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The Role of Behavioral Interventions in Reducing Residential Water Usage: Case Study from Cape Town, South Africa
Last registered on January 31, 2016

Pre-Trial

Trial Information
General Information
Title
The Role of Behavioral Interventions in Reducing Residential Water Usage: Case Study from Cape Town, South Africa
RCT ID
AEARCTR-0000921
Initial registration date
November 06, 2015
Last updated
January 31, 2016 5:54 AM EST
Location(s)
Region
Primary Investigator
Affiliation
School of Economics, University of Cape Town
Other Primary Investigator(s)
PI Affiliation
Cicero, Oslo, Norway
PI Affiliation
Sussex University
PI Affiliation
EPRU, UCT
Additional Trial Information
Status
In development
Start date
2014-06-01
End date
2016-12-31
Secondary IDs
Abstract
South Africa has moved from a situation of water stress to one of water scarcity as a result of pressures from population growth, climate change, over-consumption and urbanization. We study the effectiveness of non-price instruments at promoting water conservation in the residential sector in Cape Town, South Africa's second largest city. Using inserts in monthly customer bills, we test nine behavioral messages (treatments) in a randomized control trial on 412,489 households. The treatments are classified into four groups: public-good framing (voluntary contributions to a common goal/good), financial framing (focusing on the financial losses/gains associated with inefficient/efficient water usage), social-comparison framing (where household consumption is compared to that of other households) and social-recognition framing (recognizing water conservation).
External Link(s)
Registration Citation
Citation
Brick, Kerri et al. 2016. "The Role of Behavioral Interventions in Reducing Residential Water Usage: Case Study from Cape Town, South Africa." AEA RCT Registry. January 31. https://doi.org/10.1257/rct.921-3.0.
Former Citation
Brick, Kerri et al. 2016. "The Role of Behavioral Interventions in Reducing Residential Water Usage: Case Study from Cape Town, South Africa." AEA RCT Registry. January 31. https://www.socialscienceregistry.org/trials/921/history/6680.
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Experimental Details
Interventions
Intervention(s)
This study considering the role of behavioral interventions in promoting water conservation is to be rolled-out to residential consumers in the City of Cape Town, South Africa. Our unit of randomization is the household and our outcome variable is household water consumption (kilo-liters per month). Participating households have been allocated into nine treatment groups (which receive the interventions or behavioral messages) and a control group (which receives no intervention). The nine behavioral messages are to be sent via billing inserts with the monthly municipal water bills.
The interventions can be described in terms of:
A. Information Provision - Increasing salience
B. Financial Gains & Losses
C. Social Norms
D. Intrinsic Motivation
E. Social Recognition
F. Public Goods

Intervention Start Date
2015-11-06
Intervention End Date
2016-05-05
Primary Outcomes
Primary Outcomes (end points)
Monthly water consumption (in kiloliters) at the household level.
Primary Outcomes (explanation)

Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The eligible population for this study is all residential households with access to piped water in the City of Cape Town. We are excluding commercial customers and only including those classified as “domestic” by the City. Furthermore, as we are limiting our sample to free standing houses, we are also excluding households with bulk meters. This obviates issues regarding flats where one meter number could be linked to multiple households. It further ensures that the recipient of the message is most likely one of the household members consuming water.

We are excluding those customers consuming only in Tariff Block 1, as this is the lowest consumption band (6 kilolitres/month) and is supplied free by the City. These households are already consuming the lowest possible amount of water each month and thus we assume they are either the most vulnerable residents in the City of Cape Town or are not residing in the household on a full-time basis (i.e. holiday home). If they are the most vulnerable residents in Cape Town, we want to safeguard against messaging them as it may be unethical (i.e. hazardous) to ask the lowest income residents to consume less water. However, as we are interested in how different behavioural messages affect consumption across all income groups, we are including households with “indigent” status in the treatments - provided they are consuming above Tariff Block 1.

The sample size is the population of the City of Cape Town excluding commercial and bulk accounts, apartment flats, and those consuming only within Tariff Block 1. This is a total expected sample size of 412,480 households: 113,312 are indigent and 299,168 are not indigent residents. We stratify the sample on suburbs (676) and tariff blocks (6 total).

Experimental Design Details
Randomization Method
The randomization is done using Stata v13.
Randomization Unit
Our unit of randomization is the household level. Specifically, we use contract account numbers.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
The treatments were not clustered.
Sample size: planned number of observations
412,480 households
Sample size (or number of clusters) by treatment arms
Control: 48,207 (households)
Treatment 1: 49,928
Treatment 2: 34,000
Treatment 3: 33,688
Treatment 4: 34,073
Treatment 5: 40,001
Treatment 6: 40,058
Treatment 7: 44,174
Treatment 8: 43,930
Treatment 9: 44,421

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We used the most recent consumption data from November 2014 to April 2015 from the City of Cape Town’s municipal database to conduct our power calculations. For the power calculations. we chose to use the months for which our study will be conducted in order to allow for seasonality effects as consumption increases in the summer months. We matched the municipal data with the list of contract accounts we received from the City's printers. We removed those consuming 6 kiloliters/month or below, as well as the 95th percentile to control for outliers due to measurement errors. We then calculated mean consumption over the treatment period last year (December-April). We include two power calculations: one where we look at the mean consumption over the treatment period with an unbalanced panel and one where we use the balanced panel. I) With our sample size, we are able to detect a 1.5% change in means per treatment. Assuming our standard deviation is 11.08, our mean is 21.47 kiloliters/month, alpha level is 0.05 and power of .8, the 1.5% detectable difference in means would be able to pick up an effect if the consumption decreases to 21.15 kiloliters/month (a difference of 0.32 kiloliters/month) with a minimum sample size of 18,579 per arm. We have tried various strategies for the power calculations, yet the strategy is not sensitive to changes in the detectable effect size. We assume there will be high variability in the effect size across income groups. We will use property values and suburb as covariates in our regression models to decrease the variance. II) Our power calculations are robust when using the balanced sample (those whose consumption we observe in each month) With our sample size, we are able to detect a 1.5% change in means per treatment. Assuming our standard deviation is 9.5, our mean is 21.1 kiloliters/month, alpha level is 0.05 and power of .8, a 1.5% detectable difference in means would be able to pick up an effect if the consumption decreases to 20.8 kiloliters/month (a difference of 0.31 kiloliters/month) with a sample size of 14,104 households per arm.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Commerce Faculty Ethics in Research Commit, University of Cape Town
IRB Approval Date
2015-10-28
IRB Approval Number
281015-1
Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is data collection complete?
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers