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Water Policy in Canada pp 29–56Cite as

Canadian Small Water Systems: Demand and Treatment Costs

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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. 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. 2.

    The definition of each variable is listed in Appendix 3.1.

  3. 3.

    In this study, the kernel estimation of nonparametric regressions was conducted using the Nadaraya-Watson (1964) approach.

  4. 4.

    The definition of each variable is listed in Appendix 3.3.

  5. 5.

    The estimated results for nonparametric model are summarized in Appendix 3.4.

  6. 6.

    The definition of each variable is listed in Appendix 3.6.

  7. 7.

    The estimated results from semiparametric models are summarized in Appendix 3.7.

  8. 8.

    The estimated results from parametric models are summarized in Appendix 3.8.

References

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Author information

Authors and Affiliations

  1. Climate Change Laboratory, Department of Economics, Brock University, St. Catharines, ON, Canada

    Mohammed H. Dore

Authors
  1. Mohammed H. Dore
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Corresponding author

Correspondence to Mohammed H. Dore .

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**

  1. Note The standard errors are reported in parenthesis; */**/*** indicates significance at the 5/10/1 % level; P-value (F) pertains to overall significance of the regression

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

  1. n/a indicates not applicable
  2. Note The standard errors are reported in parenthesis; */**/*** indicates significance at the 5/10/1 level; F pertains to overall significance of the regression; B-Pagan is the Breusch-Pagan test for heteroscedasticity; Heteroscedasticity White is White’s test for heteroscedasticity; RESET is the RESET test for model specification; and R2 for the Semiparametric model is the square of the correlation coefficient between the actual value of the dependent variable and its predicted value

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

  1. Note The p-values are reported in parenthesis; */**/*** indicates significance at the 10/5/1 % level

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)

  1. Note The p-values are reported in parenthesis; */**/*** indicates significance at the 10/5/1 % level; F pertains to overall significance of the regression; B-Pagan is the Breusch-Pagan test for heteroscedasticity; White is White’s test for heteroscedasticity; RESET is the RESET test for model specification; and R2 for the Semiparametric model is the square of the correlation coefficient between the actual value of the dependent variable and its predicted value

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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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