The urban informal sector as a means of livelihood improvement among youth: Evidence from Hawassa city, Ethiopia

Figures & data

Figure 1. Location of the study area.

Table 1. Distribution of sample by sub-city and kebele

Four sub-cities were selected out of eight sub-cities	Kebeles	Sample size	Percept
Hayek Dar	Gudumale	25	12
	Dume	24	12
Tabor	Gebeyadar	27	13
	Fura	26	13
Menehariya	Tesso	24	12
	Millennium square	26	13
Misrak	Wukro	21	10
	Piassay	29	15
Total		202	100

Table 2. Definition and measurements of variables and expected signs

Variable	Code	Measurement	Types of variable	Expected sign
Dependent
Livelihood status	LD	1 if the participant perceived a livelihood improvement due to informal business, and 0 otherwise	Dummy
Independent variables
Age	AGE	In years	Continuous	(+)
Education	EDUC	Last grade completed	Continuous	(+)
Income	LINCOM	Monthly income in Ethiopian Birr	Continuous	(+)
Training	LTRAING	1 = if yes, 0 = otherwise	Dummy	(+)
Credit	CREDITU	1 = if yes, 0 = otherwise	Dummy	(+)
Family size	FSIZE	Number of people in the household	Continuous	(+)
Saving	SAVING	Monthly savings in Ethiopian Birr	Dummy	(+)
Sex	SEX	1 if male, 0 otherwise	Dummy	(-)
Marital status	MARTST	1 = single,0 = otherwise	Dummy	(+)
Experience	EXPER	In year	Continuous	(+)
Working hours	WHOURS	In hours	Continuous	(+)
Capital	CAPITAL	In Ethiopian Birr	Continuous	(+)
Migration	MIGRATI	1 if migrants, and 0 otherwise	Dummy	(-)

Table 3. Types of informal sector business activities in Hawassa city

Business sector	Trade	Service	Production	Construction	Urban agriculture
Activities	Selling clothes/shoes, spiritual books, pictures, candles, soft drinks, candles various items, such as vegetables and fruits	Selling cooked foods, roadside beauty works/salons, shoe polishing,	Home-made food staff producers, weaving	Bicycle/motor bicycle, mobile, tape, and battery maintenance and repair; providing construction services along streets	Selling small trees and milk (Cow)
Frequency	73	65	30	8	6
Percentage	40.10	35.72	16.49	4.40	3.30
Total	182	182	182	182	182

Source: Own computation based on a survey, 2020.

Table 4. Livelihood status of informal sector participants

Variables livelihood status	The livelihood of informal sector participants
	Frequency	%
Improved	123	67
Not improved	59	33

Source: Survey result, 2020.

Figure 2. Types of livelihood improvements of the informal sector participants.

Source: Own computations based on survey, 2020.

Figure 3. Challenges and constraints of informal sector operators.

Source: Own computations, 2020.

Table 5. Maximum likelihood estimation of logit model

Maximum likelihood estimation of logit model
Variables	Coef.		Robust std. err.		Z	P>|z|		ME (dy/dx)
SEX|	−2.38		1.01		−2.34	0.019**		−0.187
AGE	0.211		0.091		2.32	0.020**		0.012
MARTST|	0.642		1.138		0.56	0.572		0.040
FSIZE	0.177		0.154		1.15	0.250		0.010
EDUC	0.459		0.313		1.46	0.143		0.026
MIGRATI|	−1.689		1.760		−0.96	0.337		−0.096
CREDITU|	4.443		1.359		3.27	0.001***		0.481
LINCOM	0.037		0.0129		2.86	0.003***		0.0022
CAPITAL	0.012		0.0045		2.66	0.004***		0.000074
SAVING|	4.137		1.808		2.29	0.022**		0.236
WHOURS	0.508		0.822		0.62	0.536		0.0271
LTRAING|	0.878		0.190		4.60	0.000***		0.0501
EXPER|	0.891		0.440		2.02	0.043**		0.0508
Constant	−20.873		3.83		−5.45	0.000***
Dependent variable: livelihood of informal sector participant
Log likelihood Number of Obs Prob>Chi^2 Pseudo $R^2$		−23.39 182 0.0000 0.79		Mean VIF Contingency coefficient Heteroscedasticity $X^2\left(1\right)$ Hosmer–Lemeshow’s $X^2\left(1\right)$ Wald chi^2(13)			[1.28] Max 0.67 [9.76] 0.0018 [5.52] (0.7008) 47.87

16.1.Note: *** and ** indicate 1% and 5% significance level, respectively.