In this blog Louise Corti provides a short review of an illuminating paper just published by Tom Harris, Mark Collinson and Martin Wittenberg entitled, ‘Aiming for a Moving Target: The Dynamics of Household Electricity Connections in a Developing Context’ in Science Direct.
This piece of work has stemmed from a collaborative UK-South Africa ESRC-NRF International Centre Partnership award that focused on scaling up the analysis of household energy data, looking at the potential for better understanding the policy context. In the UK policy work surrounding energy usage focuses on mitigating relative fuel poverty, in the first world sense, but for South Africa, research is inequality in public service delivery; despite significant progress over the last 20 years, basic access to electricity is by no means stable or guaranteed, and remains one of the largest development issues faced by post-apartheid South Africa.
While you can read the findings yourself in the open access paper, here I want to draw out the innovative methodological approaches of the research, which has focussed on using data from a long-running health and demographic surveillance system (HDSS) site. From my point of view, the approach taken by the authors offers an immensely promising avenue for opening up access to other complex HDSS data by considering how data can be restructured using traditional panel study methodology and considering carefully designed units of analysis.
In their analysis, the investigators investigated household electricity access in a poor rural setting in South Africa. Their approach critiques the existing literature in this field, which has tended to focus on investigating changes in an individual’s access to electricity, rather than taking into account the importance of the household unit and changes in household access. They acknowledge that that while many existing studies portray the process of electricity roll-out as a simple, monotonic progression, it is often more a far more complex picture than that. Recent literature typically uses binary indicators to measure progress in electricity access, to assess energy poverty, which suggest a strong association between electricity access and poverty. However, the authors note that the complexities of access transitions among the poor are not taken into account and that the aggregate data sources used do not offer rich-enough information on changes over time, for example, looking at access rates at provincial level.
A richer picture can be gained by investigating the short-term dynamics of electricity access using large-scale longitudinal data. The authors find short-lived deviations (in fact, periods of declines in access) from the long-term upward trend in electricity access, which they then go on to explore in more depth. The analyses were facilitated by the creation of new datasets derived from existing longitudinal studies: the National Income Dynamic Study (NIDS) and Agincourt Health and Demographic Surveillance System (HDSS). Longitudinal data analysis techniques were based around the unit of a new category of household type, defined by when a household forms and whether it continues to exist or not (persistence). Of note here is the novel approach used to re-present data from a typical HDSS.
The longitudinal data sources: sample and coverage
The National Income Dynamics Study (NIDS) is a panel study commissioned by the national Presidency in an effort to track long-run poverty and well-being and focuses on income, expenditure, labour market participation, education, health (including anthropometrics) and household well-being (e.g., access to services). The baseline sample was designed to be nationally representative and consists of around 7,300 households and about 28,000 individuals, who became Continuing Sample Members (CSMs) for the subsequent waves. Babies born to CSM women become CSMs themselves and individuals who were co-resident with CSMs were also interviewed (Temporary Sample Members – TSMs).
The Agincourt Health and Demographic Surveillance System (HDSS) monitors key demographic events and socio-economic variables in the Agincourt sub-district in north-eastern Mpumalanga Province, South Africa. A baseline census was conducted in 1992 with annual census rounds being conducted since and since 1999. Key variables measured routinely by the HDSS include: births, deaths, in- and out-migrations, household relationships, resident status, refugee status, education, antenatal, and delivery health-seeking practices. One-off modules are also added such as every second year since 2000, collection of a household asset module, which includes information on household access to services, such as electricity. Temporary migrants are classified by including on the household grid non-resident members who retain significant contact and links with the rural home and ‘share a common pot’.
Novel data structures for measuring household outcomes
The authors enable their longitudinal analysis of household outcomes from the ability to identify the same household in each wave in each study, in other words the surviving or continuing households. This approach was developed by the authors themselves in previous publication, cited in the paper. Applying their new definition to NIDS and HDSS data allowed them to set up panels of individuals as new panels of households, through which longitudinal changes in household electricity access could be explored (using transition matrices). In order to account for bias in their chosen panel definition, for example from varying household formation and dissolution rates, they also created a comparison panel using the traditional “headship-based” definition. Patterns of electricity access statistics estimated on each panel were found to be similar.
Conclusion
From a methodological point of view, the authors conclude that aggregate statistics can conceal a considerable degree of the complexity and volatility inherent in the development of electricity access. Viewing access as a time-variant process rather than assuming linear roll out of services needs to be appreciated and studied. The authors conclude that further studies into service delivery in LMICs should consider using the longitudinal techniques applied in their paper, taking into account household dynamics.
While I am by far no expert in the analysis of service delivery or utilities, my appreciation of the collection, preparation, structuring and curation of long-running social surveys leads me to believe that there is great hope for improving access to complex longitudinal data from multi-million pound development and evaluation studies conducted in low and middle income . If we consider the number of million-pound population surveillance and intervention investments across the world that generate immensely rich data over many years, such as HDSS, Millennium Villages Project rural development sites, and other large-scale intervention projects, like Girls’ Education Challenge, we can envisage the great scope for research opportunities through access beyond the research teams. The sometimes highly complex structure, inherent in HDSS–type data collections, could be reworked to create new datasets that are able to be better understood by social scientists and those sitting outside of the population, migration and epidemiology research domains
The approach the authors have taken in repurposing data to create traditional ‘panel data’, would benefit from being widely showcased and exploited as an inspiration for data-hungry researchers and, of course, for maximising impact opportunities for funders.
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