Ramos Mabugu - Social Grant, Poverty Alleviation and Economic Growth in South Africa
Presenting author: Ramos Mabugu (Sol Plaatje University South Africa)
Authors: Ismael FOFANA, Alhassane CAMARA, Ramos MABUGU
Session: C03A - Dynamic / Long term [4] - Wednesday 14:00-15:30 - Ceremonial Hall
South Africa is tackling a triple challenge of reducing unemployment, inequality, and poverty. Unemployment, having reached a record high of 34.9% in 2021, remains stubbornly high and averaged 28.8% in 2022 (World Bank[1], 2023). The country’s inequality and poverty have also remained high in the post-apartheid era with a Gini coefficient estimated at 0.63 and poverty headcount ratios at 40.0% and 55.5% for lower and upper bound poverty lines respectively (World Bank[2], 2021). This is despite redistributive fiscal policy tools, such as provision of the “social wage”, which extends protection to the most vulnerable groups. Furthermore, South Africa has experienced slower economic growth since the great recession of 2008, with the annual economic growth rate averaging 1.1% over the period 2009-2021 (World Bank[3], 2023). The government is likely to fall short on key development targets committed to under the National Development Plan 2030, the “”government’s development blueprint document”” whose overarching objectives are to eradicate poverty, reduce unemployment, and considerably reduce inequality (National Planning Commission , 2010). Thus, making substantial progress in unemployment, inequality and poverty reduction would require a different development strategy. Social grants are considered as important instruments to fight poverty and inequality in South Africa. Because of their size in government spending – i.e., an average of 16% over 2015-2020 (World Bank[4], 2021), the impact of social grants can go beyond the direct effects on beneficiary populations. In other words, social grants can produce sizeable multiplier effects in the economy. The central question remains, what is the benefit to society when a large share of the public budget is transferred to poor households? We have developed a recursive micro-macro model to assess the socioeconomic impacts of social grants in South Africa. More precisely, we simulate the economic implications of a hypothetical South Africa with lower poverty and inequality outcomes. The analytical framework which links the micro and macro models in a bottom-up fashion is developed in two steps. In step 1, the reweighting Micro-Simulation (MS) approach, pioneered by Meagher[5] (1993), is used to generate a counterfactual sample of individual households that displays lower poverty and inequality rates. The poverty headcount ratio at lower bound poverty line is set at 5% in the counterfactual sample. To be lifted out of poverty, target households, or households with per capita consumption expenditures below the lower bound poverty line, received an additional grant of 1,450 Rand on average, which represents 16% of the national poverty line. Thus, the current amount of public transfer received by target households increased by more than three-fold on average, representing an increase of their income by 75% on average. The amount of transfer received by poor households represents 0.5% of GDP and 1.6% of government spending in 2021. After the implementation of the MS model, changes in labor supply and consumption behaviors of target households are estimated by comparing their outcomes in the counterfactual and real samples of households. In step 2, a review of the literature is conducted to gather evidence on the direct economic impact of social grants. Changes in food and nonfood products expenditures by households and individual labor supplies that are likely to be affected in the short and medium run by social grants are considered in building of the counterfactual simulation scenarios. The latter are then used in a recursive dynamic Computable General Equilibrium (CGE) model developed for the South African economy. Two counterfactual samples are generated to build two counterfactual scenarios in addition to the baseline scenario: i.e., the unconditional social grant scenario, and the conditional social grant scenario. The unconditional social grant scenario implements increases in food and nonfood consumption expenditures and decreases in labor market participation by members of target households. The conditional social grant scenario implements increases in food and nonfood consumption expenditures combined to increases in labor market participation by members of target households. The conditionality in the later scenario is related to the labor market participation of members of the target households. In the two scenarios, the additional grant expenditure is externally funded by assumption. Key findings of the implementation of the micro-macro model are South African economy - measured by the level of gross domestic product (GDP) - grows faster (by 0.5 pp) under the conditional social grant scenario compared to the baseline scenario. Demand increases created by income transfers to poor households are met with supply increases driven by constrained labor market participation of target household members. The inflationary effects, in particular food price increases, are limited under this scenario. On the other hand, GDP deteriorates (by 1.0 pp) under the unconditional social grant scenario compared to the baseline scenario, as food demand increase and related price increase contribute to reduce consumers’ purchasing power. [1] “World Development Indicators Database.” [2] “South Africa: Social Assistance Programs and System Review.” [3] “World Development Indicators Database.” [4] National Planning Commission. 2010. The National Development Plan for the 2030 Vision: Our Future – Make it Work. South Africa. [5] Meagher, G . A. 1993. “Forecasting Changes in Income Distribution: An Applied General Equilibrium Approach.” Centre of Policy Studies and the IMPACT Project Working PaperOP-78. Monash University, Victoria, Australia.