Default Effects and Follow-On Behavior: Evidence from an Electricity Pricing Program
Bibliographic record
Abstract
We study default effects in the context of a residential electricity pricing program. We implement a large-scale randomized controlled trial in which one treatment group is given the option to opt-in to time-based pricing while another is defaulted into the program but allowed to opt-out. We provide dramatic evidence of a default effect – a significantly higher fraction of households defaulted onto the time-based pricing plan enroll in the program, even though opting out simply involved making a phone call or clicking through to a website. A distinguishing feature of our empirical setting is that we observe follow-on behavior subsequent to the default manipulation. Specifically, we observe customers’ electricity consumption in light of the pricing plan they face. This, in conjunction with randomization of the default provision, allows us to separately identify the electricity consumption response of “complacent” households (i.e., those who only enroll in time-based pricing if assigned to the opt-out treatment). We find that the complacent households do reduce electricity use during higher priced peak periods, though significantly less on average compared to customers who actively opt in. However, with complacents comprising approximately 75 percent of the population, we observe significantly larger average demand reductions among consumers assigned to the opt-out group. We examine the extent to which the behavioral responses we observe are consistent with a standard model of switching costs, or with alternative mechanisms including inattention, and preferences constructed based on contextual features of the choice setting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".