1. Using Feedback as a Tool for Household Energy Conservation: An Experimental Approach Kannika Thampanishvong Policy Dialogue “Transition to Green Economy for Southeast Asia” 6-7 March 2015

2. Background In Thailand, little emphasis has been placed on energy conservation by consumers in the residential sector. There is a good potential for electricity saving Behavioral changes in the use of residential energy could contribute towards overall energy consumption. This research project puts emphasis on tools that encourage household energy conservation.

3. Why Electricity Conservation Nudge? Residential electricity market tends to suffer from asymmetric information Study the effect of information provision on behavioral changes. Highlight the importance of information communicated to households and the way in which information is presented and framed. What type of information ?  Energy saving “hints”  Telling people how their energy consumption compares to that of their “neighbours”

4. Why Electricity Conservation Nudge? Residential electricity market tends to suffer from asymmetric information Study the effect of information provision on behavioral changes. Highlight the importance of information communicated to households and the way in which information is presented and framed. What type of information ?  Energy saving “hints”  Telling people how their energy consumption compares to that of their “neighbours”

5. What do other studies tell us? Ferraro and Price (2011 found that social comparison messages are most effective among high-users but the effectiveness of such messages wanes over time. Ayres et al. (2009) investigated the impact of provision of peer-comparison feedback to customers and found that mailed energy feedback has effect on energy consumption.

6. Key Research Questions 1. Does the feedback on electricity consumption successfully motivate households in the treatment groups to reduce energy consumption? 2. Which form of feedback is the most effective? Which treatment group experiences the largest reduction in electricity consumption?

7. Data Authorization Process ❶ ❷ ❸ ❹

8. Who are the Participants? Minb uri Nongjo k Ladkraba ng Sapansu ng Kanna yao Klongsa mwa 161 households from 10 housing estates

9. How Participants are Assigned into Groups? 161 HHs G1: Control Group, no feedback G2: Treatment Group1, peer comparison feedback G3: Treatment Group 2, energy saving tips G4: Treatment Group3, peer comparison and energy saving tips Randoml y assigned

10. Information Kits for Group 2

11. Information Kits for Group 3

12. Information Kits for Group 4

13. Variable Name Treatment 1 (n= 31) Treatment 2 (n= 36) Treatment 3 (n= 36) Control (n= 35) Average Household Size (persons)3.3 3.13.2 Average Proportion of Elderly Household Members Aged 60 and above in the Household (percent) 2314168 Average Proportion of Young Household Members Aged 15 and below in the Household (percent) 9101418 Average Proportion of Student in the Household (percent) 14193139 Average Household Income (THB)61,93657,19675,57971,152 Average Proportion of Households that Own Property (percent) 94 97 Average Proportion of Household Members who Stay at Home during Daytime (percent) 37293729 Average Length of Residence in Current Home (years) 9.58.610.69.8 Average House Size (squared wa)33.832.232.637.3 Average Proportion of Household Member with at least Secondary Education (percent) 77818277 Average Proportion of Female Member in the Household (percent) 57505256 Mean Comparisons of Pre-Treatment Variables

14. Pre-treatment Electricity Consumption Source: Author’s own computation using data from MEA

15. Impacts of Households’ Characteristics on Pre-treatment Consumption Households’ Characteristic Variables[1][2] Household size 73.3537 *** (14.8797) Dummy (whether some members in the household stay at home during daytime) 79.1410 *** (30.1856) 130.2590 *** (38.6551) Dummy (whether the house is owned by household) 0.7852 (52.8593) -10.7158 (46.7181) Length of residence in current home 3.9063 (4.4709) 2.2910 (4.9951) House size 2.8313 ** (1.1227) 3.1962 *** (1.2247) Dummy (whether there are elderly members aged 60 and above in the household) 12.2098 (47.9286) Dummy (whether there are young members aged 15 and below in the household) 68.9946 ** (37.2236) Constant-54.1696 (67.3181) 133.3841 (52.3726)

16. Which Group Perform Better? Source: Author’s own computation using data from MEA

17. Difference-in-Difference Results Group Mean Electricity Consumption during Pre-Treatment Period [1] Mean Electricity Consumption during Post-Treatment Period [2] [1] – [2] Difference-in- Difference (kWh) Control Group402.58400.711.87 Treatment 1346.48324.2722.2020.34 Treatment 2415.53404.1011.439.56 Treatment 3480.92451.9528.9727.10

18. Fixed Effect Model Estimations, [1][2] Treatment Dummies d_Treatment1 d_Treatment2 d_Treatment3 -20.6128 (13.8932) -7.4584 (17.4705) -25.0497 * (12.8066) -21.1481 (13.7610) -7.5574 (17.4691) -24.8587 * (12.6382) Climate Variable Temperature18.9252 (12.0741) Number of Observations R-squared within 3025 0.3986 3025 0.3995 Treatment Group 3 that received both peer comparison feedback and electricity saving hints make significant reductions in electricity consumption.

19. Percentage Reduction in Electricity Consumption Pre-treatment consumption (unit: kWh) Reduction in Consumption (unit: kWh) Percentage Reduction in Electricity Consumption (unit: Percentage) Treatment 1346.48-21.0948-6.09% Treatment 2415.53-7.3213-1.76% Treatment 3480.92-24.8587-5.17% *

20. Average Changes in Electricity Consumption for Treatment 3

21. Policy Messages Electricity conservation nudge in the form of peer comparison and electricity saving tips works in term of influencing reduction in electricity consumption. MEA and PEA: cost-effectiveness of sending electricity consumption feedback together with electricity bill Possible extensions:  Further testing for different forms of “hints” with different treatments containing more specific electricity saving tips  Testing whether effects wane over time