Determinants of urban sanitation costs – ‘willingness to connect’ and scale effects

The Daudey 2017 paper (open access) I reviewed in this post has a useful table (p.7) of 9  determinants of urban sanitation costs. I would tend to group them more simply into three headings as below – I won’t go into these more here as the table in the paper is good.

1. Technology: technology type, level of service (e.g. shared or not)

2. Input prices: labour, materials, energy,

3. Geography: population density, topography, soil condition, distance to treatment

However, I would also add a fourth set of determinants which Daudey doesn’t include (or are implicit), namely broader economic ones. Each in turn is discussed in this blog.

4. Economics. willingness and ability to pay, macroeconomy and business envt.

For sewer networks in particular, an oft-forgotten determinant of medium- to long-term per capita costs is willingness and ability to pay. Or rather, willingness to connect. I underline per capita above because many networks operate below capacity, spreading fixed costs of trunk lines and treatment plants over a smaller number of connections than initially planned. Even though the overall CapEx doesn’t change much, the cost per capita is driven up by the fact that there are fewer users (capita…).

This is demonstrated in several of Guy Hutton’s East Asia studies under the Economics of Sanitation Initiative. For example, in their Cambodian study, only about 20% of targeted households were actually connected to the sewerage system. This meant that while the “ideal” scenario had a cost per private latrine with sewer connection was US$ 5,263, in the “actual” scenario it was US$ 17,537 at the current connection rate. This ‘willingness to connect’ issue is something the World Bank have explored elsewhere – see here.

Willingness to connect could either stem from (i) people possibly being keen to connect but not affording the connection fee (ability to pay, ATP), or (ii) able to pay but still not wanting to connect as they don’t perceive the benefits (to them as a private citizen) to be greater than costs (willingness to pay, WTP). In most cases, social benefits from a sewer system should be greater than social costs if everyone connects, or the system would have been unlikely to be approved.*

In theory this problem of higher than expected per capita costs happens with non-networked systems. However, the key difference is that they are more easily scaled up or down. Here’s an FSM example, quite basic, to keep things simple – market failures mean it is unlikely to happen quite this way in reality: Emptying services are privately provided and the market supplies Y vacuum trucks if demand is presently X. When demand rises to 2X (and this is perceived to be stable), providers are incentivised by rising prices (invisible hand etc.) and will accordingly invest in more trucks. Supply then  rises to 2Y or similar. While excess capacity is still possible, it is less likely to occur than with a sewer system that must necessarily be designed for the maximum connected population expected within a 20-30 year time horizon, i.e, some anticipated demand of anything between 10X-40X. A related aspect is that the FSM system is that isolated failure of components may not have big repercussions – e.g. a vacuum truck being out of service reduces FSM service supply marginally, whereas a pumping station being out of service can reduce sewerage services dramatically.

However, an FSM-based system clearly still has the same scale / time horizon issue for the treatment part of the chain – i.e. you need to design a FSTP for maximum projected demand. So,  there may well be excess capacity there in the short-to-medium-term. But that does not matter as much if it is a simple treatment technology with low running costs, as compared to a sewer system which requires a minimum of energy to run at all, regardless of wastewater volumes.

Considering the second part of my #4 bullet back at the start, the macroeconomic situation and business environment can be seen as more distal determinants of the input prices under #2. This is nicely demonstrated by EAWAG’s report on costing on-site sanitation – Ulrich et al 2016 – which includes a useful figure (pasted below) on ‘cost factors’ (ovals in the below) and how these influence material and labour costs.

Prices of materials are determined by many things, including taxes, exchange rates, trade barriers, competition, regulation etc. and the broader “business environment”. For example, if a land-locked country is importing key materials and customs/ports are inefficient, that will drive up costs. Some of this is implicit in Daudey’s table under input prices. Likewise for labour prices, the competitiveness of the broader labour market, and associated regulation, will strongly determine labour costs. So will other macro-economic factors like unemployment (not straightforward in LMICs) and inflation.

In conclusion, many, many factors determine per capita costs of urban sanitation. This is why it is quite hard to compare costs across countries. Other sectors such as health also face this issue. Accordingly, systematic reviews of economic evaluations in health tend to tabulate and compare results, stating contextual factors, rather than doing a meta-analysis (as would be done for health interventions where a more uniform estimate might be expected across contexts).



*This disconnect between private and social benefits occurs because sanitation has public good characteristics. If discharging fecal waste untreated incurs no costs/fines (as is the case in Dhaka for example, where most septic tanks discharge direct to drains), then society pays for the consequences of that  negative externality.

2 thoughts on “Determinants of urban sanitation costs – ‘willingness to connect’ and scale effects”

  1. Guy Norman of WSUP comments on this post on the WSUP blog here
    I agree that the jury is out on costs of ST & FSM vs simplified sewerage & likely to be for some time. Population density and connection rates are key. We need more work like Sinnatamby 1986 (figure here).

    Also agree that the devil is in the detail re FSM scalability – Sinnatamby’s assumption in the figure of constant OSS costs re density is too simple. We need more comparative studies in the same context like Dodane et al. 2012 ( IFI projects are key for this, as Guy notes, and arguably present a great opportunity given IFI knowledge and research arms.


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