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

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.

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 1^st to 4^th 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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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?

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

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?

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.

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