"Estimation of non-tap water demand for connected and nonconnected households in urban districts of Rwanda" Claudine Uwera, Department of Economics, University of Gothenburg
Overview
Few households connected to tap water in developing countries( Baisa, Davis et al.2010)
Complexity in sources choice and Specific modeling specification (Whittington et al., 2008)
Separate single water demand equations : (Aburizaiza, 1991); (Crane, 1994); (David and Inocencio, 1998); (Rietveld et al,2000); (Basani. et al.,2008)
Single equation not helpful
System of simultaneous demand equations works better : (Cheesman. et al., 2008); ( Nauges and Whittington 2010);
IDEA Motivation Water an heterogeneous good in DC :Different sources Main points Household’s decision on using a specific source among other alternatives.
Relationship between water consumption , price and other socio-economics characteristics.
Form of new improved service and policy implication.
Contribution Short existing literature particularly on non-tap water demand in developing countries
We assume access to non-piped sources not exogenous in the water demand model for nonpiped households.
Background
Water supply sector divided into 2 subsectors: Urban and rural water supply system.
We distinguish households connected to piped water into their houses; and those who lack piped connections in Rwanda
Multitude of coping sources.
•
Only 3.4% connected to piped water within house or plot.
• Connected and non-connected deflect demand to the available coping sources. •
Daily per capita consumption very low (6 to 8 liters),
•
Poor households are more the most affected
Survey design & Data
Household survey conducted in 5 urban cities of Rwanda from January-April 2011
Sample:700 households in total from 3 districts that compose the capital city; and 2 other selected cities.
Data set covers 2 groups of households: currently connected to the tap water ; those unconnected and use different coping water sources.
205 connected households of which 83% rely on coping sources and 495 non tap households.
Connected households : 30% in the capital city and 19% and 33% respectively in the two other districts.
91% of households who use tap water rely on water in yard
Descriptive statistics Non connected
Connected
Variable
Mean
S.D.
Mean
S.D.
Monthly income(US$)
267.97
370.77
385.03
511.73
Years of schooling
7.78
4.947
9.36
5.01
Household size
5.49
2.38
5.59
2.68
Children less than five
1.24
1.67
1.33
1.69
Access to electricity(0/1)
0.60
0.49
0.80
0.40
Number of bedroom
3.20
1.16
3.24
1.27
Hauling time(minutes/cubic meter/month)
346.15
349.03
220.43
410.05
Source: Household’s survey in Rwanda
Average water consumption (m3/capita/month) & Average cost Connect. Unconnect. AWC Variable dwelling 0.0 yard 0.0 SE private tap 0.67 public tap 0.60 Tubewell 0.02 Protected dug 0.01 Protected spring 0.08 Unprotected spring 0.17 Cart with small tank 0.003 Surface 0.03 other 0.01 Total non-tap water 0.18 Overall 0.22 Source: Authors’ survey NB: Average tap water price is 0.25USD/m3
AC
AWC
AC
0.0
1.54 3.02 0.0 0.02 0.01 0.01 0.04 .004 0.01 0.02 0.02 0.04 0.44
0.10
0.0 .09 1.27 0.07 0.02 0.45 0.12 0.02 0.10 0.05 0.40 0.22
0.45 0.0 0.54 0.07 0.02 0.21 0.02 0.04 0.08 0.42 0.22 0.20
1. Econometric specification: Non-connected households
Assumptions:
Household’s choice as a complex decision.
Hh combines different types of coping sources but rely more on one source.
Hh makes a choice of his preferred coping source 𝑗 among 𝐽 available water sources.
Set of explanatory variables:
full cost of water as the sum of price of water (𝑃) and the pecuniary time cost 𝑇 .
income(𝐼) and a vector (𝑍) of socioeconomic characteristics variable (𝑆) as money saving from using free water.
quantity of water used 𝑸 as the dependent variable
Multinomial logit-OLS regression : non-connected households
Two-step estimators
Lee method used to correct selection biases in the choice of 4-alternatives of coping sources
Selectivity is modeled as a multinomial logit
Estimation run by step (multi logit, then linear regression with selectivity.
Selmlog adds to the explanatory variables a series of variables labeled 𝑚1 , 𝑚2 , 𝑚3 , 𝑚4 .
Multinomial logit model
obability to use water from a public tap Households income(US$) Years of schooling hhsize Number of bedroom Access to electricity(0/1) (0/1) Hauling time Children less than five obability to use water from a protected spring Households income(US$) Years of schooling hhsize Number of bedroom Access to electricity(0/1) (0/1) Hauling time Children less than five Probability to use water from surface Households income(US$) Years of schooling hhsize Number of bedroom Access to electricity(0/1) (0/1) Hauling time(hours) Children less than five obability to use water from a private tap Households income(US$) Years of schooling hhsize Number of bedroom Access to electricity(0/1) Hauling time(hours) Children less than five
Non connected households Marginal effects a
Robust standards errors
0.0002* -0.002 0.028** -0.017 0.060 -0.500*** -0.007
0.000 0.051 0.014 0.023 0.049 0.002 0.018
0.0001* -0.001 -0.019** 0.005 -0.044 0.164*** -0.009
0.000 0.034 0.008 0.014 0.034 0.032 0.011
-0.0001* -0.007* -0.012 -0.003 0.088 0.266** 0.012
0.000 0.004 0.011 0.015 0.038 0.042 0.013
0.0001* 0.005* -0.002 0.015 0.103*** 0.068** 0.003
0.000 0.003 0.006 0.014 0.034 0.037 0.012
Marginal effects of each characteristic on the probability of using each of the four non-tap sources. a ***,** and * significance at 1,5 and 10% level, respectively
Source: Authors’ survey
Second step: Estimation of water demand function : non-connected households
Estimated coefficientsa Constant Log(total cost(public tap)) og(total cost (protected spring)) Log(total cost private tap)) Log(income) Log(savings) g(lot size(number of bedroom)) Log(kids under5 ) Kicukiro dummy distr Gasabo dummy distr Lee correction parameter 1c Lee correction parameter 2 Lee correction parameter 3 Lee correction parameter 4 observations Wald test of parameter equality(three sources) p-value
-0.01 -0.142** -0.014 -0.738*** 0.033* 0.199*** -0.752** 0.254*** -0.011 0.064 -0.863** 0.702 -0.789 -0.011 495
Boostrapped standard errorsb
Student’s t-test
0.511 0.063 0.052 0.283 0.021 0.071 0.359 0.091 0.110 0.086 0.463 1.534 2.044 0.731
-0.01 -2.26 -0.27 -2.61 1.61 2.82 -2.09 2.81 -0.11 0.74 -1.87 0.46 -0.39 -0.02
14.66 0.002
Unconnected sub-sample in all districts a
***,** and * significance at 1,5 and 10% level, respectively. b replications. c Water sources: Public tap, protected spring, private tap Source: Authors’ survey
2. Econometric specification : connected households
System of simultaneous demand equations to estimate overall demand for connected-households.
Assumptions :
demand for water from the piped network 𝑞1 and a demand for water from non-piped network 𝑞 2 .
water
𝑞 2 can be zero for connected households who don’t rely on coping sources Ordinary Least Squares might be biased
Equation for 𝑞 2 as a tobit model for variable censored at zero
The general system of water demand can be specified as follow:
𝑞1 = ∑𝐽𝑗=1 𝛾𝑗1 𝑝𝑝 + 𝑥 1 𝛽1 + 𝑢1 � ⋮ 𝑞 𝐽 = ∑𝐽𝑗=1 𝛾𝑗𝐽 𝑝𝑝 + 𝑥 𝐽 𝛽 𝐽 + 𝑢 𝐽
First step: Probability of having a piped in house for connected households Probabilty of having a piped in house income If the piped water available (0/1) Years of schooling Kicukiro district (0/1) Gasabo district (0/1) Nyarugenge district (0/1) Huye district (0/1) _cons Number of observations Likelihood-ratio test:test statistic (pvalue)
Coef. 0.001 1.172 0.053 0.860 -0.191 0.408 -0.389 -1.422 209
Std. Err. 0.000 0.211 0.013 0.206 0.156 0.188 0.194 0.252
z 4.56 5.56 4.01 4.16 -1.22 2.17 -2.00 -5.63
P>z 0.000 0.000 0.000 0.000 0.222 0.030 0.046 0.000
a
***,** and * significance at 1,5 and 10% level, respectively Source: Authors’ survey
Two steps
1.
The decision to have or not a piped connection.
The probit model : the probability of having a connection.
To control for selection bias, the estimated parameters from the first stage are used to compute the so-called inverse Mill’s ratio that will be added into the water demand model.
2.
Tobit estimation of water demand of piped households
Second step: Tobit estimation of a system of water demand for connected households Coef.
Std. Err.
z
P>z
-0.367
0.201
-1.82
0.068
-0.365
0.176
-2.07
0.038
Income (log)
0.155
0.039
3.89
0.000
Full cost (log)
0.198
0.201
0.98
0.325
Household size (log)
-0.965
0.121
-7.94
0.000
Mill’s ratio
0.033
0.037
0.88
0.377
Kicukiro district
-0.077
0.159
-0.48
0.628
Gasabo district
-0.096
0.160
-0.60
0.549
Constant Dependent variable: Non- Piped water consumption per capita per month
0.999
0.371
2.69
0.007
Instrumented average price for piped water (log)
0.181
0.130
1.38
0.166
Income (log)
-0.032
0.034
-0.95
0.340
Numebr of kids under five (log)
0.316
0.129
2.44
0.015
Number of bedroom (log)
1.150
0.234
4.91
0.000
Full cost (log)
-0.545
0.215
-2.53
0.012
Mill’s ratio
-0.237
0.046
-5.09
0.000
Kicukiro district
-0.326
0.197
-1.65
0.099
Gasabo district
0.601
0.189
3.18
0.001
constant
-1.665
0.361
-4.60
0.000
Dependent variable: Piped water consumption per capita per month Instrumented average price for households combining piped and non-piped water (log) Instrumented average price for households using piped water only (log)
Number of observation
205
Conclusion
Cross sectional data collected in 5 urban areas of Rwanda ….
Substitutability between tap water and Public tap water….
Welfare effect of extending public tap connections might be very large…..
Connected households less sensitive to price change than non- connected.
Improving current price schemes as good instrument for extension….
However different reactions……………
Further applications:
Cost-benefit analyses for either extending current tap water system or improving current non-tap distribution system……………
