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Water conservation through plumbing and nudging

Nature Human Behaviour volume 6, pages 858–867 (2022)Cite this article

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Abstract

In this paper, we investigate two solutions to urban water security challenges: plumbing and nudging. Using anonymized monthly billing data from 1.5 million accounts in Singapore over ten years, our staggered difference-in-differences estimates show that a nationwide Home Improvement Programme that improves the efficiency of plumbing reduces residential water consumption by 3.5%. This effect persists over a decade and is robust across population subgroups. Efficiency improvements could enhance the efficacy of other conservation polices and mitigate the effects of excessive heat, rainfall and air pollution. The savings from efficiency improvements on utility bills are small, but the increase in housing value exceeds the private cost of the Home Improvement Programme. However, an evaluation of a nationwide peer-comparison nudging programme finds no evidence of reduced water consumption. Overall, we show that plumbing improvements generate long-lasting effects on water conservation.

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Fig. 1: Trend of monthly water consumption.
Fig. 2: Heterogeneous effects by housing characteristics, water demand and block-level demographics.
Fig. 3: Evolutionary effect of HIP on water consumption.
Fig. 4: Interaction effect between HIP and other conservation policies.
Fig. 5: Interaction effects between HIP and extreme environmental conditions.
Fig. 6: Effect of social comparison by baseline water consumption and message received.
Fig. 7: Heterogeneous effects of social comparison by message received.

Data availability

The water consumption dataset for this study is provided by PUB, Singapore’s National Water Agency, under a non-disclosure agreement for the current study. Upon reasonable request to PUB and with the necessary non-disclosure agreements signed with NUS, the dataset is available onsite at NUS to replicate all the results from the deposited Stata code. The data on block-level housing characteristics, block-level demographics, weather, air pollution and HDB resale transactions are provided on GitHub: https://github.com/fmsgp/DataCode-HIP.git. Information on block-level housing characteristics, such as year of construction, HIP status and public rental status, was obtained from https://services2.hdb.gov.sg/web/fi10/emap.html, while block-level private rental information was obtained from https://www.srx.com.sg/hdb/. Block-level demographic data were processed from administrative records. Weather and air pollution records were retrieved from http://www.weather.gov.sg/climate-historical-daily/ and https://www.haze.gov.sg/resources/historical-readings, respectively. HDB resale transactions were collected through https://services2.hdb.gov.sg/web/fi10/emap.html. Source data are provided with this paper.

Code availability

The Stata code used for data analysis in this study is available on GitHub: https://github.com/fmsgp/DataCode-HIP.git.

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Acknowledgements

All the authors acknowledge the funding and water consumption data support from PUB, Singapore’s National Water Agency. The funder had no role in study design, data analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Department of Finance, NUS Business School, National University of Singapore, Singapore, Singapore

    Sumit Agarwal

  2. Lee Kuan Yew School of Public Policy, National University of Singapore, Singapore, Singapore

    Eduardo Araral & Huanhuan Zheng

  3. Center for Advanced Study in the Behavioural Sciences, Stanford University, Stanford, CA, USA

    Eduardo Araral

  4. Institute of Water Policy, Lee Kuan Yew School of Public Policy, National University of Singapore, Singapore, Singapore

    Mingxuan Fan

  5. Department of Real Estate, NUS Business School, National University of Singapore, Singapore, Singapore

    Yu Qin

Authors
  1. Sumit Agarwal
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Contributions

All authors contributed to the research design, implementation, data analysis and writing.

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Correspondence to Mingxuan Fan.

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Nature Human Behaviour thanks Casey Wichman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Map of HDB blocks by HIP status.

The map shows the location of all HDB blocks in Singapore. The blue circles represent 2,262 HDB blocks that completed HIP by December 2019; the grey circles represent 3,342 HDB blocks that qualify for HIP but did not go through or complete the upgrade by December 2019. Out of which 1,777 blocks qualify for the initial program (that is, built before 1986) and 1,565 blocks qualify for the expansion (that is, built between 1986 and 1997); the black circles represent 4,586 HDB blocks that do not qualify for HIP (that is, built after 1997). The base map used is available at: https://data.gov.sg/dataset/master-plan-2019-subzone-boundary-no-sea.

Extended Data Fig. 2 Distribution of HDB flats by HIP cohort.

The figure shows the number of HDB flats in each region (Central, East, North, Northeast, and West) by year of HIP completion. Projects are considered complete 18 months after the announcement of a successful poll. The total number of flats that has completed HIP by December 2019 is 359,496.

Source data

Extended Data Fig. 3 Flat and household characteristics for HIP vs Non-HIP flats.

The figures show the distribution and mean of flat size (sub-figure (a)), flat age (sub-figure (b)), family size (sub-figure (c)), proportion of male (sub-figure (d)), proportion of Chinese (sub-figure (e)), proportion of elderly (sub-figure (f)) and proportion of young adults (sub-figure (g)) for HIP and non-HIP flats. The boxes represent the 25th to 75th percentile and the white lines inside represent medians; the error bars show the non-outlier limits, which are 1.5 times the interquartile range. The blue circles show the outliers while the green circles represent the sample mean. In our baseline sample, there are a total of 359,496 HIP flats and 1,143,854 non-HIP flats.

Source data

Extended Data Fig. 4 Flat and household characteristics by HIP cohort.

The figures show the mean and 95% confidence intervals for flat size (sub-figure (a)), flat age (sub-figure (b)), family size (sub-figure (c)), percentage of male (sub-figure (d)), percentage of Chinese (sub-figure (e)), percentage of elderly (sub-figure (f)) and percentage of young adults (sub-figure (g)) for HIP flats by cohort. The number of HIP flats by cohort are 9,426, 11,755, 13,525, 34,331, 7,061, 54,864, 38,563, 39,542, 84,413, 24,001, and 42,639 for project completion from 2009 to 2019, respectively. Detailed estimates are provided in Source Data Extended Data Figure. 4.

Source data

Extended Data Fig. 5 Number of housing transactions around the time of HIP completion.

The figure shows the number of HDB resale transactions each month, the linear trends and 95% confidence intervals before and after HIP completion. There is a total of 18,160 transactions within 24 months before and after HIP completion, out of which 8,719 transactions are pre-HIP and 9,441 post-HIP. The vertical line indicates the time of HIP completion defined as 18-month post the announcement of a successful poll.

Source data

Extended Data Fig. 6 Event study by year of HIP completion.

Extended Data Fig. 1 The figures show the estimated coefficients and corresponding 95% confidence intervals (error bars) for the effect of HIP in each 12-month period after project completion for each HIP cohort by estimating equation 3. The number of observations used for are 936,619 (sub-figure (a)), 2,327,234 (sub-figure (b)), 490,385(sub-figure (c)), 3,705,615 (sub-figure (d)), 2,632,263 (sub-figure (e)), 2,735,076 (sub-figure (f)), 5,841,537 (sub-figure (g)), and 1,645,165 (sub-figure (h)), respectively. Detailed estimates are provided in Source Data Extended Data Figure. 6. The vertical lines indicate the time of HIP completion defined as 18-month post the announcement of a successful poll.

Source data

Extended Data Fig. 7 Distribution, trend and spatial variation in temperature, rainfall, and air pollution.

The figures show the distribution, trend, and spatial variation (in terms of standard deviation) in monthly mean temperature (sub-figure (a)), number of rainy days (sub-figure (b)), and PSI (sub-figure (c)) from January 2011 to December 2019 using the baseline sample of 98,291,320 observations.

Source data

Extended Data Fig. 8 Event study for peer comparison by month.

This figure shows the estimated coefficients and corresponding 95% confidence intervals (error bars) for the effect of nudging through peer comparison in each month for the first year after its implementation by estimating equation 10 using the baseline sample of 98,291,320 observations. Detailed estimates are provided in Source Data Extended Data Figure. 9. The vertical lines from left to right indicate the timing of nudging, drought, and the announcement of water price increase.

Source data

Extended Data Fig. 9 Regression discontinuity around national and neighbourhood average.

The figures show the monthly water consumption by the distance to neighbourhood mean for households who consume below (sub-figure (a)) and above (sub-figure (b)) the national mean; and by the distance to national mean for households who consume below (sub-figure (c)) and above (sub-figure (d)) the neighbourhood mean. Monthly water consumption variable is residual of account fixed effects, year-month fixed effects, and weather and pollution controls. The average residual water consumptions below the cut-offs (distance<0) are shown in black dots and above the cut-offs (distance>0) are shown in blue dots. The fitted lines from robust locally weighted regressions below the cut-offs (distance<0) are shown in green and above the cut-offs (distance>0) are shown in yellow.

Source data

Supplementary information

Supplementary Information

Supplementary Discussion, Figs. 1–10 and Tables 1–15.

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Agarwal, S., Araral, E., Fan, M. et al. Water conservation through plumbing and nudging. Nat Hum Behav 6, 858–867 (2022). https://doi.org/10.1038/s41562-022-01320-y

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