Economies of scale and the equity-efficiency trade-off: Evidence from a piped water network in rural Bangladesh

Md. Ahasan Habib; Anna Tompsett

AI summary · arXiv: 2611.08031v1 · econ.GN

Economies of scale and the equity-efficiency trade-off: Evidence from a piped water network in rural Bangladesh

Md. Ahasan Habib, Anna Tompsett

Original paper Submitted to arXiv on 05 Nov. 2026 License: NONEXCLUSIVE-DISTRIB 1.0 Also in: fr

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The essentials

In a rural Bangladesh water-network study, economies of scale and price-sensitive demand made free household connections the most cost-effective option, removing the modeled equity-efficiency trade-off.

Question

Can user fees improve the cost-effectiveness of household connections to piped water, or do network economies of scale make free provision more efficient? The paper also examines the conditions under which a zero price can be cost-effectiveness-maximizing.

Method

The authors elicited willingness to pay from 330 households using an incentivized Becker–DeGroot–Marschak design linked to the possibility of connecting to a real pipeline, and combined estimated demand with construction-cost data. They estimated a demand curve and compared prices by net cost per connected household, then developed a model identifying conditions for zero-price optimality.

Findings

Result The fitted demand curve implies a revenue-maximizing price of US$9.7, at which about one-third of households would connect.
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The revenue-maximizing price is US$9.7, but at this price only around a third of households connect.
Result Free connections minimize modeled net cost per connected household because the effect of lost scale from lower take-up outweighs fee revenue.
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Strikingly, the most cost-effective approach is to offer connections for free. This is because economies of scale strongly dominate the revenue-raising effect of charging for household connections.
Result Free provision costs US$98 per connected household, compared with US$89 under the idealized case in which each household is charged its willingness to pay.
See the passage in the paper
Implementing the programme with free connections would cost US$98 per connected household. A policymaker who could charge individual households their willingness to pay would raise average revenues of US$9 per household, resulting in a programme cost per connected household of US$89, only a 9% saving relative to free provision.
Result In the modeled demand scenarios, willingness to pay must be scaled up by at least 3.4 for a positive price to improve cost-effectiveness.
See the passage in the paper
The cost-effectiveness-maximizing price is zero for scaling factors below 3.4 and then jumps to a positive price.
Authors’ interpretation The model identifies substantial fixed-cost economies of scale and high demand sensitivity to small prices as the conditions supporting a zero cost-effectiveness-maximizing price.
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In other words, for zero pricing to minimize average cost, two conditions must be met: i) the fixed cost contribution to average cost ($\nicefrac{{F}}{{\overline{M}}}$) is large; and ii) demand is highly sensitive to introducing even small prices.

Limitations

The empirical evidence comes from one rural Bangladesh piped-water-network case with a relatively small sample, limiting how broadly the specific findings can be generalized.
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Our empirical analysis is limited to the case of piped water networks in rural Bangladesh, and our sample size is relatively small
The cost-effectiveness analysis abstracts from the costs of collecting and administering user fees.
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Our analysis also abstracts from the significant overhead costs necessary to collect and administer payments, which further reduce cost-effectiveness [11, 34].
The cost model assumes variable costs rise linearly with connections, although the authors note that this may understate scale economies.
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We assume that the total variable costs scale linearly with the number of households connected. In practice, this assumption probably underestimates economies of scale.

Why it matters

The authors argue that infrastructure pricing decisions should account for how user fees can reduce take-up and increase average costs when fixed costs are substantial. In such settings, free basic connections may maximize cost-effectiveness while also maximizing equity, though the evidence on scale economies across contexts remains limited.

Written by AI from the paper. Passages are checked word for word against the text we analysed; the rest may contain errors.

AI-generated keywords: Public infrastructure Connection fees Willingness to pay Economies of scale Rural water supply

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Comprehensive Summary
Layman's Summary
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Original abstract

The paper asks whether charging households for access to public infrastructure necessarily improves cost-effectiveness by raising revenue, or whether prices can increase the cost per person served by reducing uptake. The authors examine this question using a piped drinking-water network in rural Bangladesh. Their central argument is that when infrastructure has substantial fixed costs, spreading those costs across more users may be more valuable than collecting connection fees. In such cases, free connections may be both more equitable and more cost-effective. The researchers elicited households’ willingness to pay for a private connection in two communities before selecting one by public lottery to receive a newly built network. In 2023, 330 households participated in an incentivized Becker–DeGroot–Marschak procedure. Respondents stated their maximum willingness to pay and drew a price voucher. If the voucher price was no higher than their stated willingness to pay, they could connect at that price if their community received the system. The authors combined the resulting demand estimates with construction costs to compare pricing strategies. The network had a theoretical capacity of about 200 households. Installation cost US$17,819, including US$4,795 in fixed costs for the borehole, pump, and water tower, and an estimated US$74 in variable costs per household connection. Demand declined sharply as prices rose. Among participating households, median stated willingness to pay was about US$6.30. The fitted demand curve predicted uptake of 88% at a price of US$1, compared with only 12% at US$20. A price of about US$9.70 would maximize revenue but attract only around 34% of households. When the authors combined the demand curve with the cost function, they found that a price of zero minimized net cost per connected household. Free provision yielded an estimated cost of US$98 per connection. Even a hypothetical policymaker able to charge every household exactly its willingness to pay would reduce this cost to only US$89—a saving of about 9%, before accounting for payment-collection costs. In the authors’ model, willingness to pay would need to increase by at least a factor of 3.4, with the demand curve’s shape unchanged, before a positive price became more cost-effective than free provision. Observed connection decisions differed in important ways from stated willingness to pay. After the elicitation, the project capped the required payment at US$8.40, and the eventual connection rate at that price was about 55%. Stated willingness to pay had little power to predict who connected. The authors suggest that information provided after the elicitation about arsenic in households’ existing water sources may have affected demand. Connection rates were 40% among households whose sources had no detectable arsenic, 64% among those with levels of 10–50 ppb, and 86% among those with levels above 50 ppb. This pattern suggests that connection decisions reflected households’ circumstances and information, not just their earlier stated valuations. The paper’s broader conclusion is conditional, not universal. In a simple model, free connections can minimize average public expenditure when fixed costs are large and demand is highly sensitive to even small prices. The authors argue that both conditions may often apply to networked infrastructure and low-income households, while emphasizing that evidence on economies of scale across settings remains limited. The study is relatively small and context-specific. Its estimates depend on an elicited demand curve and assumptions about costs; it also excludes ongoing operating costs and does not establish that free pricing is best for every infrastructure service. The authors recommend that policymakers assess economies of scale before imposing connection fees. They also suggest considering other designs, such as free basic access with paid upgrades.

When Free Water Connections Can Cost Less

Charging households to connect to public infrastructure may seem like a straightforward way to recover costs. But fees can also discourage people from using a service, leaving expensive infrastructure serving fewer households. A study of a piped water network in rural Bangladesh examines this tension—and finds that, under some conditions, free connections can be both more equitable and more cost-effective.

In “Economies of scale and the equity-efficiency trade-off: Evidence from a piped water network in rural Bangladesh,” Md. Ahasan Habib and Anna Tompsett ask whether connection charges necessarily make infrastructure spending go further. Their answer depends on the balance between fixed costs, the cost of adding each user, and how strongly households respond to prices.

Why the number of users matters

A water network requires substantial investment before it can serve anyone. In this project, construction cost US$17,819. Of that amount, US$4,795 covered fixed infrastructure: the borehole, pump, and water tower. Each additional household connection cost an estimated US$74. The system had a theoretical capacity of about 200 households.

Once the fixed infrastructure is in place, connecting more households spreads its cost across a larger group. That creates a potential trade-off: a fee brings in revenue, but may keep households away. If enough people decline to connect, the fee’s revenue may not make up for the lost opportunity to share the fixed cost more widely.

This trade-off can matter especially when households are sensitive to even small prices. If a modest fee substantially reduces uptake, charging for access may raise the public cost per person served—even while generating revenue from those who do connect.

Measuring willingness to pay

In 2023, the researchers elicited willingness to pay from 330 households across two communities before choosing one community by public lottery to receive a newly built network. Participants took part in an incentivized Becker–DeGroot–Marschak procedure. Each household stated the maximum it would pay, then drew a price voucher. If the voucher price was at or below the household’s stated maximum, it could connect at that price if its community received the system.

The resulting responses showed that demand dropped sharply as prices rose. Participating households had a median stated willingness to pay of about US$6.30. The fitted demand curve predicted that 88% would connect at US$1, compared with only 12% at US$20. A price of about US$9.70 would maximize revenue, but was predicted to attract only around 34% of households.

More revenue did not mean lower cost per connection

The authors combined the estimated demand curve with the network’s cost structure to compare pricing strategies. In their model, a price of zero minimized net cost per connected household. Free provision produced an estimated cost of US$98 per connection.

Even under an unusually favorable pricing scenario—where a policymaker could charge every household exactly what it was willing to pay—the estimated cost fell only to US$89 per connection. That is a saving of about 9% compared with free connections, before accounting for the costs of collecting payments. In the model, willingness to pay would have to rise by at least a factor of 3.4, with the same demand-curve shape, before charging a positive price became more cost-effective than free provision.

The findings do not mean that fees never help fund infrastructure. Rather, they show why revenue alone is an incomplete measure of a pricing policy’s performance. Where fixed costs are large and demand falls quickly as prices rise, attracting more users can matter more than collecting a connection charge.

Stated preferences and actual connections

What households said they would pay did not closely predict who eventually connected. After the elicitation, the project capped the required payment at US$8.40. At that price, about 55% of households connected, but stated willingness to pay had little power to identify which households would do so.

One possible reason is that households’ choices reflected information and circumstances that changed or became clearer after the survey. The researchers report that connection rates varied with arsenic levels in households’ existing water sources: 40% of households with no detectable arsenic connected, compared with 64% among those with levels of 10–50 parts per billion and 86% among those above 50 parts per billion. This pattern suggests that decisions were shaped by the perceived need for safer water, not simply by earlier stated valuations.

A conditional lesson for policymakers

The paper’s conclusion is not that every public service should be free. It is that policymakers should examine the economics of each system before imposing connection fees. Free access may make sense when fixed costs are high, the cost of serving another household is relatively low, and even small charges would sharply reduce participation.

The evidence also has limits. This is a relatively small study in a particular setting. Its estimates rely on an elicited demand curve and assumptions about costs, and they exclude ongoing operating expenses. The paper also notes that evidence about economies of scale across different infrastructure settings remains limited. The results therefore offer a useful framework for decision-making, not a universal pricing rule.

Policymakers might also consider designs between fully free service and a charge for every connection—for example, free basic access paired with paid upgrades. The central question is not simply how much revenue a fee can raise, but whether that revenue outweighs the cost of serving fewer people. In rural Bangladesh, the researchers’ estimates suggest that expanding access could be the better value.

Created on 09 Nov. 2026

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Economies of scale and the equity-efficiency trade-off: Evidence from a piped water network in rural Bangladesh

The essentials

Question

Method

Findings

Limitations

Why it matters

Read further

Definitions

When Free Water Connections Can Cost Less

Why the number of users matters

Measuring willingness to pay

More revenue did not mean lower cost per connection

Stated preferences and actual connections

A conditional lesson for policymakers

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