Evaluating Transport Infrastructure Projects in Low Data Environments: An Application to Nigeria

Abstract

Transport infrastructure consumes a large fraction of the development assistance envelope. Yet procedures for evaluating transport infrastructure projects are typically ad-hoc. This paper proposes an approach to assess the differential impacts of alternative investment proposals in data constrained environments where conducting reliable impact analyses is difficult. We first demonstrate a technique for estimating the cost of transporting products to markets. We then estimate the impact of improving the road network on economic activity. Finally, we perform simulations to demonstrate a methodology for prioritising alternative investments. The analysis demonstrates a pragmatic, though rigorous, approach for assessing transport infrastructure benefits.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Note that we do exploit alternative instrumental variables and higher quality, local datasets where available (for example, household surveys). However, this is done to test the robustness of our results and these datasets are not necessary for the overall methodology.

2. See Berg et al. (2017) Supplementary Materials, for further details on how these alternative measures of cost to market have been applied in the literature.

3. For more information on HDM-4, see Section A of the Supplementary Materials.

4. Delorme is a company that specialises in mapping and GPS solutions and has the most comprehensive GIS dataset on African roads.

5. Free alternatives do exist, for instance, Global Roads Open Access Dataset (gROADS) http://sedac.ciesin.columbia.edu/data/collection/groads

6. For more information about the survey and GIS methodology see ‘Spatial Analysis and GIS Modeling to Promote Private Investment in Agricultural Processing Zones: Nigeria’s Staple Crop Processing Zones’ presented at the Annual World Bank Conference on Land and Poverty 2013.

7. Note that data from FERMA and FADAMA are Nigeria specific and therefore would not be available for analyses in other countries. However, in most African countries, AICD data may be sufficient.

8. The data used in this model assumes climactic conditions similar to the 1961–1990 baseline level, and is calculated assuming low input systems.

9. Data on mining facilities throughout Nigeria was obtained from the National Minerals Information Center of the USGS. Mining facilities that were in the USGS dataset but not included in this analysis include facilities like cement plants, or steel mills, which are likely concentrated in large cities or manufacturing areas. We also excluded plants that were labelled as being closed.

10. Landscan data is available here: http://web.ornl.gov/sci/landscan/

11. Given the impact that adding population to the regression has on the market cost variable, we test how the market cost coefficient changes when we split the sample based on population quantiles. We find that the impact of market cost remains relatively stable amongst all four population quantile samples, with the largest impact of market cost occurring in the densest populated areas. Results are presented in the Supplementary Materials Table A3.

12. Interaction terms were instrumented by the interaction of the natural path variable and the geo-political zone FE.

13. The system of Trans African Highways consists of nine main corridors with a total length 59,100 km. The concept as originally formulated in the early 1970s, aims at the establishment of a network of all-weather roads of good quality, which would: a) provide as direct routes as possible between the capitals of the continent, b) contribute to the political, economic and social integration and cohesion of Africa and c) ensure road transport facilities between important areas of production and consumption.

14. This assumption is reasonable in the short- to medium-run. As the economic structure evolves over time, the estimated elasticity could well change. Given the length of time under consideration, that is, the time it takes to upgrade a road, this seems reasonable.

15. The four roads being rehabilitated were: Enugu – Abakaliki (72 km), Ogoja – Ikom road (52 km), Akure – Ilesha (74 km) and Mokwa – Bida (121 km).