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Economic tools help us better understand handpump breakdown.
Which factors predict the functionality of hand pumps? Do communities free ride on their neighbors’ water sources? Are there positive spillover effects in the maintenance of nearby pumps? And what does this all mean for practitioners? This post gives an overview of my ongoing Economics PhD research, which tries to answer these questions.
Note: this research is still in progress, and I am seeking survey responses to complement my quantitative work, and help understand and interpret my results. If you have knowledge of how decisions are made in the installation and/or maintenance of hand pumps, please take this 8 minute survey.
Economics and rural water supply
My first job after graduating was conducting Monitoring and Evaluation for a medium-sized international water NGO in Malawi, where I saw first hand the large number of non-functional hand pumps. I started thinking about the economic factors affecting pump functionality, and how I might use economic tools to better understand breakdown.
Where did the data come from?
I started out with a statistical exercise, looking at which community or pump characteristics predict whether a pump is functional or not. Step one was finding data to analyze - Joseph Pearce (then at WaterAid, now at IRC), was very helpful in this regard, as was Brian Banks at GETF, and the Water Point Data Exchange (WPDx) has been incredibly useful in helping to locate data as I have updated and expanded the research.
Although I was most familiar with the Malawian context, I started using Tanzanian data, as it had good detail on pump and community characteristics. The data can be seen in the map below, where different colors represent different types of water sources. My analysis focuses on 10,747 hand pumps in the data, 63% of which were recorded as functional (producing water) at the time of data collection.
Predictors of pump functionality
The results of this statistical exercise build on the excellent research by Tim Foster which uses data from Liberia, Sierra Leone and Uganda, a great summary of which can be found here. A selection of these results are summarized in the table below, where (S) denotes a finding that is similar to that in Foster (2013), and (D) denotes a finding that is somewhat different. (Note that if a variable is a significant predictor of pump functionality, this does not imply that there is a causal relationship!)
Significant positive predictors of pump functionality
Variables that are not significant predictors
Significant negative predictors of pump functionality
Do neighbors’ pumps matter?
I also conducted some spatial analysis: I looked at whether the number, type and technology of nearby water sources help predict whether a pump will be functional or not. I found some interesting results:
A pump of a specific technology (e.g. India Mark II) is more likely to work if there are more pumps of the same technology nearby
A pump is less likely to work if there are more pumps of a different technology nearby
The number of non-pump water sources does not predict pump functionality.
There are a number of potential explanations for these spatial correlations in pump functionality, and I explore each of these in detail in my research.
I think the most convincing explanation for result (1) is that there are positive spillovers in the maintenance of very similar water sources - i.e. it is easier to maintain a pump if there are many similar pumps nearby. These spillovers might be a result of: increased availability of spare parts and pump mechanics familiar with the technology, explicit cost sharing in maintenance of similar pumps, the development of skills, or sharing of information between communities.
Results (2) and (3) give some (weaker) evidence for free riding, which occurs if a community is less willing to pay for their pump’s maintenance when there are other working water sources nearby, because if their pump breaks down there is an alternative source available.
Using an economic model to explain functionality
Positive spillovers and free riding effects are opposing forces: having other pumps nearby can increase both, and we only observe the ‘net effect’ in the data. I developed an economic ‘network’ model to disentangle the two effects, measure their magnitude, and test the extent to which they depend on different community and pump characteristics. For example, my initial results suggest that the strength of positive spillovers depends on the distance between pumps, and whether they are of the same technology; free riding seems to depend more on whether an alternative pump charges user fees than the distance a community has to travel to use it.
What would happen if we standardized pump technology?
Estimating this model also allows me to perform ‘counterfactual analysis’ - i.e. estimate how outcomes would change if we changed some of the model inputs. Focusing on a subset of 4116 pumps with the richest data, I estimate that if pump technology was standardized to fully exploit positive spillovers in their maintenance, the functionality rate would increase from 67% to 74%. I caution against interpreting this preliminary finding as a policy prescription, as there may be benefits to having different technologies: for example, perhaps some technologies are easier to install in different terrains, or are resistant to different types of physical shocks. However, I think this research gives a good estimate of the cost of fragmentation of pump technologies.
Future work: health, education, dependency and other countries
I am currently using the model to estimate the effect of non-functionality of pumps on health and education outcomes, using Tanzanian census data. In future work I hope to explore the dynamics of pump maintenance decisions. If communities respond to new installations, and form expectations about when a new pump will be installed in the future, this might induce ‘dependency’, with communities preferring to wait for a new pump to be installed rather than repairing their older pump. A dynamic analysis will allow me to explore such effects. I’m also hoping to apply my framework to data from other countries.
Your input is very valuable!
This is ongoing work, and I am still refining my model so my estimates are subject to change - you can find the latest draft of my paper at my website. I am also working to better understand my results, which is where drawing on the knowledge and experience of a wide variety of stakeholders is incredibly valuable. If you have experience in the sector, please take my survey and share it with others who might have useful insights! And please get in touch (firstname.lastname@example.org) if you have any comments, suggestions or feedback on this research - hearing from practitioners is extremely valuable, and I hope that this research can be useful for decision-making in the sector!
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