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Macroeconomic analysis of transport pricing regimes for the EU

The Tipmac project analysed '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. This 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.

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 employment 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 employment 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 employment 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 is no employment change from BAU). For the SMCP and SMCP+TEN-T scenarios, ASTRA also has small impacts (only 0.1 % increase and –0.2 % decrease from the BAU respectively). Thus ASTRA shows no net significant change from the BAU employment. The Scenes/E3ME model confirms the general conclusions of ASTRA for the Fuel tax + TEN-T scenario. Most countries have a negligible change from the BAU. Only Denmark, Portugal, and Sweden have large employment changes. However for the SMCP and SMCP+TEN-T scenarios there are much larger changes (3.3 % and 3.5 % increase from the BAU respectively).

The overall change in CO2 emissions from the BAU across the EU is very small for all the scenarios considered.

The other way of dividing the EU economy is by industrial sector. The overall pattern of results across sectors reflects the average across countries within both models. Both models have negligible effects for the Fuel tax +TEN-T scenario. The Scenes/E3ME model has significant increases in output for the SMCP and SMCP+TEN-T scenarios (3.2 % and 3.0 %), and also increases in employment than are proportionally larger than those for output (4.2 % and 4.1 %). ASTRA has small decreases in output (-0.8 % and –1.1 %) and increased impacts on employment (-1.6 % and –1.7 %) for the SMCP and SMCP+TEN-T scenarios. There is no effective difference for either model between the SMCP and SMCP+TEN-T scenarios. Together with the negligible response of both models to the Fuel tax+TEN-T scenario, this shows that there is no large aggregate effect on industrial activity from the more rapid construction of the TEN-T infrastructure projects.

The overall transport results have similar patterns to the macroeconomic results in that the SMCP and SMCP+TEN-T scenarios generate much larger changes than the Fuel tax+TEN-T scenario. Also, the stronger response to the adoption of SMCP of the Scenes/E3ME model in comparison to ASTRA is shown as larger decreases in transport activity. In general, there are considerable differences between the two models.

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.

Reported by

Cambridge Econometrics Ltd
Cogent Garden
CB1 2HS Cambridge
United Kingdom
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