USING COST-EFFECTIVENESS ANALYSIS TO SCREEN AND RANK ROAD PROJECTS IN NAMIBIA

1. USING COST-EFFECTIVENESS ANALYSIS TO SCREEN AND RANK ROAD PROJECTS IN NAMIBIA Matthew TownshendSchool of Economics, University of Cape Town, South Africamatthew@cornerstonesa.netProfessor Don RossSchool of Economics, University of Cape Town, South AfricaSchool of Sociology, Philosophy, Criminology, Government, and Politics, University College Cork, IrelandCenter for Economic Analysis of Risk, J. Mack Robinson College of Business, Georgia State University, Atlanta, GA, USA don.ross931@gmail.com University of Cape Town Southern African Transport Conference, 8-11 July 2019

2. The technical analyst’s problem • The network is given • Maintenance mandate in place • Budgets are exogenously set • Full funding = 100% maintenance • Less than full funding = prioritisation • This shifts the practical focus from cost-benefit analysis (CBA) • Net Present Value (NPV) determines if a project generates a positive return • But because all projects should at some point proceed, the focus shifts to an accurate project ranking • Cost-effectiveness analysis (CEA) is a practical methodology to distribute a set budget in order of project importance University of Cape Town

3. Presentation structure • Problem statement • Investment objective • Prioritisation methodology • Example application • The set of road projects • Screening • Effectiveness measure • Maximising effects • Maximising efficiency • Optimal project combination • Conclusion University of Cape Town

4. Problem statement • Context: • The Namibian Roads Authority has limited funding, with a backlog 25x the annual budget • Situation: • There are 8 road projects with approved feasibility/design studies amounting to N$6.9 billion, but there is only N$1.0 billion available in donor funding • Problem: • The economic evaluations in the feasibility studies have methodological discrepancies and are thus not suitable for prioritisation purposes • Cost-benefit analysis not appropriate • Solution: • This paper uses cost-effectiveness analysis (CEA) as an alternative method to screen and rank the projects University of Cape Town

5. Investment objective • Logistics infrastructure is a focus of the 5th National Development Program • Objective is to develop “a sustainable transport system supporting a world-class logistics hub connecting SADC to international markets” • The donor aim to finance projects that support the government’s objective to raise growth by strengthening the country’s logistical hub position • The road projects must consequently be assessed and prioritised according to their contributions to this goal University of Cape Town

6. Motivations for CEA • General conditions that motivate the use of CEA: • Analysts are unwilling or unable to monetise the most important impacts of a project; • Analysts may recognise that a particular effectiveness measure does not capture all of the benefits of each alternative, but the outstanding benefits are difficult to monetise; • Analysts are missing information on utilities that would allow for estimation of welfare effects; or • Technical analyst gets called in after political processes have already fixed the budget University of Cape Town

7. Prioritisation methodology • CEA prioritises projects with the highest output • Output is measured in real not monetized units • Costs are recorded as the financial cost of a project • Generates an index of output over cost for projects • Unable to guarantee that benefits exceed costs • Effectiveness measure reflects investment objective • Number of heavy-vehicle-km on each road University of Cape Town

8. Prioritisation methodology • Step 1: Estimate cost-effectiveness ratio • Step 2: Select the project(s) that most cost-effectively meet the imposed cost constraint Minimise CEi Subject to Ci ≤ University of Cape Town

9. The set of road projects Figure: Road project details University of Cape Town

10. CEA: Screening Figure: Project costs compared against the budget constraint University of Cape Town

11. CEA: Effectiveness measure • Use historical AADT data to forecast road traffic • Sensitivity analysis accounts for uncertainty in traffic demand • Monte Carlo simulations most accurate method to forecast ADA Table: Sensitivity tests of the heavy vehicle traffic forecasts University of Cape Town

12. CEA: Maximising the effects Figure: Project effectiveness University of Cape Town

13. CEA: Maximising efficiency Figure: Project efficiency University of Cape Town

14. CEA: Optimal project combination Figure: Possible project combinations and their effects University of Cape Town

15. Conclusion • This paper demonstrates how financially constrained authorities can apply CEA to screen and rank road projects • CEA is the best alternative when insufficient data are available to undertake comprehensive and comparable CBA studies across road projects • Although CEA is relatively simple for officials to apply, it generates enough information to enable authorities to allocate their available budget to the most efficient road projects according to their specific investment objective • If sufficient funding is available for multiple projects, then CEA can prioritise the combination of projects that provide the maximum effect on the investment objective University of Cape Town

16. Thank you! Questions? matthew@cornerstonesa.net don.ross931@gmail.com University of Cape Town Southern African Transport Conference, 8-11 July 2019