Where stated WTP simply attempts to ascertain a respondent’s pre- disposition to the water service scenario presented, maximum WTP
attempts to extract the value of that service for those who are will- ing to pay. For WTP elicited using a bidding game or payment card method, values actually represent an interval where the elicited values represent a minimum corresponding to that interval217
. 217
To make this clear, consider the case where a person accepts the originally proposed value. Bids are then incremented until the respondent rejects a bid, at which point the highest bid that was accepted is recorded. The true value can lie anywhere between the last accepted bid and the rejected bid. It can be easily shown that we get a similar result if the first bid is rejected.
Cameron and Huppert[1989] propose a maximum likelihood inter- val regression approach that reflects the fact that the true value lies somewhere in this interval, and this approach is less prone to bias than ordinary least squares (OLS) estimation based on the midpoint of the interval218
. This approach has its own assumptions in regard
218
T.A. Cameron and D.D. Huppert. OLS versus ML estimation of non- market resource values with payment card interval data. Journal of Environ- mental Economics and Management,17 (3):230–246,1989
to the distribution of values over this interval, however, and we are interested in providing a conservative estimate of value based on WTP, so we use the minimums corresponding to each interval, in which case OLS would be expected to provide unbiased estimates of the minimum.
In determining the factors that explain maximum WTP, we con- sider the same set of variables as when determining factors that explain stated WTP219
. Here, we restricted analyses to respon- 219
One slight modification we make is removing the variable corresponding to interview length. This was included in models for stated WTP to account for some respondents potentially stating unwillingness to pay because they were impatient for the interview to end.
dents who had stated WTP, and the response variable used was log-transformed maximum WTP. Effects for the model incorporat- ing reported numeric incomes are provided in Table32. Key results are presented in Box8and are largely in line with what we would expect. Households where the head of household has a higher level of education may understand the value of piped water and, con- sequently, be able to pay more220
. At the same time, those who 220
There may be a bit of an income effect here as well, as higher levels of education tend to correlate with higher income. Education is not strictly a proxy for income, though, hence the need to include it in this model.
understand the scenario better may recognise the full set of benefits from the presented scenario and, consequently, be willing to pay more. The income effect is also significantly positive, so we see that WTP is correlated with capacity to pay. The significance of effects corresponding to water points is likely representing an income ef- fect more than anything, as these effects simply show that those with household connections report the highest WTP.
Level of understanding of scenario: • Those reporting a better level of understanding of the scenario are willing to pay more.
Education level of head of household: • Households where the head of household has a higher level of education report increasingly higher and signifi- cant WTP.
Primary water point: • Maximum WTP is significantly higher for users of household connections than most all other primary water point users.
Income: • As income increases, so does maximum WTP.
Perceived change in water access: • Households that believe that there has been a signifi- cant increase in water quality in their neighbourhood or do not know report higher maximum WTP.
Box8: Key results for linear regression of log-transformed maximum will- ingness to pay on relevant variables, including numeric income.
m e a s u r i n g t h e va l u e o f p i p e d wat e r t o h o u s e h o l d s 99
Estimate Std. Error z-value Pr(>|z|) (Intercept) 3.4984 0.8843 3.96 0.0001 Female respondent -0.0855 0.1512 -0.57 0.5723 Age of respondent -0.0008 0.0057 -0.13 0.8958 Household size 0.0283 0.0299 0.95 0.3455 Level of understanding of scenario: (Reference: Very well)
Well -0.2688 0.1304 -2.06 0.0406∗∗ Town/City: (Reference: Nampula)
Liúpo -0.0763 0.5262 -0.14 0.8849 Ribáuè 0.1936 0.3253 0.60 0.5523 Education level of head of household: (Reference: None)
Primary of1stdegree 0.5222 0.3242 1.61 0.1088 Primary of2nddegree 0.5852 0.3414 1.71 0.0880∗ Secondary of1stdegree 0.7098 0.3460 2.05 0.0415∗∗ Secondary of2nddegree 1.1126 0.3394 3.28 0.0012∗∗∗ Higher level 1.4438 0.4868 2.97 0.0034∗∗∗ Do not know 0.8566 0.5331 1.61 0.1096 Primary water point: (Reference: Household connection)
Yard tap -0.9021 0.4373 -2.06 0.0404∗∗ Standpipe -0.9771 0.5383 -1.82 0.0710∗
Borehole -0.9446 0.5105 -1.85 0.0657∗ Unprotected well -1.1927 0.5361 -2.22 0.0272∗∗ River, stream, lake -0.9935 0.6534 -1.52 0.1300
Neighbour’s tap -0.2971 0.6099 -0.49 0.6267 Hours operational -0.0104 0.0094 -1.11 0.2675 Time to collect water 0.0772 0.1238 0.62 0.5335 Good water quality -0.1720 0.1465 -1.17 0.2419 Household has access to sufficient water 0.1943 0.1471 1.32 0.1879 Incidence of diarrhoea 0.0131 0.1769 0.07 0.9408 Household treats water -0.0988 0.1380 -0.72 0.4750
Household income 0.1529 0.0557 2.75 0.0066∗∗∗ Perceived change in access to water: (Reference: Decreased)
Increased 0.6451 0.2613 2.47 0.0144∗∗ Neither decreased nor increased 0.2773 0.2556 1.08 0.2792
Do not know 0.9213 0.4005 2.30 0.0224∗∗ Perceived change in quality of water: (Reference: Decreased)
Increased -0.0032 0.3460 -0.01 0.9926 Neither decreased nor increased 0.0374 0.3160 0.12 0.9060 Do not know -0.4011 0.4315 -0.93 0.3537 Liúpo:Time to collect water -0.0863 0.1820 -0.47 0.6359 Ribáuè:Time to collect water -0.0796 0.1309 -0.61 0.5436
Note: ∗p<0.1;∗∗p<0.05;∗∗∗p<0.01
Table32: Linear regression of log- transformed maximum willingness to pay on relevant variables, including numeric income.
Again replicating this linear regression model for maximum WTP but replacing numeric income with proxies for income, we obtain the results presented in Table33. These results are again largely consistent with that of the model incorporating numeric in- come, and proxies for income that are significant all have a positive relationship with maximum WTP. Key results are presented in Box 9.
Sex of respondent: • Women report significantly lower maximum WTP than men.
Level of understanding of scenario: • Those reporting a better level of understanding of the scenario are willing to pay more.
Education level of head of household: • Households where the head of household has a higher level of education report increasingly higher and signifi- cant maximum WTP.
Primary water point: • Maximum WTP is significantly higher for users of household connections than most all other primary water point users.
Occupation of head of household: • Households where the head of household is in a man- agerial position report higher maximum WTP than households where the head of household is in other professions.
Household pays for water: • Households that pay for water report significantly higher actual WTP.
Household has electricity: • Households that are connected to the electrical grid report significantly higher maximum WTP.
Time to collect water: • In Liúpo, households that spend more time collecting water tend to report lower maximum WTP.
Box9: Key results for linear regression of log-transformed maximum will- ingness to pay on relevant variables, including proxies for income.
Validity of Maximum Willingness to Pay Values
Gunatilake et al.[2007] provide a small set of simple checks to as- sess the validity of results for a WTP instrument. These again tend to be in the context of competing options, so not all are applicable. Those applicable to our situation are those related to income, inci- dence of water borne diseases, and education level, which should all be positively associated with maximum WTP. In both of our models this is the case except in terms of incidence of water borne diseases (specifically diarrhoea), which is not statistically signifi- cant. This should not be cause for concern in that, when reported incidence of diarrhoea is low (as it was in the case of our survey), it
m e a s u r i n g t h e va l u e o f p i p e d wat e r t o h o u s e h o l d s 101
Estimate Std. Error z-value Pr(>|z|) (Intercept) 4.8189 0.4302 11.20 0.0000 Female respondent -0.2101 0.0802 -2.62 0.0090∗∗∗
Age of respondent 0.0053 0.0033 1.59 0.1115 Household size -0.0016 0.0168 -0.10 0.9237 Level of understanding of scenario: (Reference: Very well)
Well -0.1672 0.0695 -2.41 0.0164∗∗ Neither well nor poorly 0.2833 0.6286 0.45 0.6524 Town/City: (Reference: Nampula)
Liúpo 0.1181 0.3063 0.39 0.6999 Ribáuè -0.0387 0.1675 -0.23 0.8176 Education level of head of household: (Reference: None)
Primary of1stdegree 0.1136 0.1815 0.63 0.5316 Primary of2nddegree 0.3776 0.1924 1.96 0.0502∗ Secondary of1stdegree 0.3979 0.1876 2.12 0.0343∗∗ Secondary of2nddegree 0.4991 0.1862 2.68 0.0075∗∗∗ Higher level 0.8700 0.2647 3.29 0.0011∗∗∗ Do not know 0.1859 0.2363 0.79 0.4317 Primary water point: (Reference: Household connection)
Yard tap -0.7435 0.2408 -3.09 0.0021∗∗∗ Standpipe -0.6293 0.2948 -2.13 0.0332∗∗
Borehole -0.7831 0.2861 -2.74 0.0064∗∗∗ Unprotected well -0.7528 0.2940 -2.56 0.0107∗∗
Protected spring -1.1695 0.5913 -1.98 0.0483∗∗ River, stream, lake -0.6714 0.3712 -1.81 0.0709∗
Neighbour’s tap -0.3474 0.3040 -1.14 0.2536 Hours operational -0.0033 0.0054 -0.62 0.5377 Time to collect water 0.0773 0.0563 1.37 0.1705 Good water quality 0.0208 0.0796 0.26 0.7938 Household has access to sufficient water 0.0898 0.0940 0.95 0.3399 Incidence of diarrhoea 0.1012 0.1048 0.97 0.3346 Household treats water -0.0130 0.0740 -0.18 0.8602 Perceived change in access to water: (Reference: Decreased)
Increased 0.1869 0.1205 1.55 0.1213 Neither decreased nor increased 0.0144 0.1142 0.13 0.8994 Do not know 0.2500 0.1729 1.45 0.1487 Perceived change in quality of water: (Reference: Decreased)
Increased 0.1326 0.1801 0.74 0.4618 Neither decreased nor increased 0.2021 0.1575 1.28 0.1999 Do not know -0.3636 0.2063 -1.76 0.0785∗ Education level of head of household: (Reference: None)
Professionals -0.3277 0.1754 -1.87 0.0621∗ Technicians -0.1826 0.1801 -1.01 0.3112 Clerical support 0.1188 0.3109 0.38 0.7024 Services, sales -0.4652 0.1868 -2.49 0.0130∗∗ Agriculture, forestry, fisheries -0.5043 0.1827 -2.76 0.0059∗∗∗
Craft and related trade -0.5759 0.1848 -3.12 0.0019∗∗∗ Plant/machine operators -0.1193 0.2646 -0.45 0.6521 Elementary occupations -0.2155 0.2031 -1.06 0.2890 Armed forces -0.9453 0.3955 -2.39 0.0171∗∗ Unemployed -0.4547 0.1968 -2.31 0.0211∗∗ Student -0.9142 0.2942 -3.11 0.0020∗∗∗ Homemaker -0.6256 0.2342 -2.67 0.0077∗∗∗ Benefits/pension -0.8288 0.2946 -2.81 0.0051∗∗∗ Other -0.7173 0.2930 -2.45 0.0146∗∗ Household pays for water 0.2977 0.1124 2.65 0.0083∗∗∗
Household has electricity 0.3446 0.0987 3.49 0.0005∗∗∗ Liúpo:Time to collect water -0.1839 0.0981 -1.88 0.0611∗∗ Ribáuè:Time to collect water -0.0613 0.0613 -1.00 0.3178
Note: ∗p<0.1;∗∗p<0.05;∗∗∗p<0.01
Table33: Linear regression of log- transformed maximum willingness to pay on relevant variables, including proxies for income.
is difficult to obtain a significant effect221
. If we examine the coeffi- 221
Admiraal and Doepel[2014] provide further explanation of this.
cient for this variable, although not significant, it is positive, in line with what would be expected.
Importantly,Griffin et al.[1995],Cameron et al.[2002],Bhatia and Fox-Rushby[2003] andGunatilake et al.[2007] all provide evidence to suggest that actual behaviour closely reflects what is predicted by CV methods in the context of water supply for properly constructed CV scenarios222
. In other words, maximum
222
C.C. Griffin, J. Briscoe, B. Singh, R. Ramasubban, and R. Bhatia. Con- tingent valuation and actual behavior: Predicting connections to new water systems in the state of Kerala, India.
The World Bank Economic Review,9(3): 373–395,1995; T. A. Cameron, G.L. Poe, R.G. Ethier, and W.D. Schulze. Al- ternative non-market value-elicitation methods: Are the underlying prefer- ences the same? Journal of Environ- mental Economics and Management,44 (3):391–425,2002; M.R. Bhatia and J.A. Fox-Rushby. Validity of willingness to pay: Hypothetical versus actual pay- ment. Applied Economics Letters,10(12): 737–740,2003; and H. Gunatilake, J.C. Yang, S. Pattanayak, and K.A. Choe.
Good Practices for Estimating Reliable Willingness-to-Pay Values in the Water Supply and Sanitation Sector. Asian Development Bank, Economic and Research Department Technical Note No.23,2007
WTP, a value based on a hypothetical scenario, should correspond quite closely to what households would pay in reality. Although we are unable to check the validity of reported WTP rigorously in our case, a basic check with NAMWASH baseline data from2012can provide a sense of whether these claims are reasonable.
Projected Actual Value (2012) (2014) ≥300MZN 0.00% 1.56% ≥200MZN 2.09% 2.49% ≥100MZN 8.38% 4.36% Table34: Projected percentages of households in Ribáuè that would pay at least specified monthly amounts for a yard tap, as estimated from the 2012NAMWASH baseline survey, along with the estimated percentages of households paying at least those monthly amounts for a yard tap in November2014.
Table34provides the percentage of households in Ribáuè in 2012who reported being willing to pay at least a specified monthly amount. This total was based on the71.83% of households who stated a yard tap as their first or second preference for water sup- ply, a total close to the66.80% of households in Ribáuè who stated WTP for a yard tap in2014. Next to these totals are the correspond- ing percentages of households from the subpopulation of house- holds in Ribáuè who are willing to pay and who use yard taps and reported monthly water costs in the specified range in Ribáuè in 2014223. We observe higher percentages paying above200MZN or
223
This subsetting is necessary for a fair comparison, as WTP values from 2012are reported only for households who specify a yard tap as a first or second choice. Those who do not fall into this group would be significantly more likely to be unwilling to pay.
300MZN per month than what would be expected based on2012 data. At the same time, we also observe lower percentages paying above100MZN.
It might be argued that this would suggest a small percentage of wealthy households who will pay exorbitant amounts for water, leading to greater percentages of households paying at least200 MZN per month than what would be expected based on2012data. At the same time, however, in general maximum WTP overesti- mates actual behaviour, as only roughly half of the percentage of households we would expect to have a yard tap at a particular price point actually do.
This argument fails to take into consideration the fact that per- centages presented for2012reflect what would be expected if all households in Ribáuè were offered a yard tap at the given price. As noted previously, four of the neighbourhoods sampled did not have access to the distribution network at the time of fieldwork224
, 224
Adjusting for this would minimally increase percentages for2014by an estimated22.45%.
and yard taps are not pushed out to all households at once, as ev- idenced by the continued steady uptake of yard taps. Accounting
for the increase in yard taps since November2014225along with the 225
This was estimated at more than 60%.
lack of availability of yard taps in certain neighbourhoods produces an adjusted estimate of8.55% of households having a yard tap and paying at least100MZN per month. Thus, at present (and ignoring the fact that the continued demand for yard taps would push this percentage significantly higher), WTP totals from2012are almost certainly conservative relative to what has been observed in terms of actual payments for2014.
m e a s u r i n g t h e va l u e o f p i p e d wat e r t o h o u s e h o l d s 103
This is important because WTP totals from2014are actually quite similar to those reported for2012despite following a more rigorous means of elicitation226
. Figure36shows cumulative per- 226
The2012NAMWASH baseline survey was not meant to be a WTP study, so it did not follow the bulk of the recommendations ofWedgwood and Sansom[2003] andGunatilake et al.[2007]. It also used a different means of eliciting maximum WTP, opting for an open-ended question.
centages of people being willing to pay particular amounts for service from a yard tap, as estimated from the surveys carried out in2012and2014227. The cumulative percentages are remarkably
227
The2014totals were categorised to match those presented in the2012 survey.
close, and a Wilcoxon-signed rank test fails to find a significant dif- ference in terms of the two distributions (p-value =0.4515). This means that, just as we would expect WTP totals from2012to be conservative, we would likewise expect WTP totals from2014to be conservative.
Figure36: A comparison of household maximum willingness to pay as recorded in September2012and November2014.
To further validate the reliability of maximum WTP across Nam- pula, Ribáuè, and Liúpo, we present mean and median maximum WTP for the three locations in Table35. As WTP values are highly positively skewed, we also present geometric means of maximum WTP, along with corresponding (non-symmetric)95% confidence intervals. These WTP values show a relationship much in line with what we would expect with maximum WTP highest in Nampula and lowest in Liúpo. These differences may partially be attributed to the length of time that piped water has been available in each of these three locations with those in Nampula understanding well both the convenience and higher cost of piped water and, conse- quently, being willing to pay more. Almost certainly, though, the primary reason for these differences is disparities in household
income, which are highlighted in Table22.
Town/City Mean Median Geometric Mean
Nampula 302.45MZN 155MZN 173.62(158.31,190.41) MZN Ribáuè 191.46MZN 100MZN 120.36(110.66,130.91) MZN Liúpo 101.09MZN 70MZN 74.20(67.06,82.11) MZN
Table35: Mean and median maximum willingness to pay by town/city, along with geometric means and corre- sponding95% confidence intervals, as estimated in November2014.
Table36provides comparisons of both incomes and maximum WTP for each of the locations, and it does so in terms of both means and medians. For example, the mean income reported in Ribáuè is1.26times larger than that of Liúpo, and the median max- imum WTP reported in Nampula is1.55time larger than that of Ribáuè. If there was some form of systematic bias in terms of re- ported maximum WTP across these three locations, then this might be expected to appear in terms of clear departures from observed relationships in terms of the incomes (which should represent ca- pacity to pay) for these locations. Here, we note that the difference in mean maximum WTP between Nampula and Ribáuè is smaller than the ratio of mean incomes for the two locations, potentially suggesting systematic bias where households in Ribáuè report higher WTP than what they would actually pay228
. At the same 228
Conversely, it could reflect house- holds stating lower WTP than what they would actually pay. A third and more plausible explanation would be that there is no bias, as maximum WTP may be linearly related with income only to a point, at which it would begin to plateau.
time, though, this relationship changes when considering median income and median maximum WTP, which would be consistent with exactly the opposite bias. This would suggest that there is no consensus in terms of bias in these reports, so differences in max- imum WTP in Nampula and Ribáuè could very well be consistent with the observed differences in income. The same relationship holds when comparing Nampula with Liúpo. When comparing Ribaúè and Liúpo, the difference in mean maximum WTP is ac- tually more substantial than the differences in mean income229
. 229
An explanation for this could be that, as households in Ribáuè had exposure to piped water, they potentially understood not only the true cost but also the greater convenience and value, so were wiling to pay more. Households in Liúpo did not have this exposure.
However, we get the reverse relationship when comparing medians, again suggesting that the relationships between maximum WTP for the two towns could be consistent with the observed differences in income. Thus, we do not have clear evidence of systematic bias in terms of maximum WTP (when comparing with reported incomes) across the three locations.
Finally, there is the question of whether reported maximum WTP exceeds what households would reasonably pay for water. It has long been claimed that, as long as water costs fall below5% of household income, then water is affordable230
. However, this rule 230
SeeFankhauser and Tepic[2007] for one such example of this claim.
has been called into question withWang et al.[2010] noting that this threshold may depend on the particular country or even par-