Why does household electricity demand not grow in some developing countries?
Recent analysis shows household growth in demand for electricity remaining stubbornly low in some less developed countries. This has been analysed in Kenya, for example, in a recent paper published by Fobi et al. in the journal Energy Policy. Similarly, in Kuungana’s project work in countries such as Uganda and Zambia, low growth has been evident in demand for electricity from residential customers. But demand from these households will surely grow in the right conditions. So, what are those conditions, and why is demand growth so stubbornly low in some countries?
To try to explain this, demand from residential electricity consumers has been analysed. Data on residential demand for electricity has been collected, together with residential connection numbers, to analyse the growth in demand per connection. The impact of grid expansion on the demand per connection has been stripped out from the raw data. This is important, especially in countries such as Kenya where grid access has grown rapidly in recent years, because these new connections typically have much lower demand, dragging down the average. For this analysis, the aim has been to construct a dataset that allows for the demand per household to be followed across a population of connections whose characteristics are reasonably constant over time.
For this population, the GDP elasticity of demand has been calculated. This is a measure of the extent to which electricity demand responds to GDP growth. An elasticity of 100% would suggest that 1% GDP growth results in 1% demand growth. The first graph below plots this demand elasticity for households against GDP per capita. Apart from in the case of the poorest countries, the graph shows declining GDP elasticity of demand with increasing GDP per capita, i.e., the responsiveness of demand to GDP growth declines as countries become richer. For high income countries, the negative numbers indicate demand reduction as more energy efficient appliances are adopted (this is driven by policies and consumer behaviour, rather than by GDP per se). But on the left-hand side of the figure the pattern is less clear: why is elasticity high for some countries (e.g., Ghana, Bangladesh), but not for others (Kenya, Uganda, Zambia)?
Analysis of poverty data explains the trends in household demand for electricity in lower income countries. For the poorest countries, affordability is a very real constraint for many households. The graph below analyses the relationship between three variables:
The poverty rate, defined as a the % of the population below the poverty line of $1.90/day in 2011 PPP terms.
The reduction in poverty rate over the last 20 years covered by the data.
GDP elasticity of demand, which is indicated by the shading of the markers in the graph. Darker red shading indicates the countries with highest elasticity.
This analysis helps to identify a ‘sweet spot’ where electricity demand grows rapidly with GDP.
For countries with high absolute poverty rates, the GDP elasticity of demand is low, meaning that the electricity demand for the average connected household is flat.
This persists even for countries where poverty has started to decline quite rapidly (e.g., in Uganda), so long as absolute poverty remains high.
When poverty drops to a level where affordability is no longer such a constraint on electricity consumption, further reductions in poverty can unlock more substantial increases in demand. This is the ‘sweet spot’ indicated in the bottom right quadrant of the graph.
Once poverty has declined to much lower levels, household demand growth slows again; households have acquired most of the core electricity-consuming appliances that they want and the potential for demand growth is more limited.
This analysis can be taken a step further by estimating the size of consumers’ ‘energy wallet’. The amount that a household can spend on electricity consumption can be estimated using distributional consumption data. For each decile of households in a country the ‘energy wallet’ size can be estimated by (a) assuming that households are willing to spend up to a given % of income on energy, and (b) that this rule is subject to some maximum monthly spend (reflecting the fact that there is only so much electricity that most households will need).
The outputs from this analysis reinforce the findings presented above. The graph below presents the estimated increase in ‘energy wallet’ size for the average household across selected countries. In Zambia, the size of the energy wallet has not increased much, reflecting persistently high poverty rates. In Ghana, rapidly declining poverty, combined with much lower absolute poverty results in much higher spending potential – the ‘sweet spot’ referred to above. As a country’s poverty rate continues to decline (e.g., the Philippines), this growth slows as many households have at least the basic appliances that they desire. In Uganda, the ‘energy wallet’ size was static during the 2000s but started to increase during the 2010s. Could this indicate that Uganda is poised to enter the ‘sweet spot’ third quadrant in the model illustrated above?
Why does this analysis matter? There are at least two important conclusions to draw from this analysis:
It is important to reflect the findings above in system planning exercises. All too often, system plans in low-income countries overestimate demand growth. This can result in bad decision making and in plans rapidly becoming out of date.
The evidence that affordability really does act as a constraint on demand should influence how we think about the role of subsidies in achieving universal access, SDG 7. For some households, a connection (whether on-grid or off-grid) will be of little use without subsidy support.