Int. J. Entrepreneurship and Small Business, Vol. 34, No. 2, 2018
Informal sector and urban unemployment: small businesses contribution to large livelihood improvements Tefera Darge Delbiso* Université catholique de Louvain (UCL), Clos Chapel-aux-Champs 30, Bte B1.30.15, 1200, Brussels, Belgium and Hawassa University, P.O. Box 05, Hawassa, Ethiopia Email:
[email protected] *Corresponding author
Fekadu Nigussie Deresse and Addisalem Ambaye Tadesse Japan International Cooperation Agency (JICA), P.O. Box 5384, Addis Ababa, Ethiopia Email:
[email protected] Email:
[email protected]
Befekadu Bezabih Kidane AMREF Health Africa, P.O. Box 20855 Code 1000, Addis Ababa, Ethiopia Email:
[email protected]
Germán Guido Calfat University of Antwerp, Lange Sint-Annastraat 7, S.S.124, 2000 Antwerp, Belgium Email:
[email protected] Abstract: Based on quantitative data collected from 450 informal sector operators and in-depth interviews with stakeholders in Hawassa City, Ethiopia, this study assesses the improvement in the livelihood of informal sector operators. Our findings show that the majority of operators (about 90%) have improved their livelihood. Operators who are native, educated, experienced, profitable, and economical are more likely to improve their livelihood than their counterparts. However, operators face challenges such as a shortage of working capital, lack of working premises, shortage of raw materials, and
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T.D. Delbiso et al. narrow local market base. Given the considerable impact of the informal sector on the livelihood of the operators, the government needs to seriously consider the role of the informal sector in combating growing urban unemployment. Keywords: informal sector; livelihood improvement; small business; urban unemployment; Ethiopia. Reference to this paper should be made as follows: Delbiso, T.D., Deresse, F.N., Tadesse, A.A., Kidane, B.B. and Calfat, G.G. (2018) ‘Informal sector and urban unemployment: small businesses contribution to large livelihood improvements’, Int. J. Entrepreneurship and Small Business, Vol. 34, No. 2, pp.169–182. Biographical notes: Tefera Darge Delbiso is a Research Associate and PhD candidate in Université catholique de Louvain (UCL). He obtained his BSc in Statistics and MSc in Population Studies from Addis Ababa University (AAU), and Advanced MSc in Public Health Methodology from Université Libre de Bruxelles (ULB). He has served as a Lecturer at Hawassa University; as a Research Advisor at Marie Stopes International Ethiopia; and as an independent consultant in different organisations. He has a multidisciplinary background and has published scientific articles in a variety of journals. Fekadu Nigussie Deresse obtained his Advanced Master in Development Evaluation and Management from University of Antwerp, MA in Development Studies and BSc in Mathematics from Addis Ababa University. He holds a position of Program Officer for the Industrial and Private Sector Development at the Japan International Cooperation Agency. He has research contribution in the area of industrial management, impact evaluation and livelihood. Addisalem Ambaye Tadesse is currently working as Economic Infrastructure Program Officer with Japan International Cooperation Agency Ethiopia Office. She completed her MA in Regional and Local Development Studies and BA in Economics both from Addis Ababa University. She has since worked as a Senior Gender Officer with a USAID funded food security program for three years. Her research interests are focused on areas on the economic empowerment of women particularly access to productive resources such as land, credit, employment, and income generation. Befekadu Bezabih Kidane is a Human Capital Manager in Amref Health Africa, Ethiopia office. He obtained his MA in Development Economics from St. Mary’s University, MA in Public Administration from Addis Ababa University, and BA in Business Management from Jimma University. He has over 12 years of proven and progressive experience in human capital, organisational development, talent management and labour market studies. Apart from his experience as practitioner, he has taught different courses to graduate and undergraduate students. Germán Guido Calfat is a full-time Lecturer at the IOB, University of Antwerp. He is holder of a Master of Arts in Economics from the KUL and a PhD in Economics from the UA. His actual research agenda is on the channels by which trade/migration impacts welfare and the links between migration/remittances and poverty and human capital. He is the current Programme Director of the three master programmes in development at IOB. He publishes in the Journal of Development Studies, Migration and Development, Journal of Common Market Studies, Journal of Economic Integration and CEPAL Review, among others.
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This paper is a revised and expanded version of a paper entitled ‘The role of informal sector employment in poverty alleviation: the case of Hawassa City, Ethiopia’ presented at the 2nd Regional Conference of the Southern, Nations, Nationalities, Peoples’ Regions Economic Development, Hawassa, Ethiopia, 27 March 2012.
1
Introduction
The informal sector constitutes a heterogeneous mix of economic activities that are unregistered and therefore unrecognised by the government. Informal economy are activities that involve paid production; unregistered, operating in hiding from the state for tax and employment law purposes and none of its economic contribution is accounted in official statistics (Dana, 2010). It involves all categories of actors including legal, illegal and migrant (Rezaei et al., 2013a). The sector provides a wide range of services and produces diversified consumption goods. Included under this category are street vendors, daily labourers, domestic servants, small-scale artisans, drug pushers, informal currency exchange services, and home based maintenance and repair services. The absence of recognition from the government means that informal agents operate in the shadow of the law with little or no access to organised markets; formal credit and financing; technology and knowledge transfer; legal protection; working hour arrangement; and working areas, among others. Thus, these activities lack an organised approach to their operation, which often leads to low levels of productivity and income. It also limits their operational scope and the possibility for future expansion and growth (CSA, 2004; Enquobahrie, 2003; Sparks and Barnett, 2010). The informal sector exists both in developing economies and in highly regulated, monitored and organised labour markets in developed countries (Rezaei et al., 2013a). The sector constitutes a sizeable proportion of national accounts around the world. The contribution of the sector to the gross domestic product (GDP) seems to vary depending on the country’s level of development: an average of 8.4% in the USA, 16.6% in rich Organisation for Economic Cooperation and Development (OECD) countries, and 35.1% in developing countries (Schneider et al., 2010). This figure reaches as high as 52% of the GDP in Ethiopia (Kolli, 2010). Hence, the sector plays an important role in urban poverty alleviation through job creation and unemployment reduction. For instance, about 60% of urban employment in Africa was estimated to be in the informal sector (World Bank, 2008). The sector provides employment opportunities mostly for semi-skilled and unskilled labour, which constitutes the majority of the young and unemployed. Furthermore, the sector plays a key role in mitigating the effects of government inefficiencies by absorbing surplus labour from the formal sector, and contributing to a reduction in crime and violence (Reddy et al., 2003; Sparks and Barnett, 2010). In Ethiopia, the informal sector accounts for 37% of employment. However, in the study area – the Southern Nations, Nationalities and Peoples’ (SNNP) region – the rate was 44%, which is slightly above the country average. In Hawassa City, the capital city of the study region, 26% of the employment was found to be in the informal sector (CSA, 2012).
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Many developing countries are increasingly recognising the informal sector as an important sector in their economy and trying to formulate policies to encourage its activity (Reddy et al., 2003). Rezaei et al. (2013a) stress on the need to recognise the informal economy’s as a by product of the country’s business structure. However, policy makers in sub-Saharan Africa often fail to recognise the potential of the sector as a source of employment and income. As a result, the sector is often separated from the development process (Enquobahrie, 2003). However, the informal sector is not a transitory phenomenon in the development process. Rather, it is a sector that will continue to function alongside the formal sector. Indeed, in some cases, the informal sector is necessary (Ruffer and Knight, 2007). In order to suggest successful development policies and unemployment reduction strategies, it is thus important to understanding the sector. Therefore, in this paper, we attempted to assess the role of informal sectors in reducing urban unemployment and poverty. More specifically, we investigated the nature of informal sector in Hawassa City; the characteristics of operators; the challenges they face; and the determinants of livelihood improvement of the operators.
2
Methodology
2.1 Sampling and data collection Hawassa is the capital city of the SNNP region and the hub of administrative, commercial, and industrial activities in the region. The city is located 275 kilometres to the south of the national capital, Addis Ababa, and is one of the fastest growing urban centres. The city also has historic and geographic advantages which makes it one of the most popular tourist destinations in the country. The city is divided into eight sub-cities which are further subdivided into kebeles, the smallest unit in the Ethiopian political and administrative structure. A multi-stage sampling method was used to select informal sector activities. The eight sub-cities provide eight clusters for sampling. In the first stage, four sub-cities – Tabor, Meneharia, Misrak and Haikedar – were selected randomly. Within the selected sub-cities, there were 12 kebeles, out of which nine kebeles were randomly selected in the second stage. Given the lack of documented information about the informal sector, the investigator then relied on personal observation to estimate the number of informal sector activities in the selected kebeles. The calculated sample size was then allocated among the selected kebeles according to the size of informal sector activities in the kebeles. In the third stage, the selection of informal sector activities within each kebele was done as follows: a random geographic location was identified in a given kebele as a starting point for a transect walk in a random direction. Then every fifth informal sector activity along the walk was chosen until the required sample size was reached. We calculated the required sample size (n = 450) using the one-population proportion sample size determination formula1. Accordingly, 127 informal sector activities were selected from Tabor sub-city, 125 from Meneharia sub-city, 117 from Misrak sub-city, and 81 from Haikedar sub-city. Then, the quantitative data was collected from the owners
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of the informal sector using a structured questionnaire. To supplement the quantitative evidence, we conducted in-depth interviews with selected informal sector operators and local government representatives using semi-structure questionnaires.
2.2 Variables and econometric analysis •
Dependent variable: The livelihood improvement of operators. Operators were asked to rate their livelihood improvement since they started working in the informal sector. Accordingly, they rated the livelihood improvement as 1 no improvement 2 satisfactory improvement 3 good improvement 4 very good improvement.
•
Independent variables: These include sex, age, educational level, marital status, religion, migration status, initial capital, monthly profit, saving status, and duration in the informal business operation.
•
Econometric analysis: By taking into account the ordinal nature of the dependent variable – livelihood improvement –we fitted an ordinal logistic regression model to determine factors that affect the livelihood improvement of operators (Agresti, 2002). This provides the likelihood of livelihood improvement a unit change in an independent variable, when other factors are held constant. To make sure that our data satisfies all the underlying assumptions of the econometric analysis, we conducted model adequacy checks including multi-collinearity, proportional odds assumption, outliers and influential values and addressed them adequately. Finally, we reported the odds ratios (OR) together with their 95% confidence intervals (CI) and p-values for variables which are significant in the model. The statistical analysis was performed using STATA/IC 12.1 for Window.
3
Findings
3.1 Sample characteristics The median age of the operators was 22.8 years. The youth (10–24 years of age) constituted 62% of the sample; nearly 45% of the operators were women; and over 63% were single. The overwhelming majority (about 90%) of the operators had basic literacy. This is mainly because there is a high level of academic and skills requirement to get a job in the formal sector. In recent years, however, it has become increasingly common to find college and university graduates in this sector. This is due to the narrow formal sector in most developing countries and the inherently slow replacement rate which results in a limited capacity to absorb the increasing number of new graduates (Amaral and Quintin, 2006). Palmer (2004) also found that informal sector operators in
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sub-Saharan Africa are better educated and have better trainings; and a study in Australia suggests that informal sector operators often perform jobs below their skill level (Rezaei et al., 2014). The proportion of rural-urban migrants was over 72% (Table 1). Table 1
Characteristics of sample informal sector operators in Hawassa City, 2011
Variables Age (in years)
Number
Percent
438
22.8 (19.8–27.2)1
10–19
20.0
20–24
42.0
25–34
28.2
35–65
9.8
Sex
450
Male
55.3
Female
44.7
Educational level
450
No education
10.2
Primary (1–8 grades)
50.4
Secondary (9–10 grades)
24.2
Certificate and above
15.1
Marital status
450
Single
63.3
Married
31.6
Others2
5.1
Religion
450
Protestant
45.8
Orthodox
43.3
Muslim
8.0
3
Others
2.9
Migration status
435
Migrant
72.6
Native 1
27.4 2
Notes: Median (25th percentile – 75th percentile); divorced or separated or widowed; 3 no or traditional religion.
3.2 Informal sector activities covered in the study There are several informal sector activities, such as selling fruits and vegetables, clothes and shoes, various items in kiosks, food processing, small-scale manufacturing, construction and maintenance of goods, daily labourers, currency exchange services, domestic servants (maids), prostitution, drug pushers, small-scale artisans, barbers and shoeshiners (Economic and Social Commission for Asia and the Pacific, 2006; Reddy et al., 2003; Yuki, 2007). Based on the investigator’s observation and assessment, seven dominant informal sector activities were selected for this study (Table 2).
Informal sector and urban unemployment Table 2
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Selected informal sector activities in Hawassa City, 2011
Activities Selling cooked foods/drinks Bicycle/motor bicycle repair Selling clothes/shoes Vegetable/fruit vending Beauty salon Selling various items in kiosk Shoes shining Total
Number
Percent
119 65 64 60 56 46 40 450
26.4 14.4 14.2 13.3 12.4 10.2 8.9 100
3.3 Skills acquisition Informal sector workers usually acquire the necessary skills either through on the job or through traditional apprenticeship (Economic and Social Commission for Asia and the Pacific, 2006; Haan, 2001). Our results show that a majority of the operators (80%) learned the skill by themselves, 11% through apprenticeship or job training, 8% from their family, and only 1% through formal training.
3.4 Source of initial capital Entry barriers to the informal sector are very low compared to formal sector. We found that the average initial capital required for the sectors was about Birr 575 with variations ranging from Birr 209 to Birr 2,270 for the middle 50% of the operators. The majority of operators (65%) indicated that their start-up capital came from a local rotary savings group called ‘Equb’ while only 3% borrowed from micro finance institutions (Table 3). This shows that only a small portion of the operators have access to formal financial institutions to start their business. This might be related to the common assumption that the poor are unbankable. Table 3
Source of initial capital for informal sector operators in Hawassa City, 2011
Variables Initial capital (in Birr)
Number
Percent
438
575.0 (209.3–2270.3)1
Less or equal to 250
32.0
Between 251 and 999
34.2
Greater or equal to 1,000
33.8
Source of initial capital
442
Own rotary saving or ‘Equb’
64.7
Borrowed from friends/relatives
17.2
Assistance from friends/relatives
13.1
Borrowed from micro finance institutions
2.9
Inherited
1.4
Assistance from governmental or NGO’s
0.7
1
Note: Median (25th percentile – 75th percentile).
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3.5 Monthly profit and saving status By comparing wage rates, it appears that workers in the informal sector earn less than their formal counterparts, even though they work longer hours (Amaral and Quintin, 2006; Economic and Social Commission for Asia and the Pacific, 2006; Rezaei et al., 2014). The low wages in the informal sector is often associated with the perceived low labour productivity in the sector due to low level of skills requirement. It could be also due to vulnerable status of operators in the shadow economy they often get inferior working conditions and wage where most labour market regulations are overlooked (Rezaei et al., 2013a). In addition, informal business owners often do not have adequate financial capacity to purchase labour saving-productivity enhancing technology. This study, however, shows that the median monthly profit was found to be about Birr 763, which was more than double the minimum monthly salary of civil servants in the county. This finding is in line with research by Haan (2001) indicating that the gross income in informal sector enterprises is 2.5 times higher than the minimum wage of civil servants. There is in fact a considerable variation in the monthly profit in this survey. The average profit ranges from Birr 500 for shoe shiners to Birr 1,148 for vegetable/fruit vendors. 70% of the operators confirmed that they save some portion of their income (Table 4). Table 4
Monthly profit and saving status of informal sector operators in Hawassa City, 2011
Variables Profit per month (in Birr)
Number
Percent
436
763.3 (447.0–1,227.0)1
< 543
32.6
543 – 1,015
34.9
> 1,016
32.6
Saving status
450
Saving
70.4
Not saving
29.6
1
Note: Median (25th percentile – 75th percentile)
3.6 Challenges and constraints of informal sector operators Informal sector operators are constrained by a myriad of challenges. About 76% of the operators indicated that in one way or another they confront challenges that impede their operation. A shortage of working capital (60%), lack of working premises (48%), inadequate market in terms of purchasing capacity (41%), and shortage of good quality affordable raw materials (41%) were the major challenges reported by the operators. Bureaucratic bottlenecks to obtain a license, credits, family responsibilities, and problems with workers were also among the constraints reported (Figure 1). Fjose et al. (2010) also noted that informal business growth opportunities in sub-Saharan Africa are primarily hindered by limited access to formal credit and lack of markets. In other words, these challenges entail the myriads of barriers and the absence of established channels to transform informal operators into formal business.
Informal sector and urban unemployment Figure 1
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Constraints and challenges in the informal sector in Hawassa City, 2011 (see online version for colours) Shortage of capital
59.5
Constraints and challenges
Lack of working premises
47.8
Lack of market
41.4
Lack of raw materials
40.8
Bureaucracy
20.1
Credit
19.8
Family responsibility
19
Problems with workers
14.9
Social responsibility
6.7
Inadequate skill
5.5
Health problem
4.7
Government regulations
2.9
Others
1.2 0
10
20
30 40 Percent
50
60
70
In-depth interviews with local government representatives reveal a strong negative perception towards informal sector operators. This is mainly because businesses in this sector are not formally registered, tax-paying enterprises. In addition, the existence of the informal sector has created a loophole allowing formal sector operators to disguise their transactions, and thus minimise the amount of tax payable or evade taxation altogether. For instance, formal sector operators hire rural migrants informally and pay them low wages, while reporting exaggerated figures as salary expenses. It is a classical case where in the formal-informal relation, the formal sector is engaged in tapping the informal entrepreneurs that risk the dependency of informal sector on the formal sector; rather searching for appropriate technical and financial incentives to upgrade them (Meagher, 2013). It is also not uncommon to find products from the formal sector being distributed via informal channels while producers report undervalued sales figures to minimise the amount of reported profit, and therefore, profit tax payable to the government. Currently, the government discourages petty traders and street vendors as a mitigation strategy to curb these challenges. The informal sector operators have also acknowledged that there are disguised formal sector operators within the informal sector – particularly in limited areas such as the selling of clothes and shoes. The informal sector operators see the government action as collective punishment. However, without addressing the prohibitive criteria by the government to have a legal personality for a business, it may prove very difficult and counterproductive to prohibit or control the proliferation of informal operations. Given its high social externalities cost the control of informal economic activities is a challenging endeavour even in an advanced and well-regulated economy such as Belgium. The decision that politicians have to make to control the informal
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economy is a choice balancing between pursuing a harder line or a laissez-faire style towards informal economy, however neither choice will eliminate the underground economy completely (Rezaei et al., 2013b).
3.7 Livelihood improvements The sample operators were asked to rate their livelihood improvement after they joined the informal sector. About 38% of them rated their livelihood improvement as satisfactory, 28% as good, and 23% as very good, while the remaining 11% indicated that their livelihood did not show any improvement at all. In general, nearly nine out of ten operators witnessed improvement in their livelihood after they started working in the informal sector. This finding echoes the results of earlier studies by Reddy et al. (2003) and Kapunda and Mmolawa (2007), which concluded that the informal sector improves the livelihood of the operators in terms of income generation and asset building. Furthermore, over 82% of the operators were able to meet their basic needs, 78% were self-employed rather than seeking employment elsewhere, and 40% were able to support their families. In addition, operators testified that after they joined the sector, they started participating in social life activities that requires financial contribution, some resumed their education and others created fixed asset (Figure 2). Figure 2
Types of livelihood improvements of informal sector operators in Hawassa City, 2011 (see online version for colours)
Type of Improvements
Meet basic needs
82.4
Free from unemployment
77.6
Support family
39.7
Participate in social life
25.4
Resumed education
13.3
Created fixed asset
5.8
Others
0.3 0
10
20
30
40 50 Percent
60
70
80
90
3.8 Determinants of livelihood improvements Our findings show that, livelihood improvement in the informal sector is significantly associated with the level of education, migration status, monthly profit, saving status, and the duration of stay in the sector (Table 5). Operators who have a certificate and above level of education had 2.4 times more chance of livelihood improvement than those with no education. This finding is consistent with Adams’ (2008) and Wamuthenya’s (2010) conclusion that informal sector earning increases with the level of education.
Informal sector and urban unemployment Table 5
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Ordinal logistic regression estimation of the association between livelihood improvement of the informal sector operators and the independent variables
Variables
OR
95% CI
P-value
Level of education No education (ref)
1.00
Primary
1.47
(0.76; 2.84)
0.251
Secondary
1.58
(0.77; 3.23)
0.214
Certificate and above
2.44
(1.11; 5.36)
0.027
Native
1.90
(1.22; 2.95)
0.004
Migrant (ref)
1.00
Migration status
Monthly profit (in Birr) Less than 588 (ref)
1.00
Between 588 and 1,008
2.01
(1.32; 3.34)
0.002
Greater than 1,008
4.43
(2.66; 7.37)
