1
Comparing the economic impacts of
different modelling scenarios to cover
the cost of producing electricity
Prof. Jan van Heerden
Dr. Heinrich Bohlmann
Presentation at Eskom
March 2012
Outline of Presentation
• Background to the electricity situation
• The UPGEM modelling approach
• Economic effects of inadequate electricity infrastructure
• Research question
• Modelling scenarios
• Macro results
• Industry level results
• Recommendations
The Electricity Situation
• ESKOM needs to expand its capacity (supply) to deliver
electricity across South Africa in order to facilitate projected
economic growth (demand) → a large fixed cost must therefore
be financed
• Basic electricity prices are too low at the moment and not
reflective of the cost of production → electricity prices must rise
in order for average revenue to equal average cost
• If electricity prices are not allowed to adjust it will require a
much larger increase in electricity supply or a downward shift in
the demand curve for electricity (relative to the baseline)
Modelling Approach
• Modelling evidence produced using the current version of
UPGEM, a dynamic CGE model of the South African economy
• Based on the state-of-the-art MONASH model developed by the
Centre of Policy Studies in Australia
• Economy-wide, substitution based on relative price changes,
high level of analytical detail, sound theoretical mechanisms,
flexible, familiarity and credibility from use in many projects and
peer-reviewed publications
• Dixon & Rimmer (2002) Dynamic CGE Modelling for Forecasting
and Policy: A Practical Guide and Documentation of MONASH.
North Holland, Amsterdam.
• Bohlmann (2011) Labour and Migration Issues in South Africa.
Monash University, Melbourne.
GDP Growth Scenarios
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
GDP Growth (Year-on-Year Percentage Change)
Standard Baseline No ESKOM Growth
Household Consumption Scenarios
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Household Consumption Growth (Y-o-Y Percentage Change)
Standard Baseline No ESKOM Growth
Additional Capacity Required
0
1
2
3
4
5
6
7
8
9
2012 2013 2014 2015 2016 2017 2018 2019 2020
Additional ESKOM Capacity Required with Low Electricity Prices (Cumulative Percentage Change)
Required Growth in Electricity Output
What Does This Mean?
• There is no such thing as a free lunch
• Electricity output must rise to meet future growth in demand
• Electricity prices must also rise for production to become cost-
reflective and reduce strain on additional expansion
• The previous slides provide us with clear evidence and
motivation for achieving both these outcomes
• The question now becomes: What is the best way to generate
the required additional revenue for ESKOM operations?
• Related questions regarding environmental impacts and the
viability of alternative/renewable energy sources are addressed
in a separate study
Modelling Scenarios
• Our main job was to analyze the different ways of achieving cost
reflective production using the current UPGEM model
• Series 1: increase electricity tariff by 26%, 25% and 25% in 2013,
2014 and 2015 respectively and determine the change in
government revenue and impact on the economy over time
• Series 2: VAT on households to fund expansion
• Series 3: VAT / tariff increase (4yr) combo to fund expansion
• Series 4: PIT, CIT to fund expansion
• Series 5: PIT, CIT / tariff increase (4yr) combo to fund expansion
Real GDP
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
1 2 3 4 5 6 7 8
2x25% VAT 3x18%+VAT Tax 3x18%+Tax
Household Consumption
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
1 2 3 4 5 6 7 8
2x25% VAT 3x18%+VAT Tax 3x18%+Tax
Why Income Tax is the Worst Scenario
-4.5
-4
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1 2 3 4 5 6 7 8 9
% c
han
ge
Time 2x25% Capital 2x25% Labour Tax Capital Tax Labour
Explaining Industry Results
• The current version of UPGEM distinguishes 27 industries and
commodities
• When interpreting industry level results from UPGEM we typically
focus on three areas:
– Shock (e.g. electricity is directly impacted on in these
scenarios)
– Macro link (e.g. construction is closely linked to I, mining to X
and R/$, services to C)
– Compositional effects (e.g. capital/labour ratios)
Macro trends influence industry results
-6
-5
-4
-3
-2
-1
0
1
2
1 2 3 4 5 6 7 8 9
2x25%
x0gdpexp x2tot_i x4tot x3tot p0realdev x0imp_c
Production factors and GDP
15
-2.5
-2
-1.5
-1
-0.5
0
0.5
1 2 3 4 5 6 7 8 9
2x25%
x0gdpexp x1cap_i emp_jobs
Electricity Industry
-7
-6
-5
-4
-3
-2
-1
0
1
1 2 3 4 5 6 7 8 9
2x25% VAT 3x18%+VAT TAX 3x18%+TAX
Agriculture Industry
-2.5
-2
-1.5
-1
-0.5
0
0.5
1 2 3 4 5 6 7 8 9
2x25% VAT 3x18%+VAT TAX 3x18%+TAX
Mining Industry
-1.4
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
1 2 3 4 5 6 7 8 9
2x25% VAT 3x18%+VAT TAX 3x18%+TAX
Motor Vehicles Industry
-4.5
-4
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1 2 3 4 5 6 7 8 9
2x25% VAT 3x18%+VAT TAX 3x18%+TAX
Construction Industry
-7
-6
-5
-4
-3
-2
-1
0
1
1 2 3 4 5 6 7 8 9
2x25% VAT 3x18%+VAT TAX 3x18%+TAX
Health & Social Industry
-4.5
-4
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1 2 3 4 5 6 7 8 9
2x25% VAT 3x18%+VAT TAX 3x18%+TAX
What Does This Mean?
• Raising electricity prices and revenues for ESKOM to become
financially sustainable is unavoidable, but should be done in a
socio-economic and politically sensitive manner
• Our analysis suggests that directly raising electricity prices (over a
period of 3 to 5 years) should be the main instrument towards
achieving cost-reflective production of electricity
• Series 1 and 3 shows a superior combination of macro and
allocative efficiency outcomes in the economy
• Users of electricity will therefore have to carry most of the burden
of increased prices, although the impact of higher electricity prices
will be felt throughout the entire economy
Looking Ahead
• A lot more work could (and should) be done on building a more
detailed industry level database and capturing the relevant
mechanisms more accurately in the CGE model
• The parameters in the database should be carefully checked to
ensure that both the economic literature and industry experts
agree on their validity
Metal Machinery Industry
-4
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1 2 3 4 5 6 7 8 9
% c
ha
ng
e
Time Series1 Series2 Series3 Series4 Series5
Radio TV Optical Industry
-4.5
-4
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1 2 3 4 5 6 7 8 9
% c
ha
ng
e
Time Series1 Series2 Series3 Series4 Series5
Financial Services Industry
-4
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1 2 3 4 5 6 7 8 9
% c
ha
ng
e
Time Series1 Series2 Series3 Series4 Series5