Democracy and the Distribution of NGOs Promoting Renewable Energy in Africa

Abstract

Roughly 60 per cent of Africans lack access to electricity, negatively impacting development opportunities. Non-governmental organisations (NGOs) have started promoting distributed generation – small-scale, localised electricity generation – to change this situation. Despite widespread need, however, the dispersion of these distributed generation NGOs (DG-NGOs) is uneven, with high concentrations in a few African countries. Drawing on an original database and field research, we analyse location variation among DG-NGOs across the continent. We find that DG-NGOs are likely to operate in democratic settings with large populations that lack access to electricity. International DG-NGOs are also likely to operate where aid allocation levels are relatively high.

Acknowledgements

We wish to express our appreciation to Elizabeth Baldwin and Shannon Lea Watkins for excellent research assistance. We thank the Mitsui & Co., Ltd. Environment Fund, the Indiana University Office of Sustainability, the Indiana University School of Public and Environmental Affairs, and the Indiana University Faculty Research Support Program for generous funding. The article’s original NGO dataset and code will be made available upon request. Earlier drafts of this article were presented at the International Society for Third-Sector Research, the African Studies Association, and the American Political Science Association annual meetings in 2012. We are grateful for comments received at these presentations and from anonymous reviewers. All remaining errors are our own.

Notes

1. This is particularly true for those living far from power lines, but even where the grid is close the cost of connecting to it can be prohibitive. In Kenya, for example, Kenya Power and Lighting Company, the sole electricity provider in the country, charges approximately $500–$1,200 for the act of connecting a new user (20-0612n; 38-0612d; 5-0612r). Citations in the form 0-0000x represent interviews done by the authors, and represent the nth interview of a month-year pair, followed by the a letter representing the type of respondent: a donor (d); company (c); beneficiary (b); NGO (n); researcher (r); or other individual(o). Names are not given to protect the confidentiality of the study’s human subjects.

2. DG systems using renewable energy sources such as windmills, solar photovoltaic panels or hydroelectric turbines have become increasingly common, due to decreasing input costs and international environmental concerns. However, diesel generators remain among the most common type of distributed generation system; solar lanterns are also very common.

3. Data from the Union of International Associations (UIA) is not an appropriate measure of our dependent variable. Associations and NGOs are not categorised or registered in the same way in developing countries, so the meaning of the UIA data is not transparent. For example, associations in Kenya are registered under the Societies Act, while NGOs (both locally and foreign-headquartered organisations) are registered under the NGO Act, so a list of the associations located in Kenya would not include most of the NGOs in our study.

4. Biogas, however, is more often used for gas cooking purposes than for electricity generation.

5. We define energy NGOs as ‘implementers’ if their activities included carrying out concrete ‘on-the-ground’ projects including the installation and maintenance of energy systems. A ‘capacity building’ classification means that the NGO conducts training programmes focused on business development, installation and system maintenance, as well as the development of supportive local institutions, including market institutions.

6. While it would be ideal to include a measure of DG private sector activity in a country, or even a measure of total private sector activity, the authors were not able to locate such variables. We considered the World Bank’s Ease of Doing Business index, as well as a number of other potential measures of private sector activity (for example, domestic credit to the private sector, export value index, and so forth), but chose the time it takes to start a business because it was the most theoretically salient measure. This decision also allowed us to use data from the correct year in relation to our dependent variable, and it had the lowest number of missing observations for Africa.

7. Even though road density was the best and most complete measure of infrastructure, the variable was missing observations for 13 countries. If we included this variable in the model, our sample would drop to only 40 cases. When included, road density is not statistically significant at any conventional significance threshold.

8. We transpose Freedom House scores such that the degree of liberalisation increases as numbers increase.

9. We also estimated a model in which we interacted electricity access with population. The inclusion of this variable did not substantively change other results, but this variable is statistically significant at the 1 per cent statistical significance threshold. This result implies that NGOs are less likely to locate in countries with higher populations and higher rates of electricity.

10. When GDP per capita is included in a model without electricity access, the relationship is both negative and significant. We find similar results using NGOs per capita as the dependent variable, rather than a raw count of NGOs.

11. Model results available upon request.

12. Four of the developing-country-based NGOs in the database are headquartered in India; the rest are based in Africa.

13. Several NGOs list multiple countries as housing their headquarters, and are therefore included as ‘domestic’ organisations in more than one country.

14. See note 1 for an explanation of interview coding.