Figures and tables in Quantifying the impacts of expanding social protection on efficiency and equity: Evidence from a behavioral microsimulation model for Ghana

Abstract

A key challenge facing developing countries when they are gradually building up their social protection system is the presence of a large informal sector. Social safety nets should be expanded to reduce poverty but financing social protection through higher taxes may reduce the number of formal-sector jobs available. The aim of this paper is to quantify the impacts of a revenue-neutral expansion of social protection in a developing country on both income distribution and efficiency which we measure via the impacts on formal sector work. Results from a new tax-benefit microsimulation model for Ghana, GHAMOD, are combined with the extensive margin elasticity of the share of formal work with respect to the tax wedge on formal labour, derived from repeated cross-section econometric estimates. The size of the estimated formality elasticity is modest and therefore the distributional gains of expanding cash transfer programmes are considerable, even when taking into account behavioural impacts.

This article contains 2 figures and 5 tables.

Figures

**Figure 1.** Share of formal sector workers out of all employed and self-employed (15 years or older).

**Figure 2.** Wage distribution of formal and informal workers across time.

Tables

Table 1. Summary statistics of persons with non-missing labour or self-employment income data by formality status in GLSS 6.

	Informal	Formal	Total
Male	0.449	0.659	0.477
	(0.497)	(0.474)	(0.499)
Age	37.46	38.51	37.61
	(15.41)	(12.93)	(15.10)
No education	0.283	0.0769	0.255
	(0.450)	(0.266)	(0.436)
Primary education	0.262	0.0949	0.240
	(0.440)	(0.293)	(0.427)
Secondary education or higher	0.455	0.828	0.506
	(0.498)	(0.377)	(0.500)
Household size	5.331	4.230	5.182
	(3.178)	(2.659)	(3.135)
Farmer	0.279	0.123	0.258
	(0.449)	(0.328)	(0.438)
Self-employed	0.298	0.000	0.258
	(0.458)	(0.000)	(0.437)
Employee	0.148	0.813	0.238
	(0.355)	(0.390)	(0.426)
Agricultural sector	0.500	0.128	0.449
	(0.500)	(0.334)	(0.497)
Manufacturing sector	0.150	0.172	0.153
	(0.357)	(0.377)	(0.360)
Service sector	0.350	0.700	0.397
	(0.477)	(0.458)	(0.489)
Public sector worker	0.000	0.417	0.0567
	(0.000)	(0.493)	(0.231)
Urban	0.442	0.758	0.485
	(0.497)	(0.428)	(0.500)
Gross annual income	2,820.7	8,158.1	3,546.5
	(13,237.5)	(27,022.4)	(15,938.1)
Net annual income	2,820.7	7,183.0	3,413.9
	(13,237.5)	(21,599.0)	(14,733.0)
Observations	28,765	3,598	32,363

Source: Authors’ calculations based on GLSS data.

Notes: Standard errors in parentheses.

Table 2. Elasticity of the share of formal work with respect to the change in the net pay between formal and informal work.

	No controls	All controls	All controls, above minimum wage
Elasticity	0.265**	0.081	0.106*
Std error	0.106	0.052	0.064
Number of cells	101	101	94

Source: Authors’ calculations based on GLSS data.

Notes: Linear probability regression results with the share of formal work as the dependent variable. The key regressor is the difference between the log of net pay in the state of formal work versus the log of net pay in the state of informal work. The model in Column 1 with no controls, the model in Column 2 with a full set of group and time dummies (groups formed based on age, sex, and education), and the model in Column 3 with the wage restricted to lie above the legal minimum wage. Instead of the regression coefficient, an elasticity estimate is shown. Standard errors calculated with the delta method.

indicates significance at the 10% level and *** at the 1 per cent level.

Table 3. Simulation results of expanding social protection on poverty and inequality.

	Status quo	Reform A: Stand-alone extension of social protection	Reform B: Revenue-neutral reform	Revenue-neutral reform with behavioural impacts
	(I)	(II)	(III)	(IV)
		FGT(0)
All	24.9	24.1	24.3	24.4
Male-headed households	26.6	26.0	26.2	26.3
Female-headed households	19.7	18.4	18.5	18.6
Households with children	27.4	26.7	26.9	27.0
Households with older persons	33.7	29.3	29.3	29.4
		FGT(1)
All	8.1	6.7	6.8	6.8
Male-headed households	8.8	7.3	7.5	7.5
Female-headed households	6.0	4.6	4.7	4.7
Households with children	8.9	7.4	7.5	7.5
Households with older persons	11.0	7.8	7.9	7.9
Gini	41.7	40.8	40.8	40.8
P80/P20	3.53	3.46	3.46	3.46

Source: Authors’ calculations based on GHAMOD v1.0.

Notes: Poverty rates measured using the consumption-based absolute poverty line of GHS 1,314 per adult equivalent per year. The Gini index is also calculated on the basis of consumption.

Table 4. Changes to the government budget based on the simulated social protection expansion.

	Status quo	Reform A: Stand-alone extension of social protection	Reform B: Revenue-neutral reform	Revenue-neutral reform with behavioural impacts	Difference between (III) and and (IV), in %
	(I)	(II)	(III)	(IV)	Difference between (III) and and (IV), in %
LEAP benefit	3.3	273.5	273.5	273.8	0.09
Old-age LEAP	0.0	436.6	436.6	436.7	0.02
Employee SSC	485.6	485.6	1191.9	1179.7	−1.02
Employer SSC	1066.5	1066.5	1066.5	1055.7	−1.02
Income tax	2059.8	2059.8	2059.8	2038.8	−1.02
Budget effects	-	706.7	3.8	44.8	-
Formal sector (%)	13.6	13.6	13.6	13.5	−0.88

Source: Authors’ calculations based on GHAMOD v1.0.

Notes: The budgetary implications are expressed in millions of GHS.

Table 5. Sensitivity analysis of government revenues and expenditure.

	Revenue-neutral reform with behavioural behavioural impacts	As (I) with greater wage change (-0.15% instead of −0.10%)	As (I) with higher elasticity (0.75 instead of 0.1)
LEAP benefit	273.8	273.9	275.4
Old-age part of	436.7	436.7	437.3
LEAP benefit
Employee SSC	1179.7	1174.0	1100.6
Employer SSC	1055.7	1050.5	985.9
Income tax revenue	2038.8	2028.9	1902.1
Change in costs vs.	44.8	65.7	333.6
status quo
Share of formal workers (%)	13.5	13.4	12.7

Source: Authors’ calculations based on GHAMOD v1.0.

Notes: The first column reproduces the numbers of Column (4) in Table 4.