The Short‐Term Economic Impact of Levying E‐Tolls on Industries

• Author: Jan Van Heerden,Francois Jacobus Stofberg
• DOI: http://doi.org/10.1111/saje.12106
• Published date: 01 December 2016
• Date: 01 December 2016

THE SHORT-TERM ECONOMIC IMPACT OF LEVYING

E-TOLLS ON INDUSTRIES

FRANCOIS JACOBUS STOFBERG

*

AND JAN VAN HEERDEN

†

Abstract

TERM is used to analyse the short-term regional economic impact of an increase in industries’

transport costs when paying E-Tolls. Market-clearing and accounting equations allow regional

economies to be represented as an integrated framework, labour adjusts to accommodate

increasing transportation costs, and investments change to accommodate capital that is fixed.1We

concluded that costs from levying E-Tolls on industries are small in comparison to total transport

costs, and the impact on economic aggregates and most industries are marginal: investments

(−0.404%), gross domestic product (GDP) (−0.01) and consumer price inflation (−0.10%). This

is true even when considering costs and benefits on industries as well as consumers. Industries that

experienced the greatest decline in output were transport, construction and gold. Provinces that are

closer to Gauteng and have a greater share of severely impacted industries experienced larger GDP

and real income reductions. Mpumalanga’s decrease in GDP was 17% greater than Gauteng’s.

JEL Classification: C68, L91, R10, R48

Keywords: Computable general equilibrium models, regional economics, policy modelling, transport cost

1. INTRODUCTION

The Gauteng Provincial Government (Department of Transport, 2011) and Pienaar

(2011) state that an inadequate transport network in Gauteng is one of the key

constraints to economic growth. The Gauteng Freeway Improvement Plan (GFIP) was

introduced as a solution to the inadequate network. Phase 1 involved a R19.5 billion

upgrading of 185 km of freeway in Gauteng (Department of Transport, 2011), as well as

the introduction of E-Tolling; additional upgrades and further phasing projects are set out

in the Socio-economic Impact of the Gauteng Freeway Improvement Project and E-tolls

Report (2014). The tolling system had to be equitable, affordable, traffic-attracting and

efficient, and as a result directional E-Tolls were introduced (Department of Transport,

2011). These tolls operate as an Open Road Tolling system to limit the impact on road

traffic; the system collects tolls 10 km apart and does not require vehicles to slow down

* Corresponding author: Economist, Efficient Group, 81 Dely Road, Hazelwood, Pretoria,

Gauteng, 0081, South Africa. E-mail: francoisstofberg@efgroup.co.za

†Department of Economics, University of Pretoria, South Africa.

The authors would like to thank and acknowledge Economic Research Southern Africa (ERSA) for

their financial support. We would also like to thank Makoma Mabitsela, Antony Boting and

WernerZwart for all their help. We are also specifically thankful to the reviewers for their insightful

comments; any further mistakes or shortcomings lie with the authors.

1TERM is a bottom-up CGE model designed for highly disaggregated regional data. The

Enormous Regional Model’s originate from Horridge et al. (2005) and are better explained in

Horridge (2011).

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or stop. These E-Tolls are only levied in Gauteng, but we show in this paper that the

introduction of E-Tolls influenced the entire economy: directly through other provinces’

use of the GFIP and indirectly through Gauteng’s share of total economic activity. For

this reason, a multiregional model of the country is utilised to measure cross-regional

economic effects.2

Most research on the effect of tolling on the economy tends to show partial

equilibrium effects and is narrowly focused with weak assumptions. Most models also

assume that tastes and technology are uniform or are represented by only a few individuals

or firms.

An assessment of the likely impact of the GFIP on the regional as well as national

economy was conducted by Economists.co.za (2011). The nature of their analysis is

analytical and limited in the observed variables chosen. In their research, they estimated

that the toll incidence on the commercial road freight industry could amount to an

equivalent 30% company tax increase. We, however, found that, except for the gold

industry, transport cost increases on industries are small in comparison to total transport

costs. Economists.co.za (2011) estimated that consumer price inflation (CPI) would

increase by approximately 0.4%, and Standish et al. (2010) concluded that consumer

prices would increase between 0.28% and 0.31%, depending on Living StandardsMeasure

(LSM) group. We found that even when considering costs and benefits to both consumers

and industries, CPI would only increase by 0.12% in the short term. As suggested in

Economists.co.za (2011), Standish et al. (2010) did a cost–benefit analysis of the GFIP

over a 20-year period. Their results showedthat at an aggregate level, benefits to road users

are greater when driving on upgraded roads and paying E-Tolls, than not upgrading, but

simply maintaining the road network. The aim of this study was to determine the

short-term economic effects of the direct increase in transport costs on industries, as a result

of levying E-Tolls. However, we briefly consider the short-term case of direct and indirect

costs and benefits on industries as well as consumers, as originally outlined in Standish et al.

(2010).

Using a general equilibrium model to forecast an increase in transport cost, the

following particular advantages arise: the model captures not only the direct impact of the

change, but also a full system wide pattern of indirect effects (Horridge, 1999), and

the so-called multiplier effects between all regions and commodities of the model. This

introduces a more accurate measurement of the macroeconomic implication of levying

E-Tolls on industries. Other dimensions can also be explored with a general equilibrium

model; the effect on unemployment, income distribution and social equity, changes in

consumption, investment patterns, and so on.

As suggested by Haddad and Hewings (2004), we introduced a more comprehensive

approach to measuring the impact of changes in transport costs by linking regional

economic modelling with those of transport models, a need emphasised by De Jong et al.

(2004). Haddad and Hewings (2004) evaluated the structural impact of Brazilian policy

developments that caused a reduction in transport costs, which reduced the gross

2Some multiregional models that have been developed include Bröcker and Carsten (2010), Das

et al. (2005), Horridge and Wittwer (2008), Ishiguro and Inamura (2005), Latorre et al. (2009),

Li et al. (2009), and Ueda et al. (2005). Donaghy (2009) is carrying out a survey of literature that

follows this direction. This type of multiregional models deal with cross-country analysis, those

that focus on disaggregating one country into separate economies follow later.

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