Geography of Infrastructure Functionality at Schools in Nigeria: Evidence From Spatial Data Analysis Across Local Government Areas

Abstract

Is functionality of electricity, sanitation and water infrastructure at schools unequally distributed geographically in Nigeria? Are there significant disparities in infrastructure functionality between Northern and Southern geopolitical zones in the country as has been posited in previous studies? In this study, we answer these questions with an examination of functionality at schools, with metrics for functionality aggregated at the smallest administrative unit available in the country, the local government area (LGA). We employ spatial statistical techniques to examine the spatial autocorrelation of power, sanitation and water (or ‘infrastructure’) non-functionality across 68,627 schools for 764 of 774 local government areas in Nigeria using a novel survey dataset courtesy of Nigeria's Office of the Senior Special Assistant to the President on the Millennium Development Goals. We find evidence for the existence of LGA clusters of infrastructure non-functionality, aligned along Nigeria's six geopolitical zones. The results also reveal a significant cluster of LGAs in the Northwest zone, the zone with the highest income-based poverty rate (70%) in the country, outperforming LGAs in both other Northern and some Southern zones on all functionality indicators. The results hold up to multiple testing correction, controlling the false discovery rate using the Benjamini-Hochberg method. These results highlight the need for a spatially targeted policy approach, at finer spatial scales, to poverty reduction efforts through infrastructure access expansion in the country.

Keywords:

• clusters
• geopolitical zone
• infrastructure access
• local government area
• Nigeria
• spatial autocorrelation

Acknowledgements

Special thanks go to Nigeria's Office of the Senior Special Assistant to the President (OSSAP), Prabhas Pokharel, Chris Natali, Salah Chafik, Zaiming Yao, Brett Gleitsmann, Carson Farmer, and the members of the Columbia University Sustainable Engineering Lab for the data and discussion of methods used in this article.

Notes

1 The MDGs from can be found at http://nmis.mdgs.gov.ng/.

2 Poverty rates are measured as the percentage of persons living on under US$1 per day (National Bureau of Statistics 2010).

Additional information

Notes on contributors

Belinda Archibong

BELINDA ARCHIBONG is a doctoral candidate in the School of International and Public Affairs, Columbia University, 500 West 120th Street, New York, NY 10027. E-mail: [email protected]. Her research interests include political economy, economic and political geography, development and environmental economics, and energy policy.

Vijay Modi

VIJAY MODI is Professor in the Department of Mechanical Engineering, Columbia University, 500 West 120th Street, New York, NY 10027. E-mail: [email protected]. His research interests include energy infrastructure, CO₂ sequestration, fuel cells, distributed sensing/control of flow, and heat transfer.

Shaky Sherpa

SHAKY SHERPA is GIS Research Analyst at the Earth Institute, Columbia University, 500 West 120th Street, New York, NY 10027. E-mail: [email protected]. His research interests include mapping and modeling approaches for geospatial planning in areas of energy, infrastructure, health, environment and community planning.