A Projection of the SA government's social security obligations 2017 2037
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A Projection of the SA government’s social security obligations 2017 - 2037
Talking real money A billion here, a billion there, pretty soon, you're talking real money. Misattributed: Everett Dirkson (1896-1969) PG 2
Talking real money The 1.67 trillion budget Learning & Culture R351.1bn Social Development R259.4bn Health R205.4bn Peace & Security R200.8bn 60.5% Economic development R200.1bn Community development R196.3bn Debt-service costs R180.1bn General public services R64.0bn Social Services 0% 10% 20% 30% 40% PG 33 PG
Our team Natalie van Zyl William Melville Dewald Muller Senior Lecturer Actuarial Associate Actuarial Associate Stellenbosch PWC EY University Cape Town Sandton 15 Years experience in pension Part-time masters student, Part-time masters student, industry. Stellenbosch University. Stellenbosch University. nataliev@sun.ac.za william.melville@pwc.com dewald.mueller@za.ey.com PG 4
00 Agenda 1. Measuring the wood 2. The South African reality: The individual trees 3. Modelling communalities 4. Modelling approaches 5. Modelling results 6. Switching gears PG 5
01 Measuring the wood Actuaries We’re good at measuring the wood PG 6
01 Measuring the wood Learning & Culture R351.1bn Social Development R259.4bn Health R205.4bn Peace & Security R200.8bn Economic development R200.1bn Community development R196.3bn Debt-service costs R180.1bn General public services R64.0bn 0% 10% 20% 30% 40% PG 7
01 Measuring the wood Social Development Old-age grant R70.5bn Child-support R60.6bn grant 80% Disability R22.1bn grant Other R9.7bn Social Grants grants R163bn PG 8
01 Measuring the wood PG 8
01 Measuring the wood PG 10
01 The South African reality: The individual trees Household income distribution 4 000 000 Number of households 3 500 000 3 000 000 2 500 000 2 000 000 1 500 000 1 000 000 500 000 0 0-R799 R800-R1399 R1400-R2499 R2500-R4999 R5000-R7999 R8000-R10999 R11000-R19999 R20000+ Monthly income *Income distribution, AMPS 2015 PG 11
02 The South African reality: The individual trees Care dependency, disability and older person’s grant PG 12
02 The South African reality: The individual trees SA grant values: National Treasury Grant 2017/18 2018/19 State old age grant R1 600 R1 690 State old age grant, over 75s R1 620 R1 715 War veterans grant R1 620 R1 715 Disability grant R1 600 R1 700 Foster care grant R920 R960 Care dependency grant R1 600 R1 700 Child support grant R380 R400 PG 13
02 The South African reality: The individual trees Grants are means tested Means test criteria for old age grants (SASSA 2018) Income threshold (single R78 120 p.a (R6 510 p.m.) person) Income threshold (married R156 240 p.a (R13 020 p.m.) person) Question How many South African households lives below the pensioner threshold income? PG 14
02 The South African reality: The individual trees 60% - 70% Household income distribution 4 000 000 Number of households 3 500 000 3 000 000 2 500 000 2 000 000 1 500 000 1 000 000 500 000 0 0-R799 R800-R1399 R1400-R2499 R2500-R4999 R5000-R7999 R8000-R10999 R11000-R19999 R20000+ Monthly income *Income distribution, AMPS 2015 PG 15
02 The South African reality: The individual trees “It’s not just money that a job provides. It provides dignity and structure and a sense of place and a sense of purpose. So we’re gonna have to consider new ways of thinking about these problems, like a universal income.” ~ Barack Obama, Nelson Mandela Centanry Address, July 2018 PG 16
02 The South African reality: The individual trees Can you imagine living on R1700 a month… Time for some reality checks PG 17
02 The South African reality: The individual trees Estimation Game Expense Type Amount Housing Table 1 Food Table 2 Transport Table 3 Clothing , Health, Communication Table 4 Recreation and entertainment Table 5 Other R276 Total R1700 *Urban pensioner CPI weights, 2012 PG 18
02 The South African reality: The individual trees Estimation Game Expense Type Amount Housing R614 Food R334 Transport R237 Clothing , Health, Communication R125 Recreation and entertainment R114 Other R276 Total R1700 *Urban pensioner CPI weights, 2012 PG 19
The South African reality: The individual trees R415 – 7 products on promotion PG 17
02 The South African reality: The individual trees “R50 here, R1 700 there (to around 16 million beneficiaries), soon you’re talking real money and real lives” PG 21
02 Back to measuring wood Who is brave enough to estimate the total projected South African social grant cost in 2030? Estimated R163 billion in 2018/2019 year per budget PG 22
02 Back to measuring wood Nominal grant cost 2018-2037 400 350 300 250 R365 bn by R Billions 200 150 2030 100 50 0 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Projection year PG 23
02 Back to the wood Sense check (AvE) Actual expected 2018 = R153 bn Model projection in 2016 for 2018 = R155 bn PG 24
AGENDA SLIDE LOOKS LIKE THIS THE MODELLING APPROACHES PG 25
03 Behind the numbers General considerations • Type of social grants assumed • Size of social grants • Eligibility criteria for social grants. Projection model(s) used • Three different approaches • Based on the eligibility criteria PG 26
03 Back to the social grants Grant 1 R70.5bn Grant 2 R60.6bn Other Grants R9.7bn Grant 3 R22.1bn SASSA admin costs Other R9.7bn grants PG 27
03 Projection models used Main modeling Eligibility requirement Qualifying for Income Age the grant Modeling Population Proportional Statistical approaches projection PG 28
03 Projection models used Time series modelling PG 29
03 Demographic and economic assumptions Population projection models South African Population Pyramid • Initially ASSA2008 2017 • Now updated for Thembisa Inflation • Assumed increase in the grant value • Based on data provided by Stellenbosch BER GDP growth rate • Used for the income distributions • Based on data provided by Stellenbosch BER PG 30
03 Grant data National treasury Stastistics South Africa SASSA PG 31
03 Data issues Change in age eligibility • OAP age equalisation • Increased coverage of child grants by age Income data • Income data surveys are sporadic PG 32
04 Age eligibility model: how it works Projected number Current cost qualifying by age Grant 12 amount Inflation Base year (2017) High road Average(2012-2017) Middle road Low road PG 33
04 Proportional income eligibility model: how it works Households Projected future qualifying for grants population by income Households receiving grants Base year (2015) Number of people Consistent Average(2012-2015) per household PG 34
04 Proportional income eligibility model: how it works Number of grant recipients Grant Households 12 receiving grants amount Households Base year (2015) qualifying by income Average(2012-2015) PG 35
04 Statistical income eligibility model: how it works Households Conceptually similar to proportional approach, also assumes: qualifying for grants • A constant number of grants per eligible household by income • A constant number of people per household It differs by: • The proportion of households assumed to be eligible • Instead an assumed income distribution is used Total number of households PG 36
04 Statistical income eligibility model: fitting the distribution Fitted the income data with a lognormal distribution Number of hosueholds Derived the μ and σ parameters Repeated for each year 2004 to 2015 Income band PG 37
04 Statistical income eligibility model: projecting Projected these parameters forward • Used simple linear regression • Predicted the mean income based on the growth in nominal GDP = × 1 + 0 • Kept sigma constant at the average value over the period Assumes that income inequality will remain unchanged Results in a projected μ and σ in each future year • Can then determine the proportion of households qualifying by income • Assumes that the means test increases by inflation PG 38
04 Statistical income eligibility model: grant cost Number of grant recipients Grant Households 12 receiving grants amount Number of hosueholds Households qualifying by Based on 2015 lognormal income distribution Income band PG 39
04 Modelling preference Which modelling approach did you like most and Why? PG 40
AGENDA SLIDE LOOKS LIKE THIS Model results and applications PG 41
04 Model Results Nominal grant cost 2018-2037 700 600 500 R Billions 400 300 200 100 0 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 Projection year PG 42
04 Model Results: other methods Nominal grant cost for the three methods 700 600 500 R Billions 400 300 200 100 0 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 Projection year Age eligibility Proportional income eligibility Statistical income eligibility PG 43
04 Model Results: Overall Grant cost as a proportion of GDP 2018-2037 3,5% 3,0% 2,5% Percentage of GDP 2,0% 1,5% 1,0% 0,5% 0,0% 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 Projection year PG 44
04 Model Results: Per grant Nominal grant cost for the three largest grants 350 300 250 R Billions 200 150 100 50 0 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 Projection year Child support grant Grant for older persons Disability grant PG 45
04 Model Results: Over to You! Password ASSASGC2018 (case sensitive) PG 46
05 Switching Gears Universal basic income Age eligibility model • Do you think it is affordable? • Did you see any surprising results Model results projection • R100 per month for all working aged people • Starts in 2018 and increases by inflation • 95% take-up rate Does a R1000 seem more reasonable? Then we are looking at R1.19 trillion in • Cost is R119 bn 2037 or about 5% of GDP. PG 47
05 Switching Gears Then there are other spending priorities • Free tertiary education • NHI • Housing We can only stretch our budget so far… PG 48
05 There’s more than meets the eye PG 49
Any Questions? Natalie van Zyl William Melville Dewald Muller Senior Lecturer Actuarial Associate Actuarial Associate Stellenbosch PWC EY University Cape Town Sandton nataliev@sun.ac.za william.melville@pwc.com dewald.mueller@za.ey.com Thank you. PG 50
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