The TIPMAC project analyses ‘The role of transport in macro-economic development and employment’ as part of a projects cluster on socio-economic impacts of transport investments and policies and network effects in the EU of the Fifth Research Programme.
The TIPMAC project aimed to overcome a major limitation of all previous macroeconomic analyses: the very simple modelling of the transport sector.
The study focused on the TEN-T infrastructure projects and transport pricing policies, using the White Papers “Fair Pricing for Infrastructure Use” and “European Transport Policies for 2010: time to decide” as a starting point.
The objective of the TIPMAC project was to combine transport modelling with macroeconomic modelling to study the indirect macroeconomic impacts of transport infrastructure investment and transport pricing policies in the EU.
Two parallel analyses employing contrasting methodologies, using models that were at the leading edge of EU analysis, as well as state-of-the-art techniques and knowledge of industrial and consumer behaviour, were undertaken:
- In the first analysis, the SCENES transport network model has been linked to the E3ME macroeconometric model. This is the first study which combines a full macroeconomic model with a detailed analysis of the transport sector, allowing for changes in both passenger and freight demand and a network analysis of the impact of new infrastructure.
- In the second analysis, the ASTRA model is applied. The model was developed under the 4th research framework programme of the European Commission for strategic assessment of long-term impacts of European transport policy with respect to economic, environmental and social implications. The technical challenge of ASTRA was to demonstrate that the applied system dynamics methodology is appropriate for such long-term policy assessments.
Three common scenarios were defined to provide common model input assumptions for the cluster projects (TIPMAC and IASON). All scenarios are revenue neutral and include alternative options for TEN-T projects funding which are offset by reductions in personal income tax. These policy scenarios were designed as variants in the implementation of the measures the White Paper considers essential to redirect the Common Transport Policy towards meeting the need for sustainable development. These were defined by juxtaposing transport taxation and infrastructure charges as alternative instruments to capture external costs generated by transport vehicles, as well as the main sources to fund a slow or a fast implementation of the Trans-European Transport Network projects in the next decades. More precisely:
- In the first scenario, Social Marginal Cost Pricing is adopted as a key criterion to harmonise infrastructure pricing in the EU together with a slow implementation of the TEN-T core projects.
- In the second and third scenarios, investments flows are anticipated (fast implementation). In the second scenario variant, the bulk of additional funds to fund the fast implementation of TEN-T core projects is made available by means of increasing taxation on fuel, while in the third variant infrastructure charges are levied at social marginal costs.
The impacts of the three scenarios are compared with those of a referenc
Common scenarios were defined to provide common model input assumptions. All scenarios were revenue neutral, with the social marginal cost pricing (SMCP) charges in the SMCP and SMCP+TEN-T scenarios being offset by reductions in personal income tax. The “Business as Usual” (BAU) projections were undertaken to provide a basis for comparison of different policies. Over the period of the projections (1995-2020), GDP increased by 82% and employement increased by 31% implying continuing increases in labour productivity.
The adoption of SMCP for transport has very significant macroeconomic impacts, as well as impacts on the transport sector. The large scale of the revenues makes the accompanying fiscal policy very significant. Given the very large scale of these changes, the E3ME/SCENES model system shows very considerable dynamic macroeconomic impacts in the SMCP scenarios, with considerable increases in GDP and employement from the BAU in the SMCP scenarios. The ASTRA model also gives increases in GDP from the BAU. The results should be considered in the context of the BAU underlying assumed GDP growth. BAU growth from 1995 to 2020 is 80+%, so that scenario changes of 2-3% are small in modelling terms. This means that ASTRA and E3ME/SCENES have produced fundamentally similar results both for GDP changes form the BAU and for employement changes from the BAU.
The Fuel Tax + TEN-T scenario has relatively small macroeconomic impacts. The differences between the SMCP scenarios with and without the fast completion of the TEN-Ts are small for both models. This indicates that the medium to long-term impact of a more rapid completion of the TEN-T projects is small in comparison to the BAU case. Given that the rapid TEN-T programme leads to a completion of the expanded infrastructure between 2 and 10 years before the BAU, this is a relatively small alteration to policy. The results of the Fuel tax +TEN-T scenario for the ASTRA and SCENES/E3ME models confirm this assessment. The macroeconomic impacts are therefore dominated by the revenue recycling.
The SCENES/E3ME model shows strong increases for both GDP and employment in response to the reductions in income tax. ASTRA has a slight response to this reduction and therefore shows small macroeconomic impacts to these policies.
The results for changes in employment by country from the BAU are very similar to those for GDP. ASTRA has negligible responses for all countries (the conclusion is that there i
This project combined a full macroeconomic model with a detailed analysis of the transport sector, and it has also compared two different dynamic macroeconomic models: ASTRA and E3ME. The great effort in developing common scenarios has enabled for the first time an assessment of the range of macroeconomic results from different models. In order to limit the expenses for developing the model, the SCENES and E3ME models were not connected automatically (data were passed through assumptions files that were changed manually for each run). The automation of this iterative process would make a model which ran SCENES for more than three years simpler and faster. This would be possible using the Tyndall Centre CIAM^n technology, which has software developed to automatically exchange data between different computer models running on different hardware and software systems, connected over the UK access grid.