Why Do Water and Sanitation Systems for the Poor Still Fail? Policy

Policy Analysis pubs.acs.org/est

Why Do Water and Sanitation Systems for the Poor Still Fail? Policy Analysis in Economically Advanced Developing Countries Markus Starkl,*,† Norbert Brunner,‡ and Thor-Axel Stenström§ †

Competence Centre for Decision-Aid in Environmental Management, University of Natural Resources and Applied Life Sciences/DIB, Gregor Mendel Strasse 33, A-1180 Vienna, Austria ‡ Center for Environmental Management and Decision Support, Gregor Mendel Strasse 33, 1180 Vienna, Austria § SAR Chair, Institute for Water and Waste Water Treatment, Durban University of Technology, P.O. Box 1334, Durban 4000, South Africa S Supporting Information *

ABSTRACT: The results of an independent evaluation of 60 case studies of water and sanitation infrastructure projects in India, Mexico, and South Africa, most of them implemented since 2000, demonstrate an ongoing problem of failing infrastructure even in economically advanced developing countries. This paper presents a meta-analysis of those project case study results and analyses whether the design of existing policies or other factors contribute to failures. It concludes that the observed failures are due to well-known reasons and recommends how the implementation of the Dublin−Rio Principles can be improved. (They were introduced twenty years ago to avoid such failures by means of more sustainable planning.)

1. INTRODUCTION International policies, such as the 1992 Rio Declaration on Environment and Development, established sustainable development in international law. Its principle of poverty eradication, measured relative to the Millennium Development Goals, has sparked an ongoing debate about the obligations of states to provide basic water infrastructure services, culminating in the formulation of a human right to water and sanitation.1 The recent Rio+20 Summit on sustainable development confirmed this critical importance of water and sanitation for sustainable development.2 Despite such commitments, inadequate provision of domestic water services to the poor is still a global challenge. The health and economic losses from inadequate water services are well-known (e.g., for India3 and China4). Three major problems have been documented in the literature: First, by far the largest problem is the lack of infrastructure. A billion people are without access to improved water supply, and 2.6 billion people are without access to improved sanitation; this number is on the rise.5 Improving health outcomes will require massive increases in water and sanitation coverage of the poorer populations.6 Second, there is a problem of improved infrastructure that does not deliver. For instance, a 2012 audit of recent European Commission cofinanced development projects in Angola, Benin, Burkina Faso, Ghana, Nigeria, and Tanzania concluded that “the needs of beneficiaries as defined in the projects were clearly met in only two of the 23 projects audited” (ref 7 in section 21). Classical top-down supply driven planning, which ignores the local context, has long been identified as a cause of stranded infrastructure investments. As a © XXXX American Chemical Society

result, in many developing economies policy changes to promote decentralization of service provision have been promoted (e.g., Water Sector Reform Policy in India; see ref 8). However, these policy changes seem not to have resolved a major obstacle to sustainable municipal development projects, namely the lack of adequate operation and management (OM) for maintenance of the provided infrastructure.9 Further, improved infrastructure services that are not delivered close to homes may not have a positive health impact.10 Third, over the last two decades decentralized “green technologies” have been developed and promoted in order to overcome drawbacks of conventional technologies (examples: alternative technologies, such as eco-sanitation, or traditional technologies for rainwater harvesting). Demand for such technologies is expected to increase.11 However, users’ lack of awareness of personal hygiene practices may reduce the positive health impact of improved infrastructure,12 as may do mismanagement and lack of coherence with OM guidelines:7 Some systems were implemented because “green technologies” were believed to be sustainable, but no adequate attention was given to economic, institutional, or social aspects, which increased risk for failures [e.g., S1 of the Supporting Information (SI)]. In view of the large population without adequate functional water and sanitation infrastructure, it is a pressing goal to understand better why system failures still occur and, overall, Received: November 30, 2012 Revised: April 16, 2013 Accepted: May 1, 2013

A

dx.doi.org/10.1021/es3048416 | Environ. Sci. Technol. XXXX, XXX, XXX−XXX

B

apparent success

technical success social success success overall

local context

external driver

demand af fordability

acceptance

hygiene

operation

case study dimension type complexity f unctionality

purpose

country

aspect

summary

initial assessment by the local experts, who identified the case studies: success (OK), failure (F), mixed

success (accepted, demanded, and affordable) or failure (at least risk in one user aspect) success (both technical and social success), partial failure (technical or user related failures), complete failure (both technical and user related)

42 systems success, 18 failures 13 success cases, 34 partial failure, and 13 complete failures 29 success, 7 mixed, 24 failures

29 case studies from India, 16 from South Africa, and 15 from Mexico domestic water supply (WS), sanitation (San), or wastewater treatment/domestic sources (WWT) 28 case studies about water supply, 18 sanitation, and 14 wastewater treatment communal (com: an off-site technology serves several households), institutional (inst: e.g. schools), on-site (on-s: but several households, e.g. village, 35 communal projects, 2 institutional systems, and considered in case study) 23 on-site systems traditional or indigenous (trad) or not = “modern” (mod) 17 traditional ideas and 43 modern concepts basic (bas, e.g. water holes, standpipes, latrines, septic tanks), advanced (adv, e.g. filtration technologies, biogas toilet) 21 basic technologies and 39 advanced technologies working (OK) or out of order (no), with intermediate assessments: mixed (if some systems are working, others not), and risk (if not usable under certain 39 systems working, 15 at least partially out of conditions, e.g. strong rain) order, 9 at risk operational (OK) or not properly operated (no), and risk (e.g., lack of skilled operator) 42 systems operational, 14 at least partially not operational, 4 at risk safe, not applicable (NA)because out of order, not safe (no), or risk (e.g., need to cook water) 22 systems safe, 6 abandoned (NA), 14 systems not safe, and 18 systems at risk accepted (OK: system is or was used), not accepted (no: e.g. used for different purpose), or risk (e.g., not willing to adapt habits to technology) 47 systems accepted, 9 at least partially not accepted, 4 systems at risk demand (OK), no demand (no: e.g. a better accepted alternative was always available), mixed (demand for some households) 50 systems satisfy a demand, but 9 not, 1 mixed affordable (OK: users contribute to the capital costs and pay OM costs), not affordable (no: unable or unwilling to pay), risk (e.g., costly changes in building 56 systems affordable, 2 are not, 2 are at risk required) government (govt: support by government and regional programs), other sources (NGO: NGOs, private institutions, research institutes implementing 25 systems government supported, 22 systems other advanced technology), other external drivers (other: local contests and awards, regional regulations enforcing infrastructure improvements), no external support, 4 systems other external drivers, 9 drivers (no: i.e. projects were user driven, and users were left on their own); see also SI systems without external drivers user priorities ignored (igno), other aspects of the local context missed, such as not assessing foreseeable future demand (miss), other planning issues, such as 5 planners ignored user priorities, 23 missed other ignoring capability of households to use or implement system (other) or mixed (mix), or local context adequately considered (OK); see also SI local aspects, 5 other issues, 2 mixed, 25 considered local context success (working, operational, and safe) or failure (at least risk in one technical aspect) 18 systems success, 42 failures

explanation

3 economically advanced developing countries (members of the outreach 5 of the G 8 + 5 countries)

Table 1. Aspects and Case Study Overview

Environmental Science & Technology Policy Analysis


Environmental Science & Technology

Policy Analysis

lab scale studies). Where the same on-site technologies (household based) were provided to several households in the same locality, this was deemed to be one project. It was further required that the technologies should have been in operation for at least one year; 43 (72%) systems were implemented or revived after 2000. Among discarded case studies were unproven technologies (i.e., not documented in international literature). In the second step, field work by international project teams (technicians, social scientists, microbiologists) evaluated projects as either a success or failure. The case study assessment and analysis of explanatory factors used standard multidisciplinary methodology.24,25 The terms “failure” and “success” are defined as follows: Water infrastructure may not deliver the expected services or does so with unacceptable risks. In this case, it is a failure (ref 7 in section 13a); otherwise, it is a success (infrastructure safely delivers the expected services). What is “expected” depends on the purpose (water supply, sanitation, wastewater treatment). The basic expectation is the same for all technologies with the same purpose (e.g., delivery of safe water, provision of safe sanitation), but if technologies have specific additional features (e.g., production of biogas), then these features need to be delivered too. Thereby, failures may be due to technical or userrelated social issues. A technical failure is manifested by malfunction (e.g., leakage), which may be caused by organizational or institutional deficiencies (e.g., lacking skills for OM) and lead to environmental or hygiene risks (e.g., pollution). Social failure is manifested by nonuse or misuse of the technology, which may be caused by sociocultural or socioeconomic deficiencies such as lack of demand, lack of acceptance, or unwillingness or inability to pay for the services. Social deficiencies may also lead to environmental and hygiene risks. Thereby, problems are multicausal and a problem can at the same time be a cause. The case studies did not investigate possible long-term infrastructure failures associated, e.g., with biodiversity losses.26 For this paper, “apparent success” is success in the view of local experts (step 1 of data collection) and “actual success” is success in the view of a closer inspection by international expert teams (step 2). “Hidden failure” is apparent success that is not actual success. This paper emphasizes hidden failures, as the subsample of hidden failures appears to be random, justifying statistical methodology. (In step 1, data could not be collected at random, as insight by the local experts about the systems was needed. However, as hidden failures result from a lack of information, one could not intentionally search for them.) “Hidden success” was not observed. The assessment of planning depends on the project outcome: For actual success cases, it is assumed that the local context (see Table 1 and the SI for further explanation) was adequately considered (and what was not considered did not hinder success). For actual failure cases, the planning is classified along three steps: The first step asks whether planning considered the local priorities of users and local stakeholders. If yes, the second step identifies those failure cases where other aspects of the local context (explanation Table 1, illustration SI) were not considered. The third step identifies planning errors, which are difficult to categorize or are not so evident (illustration SI). The remaining failure cases are not attributed to planning, i.e. failure occurred although planners considered the local context adequately (illustration SI). For the meta-analysis the following methodology was applied:

how they could be avoided in the future. This paper presents a meta-analysis of published case studies of water and sanitation systems in India, Mexico, and South Africa (see refs 13−18 where there is also cost data). The countries studied exemplify the situation of economically advanced developing countries across three continents (they are among the “outreach five” at the yearly G 8 + 5 summits, the other two being Brazil and China). Their rapid economic growth, which also attracts investments, allows them to support improved water infrastructure for the poor. As an example, the Indian Union State of Madhya Pradesh 19 envisages 100% safe disposal of wastewater by 2025, using a mixture of centralized and decentralized systems financed by public and private capital. The study framework differs as follows from the more common internal reviews by donors: The purpose was to investigate the reasons for success and failures by means of a large independent in-depth analysis across three continents, which includes both success and failure cases and which applies a uniform evaluation approach. As the present data confirm, a surprisingly large number of failures may still be explained by well-known factors.20 However, the key finding of this paper is the high occurrence of a fourth obstacle to water service provision, hidden inf rastructure failures: Many supposedly successful systems on close inspection turned out to fail. Thus, households may receive water that is hazardous to health, sanitation may become unsafe, or wastewater treatment become inadequate, but the problem goes undetected and an unsuspecting population may be exposed to avoidable risks of waterborne diseases. A literature search found only few studies about this problem: A WHO study in five developing countries (Ethiopia, Jordan, Nicaragua, Nigeria, Tajikistan) found thermotolerant Coliforms in 11% of samples of piped water and in up to 57% from other improved water sources.21 Also, in industrialized countries with drinking water chlorination, there are occasional outbursts of waterborne diseases, e.g. due to Cryptosporidium.22 This issue is of great importance for policy analysis, as policies that are designed to avoid hidden infrastructure failures may be robust enough to actually provide safe and sustainable water services. Further, such failures are hardly compatible with the people centered approach of the 1992 Dublin−Rio Principles, which are now widely applied in the planning and implementation of infrastructure (see refs 8 and 23 for demanddriven planning). Rather, they are reminiscent of past experiences (e.g., supply driven policy provided communities accustomed to open defecation with latrines but forgot about hygiene promotion). The paper develops a diagnostic tool to explain, what policies may help to prevent such failures. (This uses a novel application of classification and regression trees.)

2. MATERIALS AND METHODS The paper analyses project case studies in Mexico, South Africa, and India with a focus on poor areas. In total, this paper used a sample of 60 case studies comprised of 25 different types of technologies. (Six more case studies were excluded for methodological reasons. A summary of evaluation results is presented in Table S1 of the SI.) The individual case study evaluation was conducted in projects led by the authors (see SI) by independent local and international expert teams in two steps. In the first step, local partners (SI) identified 29 apparent success cases and 31 failure cases (definition below). Case studies were considered only where a technology was implemented in full scale (no pilot or C



Policy Analysis

Table 2. Confidence Intervals for Conditional Percentagesa significance (one-sided) 95% No. 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18

sample apparent apparent apparent apparent apparent apparent apparent apparent apparent apparent apparent apparent apparent apparent apparent apparent apparent apparent

deficiency success success and success and success and success and success and success and success success and success and success and success and success and success and success and success and success and

adequate planning inadequate planning basic technology advanced technology no external driver funding (government, NGO) adequate planning inadequate planning basic technology advanced technology no external driver funding (government, NGO) India Mexico, South Africa adequate planning, but hygienic risk

97.5%

no.

parameter

no.

ML

lower

upper

lower

upper

31 29 25 4 6 23 4 21 29 25 4 6 23 4 21 20 9 10

actual failure

31 13 13 0 1 12 0 11 16 15 1 1 15 2 12 10 6 10

100% 45% 52% 0% 17% 52% 0% 52% 55% 60% 25% 17% 65% 50% 57% 50% 67% 100%

91% 29% 34% 0% 1% 34% 0% 33% 38% 42% 1% 1% 46% 10% 37% 30% 35% 74%

100% 62% 70% 53% 58% 70% 53% 71% 71% 76% 75% 58% 81% 90% 75% 70% 90% 100%

89% 26% 31% 0% 0% 31% 0% 30% 36% 39% 1% 0% 43% 7% 34% 27% 30% 69%

100% 64% 72% 60% 64% 73% 60% 74% 74% 79% 81% 64% 85% 93% 78% 73% 92% 100%

actual success

safe

social success

ML = maximum likelihood estimate, no. = number of cases, lower/upper = boundaries of confidence intervals (percentage of “parameter” observations amongst “sample” observations). a

• First, the aspects that caused failure cases in the studied sample of 60 project case studies were summarized. In order to uncover artificial causal relationships (study design), the aspects were pairwise compared by means of contingency tables. Contingency tables are a well-known method to identify relevant factors for planning and patterns of decision making.27 • In view of the unknown underlying distribution and the small sample sizes, frequencies of hidden failures are estimated by means of Clopper−Pearson exact confidence intervals.28 This method is known to be conservative: the actual level of significance is higher than stated as the nominal level; Table 2 displays confidence intervals. • Classification and regression trees (CaRT) are used for failure identification, because the results are easy to interpret and they make no assumptions regarding the underlying unknown distribution of values of the predictor variables. This is a decisive advantage over other classification methods, such as multivariate regression. The problem of this paper asks for the simultaneous solution of two classification problems under the constraint of a similar tree structure, using information gain (Shannon entropy reduction) as a criterion for the goodness of fit to the data. In the first step, classification trees were generated with chi-squared automatic interaction detection, CHAID.29 Then these trees were modified to display the same branching structure and combined into one tree. The branching successively reduces, in each level, the (expected) Shannon entropy for each mixture of apparent failure, hidden failure, and actual success case studies. CaRT methods are common in medical sciences to extract meaningful prognostic groups.30 As in these applications, CaRT is used as a diagnostic tool to identify failure types by successively checking for certain “symptoms” (e.g., known reasons for failure listed in ref 20). Similar

applications in environmental management and policy analysis are emerging (e.g., refs 31 and 32).

3. RESULTS Table 1 summarizes the relevant aspects that were used to explain the case study outcomes. Figure 1 summarizes the reasons for failures (based on Table S1 in the SI), whereby hygienic risks were a major concern both for apparent and for hidden failures.

Figure 1. Observed sample, failures.

Table 2 summarizes confidence intervals, whereby the following presentation is based on the one-sided 95% level of significance (computation by the Clopper−Pearson formulas28). As will be explained below, the key result from Table 2 is the observation that with 95% confidence at least 10% of all apparently working water infrastructure have hidden hygiene risks, i.e., the technology is unsafe or may become unsafe under realistic conditions (example for rainwater harvesting: the system robustness has not been considered in the case of strong D



Policy Analysis

Figure 2. Significant contingencies between the aspects. Nodes represent aspects and lines represent significant contingencies (95% significance, using chi-squared test for the contingency table between the nodes). The node size symbolizes the degree (number of links).

for example, due to implementation, as the local context may change (change in local government). Second (Figure 2), advanced technology improves over basic systems, among which with 95% confidence there are at least 42% with hidden failures; nevertheless more than 30% of advanced systems still have hidden failures (Table 2, lines 5 and 6). Third, with 95% confidence, among systems without external funding or other support there are at least 47% hidden failures (Table 2, line 7), in addition to the apparent ones and the projects that were not begun owing to lack of support. (Among funded systems, there are at least 29% hidden failures: Table 2, line 08.) However, in view of the small number of systems without any external support, Figure 2 does not recognize a significant contingency between funding and success. The dominating observation for Figure 2 is the fact that there is no 95%-significant contingency between success and either government or NGO support (additional contingency table, computed from Table S1). Further conclusions from Figure 2 are the following: Although the distribution of hidden success or failure over countries contrasts with the distribution of apparent success or failure (in Mexico and South Africa fewer than 50% apparent success cases, but in India 69%), this did not significantly affect the overall success or the hygiene assessment (there are no links to the country node). Actually, some partners in Mexico and South Africa were involved in planning and implementation of failure cases and this eased access to these cases for the subsequent independent analysis. Further (section 2), most case studies were in poor areas. Surprisingly, there were only four cases in which affordability or willingness to pay was an issue (SI Figure S1). This is because the case study selection was restricted to implemented projects, where cost recovery is a key condition for implementing institutions. Figure 3 identifies the root causes of success or failure, further explained in Table 3. Thereby, Figure 3 displays combined classification trees for the initial local expert opinions about success and failure and for actual success and failure. These are descriptive empirical models, fitted to the full set of 60 data (Table 3 and SI). While the unstructured data carry almost no information content (close to maximum entropy), the expected entropy of the structured data is close to 0 (=

rain and with possible contamination of water sources; c.f. ref 33). The reason why “hidden failures” could be identified was the case study selection: Local experts were asked to identify about the same number of success and failure cases. Among the 29 preliminary assessments of success by local experts, international experts confirmed only 13 actual success cases and refuted 16; they were actually failures. These 55% hidden failures in a sample of 29 translate with 95% confidence into the statement that at least 38% of systems that appear to be successful are hidden failures (Table 2, line 2). The perception of the hygiene risks was a key reason for this discrepancy. Among the 29 systems that local experts assessed as apparently successful, only 16 were safe (among them three failures for other reasons), four were unsafe and nine posed hygiene risks. Thus, with 95% confidence at least 29% of water systems, which appear as success, have hygiene risks; 30% for India and 10% for Mexico and South Africa (Table 2, lines 6, 16, and 17). Regardless of different national water policies, this paper therefore supports a lower bound of 10% undiscovered hygiene failures (observation corroborated by ref 21). Figure 2 displays the significant contingencies (chi-squared method, 95% level); the 136 contingency tables (not displayed) to compare the 17 criteria of Table 1 were automatically generated from SI Table S1. Figure 2 shows (by means of the displayed links) that hygiene risk and actual success are contingent on the adequate consideration of the local context by the planner (definition: Table 1 and SI) and on the complexity of the technology. Further, in view of Table 2 there is an influence from external support (e.g., funding). These factors (planning, complexity, and support) can be controlled in the planning phase. However, Table 2 also shows (see below) that no factor alone suffices for success. First, good planning contributes to success (Figure 2). In addition to the apparent failures, inadequately planned projects have a hidden failure rate of at least 47% (Table 2, line 4). However, good planning alone does not guarantee success and with 95% confidence there are at least 30% hidden failures despite adequate planning (Table 2, line 3): A project may fail, E



Policy Analysis

Figure 3. Classification trees to explain actual and apparent success or failure. This figure illustrates the structure of the observations, definitions in Table 1, and explanations in Table 3.

proper OM, no leakages or other technical mal-functions), as they lacked data and laboratory resources to analyze the systems more closely. Figure 4 relates the classification of Figure 3 with the time line of the planning process and chooses recommendations for policy instruments that in hindsight could have avoided the observed failures. On this basis, a policy workshop with Mexican stakeholders (SI and ref 25) confirmed the ongoing need to implement such measures to cope with the challenge of providing sustainable water and sanitation infrastructure in periurban areas. The considered measures are well-known, see refs 8, 20, 24, and 34−40 as displayed in Figure 4.

perfect order). Actual success cases are in end node 11. All other cases in nodes 4, 6, 8, 9, and 10 are actual failures. Apparent failures are in nodes 4 and 8, which experts linked to inadequate planning. Hidden failure cases are in nodes 6, 9, and 10. Overall, this tree model faithfully replicates 100% of the international experts’ assessments of actual success and failure, and it replicates faithfully 98% of the local experts’ assessments of apparent success (with one exception in node 9; see SI). A simulation (see SI) indicates that this goodness of fit is statistically significant (95% level). By comparing the left and the right sides of the tree, this model suggests a hypothesis for why hidden failures occur: Judging from the authors’ personal acquaintance with them, local experts were as well-trained as their international colleagues. Moreover, the contingencies (Figure 2) show that, in their initial assessments, local experts at least implicitly considered all aspects known to be related to success or failure. However, the differences in the classification trees suggest that local experts did not directly assess hygiene (it does not occur on the left side) but rather assessed hygiene indirectly in terms of other factors (good planning, use of advanced technology,

4. POLICY ANALYSIS The paper identified systematic water infrastructure problems in India, Mexico, and South Africa. Thus, 20 years after the formulation of the Dublin−Rio Principles, the observed case study failures beg the question of how reform policies in the water sector failed (see also ref 8), although they were based on these principles. However, the results of this meta-study support a different conclusion. It follows from Figures 3 and F



Policy Analysis

Table 3. Information Gains in the Classification Tree of Figure 3a node no.

level

branching from node

apparent failures

description

hidden failures

actual successes

entropy: % of maximum

16

31

93.3%

4

31

32.3%

12

0

63.0%

1 0 raw data from published project evaluations 13 query at no. 1: Did planners consider the local context adequately? 2 1 1 no: inadequate consideration of the local context is an actual failure (not 0 causation, but a classification rule) 13 3 1 1 yes: consideration of the local context is assessed as apparent success (these systems were all operational) weighted average of level 1 node entropies (nos. 2, 3) query at no. 2: Is the inadequately planned system nevertheless running? Otherwise a branching follows. 4 final 2 2 no, it is (partially) out of order, not operated properly or at least a risk in 0 these aspects: apparent and actual failure 5 2 2 yes, the system is functioning, but may fail for other reasons (subsequent 0 query) query at no. 3: Is the adequately planned system (which is perceived as apparent success) safe? 6 final 2 3 no, it is not safe and therefore an actual failure, but hidden 0 7 2 3 yes, it is safe, but may fail for other reasons (subsequent query) 13 weighted average of level 2 node entropies (nos. 4−7) query at no. 5: Does the inadequately planned (actual failure) but nevertheless running system use basic technology? 8 final 3 5 yes and use of basic technology was associated to apparent and actual failure 0 (e.g., lacking safety) 9 final 3 5 no, and in 80% of cases use of advanced technologies was perceived as 0 apparent success: hidden failure query at no. 7: Was the safe and adequately planned system externally driven? 10 3 7 no, users were left alone (no financial support, capacity building): actual 0 final failure (e.g., not affordable), but hidden 11 3 7 yes, someone outside the local community has influenced the selection of 13 final technology: actual success weighted average of end node entropies (nos. 4, 6, 8, 9, 10, 11) a

45.1% 0

24

0.0%

4

7

59.7%

10 2

0 0

0.0% 35.7% 19.9%

0

6

0.0%

4

1

45.5%

2

0

0.0%

0

0

0.0% 3.8%

Overall information gain: Entropy reduction from 93.3% of log2(3) to 3.8% = 95.9% relative information gain.

4 that all failures in the data could be attributed to well-known reasons, discussed extensively in policy guidelines (such as ref 20). This reaffirms the continued importance of the correct implementation of the Dublin−Rio Principles by adhering to well-known measures, asking for transparent, participative, and democratic demand-driven planning, aiming at sustainable solutions.34 Why did planners not implement these guidelines correctly, although in general they were aware of them and although the recommendations of the guidelines are straightforward? The authors propose three possible explanations: First, it is not enough to just implement “improved technologies”. This term was introduced by WHO and UNICEF as a proxy for “safe” to support infrastructure planning in developing countries, where the costs of systematic hygiene risk assessment may be excessive. If policies focus on “improved technologies”, which just avoid apparent failure sources, this may lead to a higher concentration of hidden failures among the remaining infrastructure. This is illustrated by Figure 4. After the first five steps, still 25% of the remaining systems had unacceptable health risks. The classification of “apparent success” in Figure 3 suggests that planners were satisfied with improved technologies: Their planning and implementation process used criteria related to “improved infrastructure”, such as consideration of the local context, technical functioning, and complexity but not hygiene (see section 3). However, “improved” does not mean “safe”:21 Although these criteria are correlated with safety (Figure 2), they cannot be used as a proxy for it. Second, the authors acknowledge that in hindsight it is easy to point out obvious fallacies, while planners may be overwhelmed by a multitude of problems typically associated

Figure 4. Hypothetical effect of policy recommendations on data.

G



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with poverty. As they may not be able to control all relevant factors, they might consider a not so perfect solution as superior to no solution at all. (For instance, insufficient funding may hinder the implementation of complex technology. However, in such a situation good planning might not suffice to ensure success; see section 3.) Sometimes guidelines (e.g., by donor institutions) are too rigid, whereas planners need sufficient flexibility to solve problems that are not foreseen in the rule books.24 Further, project drivers may ask for the implementation of technologies that may not be suitable under the given circumstances (e.g., an NGO may wish to implement a pilot in the hope for future demand). A good process for participatory planning is therefore considered crucial in this context. An innovative combination of participatory scenario development with participatory planning is documented in ref 11. Third there is an institutional problem about monitoring: Even if some policies ask for safe systems (instead of improved ones) and monitoring, as e.g. for India,41 to ensure this an institutional framework needs to be built up. At least in economically advanced developing countries, where there are sufficient means for monitoring and risk analysis, it is necessary to enforce standards and procedures that are comparable with those of industrialized countries. For instance, the European Commission recognized the need to reinforce development projects in Angola by providing laboratory equipment to facilitate water quality monitoring on the spot (ref 7, p 39). However, building up laboratories at each village may not be economical. On the other hand, in India, there are monitoring institutions, regulated by the Union States,41 but they seem to be too remote from the users, considering the hidden failure rate displayed by the present data. (refs 42 and 43 provide possible explanations for insufficient activity levels.) Thus, monitoring is an example, where a combination of bottom-up (demand-driven) and top-down (supply-driven) approaches (proposed by ref 44 in the context of capacity building) may be needed to define the right scale for setting up the monitoring institutions. As most of the reasons why the discussed infrastructure failures still occur, are well-known, this gives hope that in the future such failures can be avoided if a few key aspects (Figure 4) are considered in policy design, planning, implementation, and monitoring. The authors consider that the problems observed in the case study countries may be typical for the situation of water system provision for the poor in the larger crop of newly industrialized countries, where the socioeconomic situation of the poor population is similar (c.f. ref 45 under a related health aspect). However, in view of the importance of the institutional aspects, outlined above, in each country there may be more specific problems. Further, institutional changes due to economic development may facilitate the shift to more advanced technologies in currently poor areas. In order to develop country specific recommendations, larger data sets are needed for each country.

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Policy Analysis

AUTHOR INFORMATION

Corresponding Author

*Tel. +43 1 47654 5057. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The research leading to these results has received cofunding from five projects under the European Union 6th and 7th Framework Programmes. Details can be found in the SI.

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REFERENCES

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ASSOCIATED CONTENT

S Supporting Information *

Table S1 (summary of case study assessments) about the collaborative projects that generated the case study data and exemplifying the paper by more specific case study information. This material is available free of charge via the Internet at http://pubs.acs.org. H



Policy Analysis

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Why Do Water and Sanitation Systems for the Poor Still Fail? Policy

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