| Field | Before | After |
|---|---|---|
| Field Abstract | Before This study examines whether recipient control over benefit timing improves outcomes in the Low Income Home Energy Assistance Program (LIHEAP), which distributes $4 billion annually to approximately 6 million vulnerable U.S. households. Current LIHEAP implementation delivers benefits as administrator-controlled lump-sum payments, and our objective is to understand whether enabling recipients to allocate benefits across utility bills throughout the year via a text-based platform leads to improved outcomes with respect to disconnection rates, on-time payments, energy consumption, arrearages, and energy burden. | After This study examines whether recipient control over benefit timing improves outcomes in the Low Income Home Energy Assistance Program (LIHEAP), which distributes $4 billion annually to approximately 6 million vulnerable U.S. households. Current LIHEAP implementation delivers benefits as administrator-controlled lump-sum payments, and our objective is to understand whether enabling recipients to allocate benefits across utility bills throughout the year via an energy savings account leads to improved outcomes with respect to disconnection rates, on-time payments, energy consumption, and arrearages. |
| Field Last Published | Before October 23, 2025 06:39 AM | After August 17, 2026 01:30 PM |
| Field Intervention (Public) | Before Our proposed intervention is an alternative way to deliver LIHEAP benefits which gives recipients control over when and how their benefits are applied. The intervention will be implemented through a text-based platform that enables recipient-controlled benefit allocation. Treatment group participants receive access to a multilingual text-based platform enabling them to: (1) view their full LIHEAP benefit allocation, (2) choose how much benefit to apply to each utility bill throughout the year, and (3) receive reminders about upcoming bills and benefit usage. | After Our proposed intervention is an alternative way to deliver LIHEAP benefits that gives recipients control over when and how their benefits are applied. The intervention will be implemented through an energy savings account that enables recipient-controlled benefit allocation. Via the energy savings account, treatment group participants are able to: (1) view their full LIHEAP benefit allocation and their savings account portal, (2) choose how much benefit to apply to each utility bill throughout the year. |
| Field Primary Outcomes (End Points) | Before Primary outcomes include utility disconnection rates, on-time bill payment rates, total energy consumption, total arrearages (accumulated utility debt), energy burden (percentage of income spent on energy). | After Primary outcomes include utility disconnection rates, energy spending trade-offs, on-time bill payment rates, total energy consumption, total arrearages (accumulated utility debt). |
| Field Primary Outcomes (Explanation) | Before Disconnection rates will be measured as any service interruption for non-payment during the study period. On-time payment rates will be calculated as the proportion of monthly bills paid by or before the due date. Arrearages represent the cumulative unpaid balance on utility accounts at study end. Energy burden will be calculated as: (total annual energy costs / annual household income) × 100. | After Disconnection rates will be measured as any service interruption for non-payment during the study period. Energy-related spending tradeoffs will be measured as a household reducing or forgoing other necessities to pay an energy bill. On-time payment rates will be calculated as the proportion of monthly bills paid by or before the due date. Arrearages represent the cumulative unpaid balance on utility accounts at study end. |
| Field Experimental Design (Public) | Before We will conduct a RCT with 1,000 LIHEAP-eligible households in Illinois to evaluate whether a self-managed delivery model improves energy security outcomes compared to traditional LIHEAP implementation. The treatment group will receive self-managed LIHEAP benefits with control over allocation timing. The control group will receive traditional LIHEAP delivery (one-time direct payment to utility). | After We will conduct a RCT with 1,000 LIHEAP-eligible households in Illinois to evaluate whether a self-managed delivery model via an energy savings account improves energy security outcomes compared to traditional LIHEAP implementation. The treatment group will receive self-managed LIHEAP benefits via an energy savings account with control over allocation timing. The control group will receive traditional LIHEAP delivery (one-time direct payment to utility). |
| Field Randomization Method | Before The randomization will be implemented through the Illinois Association of Community Action Agencies and its member organizations at the time of application. Recruitment of research participants will occur when utility customers apply for LIHEAP benefits. After securing written consent and determining LIHEAP eligibility, participants will be randomly assigned to either the treatment or control group. | After Recruitment of research participants will occur after utility customers apply for LIHEAP benefits and meet the study requirements, as determined via a study screening form. After meeting these requirements and providing written consent, participants will take a baseline survey. Following this, study participants are sorted into treatment and control groups based on stratified randomization. Participants that meet LIHEAP eligibility remain in the study and those who do not are excluded. |
| Field Randomization Unit | Before Households will be randomly assigned at the individual level using stratified randomization to ensure balance across key characteristics that may influence treatment effects: geographic location (urban/rural/suburban), household composition (presence of elderly, children, medically compromised, and disabled members), housing type (owner/renter), historical energy burden, and race/ethnicity (household representative that identifies as Black, White, Hispanic, or mixed race). | After Households will be randomly assigned at the individual level using stratified randomization to ensure balance across key characteristics that may influence treatment effects: poverty level (by income levels), race/ethnicity, and age. |
| Field Power calculation: Minimum Detectable Effect Size for Main Outcomes | Before Based on a small pilot study, we have calculated the required sample size to detect meaningful effects. The pilot showed a 60-percentage point difference in bill payment rates between treatment (95%) and control (35%) groups. Using a two-sided test with α=0.05 and 80% power, we would need approximately 11 participants per group to detect this effect size (Cohen's h=1.34), however, we anticipate smaller effect sizes at this scale due to the reduced intensity of support compared to the pilot. Comparable interventions in utility bill management suggest a potential reduction in disconnection rates from 15% to 7.5%. To detect this effect size, we would need 376 participants per group. Conservatively assuming a 20-percentage point difference in payment rates, we would need 97 participants per group. The pilot demonstrated a 20% reduction in electricity usage and 10% reduction in gas consumption. For a conservative 7% reduction in overall energy consumption, which aligns with findings from behavioral interventions in energy consumption, we would need approximately 283 participants per group to achieve 80% power. We would need approximately 156 participants per group to detect a 15-percentage point difference in arrearages. For a 10% relative reduction in energy burden, consistent with outcomes from other energy assistance interventions, we would need approximately 425 participants per group. Our planned sample of 500 participants per group exceeds all these requirements, providing sufficient power even with anticipated attrition of up to 15%. This sample size also enables us to detect relatively small effects (5-7 percentage point differences) in secondary outcomes, conduct meaningful subgroup analyses by demographic characteristics to identify heterogeneous treatment effects, and maintain adequate power for stratified analyses examining impacts by household composition, geographic location, and baseline energy burden. We will supplement these a priori power calculations with sensitivity analyses during the study to refine our estimates of minimum detectable effects based on observed variance in outcomes, correlation between baseline and follow-up measures, and actual attrition rates. | After We conducted power analysis for the two primary outcomes, with a significance level of 0.05 and power of 0.8. Using the J-PAL Power Calculator, we computed the Minimum Detectable Effect under different assumptions about sample size and attrition. In all cases we assume the proportion in treatment is 0.5. Primary Outcome 1: Household disconnected during heating season. We assume an outcome proportion of 0.1 based on the 2024 EIA Disconnections Report. With our target sample size and no attrition, we will be able to detect about a 5 percentage point difference in disconnections between the treatment and control groups. Under more conservative assumptions of a sample of 600 and 20% attrition, we will be able to detect about a 7 percentage point difference. Sample MDE Attrition (%) 1000 0.0532 0 1000 0.0561 10 1000 0.0595 20 800 0.0595 0 800 0.0627 10 800 0.0665 20 600 0.0687 0 600 0.0724 10 600 0.0778 20 Primary Outcome 2: Household energy spending tradeoffs during heating season. We assume an outcome proportion of 0.2 based on the 2024 EIA Residential Energy Consumption Survey. With our target sample size and no attrition, we will be able to detect a 7 percentage point difference in households trading off energy bills for other expenses between the treatment and control groups. Under more conservative assumptions of a sample of 600 and 20% attrition, we will be able to detect about a 10 percentage point difference. Sample MDE Attrition (%) 1000 0.0709 0 1000 0.07475 10 1000 0.0793 20 800 0.0793 0 800 0.0836 10 800 0.0886 20 600 0.0915 0 600 0.0965 10 600 0.1024 20 |
| Field Public analysis plan | Before No | After Yes |
| Field Secondary Outcomes (End Points) | Before Secondary outcomes include self-reported financial stress related to energy bills, bill management, ability to maintain comfortable in-home temperatures, need to reduce food or medicine purchases to pay energy costs, understanding of energy usage and costs, sense of control over energy decisions, and household thermal health indicators. | After Secondary outcomes include self-reported financial stress related to energy bills, bill management, ability to maintain comfortable in-home temperatures, understanding of energy usage and costs, sense of control over energy decisions, and household thermal health indicators. |
| Field Secondary Outcomes (Explanation) | Before Financial stress will be measured using validated insecurity surveys, including questions about worry over paying bills, difficulty affording energy costs, and trade-offs between energy and other necessities. Comfortable temperature maintenance is a binary indicator based on whether households report being able to keep their home at a comfortable temperature during the heating season without compromising health or safety. Food/medicine trade-offs capture whether households report reducing or forgoing food purchases or skipping/reducing medication doses to pay energy bills in the past 3-6 months. Energy usage understanding assesses comprehension of how energy use translates to costs, how to reduce energy costs, and self-reported understanding of why energy costs are what they are. Sense of control measures perceived agency using items like "I feel in control of my energy costs" and "I worry about whether my home energy bill will become overdue before I can pay it." on 5-point agreement scales. | After Financial stress will be measured using validated insecurity surveys, including questions about worry over paying bills, difficulty affording energy costs, and trade-offs between energy and other necessities. Comfortable temperature maintenance is an ordinal indicator based on whether households report being able to keep their home at a comfortable temperature during the heating season without compromising health or safety. |
| Field Pi as first author | Before No | After Yes |
| Field | Before | After |
|---|---|---|
| Field Document | Before |
After
SMEA Pre-Analysis Plan Aug2026.pdf
MD5:
44f85db0f0b9cedf8515aba36ddf212a
SHA1:
340ec785e048f9019dd4ba54e7953468357f9fa7
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