South Africa Food Price Analytics
Food Prices Analysis
Historical Data 2015-2025Current Food Inflation
5.1%
+2.8pp from January 2025
Food Basket Cost
R1,336
+160% since 2015
Highest Category
21.2%
Meat (particularly beef)
Affordability Impact
43.8%
Of income for very poor households
Current Alert
Food inflation has accelerated from 2.3% in January to 5.1% in June 2025, now exceeding headline CPI by 2.1 percentage points. Meat prices show particular concern at 21.2%.
Food Inflation vs Headline CPI
Annual average inflation rates (2015-2025)
Food Category Inflation Rates
Current inflation by food category (2025) - Color coded by severity
Current Inflation Rates:
Dairy & Eggs
3.7%
Sugar & Sweets
3.8%
Fish & Seafood
4.8%
Oils & Fats
5.4%
Bread & Cereals
6.8%
Fruit
13.3%
Vegetables
15.8%
Meat
21.2%
Critical (>15%)
High (10-15%)
Moderate (5-10%)
Low (<5%)
Food Affordability Crisis
Food expenditure as % of household income
Red line at 30% indicates food insecurity threshold
Affordability Metrics
Current household burden (2025)
Minimum Wage Households23.9%
Very Poor Households43.8%
Critical
Above 30%
At Risk
20-30%
Critical threshold: When food expenditure exceeds 30% of income, households are considered food insecure.
NAMC Urban Food Basket Cost
Monthly cost progression (Rand) - Color coded by growth rate
High Growth (>30%)
Moderate Growth (10-30%)
Low Growth (<10%)
Cost Breakdown & Impact
Key statistics and trends
R514
2015 Baseline
R1,336
2025 Current
Total Increase+160%
Largest Jump2020-2024 (+R447)
Annual Average Growth+10.0%
Wage Growth Comparison
Food +160% vs Wages +45%
The food basket cost has significantly outpaced wage growth, placing increasing pressure on household budgets across all income levels.
Key Crisis Periods
Major food inflation events
2016 Drought Crisis
Peak: 12.0% inflation (Oct/Dec). Worst drought in SA history.
2022-2023 Global Crisis
Peak: 13.8% (Jan 2023). Russia-Ukraine conflict impact.
2018 Recovery
Lowest: 3.3% average. Post-drought normalization.
Key Insights & Policy Implications
Meat Price Crisis: 21.2% inflation in meat products, particularly beef, threatens protein access for vulnerable households.
Affordability Crisis: Food represents 43.8% of income for very poor households, requiring urgent policy intervention.
Cost Simulation Model
Advanced AnalyticsPrimary Cost Drivers
10
Major categories identified
Exchange Rate Impact
0.45
Elasticity coefficient
Climate Volatility
9.8
Volatility index
Simulation Iterations
10,000
Monte Carlo runs
Primary Cost Drivers Impact
Elasticity coefficients showing price sensitivity to each driver
Cost Driver Details:
Exchange Rate
0.45 elasticity
2 month lag
Global Commodities
0.32 elasticity
8 month lag
Local Agriculture
0.68 elasticity
Immediate
Climate Shocks
0.85 elasticity
12 month lag
Energy Costs
0.85 elasticity
1 month lag
Labor Costs
0.35 elasticity
3 month lag
Price Elasticity of Demand
How responsive demand is to price changes (negative values)
Beverages
Elastic-0.79
High price sensitivity - demand changes significantly with price
Meat Products
Elastic-0.73
High price sensitivity - demand changes significantly with price
Vegetables
Inelastic-0.6
Low price sensitivity - demand relatively stable despite price changes
Cereals
Inelastic-0.58
Low price sensitivity - demand relatively stable despite price changes
Fruits
Inelastic-0.45
Low price sensitivity - demand relatively stable despite price changes
Eggs
Inelastic-0.34
Low price sensitivity - demand relatively stable despite price changes
Sugar
Inelastic-0.27
Low price sensitivity - demand relatively stable despite price changes
Price Elasticity Interpretation:
• Elastic (>0.6): Consumers reduce consumption significantly when prices rise
• Inelastic (<0.6): Consumption remains relatively stable despite price changes
• Most Elastic: Beverages (-0.79) - luxury/discretionary spending
• Least Elastic: Sugar (-0.27) - essential ingredient with few substitutes
Income Elasticity of Demand
How demand changes with income (>1 = luxury, <1 = necessity)
Fruits
Luxury1.16
Luxury good - demand increases more than proportionally with income
Vegetables
Luxury1.04
Luxury good - demand increases more than proportionally with income
Meat Products
Necessity0.92
Necessity - demand increases less than proportionally with income
Beverages
Necessity0.85
Necessity - demand increases less than proportionally with income
Eggs
Necessity0.78
Necessity - demand increases less than proportionally with income
Cereals
Necessity0.62
Necessity - demand increases less than proportionally with income
Sugar
Necessity0.45
Necessity - demand increases less than proportionally with income
Income Elasticity Interpretation:
• Luxury (>1.0): Demand grows faster than income - premium foods
• Necessity (<1.0): Demand grows slower than income - staple foods
• Highest Luxury: Fruits (1.16) - premium nutrition choice
• Most Essential: Sugar (0.45) - basic ingredient regardless of income
Simulation Parameters: Volatility vs Impact
Relationship between parameter volatility and food price impact
Parameter Details:
Exchange Rate Volatility
15.8% vol, 0.45 impact
Oil Price Fluctuations
22.5% vol, 0.35 impact
Weather Shocks
9.8% vol, 0.85 impact
Global Commodity Prices
18.2% vol, 0.32 impact
Local Agricultural Prices
12.4% vol, 0.68 impact
Energy Costs
25.1% vol, 0.85 impact
This scatter plot shows the relationship between how volatile a parameter is (X-axis) and how much it impacts food prices (Y-axis). Parameters in the top-right quadrant (high volatility, high impact) require the most attention in policy planning.
Primary Cost Drivers
Key factors affecting food prices with elasticities and transmission lags
Exchange Rate Effects
0.45
2-month lag
Global Commodity Prices
0.32
8-month lag
Local Agricultural Prices
0.68
Immediate
Climate & Weather Shocks
0.85
6-18 months
Demand-Side Elasticities
Price responsiveness by food category
Beverages-0.79
Meat Products-0.73
Vegetables & Cereals-0.58 to -0.60
Eggs & Sugar-0.27 to -0.34
Scenario Testing Framework
Six core scenarios for policy analysis
Baseline Scenario
Current trend continuation with normal conditions
Drought Scenario
30% rainfall reduction impact analysis
Currency Crisis
25% rand depreciation effects
Global Food Crisis
40% international price increase
Energy Crisis
50% fuel/electricity cost increase
Policy Intervention
VAT removal and grant increases
Model Validation Targets
Performance benchmarks for accuracy
Mean Absolute Percentage Error< 5%
Directional Accuracy> 80%
Validation Period12 months
Technical Specifications
Implementation requirements
Simulation Horizon60 months
Update FrequencyMonthly
Monte Carlo Iterations10,000