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