3.3. MODELO TEÓRICO DE LA PROPUESTA: “CLIMA INSTITUCIONAL PARA
3.4.6. DESCRIPCIÓN METODOLÓGICA DE LAS ACTIVIDADES PROGRAMADAS
When it comes to stated willingness to pay, there is not much vari- ability across Nampula, Ribáuè and Liùpo with68.50% (64.56%, 72.44%) of households in Nampula stating WTP,66.80% (62.65%, 70.96%) in Ribáuè, and64.73% (58.47%,70.99%) in Liúpo. The over- whelming reason why households said they were unwilling to pay was that they felt they could not afford the service (percentages of 54.71%,79.88%, and66.28% for Nampula, Ribáuè, and Liúpo, re- spectively). Other reasons presented included that they felt that the government should cover the cost/subsidise the service (18.24%, 9.47%, and20.93%, respectively) or the service was not worth the cost (21.18%,5.33%, and10.47%, respectively).
It is also important to note that a major driving force for stated unwillingness to pay is reticence in terms of supplying any infor- mation related to willingness or capacity to pay. A cross-section of the population may exercise a heightened level of caution when confronted with questions which they believe could potentially be used to determine the pricing of water or other services, and we note that this could be the case here, as there was a significant re- lationship between respondents stating that they were unwilling to pay for the presented water service scenario and also being unwill- ing to provide their income (chi-square test p-value<0.001). Those saying that they were unwilling to pay for the presented water ser- vice scenario were more than two times as likely to be unwilling to report income data than what would be expected.
Table30provides output from a logistic regression model of stated WTP on the previously mentioned set of variables, using numeric income. Some of the key results are presented in Box6. These results are largely in line with what we might expect. Larger households would be expected to have a greater demand for a yard tap because they experience a more significant burden associated with collecting water. Those using unprotected wells are near the
Estimate Std. Error z-value Pr(>|z|) (Intercept) -2.5083 2.9309 -0.86 0.3921 Interview length 0.0437 0.0233 1.87 0.0610∗ Female respondent 0.1685 0.4770 0.35 0.7239 Age of respondent -0.0259 0.0153 -1.70 0.0895∗ Household size 0.2962 0.1145 2.59 0.0097∗∗∗ Level of understanding of scenario: (Reference: Very well)
Well 1.5255 0.4177 3.65 0.0003∗∗∗ Town/City: (Reference: Nampula)
Liúpo 1.6849 1.9359 0.87 0.3841 Ribáuè 2.3800 1.1543 2.06 0.0392∗∗ Education level of head of household: (Reference: None)
Primary of1stdegree 0.4192 0.8310 0.50 0.6140 Primary of2nddegree 1.3448 1.0296 1.31 0.1915 Secondary of1stdegree 0.0578 0.8991 0.06 0.9487 Secondary of2nddegree 0.8821 0.9085 0.97 0.3316 Higher level 0.2182 1.2896 0.17 0.8656 Do not know -0.1653 1.4506 -0.11 0.9093 Primary water point: (Reference: Household connection)
Yard tap -0.5992 1.1259 -0.53 0.5946 Standpipe 1.4971 1.6663 0.90 0.3690 Borehole 2.4735 1.6143 1.53 0.1255 Unprotected well 3.7341 1.7985 2.08 0.0379∗∗ Neighbour’s tap 0.0295 1.7854 0.02 0.9868 Hours operational -0.1028 0.0334 -3.08 0.0021∗∗ Time to collect water -0.3956 0.4064 -0.97 0.3303
Good water quality -0.1431 0.4912 -0.29 0.7709 Household has access to sufficient water -0.3652 0.4859 -0.75 0.4522 Incidence of diarrhoea 1.0091 0.7577 1.33 0.1829 Household treats water 0.8984 0.4755 1.89 0.0589∗
Household income 0.2175 0.1594 1.36 0.1725 Perceived change in access to water: (Reference: Decreased)
Increased 0.9872 0.9459 1.04 0.2967 Neither decreased nor increased -0.6083 0.8270 -0.74 0.4620 Do not know -0.7242 1.1854 -0.61 0.5412 Perceived change in quality of water: (Reference: Decreased)
Increased 0.3282 1.4931 0.22 0.8260 Neither decreased nor increased -0.5267 1.3656 -0.39 0.6998 Do not know -0.4522 1.5194 -0.30 0.7660 Liúpo:Time to collect water -0.1146 0.5941 -0.19 0.8470 Ribáuè:Time to collect water -0.4931 0.4035 -1.22 0.2217
Note: ∗p<0.1;∗∗p<0.05;∗∗∗p<0.01
Table30: Logistic regression of stated willingness to pay on relevant vari- ables, 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 95
bottom of the ladder in regard to water point usage, so they would likely exercise the highest level of demand for any form of im- proved water point. Indeed, we note that, even though effects are not significant, coefficients become increasingly positive when mov- ing from standpipes to unprotected wells, suggesting increasing demand for a yard tap for those with less improved water points215
.
215
Coefficients for those using a yard tap or neighbour’s tap are non- significant and quite modest, sug- gesting fairly similar levels of stated WTP for a yard tap for all those cur- rently benefiting from piped water to the yard (or a yard in close proximity).
At the same time, if a household must treat its water or has access to water for very limited periods of time, then we would expect demand to be higher for a water scenario (clean water for24hours per day,7days per week) that would resolve these issues.
Age of respondent: • Older respondents are slightly less likely to state WTP. Household size: • Larger households are significantly more likely to be will-
ing to pay.
Town/City: • Households in Ribáuè are significantly more likely to be willing to pay than those in Nampula.
Level of understanding of scenario: • Those who say that they understand the scenario well are more likely to state WTP than those who say that they un- derstand the scenario very well. (Note that all households said that they understood the scenario either well or very well.) The reason for this significant effect is not clear. Primary water point: • Households using an unprotected well are significantly
more likely to be willing to pay than those with a house- hold connection
Hours operational: • Households’ stated WTP decreases the longer their current primary water point is operational.
Household treats water: • Households that treat water are significantly more likely to be willing to pay.
Box6: Key results for logistic model of stated willingness to pay on relevant variables, including numeric income.
If we instead consider the same logistic regression model but with numeric income replaced by proxies for income, we obtain the results presented in Table31, which reinforce many of the key findings of the previous model216
. Key results are summarised 216
Note that the use of proxies means that we have far fewer entries excluded due to missing data, explaining the appearance of levels for some factors that did not appear in Table30. in Box7and largely are in line with what would be anticipated.
Female respondents might be more likely to state WTP because they are responsible for collecting water at much higher rates than men. Results in regard to household size, primary water point us- age, treatment of water, and hours of operation of a household’s primary water point are consistent with what we observed for the previous model. Results pertaining to the occupation of the head of household are not completely clear, although they would be consis- tent with those in generally higher paying professions being more likely to state WTP. Thus, before any amount has been assigned
Estimate Std. Error z-value Pr(>|z|) (Intercept) -1.3843 1.1971 -1.16 0.2475 Interview length 0.0122 0.0097 1.26 0.2091 Female respondent 0.3565 0.2115 1.69 0.0919∗ Age of respondent -0.0000 0.0084 -0.00 0.9989 Household size 0.1054 0.0460 2.29 0.0219∗∗ Level of understanding of scenario: (Reference: Very well)
Well -0.0101 0.1837 -0.06 0.9560 Neither well nor poorly -0.5210 1.2776 -0.41 0.6834 Town/City: (Reference: Nampula)
Liúpo 0.8819 0.7731 1.14 0.2539 Ribáuè 0.6535 0.4478 1.46 0.1445 Education level of head of household: (Reference: None)
Primary of1stdegree -0.2890 0.4487 -0.64 0.5195 Primary of2nddegree 0.1191 0.4902 0.24 0.8080 Secondary of1stdegree -0.0015 0.4774 -0.00 0.9975 Secondary of2nddegree 0.1187 0.4832 0.25 0.8059 Higher level -0.5118 0.6936 -0.74 0.4606 Do not know -0.9367 0.5816 -1.61 0.1073 Primary water point: (Reference: Household connection)
Yard tap 0.7477 0.6041 1.24 0.2159 Standpipe 1.9270 0.7672 2.51 0.0120∗∗
Borehole 2.0832 0.7340 2.84 0.0045∗∗∗ Unprotected well 3.4839 0.7586 4.59 0.0000∗∗∗
Protected spring 3.8762 2.3956 1.62 0.1057 River, stream, lake 5.1940 0.9604 5.41 0.0000∗∗∗
Neighbour’s tap 1.1737 0.7687 1.53 0.1268 Hours operational -0.0766 0.0140 -5.47 0.0000∗∗∗ Time to collect water 0.0156 0.1549 0.10 0.9197
Good water quality 0.1550 0.2093 0.74 0.4592 Household has access to sufficient water 0.2071 0.2385 0.87 0.3853
Incidence of diarrhoea 1.5602 0.4576 3.41 0.0006∗∗∗ Household treats water 0.4734 0.2057 2.30 0.0213∗∗ Perceived change in access to water: (Reference: Decreased)
Increased 0.1961 0.3429 0.57 0.5673 Neither decreased nor increased -0.1943 0.3195 -0.61 0.5430 Do not know -0.2124 0.4056 -0.52 0.6005 Perceived change in quality of water: (Reference: Decreased)
Increased -0.9643 0.5289 -1.82 0.0683∗ Neither decreased nor increased -0.5572 0.4768 -1.17 0.2425
Do not know -1.7567 0.5372 -3.27 0.0011∗∗∗ Occupation of head of household: (Reference: Managers)
Professionals -0.6507 0.5774 -1.13 0.2598 Technicians -1.0820 0.5626 -1.92 0.0545∗ Clerical support -1.2414 0.8694 -1.43 0.1533
Services, sales -0.7301 0.5895 -1.24 0.2156 Agriculture, forestry, fisheries -0.7253 0.5698 -1.27 0.2030 Craft and related trade -0.6933 0.5868 -1.18 0.2374 Elementary occupations -1.3259 0.5980 -2.22 0.0266∗∗ Armed forces -0.4277 0.9971 -0.43 0.6680 Unemployed -0.5869 0.5989 -0.98 0.3270 Student -1.3989 0.7325 -1.91 0.0562∗ Homemaker -1.1018 0.6727 -1.64 0.1014 Benefits/pension -1.2497 0.7815 -1.60 0.1098 Other -0.6279 0.7884 -0.80 0.4258 Household pays for water 2.8090 0.2248 12.50 0.0000∗∗∗
Household has electricity 0.0718 0.2469 0.29 0.7712 Liúpo:Time to collect water -0.4601 0.2472 -1.86 0.0627∗ Ribáuè:Time to collect water -0.5050 0.1645 -3.07 0.0021∗∗∗
Note: ∗p<0.1;∗∗p<0.05;∗∗∗p<0.01
Table31: Logistic regression of stated willingness to pay on relevant vari- ables, including proxies for 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 97
to the service, poorer households may be more likely to eliminate themselves, much in line with the primary rationales stated for unwillingness to pay. This would also explain the significantly posi- tive coefficient corresponding to households that pay for water. The significant effects related to perception of water quality may not be so clear, but this could reflect households that are satisfied with the current water situation (and, consequently, respond affirmatively or noncommittally to the question in regard to perceived change in water quality in the neighbourhood), so they do not have the same demand for clean water as other households.
Sex of respondent: • Female respondents are slightly more likely to state WTP. Household size: • Larger households are significantly more likely to be will-
ing to pay.
Primary water point: • Households using less improved water sources are increas- ingly and significantly more likely to be willing to pay than those with a household connection.
Hours operational: • Households’ stated WTP decreases the longer their current primary water point is operational.
Incidence of diarrhoea: • Households reporting incidence of diarrhoea are more likely to state WTP.
Household treats water: • Households that treat water are significantly more likely to be willing to pay.
Perceived change in water quality: • Households that believe that there has been a significant increase in water quality in their neighbourhood or do not know are less likely to state WTP.
Occupation of head of household: • Households where the head is employed as a technician, in an elementary occupation, or is a student are less likely to be willing to pay than households where the head is in management.
Household pays for water: • Households that pay for water are significantly more likely to state willingness to pay.
Time to collect water: • In Liúpo and Ribáuè, households that spend longer collect- ing water are less likely to state WTP.
Box7: Key results for logistic model of stated willingness to pay on rele- vant variables, including proxies for income.