1. Analysis of Consumer Preferences for Residential Lighting through Consumer Panel Data Jihoon Min Research Scholar International Institute for Applied Systems Analysis (IIASA) Inês Azevedo Associate Professor Carnegie Mellon University Presentation provided by Brock Glasgo due to scheduling conflicts in the USAEE agenda October 27, 2015 33 rd USAEE/IAEE North American Conference Oct 25-28 Pittsburgh, Pennsylvania

2. In the U.S. there are still large opportunities to improve lighting efficiency 2 U.S. Lighting Electricity Consumption by Sector and Lamp Type in 2010 (Navigant, 2012)

3. The FTC mandated a new “Lighting Facts” label starting from Jan 2012. 3 Source: FTC, http://www.ftc.gov/opa/2010/06/lightbulbs.shtm

4. The federal government sets light bulb efficiency standards. 4 Rated Lumen Ranges Typical Current Lamp Wattage Maximum Rate Wattage Minimum Rated Lifetime Effective Date 1490-2600100721,000 hrs1/1/2012 1050-148975531,000 hrs1/1/2013 750-104960431,000 hrs1/1/2014 310-74940291,000 hrs1/1/2014  The Energy Independence and Security Act (EISA) was signed into law in 2007 and went into effect in 2012.  Set wattage and lifetime requirements for general service lamps, based on lumen output

5. Big promotion by retailers 5 Source: Wal-Mart news archive, http://news.walmart.com/news-archive/

6. Questions 6 1. How do lighting- and consumer-specific attributes on labels affect lighting choices? 2. Which factors can affect the consumer choices or how are they related?  How does disclosing annual operating cost impact the decisions made by consumers?  How do consumers value operating cost savings?  How can this information guide policy and promotion efforts?

7. Past research 7  Our study based on a choice-based conjoint experiment  Min et al., (2014). "Labeling energy cost on light bulbs lowers implicit discount rates." Ecological Economics 97 (0): 42-50. Source: The Center for Behavioral and Decision Research, http://www.cbdr.cmu.edu/datatruck/

8. 8  Providing the operating cost information can foster efficient lighting technology due to:  Preference shifts toward longer lifetime and lower energy use  Large drop in implicit discount rate for light bulb choices.  100% discount rate still higher than other energy technologies  The FTC label that includes operation costs can be a good improvement. Past findings Implicit discount rates Income level Low (below $30k/yr) Middle ($30k-75k/yr) High (over $75k/yr) Overall Operating cost not shown764% (315%)491% (49.2%)203% (73%)560% (70%) Operating cost shown182% (38%)57% (19%)36% (35%)100% (22%) Standard errors in parentheses

9. Goal of this study 9  Impact of interventions 1. How are relevant policy changes related to changes in choice patterns? 2. Can a retailer significantly affect adoption of an efficient technology?  Cross-validation 1. Which factors affect choices and how do these compare with the findings from the previous study? 2. Will the new implicit discount rates be similar to the values estimated earlier?

10. Data (1/2) 10  Consumer Panel Data (collected by Nielsen)  Collected from 132,000 participant households through barcode scanners,  Nationally and regionally representative dataset (U.S.) between 2004 and 2012  Available information  Product: bar code number (UPC), price, category, description, brand, etc.  Household: income, race, education, size, residence type, location, weight, etc.  Shopping trip: retailer type, total dollar spent  Records of general service light bulbs are used for this analysis.

11. Data (2/2) 11  Retailer Scanner Data (also from Nielsen)  Weekly POS (point-of-sale) sales data at each store level  Available only for groceries, drug stores, and discount stores (65 retailer types)

12. Incandescent bulb sales decreasing, CFL sales not increasing much, prices not changing much 12

13. Light bulb sales are concentrated to a few retailers. 13 Total light bulb package sales by retailer chain Total 639 retailer chains Top 5 takes 43% of total sales.

14. Key observations 14  Overall light bulb sales are decreasing.  Before 2008: CFL replacing INC  After 2008: Longer life of CFLs (low turnover rate)  CFL sales peaked in 2007 and decrease afterward.  INC sales peak observed in 2011  Potentially linked to policy changes or promotions  Sales are concentrated to several key retailers.  A strong promotion effort can be effective.

15. Model specification 15  Similar to the stated preference models  Basic choice model  Utility is a function of preference coefficients (β k )  …and explanatory variables (x jk ): bulb attributes, year, region, brand, demographics, retailer, and channel type  Model for implicit discount rate estimation  Term in parentheses represents equivalent annual cost  β 2 is the implicit discount rate

16. Unobserved choice sets 16  Consumer data do NOT have information on alternatives that were considered but not selected by consumers.  Problem of unobserved choice sets  required for the estimation of a choice model  What to do? 1. Complement it with Retailer Scanner Data. 2. Make assumptions based on purchases from other consumers

17. Result: Basic Multinomial Logit 17  Generally decreasing preference over time for CFL types  Policy and marketing interventions in 2007 are related to a significant increase in CFL preferences  Not observed so for year 2012  Preference for bulb energy consumption (W) is not much related to or affected by these interventions Unit price-0.186 (0.00200)*** CFL0.464 (0.0282)*** CFL & year20120.0576 (0.04340) CFL & after20070.369 (0.0346)*** CFL & year-0.103 (0.00693)*** watt_nielsen-0.00536 (0.000409)*** year2012 & watt_nielsen0.000241 (0.00069) after2007 & watt_nielsen0.00127 (0.000509)* watt_year0.000078 (0.00011) retailerA_CFL-0.611 (0.0327)*** retailerA_CFL & year20120.387 (0.0626)*** retailerA_CFL & after20070.379 (0.0398)*** retailerA_watt0.00152 (0.000405)*** year2012 & retailerA_watt0.00213 (0.000988)* after2007 & retailerA_watt0.000436 (0.00054) lumen_est0.156 (0.00445)*** lumen_sq-0.00692 (0.000173)*** size1_amount0.0134 (0.00105)*** Observations23123522 Log-lik-3.71E+09

18. Results: Willingness-to-pay 18  Estimated WTPs for type, wattage, and brightness changes

19. Results: Willingness-to-pay (continued) 19  Smaller magnitudes of WTPs in revealed preference case than in stated preference  Potentially linked to  Confounding between unobserved attributes  Underestimated price coefficient in SP model

20. Results: Implicit discount rate 20  Estimated for two periods before and after 2012, when the FTC labeling was mandated and the EISA came into effect.  The ranges of discount rate values from the two different dataset are comparable.  Both stated preference and revealed preference models show discount rates higher than 100%. Revealed PreferenceStated Preference Before 2012After 2012Overall Operating cost shown Operating cost not shown Implicit discount rate 371% (0.79%)270% (1.72%)343% (0.68%)100% (22%)560% (70%) Standard errors in parentheses

21. Implications and conclusions 21  The new ‘lighting facts’ labeling on light bulb packages can help facilitate adoption of efficient light bulbs.  However, other types of barriers persist, which is reflected in the high implicit discount rates.  The EISA of 2007 is expected to lower these further.  Efforts by major retailers can have a significant impact on adoption of energy efficient lighting.  Can we mandate or incentivize large retailers to increase sales of efficient products?

22. 22 Thank you. Any questions? Jihoon Min min@iiasa.ac.at