Assessing gender gaps in employment and earnings in Africa: The case of Eswatini

Figures & data

Figure 1. Eswatini’s fiscal challenges. (a) Fiscal balances in SADC members, average for 2010–19 (% of GDP). (b) Government expenditures on education in SADC members (% of GDP). (c) Gross tertiary enrolment rates in SADC members (%), average of the last 10 years. Source: World Bank Development Indicators and IMF World Economic Outlook databases.

Note: DRC for Democratic Republic of Congo.

Figure 2. Female unemployment rates in SADC members. 2014–19 average (% of female labour force). Source: World Bank Development Indicators, ILO estimates.

Table 1. Overall labour market outcomes in Eswatini, by gender, 2007, 2010 and 2013.

	Unemployment	Employment	Labour force	Self-employment
	(% of labour force)	(% of population)		(% of employment)
	2007
Men	24.0	46.4	61.0	15.6
Women	30.3	32.5	46.6	28.3
Stat. significance of difference between men and women	***	***	***	***
	2010
Men	22.5	38.4	49.3	14.1
Women	29.4	32.2	45.3	27.2
Stat. significance of difference between men and women	***	***	***	***
	2013
Men	27.4	38.8	53.4	20.7
Women	35.5	28.9	44.8	30.0
Stat. significance of difference between men and women	***	***	***	**

Source: Authors’ calculations based on the Eswatini Labor Force Surveys. ***, ** and * respectively denote significance at 1%, 5% and 10%.

Table 2. Unemployment including discouraged workers, (% of relevant labour force).

	Male	Female	Total	Male	Female	Total
		2007			2013
Total (15+)	31.1	42.9	36.8	37.1	46.5	41.8
Youth (15–24)	59.2	66.7	63.2	61.6	70.6	66.2
Young adults (25–34)	26.7	39.4	32.8	39.2	50.7	44.9
Adults (35+)	18.7	31.7	24.9	23	31.7	27.2

Source: Authors’ calculations based on the Eswatini Labor Force Surveys.

Table 3. Employment (% of work. age pop. with relevant education), by gender.

	Primary	Secondary	Tertiary
	2007
Men	37.6	51.1	72.9
Women	26.3	36.8	69.5
Stat. signif. of difference	***	***	–
	2010
Men	30.2	44.8	76.0
Women	25.1	35.5	68.1
Stat. signif. of difference	***	***	**
	2013
Men	38.7	47.6	76.6
Women	27.0	32.5	69.8
Stat. signif. of difference	***	***	**

Source: Authors’ calculations based on the Eswatini Labor Force Surveys. ***, ** and * respectively denote significance at 1%, 5% and 10%.

Figure 3. Changes in labour market outcomes, in percentage points, from 2007 to 2013. Source: Authors’ calculations based on the Eswatini Labor Force Surveys.

Note: pp = percentage points.

Figure 4. Security of wage employment, by gender, 2007 and 2013. Share of wage employees with formal contract (%). Source: Authors’ calculations based on the Eswatini Labor Force Surveys.

Table 4. Sectoral distribution of employment, by gender and age, 2007 (% of total employment in each age category).

	15–24		25–34		35+
	Men	Women	Men	Women	Men	Women
Public	9.9	10.2	17.4	22.6	27.2	26.5
Formal private	77.3	69.1	71.3	58.8	60.4	36.9
Informal private	9.5	16.6	10.3	15.5	11.7	32.8
Domestic workers	3.2	4.1	1.1	3.1	0.7	1.8

Source: Authors’ calculations based on the Eswatini Labor Force Surveys.

Figure 5. Labour force participation in 2007 and 2013, by gender (% of population). Source: Authors’ calculations based on the Eswatini Labor Force Surveys.

Table 5. Official remittance inflows to Eswatini and other countries in the region.

Countries	Remittances (million US$)	Remittances % GDP
Eswatini	98	2.7
Botswana	43	0.3
Lesotho	398	20.6
Malawi	219	1.5
Mozambique	189	2.3
South Africa	840	0.3
Zambia	91	0.5
Zimbabwe	1730	9.9

Source: World Bank (2020).

Table 6. Share of employed with tertiary education, by gender and type of employment.

	Wage employment	Self-employment
(% of relevant employment, ages 15+)
	2007
Men	15.4	14.7
Women	21.6	5.9
Stat. significance of difference	***	***
	2010
Men	17.0	15.6
Women	21.6	6.3
Stat. significance of difference	**	***
	2013
Men	23.6	13.9
Women	27.7	7.8
Stat. significance of difference	**	***

Source: Authors’ calculations based on the Eswatini Labor Force Surveys. **, ** and * respectively denote significance at 1%, 5% and 10%.

Table 7. Gender and self-employment (logit regressions; dependent var. is self-employed = 1).

	(1)	(2)	(3)
	2007	2010	2013
Personal characteristics
Gender (women = 1)	0.90***	0.91***	0.35**
	(0.1)	(0.11)	(0.09)
Eswatini citizen	−1.32***	−1.33***	−0.66**
	(0.21)	(0.19)	(0.30)
Age (in years)	0.04*	0.06***	0.033*
	(0.02)	−0.02	(0.02)
Age (in years squared)	0.00	0.00	0.00
	(0.0)	(0.0)	(0.0)
Household-related characteristics
Head of household	0.16	0.09	−0.15
	(0.11)	(0.11)	(0.11)
Marital status (married = 1)	0.21**	0.35***	0.04
	(0.1)	(0.1)	(0.1)
Mobility, location
Urban location (urban = 1)	−0.29***	−0.34**	−0.45***
	(0.1)	(0.11)	(0.11)
Manzini resident	0.25***	0.27***	0.37***
	(0.09)	(0.10)	(0.09)
Migrant (moved into area = 1)	−0.02	−0.07	−0.03
	(0.11)	(0.11)	(0.09)
Education
Secondary	−0.16*	−0.27***	−0.25**
	(0.09)	(0.1)	(0.1)
Tertiary	−0.90***	−1.10***	−1.21***
	(0.15)	(0.16)	(0.14)
Intercept	−1.76***	−2.25***	−1.46***
	(0.46)	(0.41)	(0.49)
Obs	3561	3267	3376
Pseudo R^2	0.075	0.093	0.085

Note: Robust standard errors are under parentheses; ***, ** and * respectively denote significance at 1%, 5% and 10%.

Source: Authors’ estimations based on the Eswatini Labor Force Surveys.

Figure 6. Gender gaps in earnings, 2007, 2010 and 2013 (in % of male income). Source: Authors’ calculations based on the Eswatini Labor Force Surveys.

Figure 7. Kernel density estimate of (log of) income from wage employment, by gender. (a) Log of income from wages in 2007. (b) Log of income from wages in 2013. Source: Authors’ estimations based on the Eswatini Labor Force Surveys.

Figure 8. Kernel density estimate of (log of) income from self-employment, by gender. (a) In 2007. (b) In 2013. Source: Authors’ estimations based on the Eswatini Labor Force Surveys.

Table 8. Oaxaca–Blinder decomposition of gender wage gaps in Eswatini, 2007–13.

Dependent variable:	Coeff. (std. err.)
Log of monthly wages	[2007]	[2007]	[2010]	[2010]	[2013]	[2013]
Reference group: men
	Log points	Share of total gap, %	Log points	Share of total gap, %	Log points	Share of total gap, %
Overall
Men	7.48*** (0.02)	–	7.66*** (0.02)	–	7.79*** (0.03)	–
Women	7.18*** (0.03)	–	7.48*** (0.03)	–	7.56*** (0.03)	–
Difference (log points)	0.30*** (0.04)	100.0	0.18*** (0.04)	100.0	0.23*** (0.04)	100.0
Explained part	0.07* (0.03)	23.4	0.042 (0.03)	23.7	0.05* (0.03)	21.7
Unexplained part	0.23*** (0.04)	76.6	0.154*** (0.03)	76.3	0.17*** (0.04)	78.3
Lives in Manzini	0.00 (0.00)	0.0	0.00 (0.00)	0.0	0.00 (0.00)	0.4
Head of household	0.07*** (0.02)	26.7	0.028** (0.01)	15.8	0.10*** (0.02)	43.5
Age	0.05*** (0.01)	16.7	0.02*** (0.01)	11.3	0.00 (0.01)	1.3
Eswatini citizen	0.00 (0.00)	0.0	0.00 (0.00)	0.0	0.00 (0.00)	0.1
Married	0.03*** (0.01)	10.0	0.03*** (0.00)	16.9	0.00 (0.00)	0.3
Moved into the area	0.00 (0.00)	0.0	0.00 (0.00)	0.0	0.00 (0.00)	−1.8
Secondary education	−0.01 (0.01)	−3.3	−0.01 (0.01)	0.0	0.00 (0.01)	1.7
Tertiary education	−0.09*** (0.02)	−26.7	−0.06* (0.02)	−33.9	−0.064** (0.03)	−28.7
In the formal sector^a	0.013** (0.01)	−26.7	0.032*** (0.01)	18.1	0.011** (0.01)	4.9
Number of observations	2676	2676	2638	2638	2481	2481
Men	1591	1591	1476	1476	1413	1413
Women	1085	1085	1162	1162	1068	1068

Source: Author’s estimations based on the Eswatini Labor Force Surveys.

^a Note: In 2013, being a manager vs. worker was used as data on the formal and informal employment was not available. Robust standard errors are under parentheses; ***, ** and * respectively denote significance at 1%, 5% and 10%.

Table 9. Oaxaca–Blinder decomposition of net income from self-employment (log).

	Log points	Share of total gap, %	Log points	Share of total gap, %	Log points	Share of total gap, %
Overall
Men	7.20*** (0.09)	–	7.54*** (0.08)	–	7.37*** (0.07)	–
Women	6.42*** (0.67)	–	7.00*** (0.5)	–	6.60*** (1.86)	–
Difference (log points)	0.78 (0.68)	100.0	0.54 (0.5)	100.0	0.77 (1.86)	100.0
Explained part	0.26** (0.11)	33.3	0.23** (0.1)	42.6	0.16*** (0.06)	20.8
Unexplained part	0.53 (0.69)	66.7	0.31 (0.51)	57.4	0.61 (1.86)	79.2
Lives in Manzini	0.01 (0.01)	1.3	0.01 (0.01)	1.9	−0.01 (0.01)	0.0
Head of household	0.04 (0.09)	5.1	0.00 (0.08)	0.5	0.07* (0.04)	9.1
Age (incl. age squared)	−0.02 (0.01)	−1.9	0.00 (0.01)	0.7	−0.01 (0.03)	−1.3
Eswatini citizen	0.03 (0.02)	3.8	0.04 (0.03)	7.4	0.01 (0.01)	1.3
Secondary education	0.04 (0.03)	4.6	0.02 (0.02)	3.7	−0.01 (0.02)	−0.5
Tertiary education	0.08** (0.03)	9.6	0.13*** (0.04)	25.0	0.07** (0.03)	9.0
In the formal sector	0.09** (0.03)	11.3	0.02 (0.02)	3.7	0.03* (0.02)	4.0
Number of observations	684	684	677	677	810	810
Men	285	285	271	271	377	377
Women	399	399	406	406	433	433

Note: Robust standard errors are under parentheses; ***, ** and * respectively denote significance at 1%, 5% and 10%.

Source: Authors’ calculations based on the Eswatini Labour Force Surveys.

Table

Variable	Description
Unemployment (%)	Share of unemployed in the labour force (%)
Employment (%)	Share of employed in working age population (%)
Labour force (%)	Share of people who are self-employed as % of employed
Self-employment (%)	Share of people who are in the labour force (employed or unemployed) as % of working age population (15+)
Gender	Dummy variable taking the value of 1 for women and 0 for men
Age	Age (in years)
Education	Variable taking the value 1 if the person’s highest education is primary or less, 2 if secondary and 3 for tertiary
Secondary	Dummy variable taking the value 1 if the person has secondary education and 0 otherwise
Tertiary	Dummy variable taking the value 1 if the person has tertiary education and 0 otherwise
Eswatini citizen	Dummy variable taking the value of 1 if the entrepreneur has an Eswatini citizenship and the value of 0 otherwise
From Manzini	Dummy variable taking the value of 1 if the person is from Manzini and the value of 0 otherwise
Contract	Dummy variable taking the value of 1 if the employed person has a formal contract and 0 otherwise
Wage	Wage for employees in a typical month (Emalangeni)
Income	Income for self-employed in a typical week (Emalangeni)
Head	Dummy variable taking the value 1 if the person is head of the household and 0 otherwise
Area	Dummy variable taking the value 1 if the person lives in urban area and 0 otherwise
Migrant	Dummy variable taking the value 1 if the person moved into the current place of residence and 0 if he/she was born there

Table

Variable	Number of observations	Mean	St. dev.	Min	Max
	2007
Gender	10,046	0.53	0.50	0	1
Age (years)	10,046	33.58	15.99	15	99
Primary education	8841	0.51	0.50	0	1
Secondary education	8841	0.40	0.49	0	1
Tertiary education	8841	0.09	0.29	0	1
Swazi citizen	10,046	0.98	0.14	0	1
Area	10,046	0.41	0.49	0	1
From Manzini	10,046	0.29	0.45	0	1
Head	10,046	0.35	0.48	0	1
Migrant	10,046	0.44	0.50	0	1
	2010
Gender	10,189	0.50	0.50	0	1
Age (years)	9628	34.79	16.99	15	98
Primary education	8509	0.51	0.50	0	1
Secondary education	8509	0.40	0.49	0	1
Tertiary education	8509	0.09	0.29	0	1
Swazi citizen	9599	0.97	0.17	0	1
Area	10,189	0.40	0.49	0	1
From Manzini	10,189	0.29	0.45	0	1
Head	9599	0.38	0.49	0	1
Migrant	9592	0.47	0.50	0	1
	2013
Gender	11,217	0.53	0.50	0	1
Age (years)	11,217	35.33	17.21	15	99
Primary education	8289	0.31	0.46	0	1
Secondary education	8289	0.57	0.50	0	1
Tertiary education	8289	0.12	0.33	0	1
Swazi citizen	11,217	0.99	0.12	0	1
Area	11,217	0.33	0.47	0	1
From Manzini	11,217	0.29	0.45	0	1
Head	11,217	0.36	0.48	0	1
Migrant	11,217	0.40	0.49	0	1

Source: Authors’ calculations based on the Eswatini Labor Force Surveys.

World Bank, 2020. The kingdom of Eswatini: Toward equal opportunity: Accelerating inclusion and poverty reduction, systematic country diagnostic. World Bank, Washington, DC.

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