Abstract
According to the USEPA (2012, http://water.epa.gov/type/drink/pws/smallsystems/basicinformation.cfm), 94 % of 156,000 public water systems in the US are small water systems , serving a population of fewer than 3,300 people. In Canada, the proportion of small systems in one survey was over 75 % (Environment Canada 2004 in http://www.ec.gc.ca/eau-water/default.asp?lang=En&n=ED0E12D7-1. Accessed 25 Dec 2014). With a smaller tax base, all small water systems face special challenges, unless the government aggressively supports small water treatment systems. In Canada, many continue to encounter boil water advisories and even disease outbreaks. With appropriate public funding, many of these problems can be reduced or eliminated. However, typically in North America, each small community or rural jurisdiction must cover the capital and operating costs of its drinking water supply, although some jurisdictions offer a subsidy for capital costs. Often a rural community has a small population, lower average income, and consequently a lower tax base. These financial constraints as well as other risk factors were highlighted at a 2004 conference on small water systems (Ford et al. 2005 in http://watercenter.montana.edu/pdfs/colloquium_report_final.pdf) . These constraints are more severe in developing countries. For small water systems, we attempt to answer the following questions: Is a price that reflects a volumetric charge an adequate tool to control water use and promote conservation? Are water consumers in small systems “different” from populations in larger cities? To what extent is their water demand sensitive to price? Is their consumer behavior conditioned by their special circumstances?
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Notes
- 1.
Despite the problem of attrition, the data that was collected for the purpose of this section is a balanced panel. More specifically, the dependent and independent variables are observed for each municipality and each time period (2001 and 2004). This is in contrast to an unbalanced panel, which has some missing data for at least one time period for at least one municipality.
- 2.
The definition of each variable is listed in Appendix 3.1.
- 3.
In this study, the kernel estimation of nonparametric regressions was conducted using the Nadaraya-Watson (1964) approach.
- 4.
The definition of each variable is listed in Appendix 3.3.
- 5.
The estimated results for nonparametric model are summarized in Appendix 3.4.
- 6.
The definition of each variable is listed in Appendix 3.6.
- 7.
The estimated results from semiparametric models are summarized in Appendix 3.7.
- 8.
The estimated results from parametric models are summarized in Appendix 3.8.
References
Environment Canada (2004) 2004 municipal water use report. http://www.ec.gc.ca/eau-water/default.asp?lang=En&n=ED0E12D7-1. Accessed 25 Dec 2014
Environment Canada (2011) 2011 municipal water use report municipal—water use 2009 statistics. http://www.ec.gc.ca/doc/publications/eau-water/COM1454/survey2-eng.htm. Accessed 7 July 2014
Ford T, Rupp G, Butterfield P, Camper A (2005) Protecting public health in small water systems. Report of an International Colloquium. Montana Water Center and Montana State University, Bozeman. http://watercenter.montana.edu/pdfs/colloquium_report_final.pdf. Accessed 10 July 2014
Municipal Water Pricing Data (2001–2006) Environment Canada. http://www.ec.gc.ca/eau-water/default.asp?lang=En&n=ED0E12D7-1. Accessed 10 Feb 2014
Municipal Water Use Data (2001–2006) Environment Canada. http://www.ec.gc.ca/eau-water/default.asp?lang=En&n=ED0E12D7-1. Accessed 10 Feb 2014
Nadaraya E (1964) On estimating regression. Theory Probab Appl 9(1):141–142
Ogwang T, Kwong L, Cyr D, Kushner J (2011) A semiparametric hedonic pricing model of Ontario wines. Canadian Journal of Agricultural Economics/Revue Canadienne D’agroeconomie 59(3):361–381
Program on Water Governance (2013) Capital Region District, BC. http://watergovernance.ca/wp-content/uploads/2010/02/CRD_page.pdf. Accessed 7 July 2014
Robinson P (1988) Root-n-consistent semiparametric regression. Econometrica 56:931–954
Statistics Canada (2013) 2001 and 2005 median household income data through the Canadian Census. http://www12.statcan.ca/census-recensement/2011/dp-pd/index-eng.cfm. Accessed 10 Feb 2014
USEPA (2012) Basic information on small systems and capacity development. http://water.epa.gov/type/drink/pws/smallsystems/basicinformation.cfm. Accessed 11 July 2014
Watson G (1964) Smooth regression analysis. Sankhya Indian J Stat Ser A 26:359–372
Western Resource Advocates (2005) Water rate structures in Utah: how Utah Cities compare using this important water use efficiency tool. Boulder, Colorado. http://www.westernresourceadvocates.org/water/. Accessed 25 Dec 2013
WHO (2013) Technical notes on drinking-water, sanitation and hygiene in emergencies. http://www.pseau.org/outils/ouvrages/wedc_who_technical_notes_water_sanitation_hygiene_in_emergencies.pdf. Accessed 11 July 2014
World Conversation Union (2006) What is water demand management? http://www.iucn.org/places/rosa/wdm/what.html. Accessed 18 Jan 2006
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Appendices
Appendix 3.1 Definitions of Variables for Eq. 3.1, Panel Data Analysis
Variable | Definition |
---|---|
C | Per capita consumption in cubic meters per day of the ith municipality |
Log (C) | Logarithm of consumption |
P | Average price for 1 m3 of the ith municipality. Values based on an average consumption of 25 m3/month |
Log (P) | Logarithm of price |
I | Median household income of the ith municipality |
Log (I) | Logarithm of median household income |
M | Degree of domestic water metering, as a fractional percentage of the population served of the ith municipality |
CUC (1 = CUC; 0 = otherwise) | Dummy variable that takes the value 1 if the municipality implements CUC, FLAT rate is reference dummy |
DBR (1 = DBR; 0 = otherwise) | Dummy variable that takes the value 1 if the municipality implements DBR, FLAT rate is reference dummy |
CUC (1 = IBR; 0 = otherwise) | Dummy variable that takes the value 1 if the municipality implements IBR, FLAT rate is reference dummy |
Appendix 3.2 Estimated Parameters of the Individual and Time-Fixed Effects Models When Log Costs is the Dependent Variable (n = 111)
Independent variable | Individual fixed effects | Time-fixed effects |
---|---|---|
Constant | –0.525 (1.819) | –1.172 (1.52) |
log (P) | –0.411 (0.113)*** | –0.377 (0.063)*** |
log (I) | 0.152 (0.187) | 0.198 (0.152) |
M | –0.732 (0.222)*** | –0.722 (0.185)*** |
CUC | 0.023 (0.197) | 0.118 (0.136) |
DBR | 0.299 (0.218) | 0.337 (0.151)** |
IBR | –0.149 (0.287) | 0.089 (0.214) |
R2 | 0.685 | 0.285 |
P-value (F) | 0.0019** | 4.32e-13** |
Appendix 3.3 Definitions of Variables for Eq. 3.6, Semiparametric Analysis
Variable | Definition |
---|---|
Costs | Annual water treatment costs in $ per cubic meter |
Log (Costs) | Logarithm of costs |
Flow | Annual quantity of water flow in cubic meter |
Log (Flow) | Logarithm of flow |
MS (1 = MS; 0 = otherwise) | Dummy variable that takes the value 1 if the treatment implemented was microstraining |
FLOC (1 = FLOC; 0 = otherwise) | Dummy variable that takes the value 1 if the treatment implemented was flocculation |
SED (1 = SED; 0 = otherwise) | Dummy variable that takes the value 1 if the treatment implemented was sedimentation |
SSF (1 = SSF; 0 = otherwise) | Dummy variable that takes the value 1 if the treatment implemented was slow sand filtration |
PH (1 = PH; 0 = otherwise) | Dummy variable that takes the value 1 if the treatment implemented was pH control |
CC (1 = CC; 0 = otherwise) | Dummy variable that takes the value 1 if the treatment implemented was corrosion control |
FL (1 = FL; 0 = otherwise) | Dummy variable that takes the value 1 if the treatment implemented was Fluoridation |
MF (1 = MF; 0 = otherwise) | Dummy variable that takes the value 1 if the treatment implemented was membrane filtration |
GF (1 = CC; 0 = otherwise) | Dummy variable that takes the value 1 if the treatment implemented was granular filtration |
Appendix 3.4 Estimated Parameters of the Parametric and Semiparametric Models When Log Costs is the Dependent Variable (n = 39)
Independent variable | OLS robust standard errors | Semiparametric |
---|---|---|
Constant | 11.230 (1.296)*** | n/a |
log(flow) | –0.932 (0.11)*** | n/a |
MS | 0.235 (0.345) | –0.245 (1.126) |
FLOC | 1.369 (0.469)*** | 1.555 (0.562)* |
SED | –0.427 (0.393) | –0.490 (0.514) |
SSF | –0.776 (0.595) | –0.750 (0.655) |
PH | –0.421 (0.445) | 0.54E-01 (0.447) |
CC | 2.221 (1.601) | 2.619 (0.912)* |
FL | –0.098 (0.827) | –0.267 (0.557) |
MF | 0.560 (0.8) | 1.312 (0.658)* |
GF | –0.515 (0.498) | –0.791 (0.47)** |
R2 | 0.809 | 0.862 |
F | 21.35* | n/a |
B-Pagan | 15.447 | n/a |
White | 15.322 | n/a |
RESET (2) | 0.016 | n/a |
RESET (3) | 6.47* | n/a |
Appendix 3.5 Summary Statistics of the Predicted Values of Water Treatment Costs per Cubic Meter for Small Municipalities (Population <5,000) using Nonparametric Model
Municipality | Population | Actual costs per cubic meter | Predicted costs per cubic meter | Flow (cubic meter) | Elasticity |
---|---|---|---|---|---|
Stonewall | 4,376 | 834.3892 | 143.8973 | 500 | –0.7039 |
Argyle | 1,073 | 57.639 | 26.0521 | 1,091 | –0.8866 |
Norman’s Cove-Long Cove | 773 | 30.1836 | 42.1443 | 1,818 | –1.0823 |
Semans | 195 | 23.7694 | 16.9353 | 2,506 | –1.198 |
Beaverlodge | 2,264 | 62.5584 | 18.7239 | 12,310 | –0.7819 |
Minburn County No. 27 | 3,319 | 3.9968 | 1.7444 | 12,530 | –0.774 |
Northern Lights No. 22 | 3,772 | 2.0493 | 5.0168 | 18,250 | –0.6332 |
Harrison | 812 | 4.9175 | 4.0346 | 26,163 | –0.5494 |
Drake | 232 | 0.5761 | 0.9373 | 30,795 | –0.5259 |
Vanguard | 152 | 1.2441 | 0.8173 | 39,242 | –0.5042 |
Claresholm | 3,700 | 10.5708 | 3.3515 | 54,220 | –0.4928 |
St. Louis | 431 | 2.5279 | 1.4817 | 55,265 | –0.4925 |
Victoria | 1,149 | 2.0383 | 0.6994 | 56,512 | –0.4923 |
Saint-Wenceslas | 1,101 | 1.5672 | 0.7592 | 74,444 | –0.4919 |
Standard | 380 | 1.5252 | 2.7205 | 78,409 | –0.4921 |
Rockglen | 366 | 0.855 | 0.5056 | 88,389 | –0.4925 |
Memramcook | 4,638 | 0.5523 | 0.7848 | 156,092 | –0.4836 |
Falher | 941 | 2.1749 | 0.7906 | 170,008 | –0.4797 |
Castor | 931 | 1.7594 | 1.6122 | 183,340 | –0.4757 |
Macklin | 1,290 | 6.3617 | 1.1774 | 188,773 | –0.4739 |
Eastend | 471 | 0.9999 | 0.9273 | 199,526 | –0.4703 |
Red Rock | 1,063 | 0.989 | 0.7695 | 283,283 | –0.4399 |
Coalhurst | 1,523 | 0.3487 | 0.541 | 299,989 | –0.4337 |
Carman | 2,880 | 1.191 | 3.4837 | 337,021 | –0.4201 |
Powerview-Pine Falls | 1,294 | 0.2287 | 0.6383 | 365,602 | –0.4098 |
Casselman | 3,294 | 1.302 | 0.6502 | 393,250 | –0.4001 |
Sundre | 2,518 | 1.0804 | 0.4336 | 465,329 | –0.376 |
Ville-Marie | 2,696 | 0.1518 | 0.4222 | 493,955 | –0.3669 |
Warfield | 1,729 | 0.5751 | 0.8924 | 510,630 | –0.3617 |
Black Diamond | 1,900 | 0.5224 | 0.8677 | 545,523 | –0.3511 |
Saint-Quentin | 2,250 | 0.5038 | 0.641 | 727,377 | –0.302 |
Burgeo | 1,607 | 0.1298 | 0.3733 | 775,684 | –0.2903 |
Bienfait | 748 | 0.0297 | 0.3484 | 810,738 | –0.2822 |
Enderby | 2,828 | 0.4223 | 0.7596 | 965,016 | –0.2493 |
Lake Cowichan | 2,948 | 0.3913 | 0.3262 | 999,989 | –0.2424 |
Elkford | 2,463 | 0.1218 | 0.2938 | 1,536,780 | –0.1568 |
Killaloe, Hagarty and Richards | 2,550 | 3.9503 | 1.6979 | 2,632,646 | –0.0529 |
Brackley | 336 | 0.2788 | 0.2118 | 6,712,621 | 0.0338 |
Souris | 1,772 | 0.0011 | 0.0011 | 497,994,704 | –0.05 |
Appendix 3.6 Definitions of Variables for Eqs. 3.8–3.17, Semiparametric Analysis of Clustered Data and Parametric Analysis
Variable | Definition |
---|---|
Costs | Annual water treatment costs in $ per cubic meter |
Flow | Annual quantity of water flow in cubic meter |
MS (1 = MS; 0 = otherwise) | Dummy variable that takes the value 1 if the treatment implemented was Microstraining |
FLOC (1 = FLOC; 0 = otherwise) | Dummy variable that takes the value 1 if the treatment implemented was flocculation |
SED (1 = SED; 0 = otherwise) | Dummy variable that takes the value 1 if the treatment implemented was sedimentation |
SSF (1 = SSF; 0 = otherwise) | Dummy variable that takes the value 1 if the treatment implemented was slow sand filtration |
PH (1 = PH; 0 = otherwise) | Dummy variable that takes the value 1 if the treatment implemented was pH control |
CC (1 = CC; 0 = otherwise) | Dummy variable that takes the value 1 if the treatment implemented was corrosion control |
FL (1 = FL; 0 = otherwise) | Dummy variable that takes the value 1 if the treatment implemented was fluoridation |
MF (1 = MF; 0 = otherwise) | Dummy variable that takes the value 1 if the treatment implemented was membrane filtration |
GF (1 = CC; 0 = otherwise) | Dummy variable that takes the value 1 if the treatment implemented was granular filtration |
SMALL (1 = SMALL; 0 = otherwise) | Dummy variable that takes the value 1 for population size 0–1,999 |
SMALL2 (1 = SMALL2; 0 = otherwise) | Dummy variable that takes the value 1 for population size 2,000–5,999 |
MEDIUM (1 = MEDIUM; 0 = otherwise) | Dummy variable that takes the value 1 for population size 6,000–15,999 |
MEDIUM2 (1 = MEDIUM2; 0 = otherwise) | Dummy variable that takes the value 1 for population size 16,000–49,999 |
LARGE (1 = LARGE; 0 = otherwise) | Dummy variable that takes the value 1 for population size 5,00,000+ |
Appendix 3.7 Summary Statistics of the Estimated Coefficients of the Treatment Components $ per Cubic Meter from Semiparametric Models (n = 102)
Treatments\population | §0–1,999 n = 22 | 2,000–5,999 n = 19 | 6,000–15,999 n = 19 | 16,000–49,999 n = 22 | 50,000 + n = 20 |
---|---|---|---|---|---|
Constant | N/a | N/a | N/a | N/a | N/a |
Microstraining | –0.0759 | –0.0847 | –0.2008 | –0.1981 | –0.1882 |
–0.861 | –0.849 | –0.646 | –0.65 | –0.667 | |
Flocculation | –0.9929 | –1.1136 | –1.053 | –1.044 | –1.0594 |
(0.002)*** | (0.001)*** | (0.001)*** | (0.002)*** | (0.001)*** | |
Sedimentation | –0.1748 | –0.1588 | –0.1863 | –0.1865 | –0.1837 |
–0.49 | –0.535 | –0.469 | –0.468 | –0.474 | |
Slow sand filtration | 0.9966 | 1.0648 | 0.986 | 1.0037 | 0.988 |
(0.003)*** | (0.002)*** | (0.005)*** | (0.004)*** | (0.004)*** | |
pH control | –0.0577 | –0.0204 | –0.0167 | –0.0315 | –0.0138 |
–0.799 | –0.929 | –0.942 | –0.893 | –0.952 | |
Corrosion control | 0.3663 | 0.2511 | 0.2502 | 0.2446 | 0.2615 |
–0.248 | –0.419 | –0.425 | –0.434 | –0.421 | |
Fluoridation | –0.1631 | –0.1698 | –0.1288 | –0.1369 | 0.1358 |
–0.497 | –0.486 | –0.598 | –0.573 | –0.576 | |
Membrane filtration | 0.7279 | 0.6765 | 0.7038 | 0.7002 | 0.6995 |
(0.017)** | (0.027)** | (0.023)** | (0.023)** | (0.023)** | |
Granular filtration | 1.005 | 1.1471 | 1.1064 | 1.111 | 1.1211 |
(0.000)*** | (0.000)*** | (0.000)*** | (0.000)*** | (0.000)*** | |
R2 | 0.35 | 0.34 | 0.33 | 0.33 | 0.33 |
F | N/a | N/a | N/a | N/a | N/a |
B-Bagan | N/a | N/a | N/a | N/a | N/a |
White | N/a | N/a | N/a | N/a | N/a |
RESET (2) | N/a | N/a | N/a | N/a | N/a |
RESET (3) | N/a | N/a | N/a | N/a | N/a |
Appendix 3.8 Summary Statistics of the Estimated Coefficients of the Treatment Components $ per Cubic Meter from Parametric Models (n = 102)
Treatments\population | 0–1,999 n = 22 | 2,000–5,999 n = 19 | 6,000–15,999 n = 19 | 16,000–49,999 n = 22 | 50,000 + n = 20 |
---|---|---|---|---|---|
Constant | 0.8522 (0.000)*** | 0.9302 (0.000)*** | 0.9155 (0.000)*** | 0.9233 (0.000)*** | 0.9585 (0.000)*** |
Microstraining | –0.2104 (0.409) | –0.3103 (0.275) | –0.3186 (0.264) | –0.3237 (0.249) | –0.2620 (0.364) |
Flocculation | –0.8342 (0.033)** | –0.9098 (0.023)** | –0.9054 (0.023)** | –0.9020 (0.020)** | –0.8871 (0.022)** |
Sedimentation | –0.2699 (0.175) | –0.2856 (0.183) | –0.2860 (0.188) | –0.2881 (0.181) | –0.2425 (0.242) |
Slow sand Filtration | 0.7485 (0.142) | 0.7686 (0.123) | 0.7659 (0.135) | 0.7591 (0.14) | 0.7058 (0.167) |
pH Control | –0.1135 (0.661) | –0.0849 (0.749) | –0.0859 (0.748) | –0.0846 (0.76) | –0.0566 (0.828) |
Corrosion Control | 0.5032 (0.110) | 0.3941 (0.213) | 0.3908 (0.214) | 0.3939 (0.21) | 0.4542 (0.137) |
Fluoridation | –0.1104 (0.53) | –0.1078 (0.541) | –0.1049 (0.566) | –0.1021 (0.573) | –0.0706 (0.701) |
Membrane Filtration | 0.8489 (0.092)* | 0.8170(0.113) | 0.8172 (0.118) | 0.8203 (0.115) | 0.8402 (0.102) |
Granular Filtration | 0.9066 (0.016)** | 1.023 (0.01)** | 1.0267 (0.009)*** | 1.0188 (0.009)*** | 1.002 (0.010)** |
R2 | 0.31 | 0.29 | 0.29 | 0.29 | 0.31 |
F | (0.214) | (0.267) | (0.243) | (0.241) | (0.162) |
B-Bagan | (0.000)*** | (0.000)*** | (0.000)*** | (0.000)*** | (0.000)*** |
White | (0.000)*** | (0.000)*** | (0.001)*** | (0.000)*** | (0.000)*** |
RESET (2) | (2.34e-007) | (1.25e-006) | (1.73e-006) | (2.15e-006) | (1.16e-006) |
RESET (3) | (8.44e-009) | (1.05e-007) | (2.01e-007) | (2.63e-007) | (1.93e-007) |
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Dore, M.H. (2015). Canadian Small Water Systems: Demand and Treatment Costs. In: Water Policy in Canada. Springer Water. Springer, Cham. https://doi.org/10.1007/978-3-319-15883-9_3
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