Electricity Access Dynamics in South Africa

In this blog post, Tom Harris and Professor Martin Wittenberg from Cape Town University (UCT), report on the survey analyses they undertook on electricity transitions. Tom is a researcher in SALDRU (Southern Africa Labour and Development Research Unit) and DataFirst, with a Master’s degree in Economics. Martin is in the School of Economics and Director of DataFirst, and a PI on the UK-ZA energy project.


Lack of access to efficient, reliable and modern energy is a prevalent issue across the developing world, with one in five people lacking access to electricity. The use of biomass fuels and other ‘dirty’ and inefficient fuels has severe negative implications, not only for public health,  but for the environment  and economic development too; transitions up the energy ladder  (to electricity particularly) are accordingly associated with improvements in well-being and economic progress [1-7]. Since the use of such fuels is most prevalent among those already in poverty, this exacerbates the plight of the poor, and widens the gap between the rich and the poor [2, 5, 6]. Electrification and improvements in electricity access are therefore of considerable interest to policy makers within countries where wide-spread poverty and inequality remain the dominant issues.

In this regard, South Africa is a success story. Access rates improved from well below 40% in the early 1990s to nearly 80% by 2002. Our own analysis of household electricity access, in Figure 1, confirms that this progress has continued in recent years, and shows national household access rates to have risen by a further 9% between 2002 and 2012.


Certainly, these improvements are encouraging, and there is much that can be learned from the successes of the South African electricity roll-out process. However, our results also reveal a striking point not yet given much attention in the literature: this long-run improvement in electricity access is not the result of a consistent, monotonic increase in access rates. Instead, there are short-term deviations from the long-term upward trend. The most salient of these deviations are periods of declines in access, which are evident in both national and small-area data. However, the cross-sectional statistics presented in Figure 1 cannot explain whether these declines in access were the result of households losing access at a location, or whether they were due to groups of people migrating from connected household units and setting up new households in locations that lack access.

Therefore, in an effort to understand what contributed to the observed declines in access, we introduce two novel approaches. Firstly, in order to explore the relationship between household formation and electricity access, we categorise households according to when they form and whether they continue to exist or not, and investigate changes in access among these different household categories. Secondly, to examine transitions in access among households that continue to exist, we apply longitudinal techniques to a unique form of NIDS (the National Income Dynamics Study, a nationally representative household panel) that allows us to track household units over time [8, 9]. These results are shown in Figure 2 and Table 1 below.



Our findings (in Figure 2) suggest that household formation contributed to the decline in aggregate access over the 2008-2010 period in two respects. Firstly, rapid growth of the household population meant that the electricity roll-out could not keep pace with net household formation: more than 400,000 household units were added to the population, and yet the total number of connections increased by less than 100,000. Secondly, newly formed households were less likely to have electricity access than those that dissolved (77.7% vs. 84.2%) – suggesting that households with electricity access dissolved and were replaced by new households without access.

Moreover, it was not only household formation and dissolution that led to the observed decline in electricity access rates between 2008 and 2010. We also see a decline in access rates among those households that continued to exist (in Table 1). More specifically, we find that even though many additional household electricity connections were added between 2008 and 2010, these positive transitions were outweighed by numerous connections losses – with more than 800,000 continuing households (8.7% of the connected household population in 2008 that continued to exist) having lost an electricity connection by 2010.

However, the processes described above are not particular to periods of declines in access – as is evident in Figure 3 and Table 2, presented below.



Pervasive connection losses are observed even over the 2010-2012 period: a period in which aggregate access actually improved. In addition, while we argued that household formation and dissolution dynamics contributed to a decline in aggregate access rates between 2008 and 2010, in investigating the 2010-2012 period we find that these processes are also able to contribute to improvements in aggregate electricity access – with households that formed over this period being more likely to have had access to electricity than both those that dissolved and those that continued to exist.

The policy and theoretical implications of our findings are applicable to those working in developing contexts well beyond the borders of South Africa. We have shown that aggregate electricity statistics, such as access rates, conceal a considerable degree of the complexity and volatility that is inherent in the development of electricity access. In this light, we suggest that policy makers involved in the area of electricity roll-out are likely to be aiming for a moving target in the following three respects:

  1. the number of households may be growing faster than the rate of growth in connections, as a result of rapid household formation;
  2. people may be moving out of connected households and setting up new households in locations that lack access; and
  3. certain connected households that survive from one period to the next may actually lose their electricity connections.

We therefore suggest that in developing countries like South Africa, electricity access rates are unlikely to show consistent improvements, even in periods of rapid electricity roll out. Access rates do not simply improve as new connections are added. Instead, we argue that household electricity access is a complex outcome of two key time-variant processes: (1) net connections (new connections less disconnections) and (2) household formation and dissolution processes.


  1. Vermaak, C., M. Kohler, and B. Rhodes, Developing an energy-based poverty line for South Africa. Journal of Economic and Financial Sciences, 2014. 7(1): p. 127-144.
  2. Kimemia, D., et al., Burns, scalds and poisonings from household energy use in South Africa: Are the energy poor at greater risk? Energy for Sustainable Development, 2014. 18: p. 1-8.
  3. van der Kroon, B., R. Brouwer, and P.J. van Beukering, The energy ladder: Theoretical myth or empirical truth? Results from a meta-analysis. Renewable and Sustainable Energy Reviews, 2013. 20: p. 504-513.
  4. Smith, K.R., National burden of disease in India from indoor air pollution. Proceedings of the National Academy of Sciences, 2000. 97(24): p. 13286-13293.
  5. Heltberg, R., Factors determining household fuel choice in Guatemala. Environment and development economics, 2005. 10(03): p. 337-361.
  6. Rao, M.N. and B.S. Reddy, Variations in energy use by Indian households: an analysis of micro level data. Energy, 2007. 32(2): p. 143-153.
  7. Vermaak, C., M. Kohler, and B. Rhodes, Developing energy-based poverty indicators for South Africa. Journal of Interdisciplinary Economics, 2009. 21(2): p. 163-195.
  8. Harris, T., Household Electricity Access and Household Dynamics: Insights into the links between electricity access and household dynamics in South Africa between 2008 and 2012, in School of Economics. 2016, University of Cape Town: Cape Town.
  9. Wittenberg, M. and M. Collinson, Household formation and household size in post-apartheid South Africa: Evidence from the Agincourt sub-district 1992-2003. 2014: A DataFirst Technical Paper 27. DataFirst, University of Cape Town, Cape Town.

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1 Response to Electricity Access Dynamics in South Africa

  1. Pingback: Cape Town Data Quality Workshop: Measurement of Development Indicators | UK Data Service – DataFirst joint project

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