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Unravelling the linkagesbetween the millenniumdevelopment goals for poverty,education, access to waterand household water use indeveloping countries: Evidencefrom MadagascarBruce Larson a , Bart Minten b & RamyRazafindralambo ca University of Connecticut , Storrs, USAb Cornell University , Antananarivo, Madagascarc Conservation International , Antananarivo,Madagascar , (Authors are listed in alphabetical order.Seniority of authorship is not assigned.)Published online: 24 Jan 2007.
To cite this article: Bruce Larson , Bart Minten & Ramy Razafindralambo (2006)Unravelling the linkages between the millennium development goals for poverty,education, access to water and household water use in developing countries:Evidence from Madagascar, The Journal of Development Studies, 42:1, 22-40, DOI:10.1080/00220380500356258
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Unravelling the Linkages between theMillennium Development Goals forPoverty, Education, Access to Water andHousehold Water Use in DevelopingCountries: Evidence from Madagascar
BRUCE LARSON*, BART MINTEN**, &RAMY RAZAFINDRALAMBO{
*UniversityofConnecticut, Storrs,USA, **Cornell University, Antananarivo, Madagascar, {Conservation
International, Antananarivo, Madagascar (Authors are listed in alphabetical order. Seniority of
authorship is not assigned.)
Final version received August 2005
ABSTRACT All members of the United Nations have pledged to meet eight MillenniumDevelopment Goals (MDGs) by the year 2015. This study looks at the MDG objectives andlinkages between poverty, education, access to water, and household water use based on primarydata collected in Madagascar. We find strong links between these MDGs. Better educated andhigher income households rely significantly more on private water supplies and use significantlymore water. Econometric results show that, for poorer households who rely on public sources,improving access to public water taps (by reducing the distance to such a water source) would notalter dramatically water use patterns. Improved access does free up a significant amount of timethat could contribute to poverty reduction. The willingness of households to pay for improvedaccess is very price sensitive, probably because of the liquidity constraints of these households.
I. Introduction
All member countries of the United Nations have pledged to meet the eight‘Millennium Development Goals’ (MDG) by the year 2015 as agreed upon at theUnited Nations Millennium Summit held in September 2000 (United Nations, 2004).For each goal, at least one target is identified along with indicators for monitoringprogress. In developing countries in general and sub-Saharan Africa in particular,fundamental changes are required. Substantial income growth is needed to achievepoverty and hunger reduction targets (MDG one, targets one and two). Primary
Correspondence Address: Bruce Larson, Department of Agricultural and Resource Economics, Unit 4021,
University of Connecticut, Storrs, CT 06268, USA. Tel: þ1-860-486-1923;
Email: [email protected]
Journal of Development Studies,Vol. 42, No. 1, 22–40, January 2006
ISSN 0022-0388 Print/1743-9140 Online/06/042022-19 ª 2006 Taylor & Francis
DOI: 10.1080/00220380500356258
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school attendance must increase to achieve the target for primary education(MDG two, target three). Household ‘access’ to clean water must improve to achievethe targets for access to safe drinking water (MDG seven, target 10), where access isdefined as sustainable availability of water (20 litres per day per capita) from animproved source (household connection, public tap, borehole, protected dug well,protected spring, rainwater collection) within one kilometre of the dwelling (WorldBank, 2004a: 268; WHO/UNICEF, 2000; United Nations Development Group,2003).
Achieving the poverty, education, and drinking water targets to meet theMillennium Development Goals are not independent endeavours. Reducing povertyand improving education will alter household choices related to water access. Yet forwater, access is only an intermediate target, while expanding actual water use byhouseholds is the intended result. By increasing quantities of potable water used byhouseholds, for example, by expanding uses for hygienic purposes, reductions inunder-five mortality rates might be achieved (MDG four, target five). To date, littleempirical analysis exists identifying the complementarities among these MDGtargets. If governments want to achieve these MDG targets, insights on suchcomplementarities are important to prioritise actions and to help in the design ofpolicies to achieve the stated targets.
The objective of this paper is to begin to study empirically these MDGcomplementarities for the case of Madagascar. Regarding poverty, the countryremains one of the poorer countries on the planet. Per-capita income was estimatedat $260 in 1998, which ranks the country 193 out of 210 countries (World Bank,2000: 230). As of 1999, 71 per cent of the total population was living under thenational poverty line, 49 per cent of the population lived below a standardinternational poverty measure of $1-per-day in purchasing power parity terms, while83 per cent of the country earned less than $2-per-day (World Bank, 2004b: 55). Lifeexpectancy at birth as of 1997 was 56 years for males and 59 for female (WorldBank, 2000: 232), and these numbers have fallen slightly since (World HealthOrganization, 2004: 115).
Regarding education, the primary school completion rate fell from 34 per cent in1990 to 26 per cent in 2001 (World Bank, 2003: 254). Regarding health, estimatessuggest that 70 per cent of all endemic illnesses in Madagascar are waterborne, andthe lack of access to clean water and poor sanitation explain in large part whydiarrhoea-related diseases are a major source of illnesses in the country(Rabemanambola, 1997). In the 1997 Demographic and Health Survey forMadagascar (Macro International Inc., 1998), over 30 per cent of all children aged6–23 months were reported to have had diarrhoea and over 35 per cent of thesechildren had fevers in the two weeks prior to the survey.
Regarding water access, recent estimates suggest that over 85 per cent of the urbanpopulation already has ‘access’ to an improved water source as defined by the MDGtarget for water access (WHO/UNICEF, 2000), while only 31 per cent of the ruralpopulation has such access. Other studies suggest that access is substantially less(Razafindralambo, 2001) although definitions of ‘access’ continue to vary acrossstudies (for further discussion, see Rosen and Vincent, 1999).
Using household survey data for the city of Fianarantsoa, we explore empiricallythe links between poverty, education, water access, household choice of drinking
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water supply, and actual levels of water use. Given that investments in improvedwater services will need to be self-financed to some degree in most developingcountries, we also investigate the links between poverty, education, and householdwillingness to pay for service improvements.
Our results suggest a strong link between income, education, water supply choice,and household water use. Better educated and less poor households, primarilythrough their housing decisions, rely relatively more on private connections (forsimilar evidence in the Philippines, see Persson, 2002). The water use differencesbetween collecting households and households with private connections are striking:around 2,300 litres per month for an average collecting household (about 14.5 litresper day per capita) compared to 14,600 litres per month for an average householdwith a private connection (88 litres per capita per day). Households with a privateconnection use more water in part because they use more for human consumptionbut most importantly because they use more for hygienic purposes (bathing, washingdishes, and washing clothes, and so forth).
To investigate price effects and willingness to pay for water service improve-ments, a contingent valuation scenario was formulated in the survey. A largenumber of water-related valuation surveys have already been conducted indeveloping countries (for example, Whittington et al., 1990; Whittington et al.,1989; World Bank, 1993; Acharya and Barbier, 2002). Whittington (1998) providesclear guidance for applications in developing countries, and these recommenda-tions remain essentially consistent with the literature not focused on developingcountries (see, for example, Hanemann, 1989; Mitchell and Carson, 1989; Arrowet al., 1993). Because the water company in Fianarantsoa was not in a position toexpand private connections, the contingent valuation analysis in this paper focusedon households who collect water and the scenario focused on improvements to thesystem of public taps in the city. Using the dichotomous choice data, we show thelarge price and income effects on willingness to pay for improved access to publictaps. Water price changes at public taps for cost recovery purposes will thereforeneed to be designed with care to be able to meet the MDG on access withadequate household water use.
The paper is organised as follows. Section II describes the survey methods andsample design used to gather the data for this analysis. Section II also provides basicsummary information on households sampled and their water supply situation.Section III develops and estimates two Heckman selection models of existinghousehold water demand: one for households collecting water and one forhouseholds with a private connection. While Section III focuses on existing wateruse, Section IV investigates for collecting households the willingness to pay forimproved water services. We finish with conclusions in Section V.
II. Data
Fianarantsoa is the capital of the province with the same name about 250 kilometressouth of the capital city, Antananarivo. The city has around 100,000 inhabitants.The province of Fianarantsoa is considered one of the poorest in Madagascar.Razafindravonona et al. (2001) show that access to basic services, such as electricity,sanitation, as well as potable water – while still being low – has improved over the
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last decade. Most of the inhabitants of the city rely on public taps or on rivers andlakes for their water use.
To begin to develop the survey, focus group discussions on water issues wereorganised in Fianarantsoa in October 2000. These discussions helped to identifyhousehold water concerns and to develop a first draft of the questionnaire. Thisfirst draft was then pre-tested on about 30 households. The survey instrumentwas revised following the pre-test. The face-to-face survey was conducted duringNovember 2000 by four enumerators. The average length of the interview was 45minutes. All focus group discussions were held in Malagasy. The surveyinstrument from pre-test to final version was developed directly in Malagasyalong with a French translation.
A two-stage sampling strategy was implemented as follows. To begin, the watercompany has the city of Fianarantsoa divided into 50 neighbourhoods (‘quartiers’).The primary sampling unit is the neighbourhood, and a household within eachneighbourhood is the primary element. While the water company has installed someprivate connections in all 50 neighbourhoods in the past, 44 neighbourhoods havepublic taps and six do not. In the sample, all six neighbourhoods without public tapswere included in the sample and then 13 of the remaining 44 neighbourhoods wererandomly selected for inclusion. So, at this first sampling stage, six neighbourhoodshad a probability of one of being in the sample compared to a probability of 13/44¼ 0.2955 for the remaining 13 neighbourhoods.
At the second stage, based on a census of households in each of the includedneighbourhoods, 10 per cent of households in each neighbourhood were randomlyselected for inclusion. Based on this sampling rate, the target sample size was 570households. Due to the above sampling strategy, households in the six neighbour-hoods without public taps were sampled with a probability of 1*0.1¼ 0.1, whilehouseholds in the other 13 neighbourhoods were included with a probability of0.2955*0.1¼ 0.02955. The econometric analysis presented in Sections IV and V useappropriate probability weights to adjust for the unequal sampling probabilities.
The survey instrument consisted of three parts. A first section dealt with thehousehold’s water use and attitudes and perceptions about water quality andservices. A second section was organised around a scenario and a contingentvaluation question for improved water service for households without privateconnections. The third section asked for information on the respondent’s educationlevel, income levels, family composition and other socio-economic variables. All dataobtained in the survey are based on self-reported information by the respondent.
While the target sample size was 570 households, 547 households agreed toparticipate in the survey, leading to a 95 per cent response rate. This high responserate is consistent with Deaton (1998), who notes that ‘non-response is typically muchless of a problem in LDCs than in the United States (US)’. Among these 547households, 355 households collect their water from public taps, wells, or naturalsources, while 192 obtain water from a private connection in their home.
Descriptive statistics by water source are presented in Table 1. The reported samplemeans describe the data set; they are not intended to be estimates for the city. Collectinghouseholds surveyed clearly report using substantially less water in their home (2,293litres per month) than households with private connections (14,692). For both groups,average household size is roughly the same (5.38 and 5.55 respectively). As a result,
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Table
1.Descriptivestatistics(unweighteddata)
Variable
notation
Collectinghouseholds
n¼355
Householdswithprivate
connectionsn¼192
Variable
description
Unit
Mean
Standard
deviation
Mean
Standard
deviation
use
Monthly
household
wateruse
(litres)
Number
2293
1326
14652
10623
edu0
Headdid
notattendschool
Yes¼1,No¼0
0.031
0.174
0.016
0.124
edu1
Headattended
primary
school
Yes¼1,No¼0
0.259
0.439
0.031
0.174
edu2
Headattended
secondary
school(firstcycle)
Yes¼1,No¼0
0.406
0.492
0.214
0.411
edu3
Headattended
secondary
school(secondcycle)
Yes¼1,No¼0
0.239
0.427
0.427
0.496
edu4
Headattended
university
Yes¼1,No¼0
0.065
0.247
0.313
0.465
income1
Householdsincome5
$18
Yes¼1,No¼0
0.217
0.413
0.000
0.000
income2
Household
income$55–$18per
month
Yes¼1,No¼0
0.423
0.495
0.094
0.292
income3
Household
income$91–$55per
month
Yes¼1,No¼0
0.211
0.409
0.203
0.403
income4
Household
income$127–$91per
month
Yes¼1,No¼0
0.085
0.279
0.318
0.467
income5
Household
income4
$127per
month
Yes¼1,No¼0
0.065
0.247
0.385
0.488
hhsize
Household
size
Number
5.38
2.71
5.55
2.40
timert
Roundtrip
walkingtimeto
watersource
Number
7.03
21.85
**
**
waitavg
Averagewaitingtimeatthesource
Number
10.49
14.01
**
**
improve
Household
improves
quality
before
consuming
Yes¼1,No¼0
0.847
0.361
0.974
0.160
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assuming a rough average of 5.5 people per household and 30 days amonth, water use inthe home is only 14 litres on average per capita per day. Thus, average water use in thehome per capita for collecting households is substantially less than theWHOminimumrecommendation of 20 litres a day per capita, while households with private connectionsuse substantially more water than 20 litres a day per capita. When water is difficult toobtain or is expensive, the poor often cut back on hygienic uses (Bosch et al., 2000). Wenote here that, in urban areas at least,many householdmembers spend some substantialportion of the day outside the home while working, looking for work, trading, andattending school. Water is clearly obtained and consumed to some degree during theseactivities. Thus, water use in the home does not measure precisely daily waterconsumption. For households with private connections, daily water use is almost 89litres on average per capita per day.
Education levels of household heads are reported to be fairly high, althougheducation levels for collecting households are substantially lower than for house-holds with private connections. For example, while 29 per cent of household headsattended primary school or less for collection households (the mean of edu0þ edu1in Table 1), only 4.7 per cent of household heads attended primary school or less forhouseholds with private connections. At the higher end of the education spectrum(edu4), 6.5 per cent of household heads in collecting households attended some levelof university while the same figure was over 30 per cent in collecting households.
Income information was obtained for five income categories ranging fromhouseholds with income lower than 100,000 Fmg/month (income15 $18 per month)to an upper range of more than 700,000 Fmg/month (income54 $127 per month).Recognising that all of these income levels are low, income levels for collectinghouseholds are substantially lower than for households with private connections.For example, almost 64 per cent of collecting households report monthly incomesless than $55 per month (income 1 or income 2) compared to only 9.4 per cent ofhouseholds with connections while only 15 per cent of collecting households reportincome levels above $91 per month (income 4 or income 5) compared to over 70 percent of households with connections.
Collecting households travel on average seven minutes roundtrip (3.5 minutes oneway) to collect water. Over 80 per cent of all households travel on average less thanten minutes, and 99 per cent of all collecting households report a round trip traveltime of 20 minutes or less. On flat terrain, a reasonable estimate is that it takes anadult about ten minutes walking at a moderate pace to cover one kilometre (thedistance part of the MDG target for water access). As a result, almost all of theseurban households collect water within one kilometre of their home and most collectfrom substantially closer distances.
The vast majority of all households report that they ‘improve’ water quality beforeconsuming it through, for example, boiling water, using filters, and using waterpurification tablets or solutions. While not shown in Table 1, we note here that 85per cent of all collecting households report improving quality while 97 per cent ofhouseholds with private connections report improving quality. Although we do nothave objective data to compare water quality across water sources, the highereducation and income levels of households with private connections probablyexplains why they reported ‘improving’ water quality relatively more than collectinghouseholds.
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III. An Econometric Analysis of Household Water Demand
The Basic Econometric Model
Conceptually, household water demand is derived from some underlying householddecision making process. A model of a household as a joint production andconsumption unit provides a logical framework for developing insights in waterdemand (see, for example, Singh, Squire, and Strauss, 1986; Pearce and Turner 1990;and Berhman and Deolalikar, 1998). The end result of a household model (see, forexample, Acharya and Barbier, 2002) is that a reduced-form water demand functionfor a household, conditional on water source (water supply technology), can bemodelled simply as:
yi ¼ jðpi; xi; ziÞ;
where yi is water consumed at home by household i, pi represents a vector of marketprices for all goods and services, zi¼ 1 if the household’s water source is a privateconnection at their home and 0 if the household collects water from outside thehome, and xi represents a vector of all exogenous variables affecting householdwelfare including household specific factors such as household size and communityor location specific factors such as distance to water source (as measured by traveltime) and quality of water available.
With cross-sectional data from one city, the price vector pi is the same for allhouseholds. Without variation in market prices, the above reduced-form waterdemand model becomes:
yi ¼ jðzi; xiÞ:
To estimate this water demand function for households collecting water andhouseholds using water from their private connection, a Heckman (1976) selectionmodel is estimated to account for the fact that households in Fianarantsoa self-selecttheir water supply technology implicitly through their housing decisions. The basicidea is that households choose their water supply (housing with a private connectionor housing without a private connection that requires collecting from someother location); and then given their water source, they choose how much water touse/collect.
For example, for households collecting water, zi¼ 1, the selection equation for aHeckman model can be formulated as:
zi� ¼ c0wi þ ui;
zi ¼ 1 if zi� > 0;
zi ¼ 0 if zi�40;
Probðzi ¼ 1Þ ¼ Fðc0wiÞ;Probðzi ¼ 0Þ ¼ 1� Fðc0wiÞ;
where zi¼ 1 if household i collects water and zi¼ 0 for non-collecting households(that is, households with private connections), F(�) is a normal cumulative
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distribution function, wi is a vector of variables linked to the choice of water supply,c is a vector of parameters to be estimated, and ui is an error term. While the factorsdriving household choice of water supply are likely to vary across countries andcultures (see, for example, Madanat and Hummplick 1993), Table 1 suggests thathousehold income and education levels are important. The above selection equationis estimated for the total sample (n¼ 547).
Based on the above selection model, the regression model used to estimatehousehold water use for collecting household i is:
yi ¼ b0xi þ ei; only if zi ¼ 1;
where yi is monthly household water collected for use in the home (litres), xi arevariables explaining water quantity collected, such as household size, b areparameters to be estimated, and ei is an error. This regression model is estimatedonly using the data for households collecting water (nc¼ 355).
For households obtaining their water from a private connection (that is tap intheir home), the basic structure of the selection model remains the same except thatnow zi¼ 1 denotes a household with a private connection in the selection equation,and the regression model for household water use is estimated using just theobservation for households with private connections (np¼ 192).
For each set of households (collecting households or households with privateconnections), results are presented for three alternative estimation strategies of aHeckman model: version (i) is estimated with robust standard errors; version (ii) isestimated using weights to account for the probability-weighted random sample asexplained in Section II (which also provides robust standard error estimates); andversion (iii) is the weighted model with the further assumption of non-independenceof households in the same neighbourhood (that is, clustered data). Weightingobservations by the reciprocal of the sampling probabilities is needed for unbiasedparameter estimates (see, for example, Deaton, 1998). As Deaton (1998) explains,two-stage sampling may lead to non-independence of households in the sameneighbourhood even if the sample does not have an explicit cluster design. In suchcases, ignoring the possible within neighbourhood correlation among errors mayyield estimates of standard errors that are substantially too small. Thus, estimationstrategy (iii) follows a weighted approach corrected for the cluster sample. Allmodels were estimated using full maximum likelihood methods and the Huber/White/sandwich estimator of variance.
Results for Households Collecting their Water
Table 2 presents Heckman results for households collecting water. For each versionof the estimated model, the selection model parameter estimate results are reportedunder the heading ‘COLLECT’, and the regression model parameter estimate resultsfor water use are reported under the heading (‘USE’). For all three estimatedversions of the model, the estimated z-statistic for atanh(r) and the Wald test ofindependent equations indicate that the Heckman selection equation is statisticallyjustified with these data. All estimated parameters are significant at least at the 5-percent level except for ‘waiting time at source’ for all three versions of the model.
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For these data, the parameter estimate differences between version (i) and version(ii) for both the selection and water use equations are fairly minor. Qualitatively theyyield similar results. As expected, when comparing version (ii) and version (iii),clustering increases somewhat standard error estimates so that the estimatedz-statistics tend to be somewhat lower for most of the parameter estimates. However,as reported for version (iii), accounting for clustered data still yields parameterestimates that are significantly different from zero at the 5 per cent level for allvariables except ‘waiting time at source’. Version (iii) of the model, which accountsfor both clustered errors and unequal sampling probabilities, provides con-ceptually the most appropriate estimates of parameters and standard errors. For
Table 2. Heckman model results for collecting households*
Version (ii) Version (iii)Version (i)
Robust standarderrors
Probabilityweighted robuststandard errors
Probability weightedclustering by
neighbourhood
Observations 547 547 547Censored obs. 192 192 192Uncensored obs. 355 355 355Wald w2(3) 36.32 31.22 23.00Prob4 w2
Coefficient z-statistic Coefficient z-statistic Coefficient z-statistic
USEhousehold size 158.79 5.69 170.07 5.01 170.07 3.77roundtrip time to 74.84 72.14 74.41 72.76 74.41 72.81Source (minutes)
waiting time atsource
2.47 0.49 1.37 0.26 1.37 0.17
Constant 1077.34 7.06 1138.27 6.30 1138.27 5.32
COLLECTedu2 70.47 72.67 70.44 72.38 70.44 72.03edu3 70.82 74.25 70.77 73.86 70.77 73.92edu4 71.26 75.67 71.13 74.88 71.13 73.80income3 70.72 74.29 70.61 73.44 70.61 73.12income4 71.30 75.77 71.20 74.68 71.20 73.39income5 71.55 76.51 71.43 75.15 71.43 73.93Constant 1.74 8.31 1.79 7.35 1.79 6.60
Athrho 1.01 5.35 1.07 4.65 1.07 3.82Lnsigma 7.17 87.93 7.21 78.46 7.21 93.48
Wald test w2(1) 28.63 21.65 14.57Prob4w2 0.0000 0.0000 0.0000 0.0001
*All models estimated using full information maximum likelihood using the Heckman routinein STATA 6.0 (with robust, probability weight, and cluster options as appropriate). Thez-statistic is distributed standard normal (1.96 is the critical value at the 5 per cent level; 2.58 isthe critical value at the 1 per cent level). Athrho is the estimate of the inverse hyperbolictangent of �, the correlation among the errors in the selection equation and the regressionequation. Lnsigma is the estimate of ln(�), where � is the standard error of the water useequation. The Wald test is for independent selection and regression equations.
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interpretation, the coefficient estimates for version (iii), which are identical to version(ii), are used in the following discussion.
Consider first the selection equation estimated as a function of education andincome levels. All parameter estimates for education and income levels aresignificant and of a reasonable sign; that is, more educated and higher incomehouseholds are less likely to collect water (and therefore are more likely to obtainwater from a private connection in their home). In the selection equation, theconstant¼ 1.79 is a ‘z’ score for a standard normal variable for the base case (ahousehold with the lowest income level and lowest education level). The individualparameter estimates for the selection equation in Table 2 then indicate the change inthis z-score relative to the base.1
Rather than discussing z scores directly, Table 3 reports the basic probabilities ofbeing a collecting household for households given their income and education status.For reference, Part A of Table 3 reports the estimated probabilities, while Part B ofTable 3 reports the change in the estimated probabilities relative to the base case.For example in Part A, for households with the lowest income and education levels(the base case), the probability of being a collecting household is 0.96. At the otherextreme, for households with the highest income and education levels (income5and edu4), the probability of being a collecting household falls to 0.22. The changein collecting probability for these high income and highly educated householdsas compared to the base is 0.2270.96¼70.74, which is reported in Part B ofTable 3.
The importance of both higher income and more education are clearly indicated inTable 3. When compared to the base, modest increases in education levels (to edu2)to achieve the MDG education target induces modest reductions in collectingprobabilities (from 0.96 to 0.91). Modest increases in both income and educationlevels reduce the probability of being a collecting household by 19 per cent.
Returning now to the household water use regression equation, the parameterestimates in Table 2, organised under the heading ‘USE’, clearly suggest that theparameter estimates for household size and distance are significantly different fromzero for collecting households. As would be expected, larger households collect and
Table 3. Selection probabilities by education and income levels (Collecting households)
Base Income3 Income4 Income5
Part A. Estimated probabilities
Base 0.96 0.88 0.72 0.64edu2 0.91 0.77 0.56 0.47edu3 0.85 0.66 0.43 0.34edu4 0.75 0.52 0.29 0.22
Part B. Change in probability estimates from base
Base 0.00 70.08 70.24 70.32edu2 70.05 70.19 70.40 70.50edu3 70.12 70.30 70.53 70.62edu4 70.22 70.44 70.67 70.74
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use more water than smaller households, while roundtrip collecting times, whichmeasure in part the shadow price of water for collecting households, reduces theamount of water collected as expected. While travel time matters, waiting time at thesource (defined as an average wait during the dry and wet seasons), is not significant.As a practical matter, carrying buckets of water is difficult work so how far you haveto carry water matters, while waiting in a queue takes time, but is not especiallydifficult.
For interpretation, the water use regression model reported in Table 2 suggests thathousehold water use for collecting households is unresponsive to travel time. Forexample, using the parameter estimates from Table 2, an average sized collectinghousehold (5.38 people from Table 1) with an average travel time (seven minutesround trip) collects about 1,898 litres per month. In this calculation it is also assumedthat waiting time is zero since its coefficient is not statistically significant. With 5.38people on average in a household and 30 days per month, this figures converts to11.76 litres of collected water per day. The same sized household with a 17 minuteround trip is estimated to collect only slightly less (1,849 litres or 11.46 litres per day).Reducing the travel time round trip to only two minutes for the average householdonly increases water use to 1,922 litres per month (11.91 litres per day).
The main implication here is that water use per collecting household in this urbanarea is fairly unresponsive to time (distance) to source. Thus, improving access towater sources for collecting households in terms of distance conveys clear economicbenefits to households in terms of collecting time saved. Closer access does notobviously lead to direct health benefits to households through substantially changedquantities collected.
The water use regression model can easily be used to measure the value tohouseholds from reducing travel times. For example, an average sizedhousehold with a ten- minute round trip collects 1,883 litres per month, whilethe same household with a two-minute round trip collects 1,922 litres per month.While the value of closer access (ten minutes to two minutes round trip)can be obtained by integrating the area under the demand schedule betweenten and two minutes, which given the linear functional form is simply(107 2)*1883þ 0.5(107 2)(19227 1883)¼ 15,064þ 1,600¼ 16,664 where theunits can be expressed as quantity per trip times minutes per month. If weassume that quantity collected per trip (qt) remains constant with the fall incollection time, the total time saved per month can be calculated as 16,664/qt.For example, if 30 litres are collected on average per trip, 16,664/30¼ 555minutes per month (9.25 hours per month) are saved by the household. Ifhouseholds begin to carry more water per trip due to shorter distances, thequantity of time saved per month would be even larger.
The implications of these findings for the development of a dense network ofpublic standpipes in urban areas need to be considered further if the city hopes tohave households, especially the poorer households, use more than minimallysufficient levels of safe water in their homes. With current conditions at collectionsites (for example, travel times, quality and so forth), closer access definitelyimproves household welfare (calculated above in terms of time saved per month) andthe quantity of water collected by households. Household welfare is improvedbecause time can be allocated to other uses, such as school lessons for girls,
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additional time for child care for mothers, and potentially additional time can beallocated to income generation, looking for work, or leisure.
At the same time, collecting households, even with close access to water, collect farless than 20 litres per day per capita. Thus, improving access through a densenetwork of public taps to achieve the MDG target for water access yields clearwelfare improvements in terms of time saved, but density alone is unlikely tostimulate household collection of water to levels that achieve the MDG for wateraccess and perhaps more importantly support substantially higher levels of water usefor private and social welfare improvements.
Results for Households with Private Connections
Table 4 presents Heckman results for households obtaining water from a privatehome connection. Because edu¼ 0 for almost all households with privateconnections (see Table 1), it was necessary to create a dummy variable edu0þedu1¼ ‘less than completing primary school’ to estimate the selection equation.Thus, the coefficients for edu2, edu3, and edu4 indicate the impacts of achieving theMDG target for primary education (edu2) and beyond. Also in the selectionequation, the lowest two income categories, which are very poor households basedon reported income levels, are also combined to become the excluded incomedummy.
For each version of the estimated model, the selection model parameter estimateresults are reported under the heading ‘PRIVATE’, and the regression modelparameter estimate results for water use are reported under the heading (‘USE’).While the selection model is comparable to the model for collecting households, awater quality variable (based on the household’s opinion about water quality) isincluded as an explanatory variable in the regression model instead of travel andwaiting time.
For all three estimated versions of the model, the estimated z-statistic for atanh(r)and the Wald test of independent equations indicate that the Heckman selectionequation is statistically justified with these data. Also for all three versions of themodel, all estimated parameters are significant at least at the 5 per cent level exceptfor ‘waiting time at source’ in the regression equation and edu2 in the selection(significant at the 10 per cent level).
For these data, the parameter estimate differences between version (i) and version(ii) for the selection equation are fairly minor, although the estimates for theconstant and the parameter for household size are substantially different in the wateruse regression. All versions of the model yield qualitatively similar results. Again asexpected, when comparing version (ii) and version (iii), clustering increasessomewhat standard error estimates for most parameters, but not all, so that theestimated z-statistics tend to be somewhat lower for most of the parameter estimates.However, as reported for version (iii), accounting for clustered data still yieldsparameter estimates in the selection equation that are significantly different fromzero at the 5 per cent level for all variables except edu2, as with the case for collectinghouseholds. Version (iii) of the model, which accounts for both clustered errors andunequal sampling probabilities, provides conceptually the most appropriateestimates of parameters and standard errors. For interpretation, the coefficient
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estimates for version (iii), which are identical to version (ii), are used in the followingdiscussion.
As with Table 3 for collecting households, Table 5 reports the predicted pro-babilities of being a household with a private connection depending on income andeducation levels (Part A) as well as the change in these probabilities compared to thebase (Part B). Not surprisingly, the results are essentially the reverse of Part A forTable 3; the probability of being a household with a private connection is only 0.02for the base case (low income and little education), while the probability of being ahousehold with a private connection increases to 0.80 for households with thehighest education and income levels.
The water use regression model for households with private connections clearlyshows that household water use is substantially higher for households with private
Table 4. Heckman model results for households with private connections*
Version (ii) Version (iii)Version (i)
Robust standarderrors
Probabilityweighted robuststandard errors
Probability weightedclustering by
neighbourhood
Observations 547 547 547Censored obs. 355 355 355Uncensored obs. 192 192 192Wald w2(2) 13.82 13.95 13.72Prob4 w2 0.001 0.0009 0.0011
Coefficient z-statistic Coefficient z-statistic Coefficient z-statistic
USEhousehold size 1201.45 3.71 1008.22 3.69 1008.22 3.63Need to improvequality beforeconsuming
7248.54 70.06 74271.34 70.73 74271.34 70.69
Constant 9878.29 2.31 15654.24 2.51 15654.24 2.33
PRIVATEedu2 0.51 2.21 0.57 2.31 0.57 1.94edu3 0.83 3.46 0.86 3.41 0.86 3.23edu4 1.25 4.61 1.17 4.20 1.17 3.49income3 0.87 4.85 0.81 4.34 0.81 4.45income4 1.53 7.55 1.46 6.76 1.46 5.91income5 1.92 9.63 1.87 8.67 1.87 8.18Constant 71.96 79.53 72.13 79.73 72.13 77.03
Athrho 70.24 72.68 70.34 73.92 70.34 73.52Lnsigma 9.23 74.73 9.29 56.81 9.29 54.60
Wald test w2(1) 7.2 15.38 12.4Prob4 w2 0.0073 0.0001 0.0004
*All models estimated using full information maximum likelihood using STATA 6.0. Thez-statistic is distributed standard normal (1.96 is the critical value at the 5 per cent level; 2.58 isthe critical value at the 1 per cent level). Athrho is the estimate of the inverse hyperbolictangent of �, the correlation among the errors in the selection equation and the regressionequation. Lnsigma is the estimate of ln(�), where � is the standard error of the water useequation. The Wald test is for independent selection and regression equations.
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connections as compared to households who collect water. Simulations show thatfor an average sized household (5.55 for households with private connections)monthly water use is estimated to be 16,425 litres, which translates into 99 litres ofwater per capita per day. In contrast to collecting households, households withprivate connections use more water in part because they use more for humanconsumption, but most importantly because they use more water for hygienicpurposes (bathing, washing dishes, and washing clothes).
IV. Household Demand for Improved Water Services
To begin to investigate the household demand for improved water services (see Aigaand Umenai, 2002; Briscoe et al., 1990; Whittington et al., 1990; Whittington et al.,1989), the survey included a contingent valuation section. Given the obviousdistinction between households with private connections and households collec-ting water, a scenario for ‘improved’ service was developed for collecting households.The scenario developed followed from focus group discussions and pre-testing inthe city.
The scenario, while presented more completely to households in Malagasy, can besummarised as follows. For collecting households, the service improvement wasdefined as access to a clean public tap that is: (1) located nearby (less than oneminute walking); (2) accessible 24 hours per day each day of the year; (3) involveslittle waiting time; and (4) provides potable water (safe to drink without boiling).
After explaining the scenario, the payment vehicle was outlined and the contingentvaluation question was asked. A simple dichotomous choice format was used. If the bidwas accepted, a follow-up question was asked to determine how the household wouldpay for these expenditures. Based on focus group discussions, pre-testing, andinformation on existing payment rates, ten bid levels ranging from one to 10 Fmg perlitre were used and were randomly distributed among the households interviewed.
For reference, the probability that a household said ‘yes’ to the bid, denoted asyi¼ 1, was estimated as a logit model (see, for example, Maddala, 1986):
Table 5. Selection probabilities by education and income levels (Private connection)
Base Income3 Income4 Income5
Part A. Estimated probabilities
Base 0.02 0.09 0.25 0.40edu2 0.06 0.23 0.46 0.62edu3 0.10 0.32 0.58 0.73edu4 0.17 0.44 0.69 0.82
Part B. Change in probability estimates from base
Base 0.00 0.08 0.23 0.38edu2 0.04 0.21 0.44 0.60edu3 0.09 0.31 0.56 0.71edu4 0.15 0.42 0.67 0.80
Pðyi ¼ 1Þ ¼ expðx0ibÞ=½1þ expðx0ibÞ�
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where xi is a vector of explanatory variables for each household i, including 1 as thefirst element, and b are parameters to be estimated.
As reported in Table 6, a short and a long model were estimated. The short modelwas estimated as a simple function of an intercept and natural log of the bid level.For the long model, the x vector included the log of the bid level (lnbid), roundtriptravel time to the existing source (timert), average waiting time (waitavg), andincome and education dummies (income2-income5, edu1-edu4). The omitteddummies are edu0 (no primary education of household head) and income1 (thepoorest income category).
For both models, the coefficient on ln(bid) is negative and significant at the 1 percent level. Using the short model, median willingness to pay is estimated to be 4.4Fmg/litre (about $2 per month per household), an important consideration forpolicies related to cost recovery and price liberalisation. As shown in Figure 1,willingness to pay for the service improvement is responsive to price. For example, aprice increase from 4.4 Fmgs/litre to 8 Fmgs/litre would reduce the percentage ofhouseholds willing to pay for the improvement from 50 per cent to about 25 per cent.
Table 6. Logit results for collecting households’ wtp for the service improvement (Maximumlikelihood estimates using probability weights and standard errors corrected for clustering by
neighbourhood)
Short modelNumber of observations 319Wald w2(1) 38.6Prob4 w2 0Log likelihood 7177.46
Variable Estimated coefficient z-statistic
Lnbid 71.825 76.213constant 2.712 7.063
Long modelNumber of observations 319Wald w2(11) 3385.58Prob 4 w2 0Log likelihood 7166.22
Variable Estimated coefficient z-statistic
lnbid 71.923 76.357timert 0.002 0.653hhsize 70.049 70.605waitavg 70.014 71.239Income2 0.375 1.076Income3 0.781 1.47Income4 1.430 2.316Income5 2.188 2.299edu1 0.706 0.652edu2 0.524 0.589edu3 0.941 0.961edu4 0.219 0.15constant 1.978 1.899
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If existing household demand water, given their existing access situation, is similarlyprice responsive, simply increasing water tariffs without access improvements – asplanned under the new Water Law (GOM, 2000a and 200b) – will have to bedesigned with extreme care to be able to achieve the MDG on water use.
Results for the long model presented in Table 6 show further the importance ofprice on willingness to pay for the service improvement. Besides the coefficients forlnbid and the highest income category (income5), which is significantly differentfrom zero at the 5 per cent level at least, the coefficients for other explanatoryvariables are not significant. This result essentially confirms the result presented inFigure 1 that price clearly drives willingness to pay, and only the highest incomehouseholds are willing to pay more than other households.
V. Conclusions
Expanding access to safe drinking water, reducing poverty, and increasing primaryschool education are three ‘Millennium Development Goals’ (MDGs) developed atthe UN Millennium Summit. While these Millennium Development Goals are notindependent endeavours, little empirical analysis exists identifying the complemen-tarities among these MDGs.
To begin to explore these complementarities, this paper uses household surveydata for the city of Fianarantsoa, Madagascar to estimate the links between poverty,education, household choice of drinking water supply, and actual levels of water use.Poverty and education clearly influence household water supply technologies, whichin turn affects the quantity of water used by households. Better educated and richerhouseholds rely relatively more on private connections. The water use differencesbetween collecting households and households with private connections are striking:around 2,300 litres per month on average for collecting households as comparedto 14,600 litres per month on average for households with a private connection intheir home. Households with a private connection use more water than collectinghouseholds in part because they use more for direct consumption, but mostimportantly because they use more for hygienic purposes.
Figure 1. Willingness to pay specific bid level (FMG per litre) – logit model results
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Regarding the current structure of demand, Heckman selection model resultsshow that income and education levels are important determinants of a household’swater supply/choice situation. Higher income and more educated households aresignificantly more likely to have a private connection than poorer and less educatedhouseholds. Conditional on being a collecting household, water collection issignificantly related to household size and travel time to source.
Using the results of a contingent valuation question related to service improve-ments for collecting households, a large price effect on willingness to pay for suchimprovements is shown. Hence, the results suggest that (not) achieving one MDGwill contribute significantly to the achievements of (failure for) the other goals. Basedon this and additional studies on water demand, future analysis needs to focus onevaluating the social impacts of alternative water pricing arrangements to sup-port cost-recovery in the supply of water services while working to achieve theMDG of expanded access to safe drinking water, complementarily with theother MDG.
Acknowledgement
This paper is based on a portion of our project report titled ‘Water pricing, the newWater Law, and the poor: An estimation of demand for improved water services inMadagascar’, Report S12, dated February 2002, from a USAID-ILO sponsoredprogramme managed by Cornell University. The Cornell programme was financedby USAID under a cooperative agreement ‘Improved economic analysis for decisionmaking in Madagascar’ (No. 687-00-00-00093-00). The surveys developed for thisstudy were financed under the environmental economics training programme of theUSAID-funded PAGE project (Projet d’Appui en Gestion Environnementale) ofIRG (International Resource Group). Special thanks go to Andy Keck and PhilipDecosse who managed this programme. The authors would further like to thankSolohery Rakotovao, Francis Andrianarison, Josiane Rarivoarivelomanana, AlainLocussol, Alex McPhail as well as participants at workshops in Antananarivo andFianarantsoa and two anonymous referees for useful comments and suggestions.The authors remain solely responsible for content.
Note
1. In some situations, it is possible that additional explanatory variables will need to be included in the
selection equation. For this Fianarantsoa study, however, additional explanatory variables related to,
for example, opinions about water quality (for example, taste, odour, colour) are not significant.
Opinions of quality are likely to be correlated with income and education; that is, higher income
households may be more likely to have higher quality expectations.
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