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Understanding Demand for Electricity Among Rural Consumers Using Pay as You Go Solar
Initial registration date
July 25, 2018
July 27, 2018 1:19 PM EDT
UC Berkeley Dept of Agricultural and Resource Economics
Other Primary Investigator(s)
Additional Trial Information
What is the willingness to pay for reliable electricty among rural consumers in developing countries, and how responsive is demand to changes in the price of electricity? Electrification is a major growth policy challenge for developing countries, as it requires large investments in infrastructure while consumers' and producers' willingness to pay for electricity in low income countries remains largely unknown. These challenges are exacerbated in rural areas, where potential consumers of electricity tend to be poorer and the costs of extending access to the grid higher than in urban markets. I use a randomized control trial (RCT) with customers of a pay as you go (PAYG) solar company in Rwanda and Kenya to study demand for electricity among rural consumers by randomly varying the effective price of electricity for different consumers. This experimental design allows me to estimate the price elasticity of demand for electricity and to trace out the demand curve for electricity. Because customers have a solar home system that includes a battery for storing electricity generated during daylight hours, consumers have reliable access to electricity as long as they purchase system access time. Therefore, my estimates of consumer demand will be estimates of demand for reliable access to electricity. Furthermore, consumers in my sample purchase all appliances except mobile phones from the solar provider, nearly all of which are primarily for consumption. These features of my experimental setting enable me to contribute to understanding demand for reliable electricity for consumption among rural consumers.
In addition to providing evidence on the nature of demand for electricity among rural consumers, I will contribute to the growing body of evidence on the benefits of rural electrification. Measuring the benefits of electricity for consumption is particularly challenging, as it requires valuing difficult to measure benefits like increased leisure time, long-term improvements in health outcomes, improvements in educational outcomes, and enhanced feelings of safety and security (CGAP 2017). Rather than attempting to directly value such benefits, I take a novel approach by applying the techniques developed in Ligon (2016) to measure households' marginal utility of expenditure. I plan to supplement this with a structural estimate of welfare. Registration Citation
I offer promotions to existing customers of a pay as you go (PAYG) solar company that randomly vary the effective price that the customer pays for a day of electricity.
Intervention Start Date
Intervention End Date
Primary Outcomes (end points)
Days of electricity purchased in a single month, marginal utility of expenditure.
Primary Outcomes (explanation)
I plan to estimate the marginal utility of expenditure using the methods in Ligon (2016). This entails eliciting household composition (number of boys, girls, men, and women living in the household) and eliciting expenditures on a range of income-elastic goods. For each good, I regress expenditures on the number of boys, girls, men, and women living in the house as well as a time by location fixed effect to account for price variation. This leaves me with a matrix of residuals, on which I perform a singular value decomposition (SVD). The marginal utility of expenditure is an object of rank one, so the SVD yields an estimate that is proportional to the marginal utility of expenditure, providing a measure of welfare for the household.
Secondary Outcomes (end points)
Frequency of remote lockouts, duration of remote lockouts, customer satisfaction.
Secondary Outcomes (explanation)
I plan to ask a series of questions eliciting customer satisfaction with the company, particularly focusing on issues of fairness and value for money. I will combine these into a customer satisfaction index.
I randomly offer payment incentives to existing customers of a solar company in Rwanda and Kenya. PAYG solar customers purchase time on their solar home systems rather than kilowatt hours. Therefore, incentives take one of two forms. The first incentive is of the form "purchase X days and receive Y days free." The second incentive is of the form "purchase X days over the course of a month and receive Y days free at the end of the month." For each type of incentive, I randomly offer some customers a high discount, or a high number of free days, and others a low discount. The discounts range between 3.5-15% on the low discount schedule and 7-24% on the high discount schedule, with discounts increasing in the number of days purchased for both types of incentives. Finally, I offer some customers discounts starting at a low minimum threshold and others discounts starting at a high minimum threshold. This cross-randomized experimental design induces random variation in the effective price of electricity. Finally, I offer a simple information treatment that merely informs customers about how well they have to pay to be considered "good customers" by the solar company.
Experimental Design Details
I am randomly offering 800 customers the bulk incentives and 800 customers the reliability incentives. Within each type of incentive, half of the customers will be randomly selected to be on a low incentive schedule and half on a high incentive schedule. Effective discounts range from 3.5-15% on the low discount schedule and from 7-24% on the high discount schedule. Finally, half of all customers within each incentive-type/incentive-level combination will be able to qualify for an incentive by purchasing at least four weeks, while the other half will only qualify upon purchasing 5 weeks. Therefore, I have eight groups of 200 customers each facing different types of incentives at a high or low incentive and at a high or low qualifying threshold. I have randomly selected an additional 800 customers to serve in the control group. I stratify all randomization by past payment performance. I am stratifying on past payment behavior to ensure that my sample contains a sufficient number of high electricity demand customers and low electricity demand customers.
Customers are being notified of the incentives that they face by a representative from the solar company's call center, and they also receive a SMS message with their incentive schedule for their future reference. Customers who are offered bulk incentives receive a SMS each time they make a qualifying purchase that informs them about how many bonus days they earned with their purchase. Customers who are offered reliability incentives will receive a SMS at the end of each month that informs them of how many bonus days they earned that month. Finally, customers receive a SMS notification when they come within three days of beginning to use their bonus days and when they come within three days of running out of time on their system.
I am collecting data in three ways. For one, I have access to the universe of administrative data from the solar company which includes information on basic customer demographics, appliances owned by each customer, system maintenance and customer calls to the call center, and the complete payment history of each customer. In addition, I am sending customers in Rwanda short monthly SMS surveys on consumption expenditures on a select set of goods, which I identified using the methods outlined in Ligon (2016). In effect, I select foods that are the most income elastic and which constitute a non-neglibile share of household expenditures among rural households in the lowest income quartile in a nationally representative survey. These SMS surveys will enable me to measure households' marginal utility of expenditures at a relatively high frequency. The other data collection I plan to do is an endline survey, which will take place with an enumerator over the phone at the end of the experiment in December, 2018 or January, 2019. This survey will provide one final measure of the marginal utility of expenditures, elicit more information about energy usage and customer satisfaction, and collect other data deemed relevant by stakeholders who I plan to engage with as I design the final survey instrument.
The randomization is done in an office by a computer.
The randomization is done at the level of the individual customer, there is no clustering. I stratify by past payment performance so that there are sufficient numbers of previously well-paying and previously poor-paying customers to examine effects on each.
Was the treatment clustered?
Sample size: planned number of clusters
2400 individual customers.
Sample size: planned number of observations
2400 individual customers.
Sample size (or number of clusters) by treatment arms
400 - pure control
400 - information treatment
200 - reliability incentive, low discount rate, low minimum threshold
200 - reliability incentive, low discount rate, high minimum threshold
200 - reliability incentive, high discount rate, low minimum threshold
200 - reliability incentive, high discount rate, high minimum threshold
200 - bulk incentive, low discount rate, low minimum threshold
200 - bulk incentive, low discount rate, high minimum threshold
200 - bulk incentive, high discount rate, low minimum threshold
200 - bulk incentive, high discount rate, high minimum threshold
Note that when performing power calculations, I am presenting power calculations for 800 treated individuals (in a given type of incentive) compared to 800 control individual (the pure control combined with the information treatment). All sub-analyses will have less power in accordance with the reduced sample.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
For days purchased in a single month and the duration of remote lockouts, the relevant units are days. If I assume that the data are distributed normally with mean of 23.44 days and a standard deviation between one and a half and three days for the days purchased in a single month, this indicates that I will have a minimum detectable effect size of 0.19-0.38 standard deviations, which would be 7-14% of the mean.
When examining the marginal utility of expenditures, the relevant unit will be utils/RWF, as I am measuring expenditures in Rwandan Francs. I have very little upon which to base my power calculations for this outcome, as it is a novel measure of welfare and I have insufficient data on the individuals in my sample to extrapolate from nationally representative surveys of Rwanda. However, using the data from Ligon (2016), I estimate that assuming my data are distributed normally with a mean MUE of 0.14 and a standard deviation of between 0.01 and 0.05, I will have a MDE of 0.001 to 0.006 standard deviations, or 8-40% of the mean.
INSTITUTIONAL REVIEW BOARDS (IRBs)
UC Berkeley Committee for the Protection of Human Subjects
IRB Approval Date
IRB Approval Number
Post Trial Information
Is the intervention completed?
Is data collection complete?