Friday 4 September 2009

Draft table of contents

Hello, everyone,

I am pleased to have been given the privilege of being lead author for this chapter, and am looking forward to working with you all. I have created a document with a draft working title, authors list, intended audience, and table of contents. I included in the list of authors everyone who has contributed to this blog, and have added a few other names. (If you don't see your name, I apologize--please let me know.) It makes for quite a long author list, but this is a synthesis chapter and many people contributed.

For the chapter outline I drew on comments made by people on the blog. I think that the outline offers a space for most of the substantive comments and recommendations from to be elaborated. I would ask you to please do the following:

  1. Download the draft outline,
  2. Edit and revise the title, outline, intended audience, and author list as you see fit,
  3. Indicate whether you are interested in contributing to any of these sections.

Please submit any revisions to this document by 10 September so that it can be prepared for the meeting in Chiang Mai.

Thanks, and best regards,
Eric

Saturday 18 July 2009

São Francisco River Basin Water and Poverty Relationship

The São Francisco River Basin of Brazil has been subject to multiple interventions to increase access to water for agricultural purposes. The analysis being conducted includes a comprehensive measurement of water availability and its impact on agricultural activities (and poverty) in the SFRB. It controls for variations in socio-economic and political variables. We use two types of measures of access to water, one traditional and one new, to try to identify the links (broadly defined) between water and poverty in the SFRB. Our analysis is done at município level (using 1990 boundaries, there are approximately 450 municípios that comprise the SFRB). We then take estimates of 2003 rural poverty, the only recent ones available for rural areas in Brazil, and have begun to examine interrelationships. Our results imply that overall explanatory power of water on poverty varies across rainfall regimes and between poverty and extreme poverty.

In particular, the results of this modeling effort seem to provide evidence that in areas with fewer natural constraints to large-scale agriculture production (areas with relatively more precipitation), the availability of water may play a more direct role in rural income generation. In areas with less rainfall, variabilty in precipitation may play a bigger role in determining income. The specific econometric results and the correlation between other factors and poverty rates will be analyzed in detail.

In general, the evidence we have on water access/ag poverty/links (distilled from this regression work and other research activities) suggests that where they do exist, the links between water and poverty may not be very strong, and will likely be ‘partial’ and increasingly ‘indirect.’ They will be partial in the sense that improving access to water may turn out to be a necessary condition for improving rural livelihoods in some areas and for some types of smallholders, but it is unlikely to be a sufficient condition for doing so. The links will be indirect in the sense that improving access to water for medium- and large-scale farming operations may help reduce rural poverty via on-farm employment generation, but this indirect link will vary quite substantially depending on product mix on these farms.

Specific outcomes and conclusions of this research are pending.

Monday 13 July 2009

Water-related poverty in the Nile basin

We gain a better understanding of the linkage between water and poverty through:-

1. Identifying key drivers

― water access conditions in agricultural systems (determinants, physical and economic)

― changing water conditions in agricultural systems (variability in water access, water productivity)

2. Understanding the conceptual framework

—characterizing poverty hotspots using household income and expenditure data. Poverty levels are higher in rural agricultural areas compared to urban areas due to income access. In the Nile basin, with the exception of Egypt, rural poverty accounts for 90 percent of total poverty and close to 80 percent of the poor depend on agriculture and farm labor as their main source of income and livelihood. Related to type 2 through disparities in safe drinking water and sanitation

—characterizing hotspots of the “water poor” meaning people who are deprived due to physical and economic water scarcity. Most of the “water poor” are found in the degraded and deforested areas of rangelands and in mixed rainfed systems that have a poorly developed water access infrastructure. Related to type2 where infrastructure determines allocation to users

—a dynamic livelihood system characterizing hotspots of biophysical and social vulnerability, given that a weak asset capital base indicates a lack of capacity to adapt to water stress as manifested through changing water conditions in agricultural systems. Areas with high vulnerability scores in rangeland and mixed rainfed systems are associated with low crop and livestock water productivity. Related to type, 4 & 5 where loss of livelihood emanates from low adaptive capacity

—a characterization of hotspots of water-related hazards (droughts, floods, diseases), highlighting areas of high exposure in marginal land such as floodplains, where there is a high risk of outbreaks of water-borne human and cattle diseases Related to type 3 due to poor ability to cope with impacts from hazards

The four concepts of water-related poverty and vulnerability

Concept 1, Measures of well-being

Deprivation from consumption estimated from lack of financial ability to meet social needs commonly defined as absolute poverty. There are three approaches for this estimate; poverty line, poverty gap and poverty inequality. Although there are other types of deprivation, we focus on two of these since they are more relevant for our analyses approach with the Nile basin data sets.

Absolute poverty being the income measure which is linked to goods and market forces that determine the allocation of goods and services to meet a minimum level of consumption. This is a good indicator for developed economies and appropriate for the case of Egypt since relationship is direct where financial deprivation leads to poverty.

Physiological poverty refers to the inability of people to meet basic needs such as food, clothing and shelter. This form of deprivation is widespread and serves as a better indicator in developing economies as is the case with most countries in the basin. We show examples of cases of northern Uganda where high poverty manifests both low and high physiological deprivation.

Hotspots of poverty in agricultural systems are a rural phenomenon. They occur in both areas of high potential for crop and livestock productivity and in rangeland systems. This is surprising and we are currently testing whether this phenomenon is related to market access. For instance, we compare poverty levels in the Kenya highlands with better market access to those found in the Ethiopian highlands having relatively poorer access to markets. Also we show that high poverty levels in rangelands are generally less variable but in some cases they may be related to environmental insecurity from water and pasture conflicts. In general our case studies suggest that education level as a proxy indicator for poverty significantly contributes to the differences we find in the prevalence of water-related poverty in the Nile basin

Concept 2, Poverty and access to water

In this approach, access is viewed as deprivation from consumption which is a function of both physical and economic factors. Together they constitute the “water poor”. In our work, we emphasize variability arising from water access due to the development of water infrastructure rather than total water availability, therefore excluding rainfall which is only weakly correlated with poverty

―measured as contribution of location of water sources, relative to the spatial demand of people and livestock in agricultural systems. For the Uganda country data set we use spatial variability in the location of water sources to explain differences in the poverty measures at the parish level. A number of water sources associated with high coefficients show unique positive as well as negative attributes in the overall model. This overrides the contribution of human and livestock population factors although we know these are major drivers of water use. (1)We provide more examples from regression analyses of poverty indicators and water access variables in Uganda and their implications for the entire basin

― by evaluating the role of water technologies and water policies in significantly influencing access to water for poor people in rural agricultural systems. There is little or no adoption of technology to adapt to changing water conditions, exposing poor people to water-related stress. (2)We provide examples from “cattle corridor” household survey of water-related poverty in 2 districts, Nakasongola (00 55N to 10 40N and longitude 31E 55E and 32 50E) and Kiruhura (00015” to 000 24” and longitude 0310 34” to 031 0 4)


Concept 3, Biophysical and social vulnerability

Vulnerability = f (sensitivity, adaptive capacity).

Sensitivity (S) and adaptive capacity (AC) [High S, Low AC = Vulnerable; Low S, High AC = Resilient]

The key point is to combine biophysical and social indicators with understanding of water-related hazards in concept 4. As opposed to poverty which is static, vulnerability is dynamic and is a characteristic of the sensitivity of the human and natural environment. In this starting point approach, poor people are sensitive to water stress since their livelihoods are dependent on rainfed agriculture. Their ability to adapt to changing water demands for agriculture is a function of how well endowed they are from natural, physical and social capital assets.

We determine vulnerability as the ability of people to deploy these assets in order to adapt to water stress while improving water productivity, given changing water quantity and quality conditions. (1) Biophysical vulnerability is assessed through scoring natural asset indicators such as water, land suitability and physical assets such as market access infrastructure. (2) Social vulnerability is assessed through scoring social asset indicators of human conditions such as agricultural labor as well as financial assets for investing in water technologies. A poor asset indicator (or index) score renders one vulnerable to impacts from variable water conditions in the agricultural systems. The linkage to poverty is in a livelihood system that modifies access to food through agricultural water management while accounting for risks associated with declining water resources, loss of pasture and cropland (or crop yields) in degraded areas. Other than rainfall, severe land degradation/deforestation and environmental insecurity are factors that cause variation in the availability agricultural water in the basin.

Concept 4, Poverty and water-related hazards

This concept is viewed as an end point assessment where poor people living on marginal land are exposed to water-related hazards. The most common events occurring in the Nile basin are associated with loss of livestock assets and crop failure from drought and flooding. A second category is in variability related to extreme events in the distribution of rainfall and temperature, for example prolonged intense dry spells and flooding events lead to crop failure and pasture loss in the rangelands as well as in floodplain regions of mixed rainfed systems. Also, extreme rainfall events may cause structural damage to infrastructure, disrupting agricultural production, transport and market functions particularly in mixed rainfed systems. The third category is water-related disease risk due to livestock and human exposure to poor quality agricultural water particularly where open water bodies are located as the main source of Water.

Outcomes of the 4 concepts

From concept 1 on poverty. Dependency on rainfed agriculture is a key driver of the high prevalence of rural poverty in agricultural systems

From concept 2 on water poverty. Poverty is exacerbated by low water access and poorly developed water infrastructure in agricultural systems

From concept 3 on vulnerability: Loss of crop and livestock productivity which occurs as a result of high exposure and low capacity to adapt to water stress under changing conditions of water access.

From concept 4 on water-related hazards: Loss of livelihoods which occurs as a result of high exposure and low ability to cope with impacts from water-related hazards

Friday 3 July 2009

The Five types of Water / Poverty in the Andes

Location, Magnitude, Causes.

The Andean region is a very diverse landscape. There are regions on which lack of water is a key characteristic such as in the western side of Peru and North Chile (Atacama Desert), on which there is virtually no rain. However, people who rely on agriculture for their livelihoods is located near main rivers from which some irrigation schemes are in place. How water is managed inside these is another issue. Absolute water scarcity is not the cause of poverty rather the way in which the one available is distributed and managed. Migrating population near those green oasis becomes marginalized since their access is restricted or limited to the existing conditions. Similar situation happens in many other locations in the Andes on which topography, infrastructure and organizations restrict access to water for easy use in farming. Even in the most humid locations is easy to find limited access to water for productive purposes. Access and rights to use water is a growing concern.

Plains and valleys are frequently affected by floods. Rainfall patterns in the highlands are uncertain and information systems and early alerts are limited to prevent these. Many people is affected every year by floods near the Atlantic coasts of Colombia, the Pacific coast of Ecuador and wetland areas in the eastern Andes on which human settlements are rapidly appearing. Risk and vulnerability schemes do work in the region and local resources are easily responding to these chronic problem. Type 3 of water - poverty does exist but in some degree society is prepared to cope with this.

Type 4 form, low water productivity, is restricted to isolated and very specific areas. In particular to those places on which infrastructure, services, access to markets and technology is also limited. This is not a extensive form in the Andes but improvements are feasible since many producers, while relying on the abundance of water do not take care of it polluting and spoiling it.

Land and water degradation is a silence but growing problem. This is causing rural displacement, changes in land use, migration to cities and empowering population. Causes are linked to the history of colonization patters. Extensive cattle ranching in the hillsides and previous agricultural areas are an example. The fight for local power control associated with competition for the best land results in social violence. Areas near big plantations or remote areas devote to ilegal crops are some examples of this type of problem.

In the suburban settlements lack of sanitation and water provision is a common problem. Main capital cities are plenty of unplanned neighbourhoods where poor people from rural and secondaty cities live. This increases the demand of water from the sources and povision areas of the highlands.

How to cope with each particular situation is what Andes BFP is trying to contribute to. Using the three selected subbasins we will address some of these issues without loosing the whole picture analyzing the region as a whole.

Manifestation of WP types in the Niger Basin

S. Cook suggested that finding examples of the five classes would help in their communication and refinement (see previous posts). This post details a few of our findings (some formal, some informal and subjective) regarding the manifestation of the five water poverty categories in the Niger Basin, West Africa. We are particularly interested in alignment between our examples and those presented by the Volta team, given the immediate proximity of these rivers (see previous post)

Where people are deprived of water for basic needs of consumption or sanitation as a result of water scarcity. (Insufficient assets to compensate for physical scarcity)

There is little correlation between physical availability of bulk water and poverty evident at a landscape level of statistical analysis, as is generally understood. Weak evidence exists in North West Nigeria and East Burkina Faso. Availability of protected water (for example, piped sources) however is often associated with poverty, not surprising given its health implications. This relationship is quite widespread. Particular incidences we found include near the east Nigeria/north Cameroon border, and north west Nigeria. A 1% decrease in the number of people accessing water from unprotected surface or well sources is associated with a 0.1 – 0.15% decrease in child mortality rates.

Where people lack equitable access to water. (Political environment & institutions that lead to inequitable access)

There’s a large disparity in the average time taken to access water which may represent differences due inequality. In some regions average time to reach the primary water source is < st="on">Niger). Whether this represents inequity depends on its definition (either as a social constraint or a physical resource scarcity issue also). Also, such geographic differentiation does not well describe inequality due to different social strata in one area. It is difficult to capture this variety of water poverty in landscape statistical models (given the complexity of representing institutions) however can be assessed using case studies.

Where people are vulnerable to water-related hazards such as floods, droughts or disease. (Physical variability & lack of assets to buffer against natural variability)

The Sahel region encompasses a large area of the Niger Basin and is particularly prone to drought. Impact of this risk on poverty is assessed by Hyman et al (2008) (see good discussion of this by the Volta team’s posting below). Parts of Sahel have seen a 30% decrease in rainfall in past 40 years – may return or may represent a new drier reality. This has had a dramatic impact on landscape of north Burkina Faso for instance, where brush described as ‘too dense to walk through’ has now considerably thinned (as shown in photo below). The combination of drought, grazing and cropping pressure has led to soil degradation (due to wind erosion) in some parts of this region also.


Brush in north Burkina Faso

Where people suffer loss of livelihood as a consequence of change.

An emerging issue is conflict between water uses, which have until now been largely avoided simply due to minimal development of the Niger’s water resources. Large dams provide water to those in a particular area who have legal rights to its provision. However, they potentially deprive downstream users. A proposed large dam in Guinea may reduce water flows across the Niger Delta, a large wetland that supports over 1 million people by the natural irrigation of rice. A decrease in flooding extent directly reduces the number of people who can be supported by this agro-ecosystem.



Niger Delta flood plain - a reduction in extent due to upstream water diversions will likely entail a loss of livelihood.





Thursday 2 July 2009

Using retrospective analysis to estimate the poverty alleviation impacts of change

I just re-read an interesting report from Sahal Darghouth and others of the World Bank about the poverty alleviation impacts of its agricultural water projects. Although it focuses strongly on blue agricultural water projects, the report makes an interesting observation – probably true of all similar projects – that ‘despite the likely poverty reduction impact….there was rarely a sense of agricultural water projects as part of a coherent poverty reduction strategy ’ (my italics).

A cause, the reports states, is the lack of clarity about agricultural water’s role in poverty reduction. The report proceeds to call for better analysis to understand the more complex (distributional) effects that can arise as water distribution and productivity changes.

My question is this: Are we making the opposite mistake? In addition to understanding the water-related causes of poverty, should we also be thinking about methods to predict the effects of ‘improvements’ (whatever we mean by this) in water distribution and productivity?

I do not want to distract teams from the excellent work they are doing of analyzing water-related poverty in basins. But I am wondering how to make the forward link with, for example, analysis that shows the impact of changes in water productivity. Who has ideas on a conceptual model for this?

Thursday 18 June 2009

5 class simplification of water-related poverty applied to the Volta basin

The concept seems useful, and at least applicable to the Volta basin. Here is a short presentation.



1. where people are deprived of water for basic needs of consumption or sanitation as a result of water scarcity.
• Insufficient assets to compensate for physical scarcity

50 % of rural population in Volta basin do not have access to good quality household water. More a result of economic (and institutional) scarcity than real physical scarcity, but with major impact on health. Has been mapped in Volta basin.


2. where people lack equitable access to water.
• Political environment & institutions that lead to inequitable access
Probably true locally for access to small scale irrigation. Especially for women.


3. where people are vulnerable to water-related hazards such as floods, droughts or disease.


Influence of dry spells and droughts in the Volta basin. Increased risk from south to north

Low rainfall in the season and dry spells are important risks for the rain fed farmers. The risk decreases from north to south.
Hyman et al. (2008) have developed a method to assess and map drought risk by estimating the probability of a failed growing season in many parts of the world. For the Volta basin this probability has been added to the description of the different systems of the basin in order to underline the effects of rainfall variability. The distribution of the crop systems is summarized below, according to the agroclimatic zones
• The Sahel, with less than 500 mm annual rainfall, is a zone of rangeland where livestock herding is the primary activity, complemented with some millet and drought resistant cowpea. The probability of a failed growing season is 53%.
• The Sahelo-Sudan, covering most of Burkina Faso and a small part of Mali, receives between 500 and 900 mm of rainfall per year. Millet and sorghum and maize are the main crops. The drought risk has been estimated as 24 %.
• The Sudan, receives between 900 and 1100 mm of rainfall per year. This is a transition zone where both cereals and root crops are produced. Maize production is increasing as a result of urban demand. Some transhumant cattle is present seasonally, and sedentary cattle is widespread. The probability of failed growing season is 17 %.
• The Guinean zone, covering the southern part of Ghana, receives in excess of 1100 mm of rainfall per year, with a bimodal regime toward the south of the basin. Yam, cassava and plantain, and also maize, are here the main food productions. The drought risk is only 8 %.
Hyman G., Fujisaka S., Jones P., Wood S., Carmen de Vicente M. & Dixon J., 2008.- Strategic approaches to targeting technology generation: Assessing the coincidence of poverty and drought-prone crop production. Agricultural Systems 98: 50-61

Influence of floods : downstream of hydroelectric reservoirs ( example of Bagré in BFA). Very high rainfall may also happen locally anywhere, with impact on crops and housings.
Influence of water related disease: widespread in the basin, with most severe impact on the living conditions of the poor. Malaria, diarrhoea, river blindness, bilharzia. Malaria prevalence has been mapped in the basin.


4. where people acquire insufficient benefit from water use. That is, low water productivity.

Low water prod because of lack of assets ( field area, tools, oxen, man power in the family) lack of access to credit, to fertilizers, to market, lack of access to dry season activities ( including small scale irrigation). Occurs mainly in the cereal part of the basin.


5. where people suffer loss of livelihood as a consequence of change.

Not really identified as such in the Volta basin

Monday 15 June 2009

Comments on the '5 classes of water-related poverty'

Comments from Akther, received by email

The note on “Water, food and poverty in river basins” is a useful synopsis of an extremely important but complex issue. Many thanks for this initiative. I have a few comments and suggestions.

• Since the title includes “food”, a couple of sentences could be added on the relationships between water and food, and poverty and food. In the present note the word “food” is not mentioned in the text.

• It seems to me that the note is written for those with relatively greater orientation in water-related issues than that of poverty or welfare issues. It would be helpful for the later group of audience if a little more clarification could be provided for some technical terminologies used in the note, such as water productivity (Type-4). Presumably the term refers to the productivity of agricultural output per unit of water, right? How is it measured? Is crop output or value of agricultural production the numerator? Is water availability (how measured) or cost of water/irrigation the denominator? By contrast, the discussion on water scarcity (Type-1) in the first paragraph is very clear. However, in the second paragraph, an explanation/definition of “ecosystem service” would be helpful for non-informed readers.

• Consider deleting the word “Inequitable” from the Type-2 sub-title. Secured access to water, particularly for irrigation, is one of the most important determinants of prosperity (or poverty reduction) for rural farm households in developing countries. However, the term “users” in this section refers to “sectors” such as agriculture, as opposed to individuals or households. Since poverty is measured at the household level, “access to water” should essentially mean “household access to water.” Here, it is worthwhile to raise the critical issue of landlessness and poverty. Landlessness is highly correlated with extreme poverty, particularly in Asia. For example, Bangladesh is one of the poorest countries in the world where more than half of all rural households are landless, living in a country with abundant water availability. Irrigation in Bangladesh is quite unique because the system mainly uses groundwater. Access to irrigation is a major determinant of agricultural productivity and hence reduced poverty in Bangladesh, but only a few landless benefit directly from irrigation through land tenure arrangements. To some extent this phenomenon holds in other South and South-East Asian countries as well. Could innovative policies/programs be designed to provide the landless with access to irrigation? Here, the implications of access to groundwater versus surface water could also be highlighted.

Another important issue that is missing in the note is gender. Women’s access to water is crucial for household welfare as women and girls are the main providers of water for domestic use, who often have to carry water from distant sources. The linkages between the role of women with respect to household water and sanitation, the burden of disease and malnutrition and hence poor health; the need for secured access to water for women for poverty reduction need to be recognized.

• In Type-3, the hazards caused by floods need to be highlighted. Devastating floods have been increasingly occurring in many countries— probably due to climate change—causing loss of lives and assets, mainly of the poor.

• References should be provided for several statements in the note.

Sunday 14 June 2009

Approach to WP1 in Niger

This is a long blog but we thought a synopsis of the Niger WP1 would facilitate some discussion. This also tackles some of the 5 items proposed by Eric. NB equitable access in the Niger was partially addressed by an analysis of ethnicity related poverty and we are hopeful the institutional research of the Niger Program will add to this. Overall the data was not longitudinal and hence temporal change was not analysed: accounting for change we see as a vital iteration. The fundamental research questions were to identify and spatially reference the three dimensions of poverty in the Niger Basin, identify where these were acute and with best available evidence and reliably estimate the relative importance of water related factors in explaining poverty. If water access or productivity was not a significant factor, we then sought to determine the significant non-water factors.

The four main questions we addressed were:

1) how to measure and spatially reference water related poverty?

2) Is scale important and if so what is an appropriate scale that captures the heterogeneity of poverty, identifies areas of critical need and the diversity of explanatory variables?

3) what is the appropriate, policy relevant scale that reveals opportunities for policy intervention and best targets the causal factors of water poverty?

4) how to address the subjective weighting of vulnerability or water poverty indices when expert opinion or participatory process are unreliable or not feasible?

The first question we tackled was how to measure poverty in the Niger Basin: ie metrics which account for a high proportion of subsistence livelihoods coupled with proportion of a non-market, hybrid economy. Hence many of the traditional monetised indicators may not be sufficiently precise to capture the magnitude of poverty and the geographic heterogeneity. Importantly, as an initial research assumption, we did not differentiate “water” poverty from any other type of poverty.

We selected the rate of child mortality (up to the age of 5), child morbidity (deviation of the ratio of height for age compared to a healthy median) and a composite wealth/asset index.

Child mortality is likely to be a function of a household’s ability to obtain essential services, nutrition and shelter. Secondly, it is, to some extent, more unidirectional than other measures: child mortality is caused by poverty, but does not in itself cause poverty to the same extent as do other socio-economic factors. Finally, child mortality provides a relatively direct method of quantifying water poverty, because poor water quality, caused by limited availability, limited access and poor infrastructure, is the direct cause of some of the most prevalent, fatal childhood diseases.

Child morbidity: There are many measures of child morbidity, however we utilise the ratio of height for age (stunting, measured as average height for age ratio (standard deviations below healthy reference median). This indicates long term, cumulative effects of inadequate nutrition and poor health, including that before birth. Height for age is thus relatively insensitive to short term seasonal variation in calorie intake, making it more appropriate when comparing data collected at different times.

Wealth Index: A wealth index, such as the DHS Wealth index was used and demonstrates the material standing of households. Wealth represents long term access to consumer and productive goods, indicating simultaneously the level of poverty and the capacity to earn a livelihood.

Metric selection was partly driven by literature based insights, a need to evaluate metrics that are a surrogate for the multi factorial nature of water related poverty and availability of reliable primary and ground-truthed data. The DHS data provided this. Hence all analysis quantified the effect of explanatory variables according to the 3 dimensions of poverty described below. We introduced and emphasised water access, water quality and water productivity variables, but did not introduce any a priori assumptions as to their relative importance in explaining poverty. We introduced many of the structural or non water variables that Jorge mentions to ensure a comprehensive vector of explanatory variables.

The 22 explanatory variables can be classified as:

Community vulnerability: the capabilities, assets and activities of the community. In the poverty model constructed here community vulnerability is represented by health variables, socio-economic variables, infrastructure and assets.

Situational vulnerability: how well these assets, capabilities and activities can withstand exposure to shock and stress. As this study focuses on water poverty in particular, situational vulnerability is represented here by the water variables.

Hazard threat: the events that challenge the community/situation. These are represented by natural disaster risk, climatic variables, population density and environmental damage.

Institutions, conflict and corruption can potentially effect all of the above vulnerabilities and threats.

Next we addressed the issue of appropriate scale of measurement. The common measures of water stress (per capita) measured at the national scale indicate that most of the Niger basin countries are not suffering water stress now or projected to 2025. Burkina Faso was the exception. Secondly the water stress or water poverty indices did not correlate with traditional national measures of poverty (HDI, social vulnerability index or genuine savings indicator). We hypothesised this was a matter of scale: i.e. national scale statistics are not sufficiently precise by compressing valuable poverty information. Hence we analysed poverty (by the 3 metrics) at basin scale, national scale and at the smallest administrative unit for each country that remained policy relevant where we had reliable data. There were 631 data points for the Niger countries. Data was interpolated and validated for those units with insufficient or low sampling points. Data was collected from a number of sources (available if required) and filtered to ensure GIS compatibility. We constructed GIS layers for analysis and to facilitate interpretation. The Null hypothesis was:

H0 :Water Poverty Basin = Water poverty national = water poverty hot spot

Water poverty represents a vector of the coefficients of the 22 explanatory variables of child mortality, child morbidity or the wealth index.

Next we deconstructed the construct of vulnerability or the equivalent of the water poverty index. Our primary motivation was to estimate evidence based coefficients of the factors of vulnerability rather than the subjective or expert determined weights commonly applied. That is we decomposed vulnerability or composite water index and, post analysis, reconstructed them with evidence based values.

Analysis utilised two spatial statistical techniques: geographic weighted regression (GWR) and a spatial lag model. We can further discuss if required. Essentially failure to account for spatial auto correlation biases traditional regression OLS models and reduces the reliability of results. In all cases spatial correlation was significant. In the spatial lag model, specific and validated spatial weightings were calculated for each poverty metric applied to each spatial scale. There was no one weighting that was valid across all spatial scales, metrics and regions. We selected from 1st to 4th nearest neighbour as the geographic weighting matrix. Only non-correlated Independent variables (variance inflation factor < style="">i, using data from neighbouring points. The extent to which proximate administrative units contributed to a particular local analysis ws dependent on the distance from i.

We were able to explain water related poverty for the basin with GWR at R2 of approx 0.89, with coefficients specific for each admin district. We reconstructed “water vulnerability” at the admin unit scale, revealing poverty heterogeneity across the Basin and the explanatory variables specific to each unit. These varied significantly. All results have been generated in map form to aid interpretation. We view this as an evidence based visual policy tool for poverty reduction in Basin sub regions. For example the question may be: with a given aid budget, how would the funds be prioritised and best distributed amongst a bundle of investment areas (education, water access, livestock densities, water quality, irrigation intensity, human footprint). With the analysis we can reliably estimate that for example a prioritised investment in education that resulted in a 1 year increase in schooling results in a 0.32 % reduction in child mortality for a specific sub region. In other areas prioritising water access has the greatest effect on reducing child morbidity or mortality.

We also used a spatial lag model that accounts for significant spatial auto correlation at the basin, national and hot spot scale. Hot spots were established as those admin units with high poverty and high spatial correlation, eg central Mali, Northern Nigeria and Eastern Burkina Faso. We rejected the null hypothesis and found that a different vector of explanatory variables were estimated for basin, national and hot spot analyses. This allows for a more and tailored response through policy intervention or priotised investment that targets high poverty areas.

There is some contradiction in the results when comparing the 3 poverty dimensions. Pragmatically we concentrated on those areas where the results were consistent for all 3 metrics or at least 2 of the 3. For GWR analysis we consider mortality and morbidity only as the wealth index cannot be compared internationally with confidence.

Below are some graphics and example applications we anticipate can be used as a participatory tool for future poverty analysis in the Niger Basin. These predictions are based on the GWR results.

A reduction in the proportion of people using surface water or unprotected well water represents a likely improvement in the quality of water used. Our model suggests that this approach could substantially alleviate child mortality in north west and central Nigeria, although other areas are affected only very slightly.

Education appears to be the most universally effective means of reducing poverty, with improvements in health predicted for much of Mali, north west Nigeria, east Burkina Faso and central Nigeria. In particular, the poverty hotspot around the Inner Niger Delta appears substantially alleviated by an additional 4 years in average education.

Also considered in this manner was both the predicted impacts from an increase in irrigation intensity and a decrease in the average distance to a dam. Neither approach led to a prediction of substantial reductions in child mortality and child morbidity (stunting). We haven’t shown these graphics because 1) they show little noticeable reduction in poverty and 2) to keep this file size to a minimum.

We would be happy to chat with anyone interested in our approach or anyone who has suggestions to make. Cheers, John Ward.


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Thursday 11 June 2009

Mappping out different classes of water related poverty in Basins

It might be useful to map out approximately how these different types of water-related poverty occur in basins. I did this on the back of an envelope a few days ago and it helped me organize my thoughts. The 5 types seemed to map out simply. Type 2 is quite widespread. Type 3 [vulnerability] is serious but restricted geographically. Type 4 [low water productivity] seemed very widespread.

This assessment was totally subjective. It could be improved further by reference to within-basin analysis. In this way, we'd have a more-or-less global scale assessment but with a reasonable connection to conditions in basin. I think people would find that very useful.

Comments?

Wednesday 10 June 2009

Where are the causes?

Hi Simon

I like very much the idea of understanding the linkages between W and P and the five cases cover the most of options. There are two points I want to put on the table when thinking in using such classification for the identification of interventions. The first deals with the causes of poverty. What is not clear yet, even the classification is the structural causes of poverty behind those scenarios. I think that the relationship of poor people and water status is a non intended consequence of more structural factors, the most rooted in historical reasons for the distribution and access to resources en general, to land in the particular case of agriculture. Poor people have been relegated by society to the corners of productive areas making them prone to lack of water, excess, extreme events, etc, but why they are poor is not precisely due to water in itself. If we want to make interventions or suggest the most appropriate interventions to reduce poverty, they wont be on modifiying water current condition rather the structural issues leading population to poverty.
The second point is when we want to map those five types in the space. All of them happend everywhere, at least in the Andes, in part due to the complex spatial variability. So figuring out where to implement interventoins that modify current conditions is a paramount task. Identification of particular cases have to go further in detail, below the basin and regional level we BFPs are addressing. I know these are arguable arguments but they are in part the trouble every time we think in potential interventions to reduce poverty associated to water.

I am redy to clarifications, discussion.

Jorge Rubiano

Sunday 7 June 2009

Equity

Minor point on the 5 species of water poverty-why is the word “equitable” needed in 2? I would think that just access covers it, at least in the first instance. If inequity in access to water leads to power differentials or something like that and then causes poverty, that is something else. But probably too far down the line for us?

Saturday 6 June 2009


Hi Simon,


This looks great. Based on some work we've been doing at SEI, I would like to embed these different "species" of water poverty within a livelihood framework. I think that in such a framework the inter-relationships between the different manifestations of water poverty become more apparent. The idea is that communities and households deploy their livelihood assets in order to buffer against variability in the physical, economic, and political environment. These translate into changes in their livelihood status as mediated by their own capabilities and the institutions in which they operate.


Looking at the 5 items, it seems to me that they can be related to this framework:



  1. where people are deprived of water for basic needs of consumption or sanitation as a result of water scarcity.
    • Insufficient assets to compensate for physical scarcity


  2. where people lack equitable access to water.
    • Political environment & institutions that lead to inequitable access


  3. where people are vulnerable to water-related hazards such as floods, droughts or disease.
    • Physical variability & lack of assets to buffer against natural variability


  4. where people acquire insufficient benefit from water use. That is, low water productivity.
    • Lack of assets: e.g., insufficient human capital, physical capital, or savings


  5. where people suffer loss of livelihood as a consequence of change.
    • And this is the basic concept behind the framework


So, I think these are related: Item 5 subsumes some of the others, and it really comes down to the interactions between: livelihood assets, the environment, and institutions.


For more details, I'm uploading a draft report (still incomplete) on some of the work we've been doing at SEI on this. We are planning to carry out some participatory exercises to test the ideas in the report in Thailand later this year.

Thursday 4 June 2009

Welcome to the blog

Here we start to share views and experience of analyzing poverty in 10 river basins: the Andes, Indo-Gangetic, Limpopo, Karkheh, Mekong, Niger, Nile, Sao Francisco, Volta and Yellow River.

This research is being undertaken by the Basin Focal Projects of the CPWF. For more information, please go to http://cpwfbfp.pbworks.com/. Other views and experiences are welcome