alleviation through creating jobs and

reducing unemployment. Consequently, many developing countries are recognizing the

sector’s importance in their economy and trying to put appropriate policies in place to

encourage the sector (Reddy, Vijay and Manoranjan 2002). In view of its contribution to

socio-economic development, an enabling environment has to be created for operators in

order to facilitate the transition of the sector to formality (Asmamaw 2004).

Shortage of working capital is the major impediment that the operators have indicated in

the sector. In this respect, the policy makers need to design imperative measures to solve

this hindrance factor, such as through providing access to microcredit and/or special credit

services. Lack of working premises is the other challenge that the operators are confronted

with, which deserves an immediate attention by the government. Similarly, it is also vital

to tackle the problems of an inadequate market and a shortage of raw materials.

Better educated operators enjoyed improvement in livelihood, which in turn has a direct

policy implication to improve and/or upgrade the educational level of the operators.

Moreover, supporting the operators by providing business trainings may help them to

generate better profit. It is essential to cultivate and beef up the culture of saving in order to

improve the livelihood of the operators. On the other hand, migrants are not enjoying

better livelihood improvement compared to their counterparts, because migrants are

partially treated in terms of getting working premises, and financial assistance. The policy

implication is that there is a need to give equal opportunities to the migrants, and to resolve

the attitude of partiality towards them.

To sum, the operators should be encouraged to join the formal sector by lessening the

bureaucracy to get license, minimizing entry cost such as lowering registration or licensing

cost, providing tax-holidays for sometimes etc.

17

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