|Title||Household Upgrade Decisions in Off-Grid Solar Services [Part 1 of study]|
|C1 Background and Explanation of Rationale||This project uses a conjoint analysis survey to elicit heterogeneous household preferences for off-grid solar technologies and services in rural Kenya. Understanding and describing these preferences is critical to off-grid energy business models. This research examines preferences beyond basic lighting to higher-margin products, such as TVs. Companies are selling solar home systems that provide enough electricity for lighting services, cellphone charging, and television. Beyond basic services such as lighting and charging, the market for “higher-margin products” such as fans and TVs is expected to grow to 7 million fans and 15 million TVs by 2020. This project aims to understand how price and quality characteristics influence decisions about upgrading solar home systems to allow for more higher-margin products. Further, this project examines household preferences for information about solar products.|
|C2 What are the hypotheses to be tested?||
Substantial attention has focused on the purchase of technologies such as solar home systems (Bensch et al., 2018) while simultaneously calling for a focus on end uses of solar (e.g., appliances). This project aims to bridge these two by examining appliances used on or compatible with solar home systems. Overwhelmingly, studies point to cost (Bensch et al., 2018), income thresholds (Gertler et al., 2016), and credit constraints (Urpelainen and Yoon, 2015) as key inputs into the household decision process for solar purchases. This study examines the multidimensional nature of quality, including technical quality indicators, service quality, and payment options. Based on the previous literature our preliminary hypotheses are the following.
H1: Households are more likely to upgrade their solar home system if they are offered financing in a pay-as-you-go daily rate.
|C3 How will these hypotheses be tested? *||To test these hypotheses we propose to use a conjoint analysis survey. Conjoint analysis surveys “ask respondents to choose from or rate hypothetical profiles that combine multiple attributes, enabling researchers to estimate the relative influence of each attribute value on the resulting choice or rating” (Hainmueller et al., 2014, p. 2). This approach allows for joint effect of different attributes on an overall judgment (Rao, 2014). Paired conjoint analysis has been shown to come closest to observed behavior (Hainmueller et al., 2015) . Data will be analyzed using a binomial logistic regression model (binary choice outcome) to determine the effect of different attributes on the stated choice preference of respondents. In addition to binomial logistic regression, we will also explore using the cjoint package in R (Oliveros and Schuster, 2018).|
|C5 Scale (# of Units)||360 respondents, each responding to 6-8 choice sets per conjoint (n = 2160 - 2280)|
|C6 Was a power analysis conducted prior to data collection?||Yes|
|C7 Has this research received Insitutional Review Board (IRB) or ethics committee approval?||Yes|
|C8 IRB Number||MIT E-1144; Strathmore University SU-IERC0353/19|
|C9 Date of IRB Approval||MIT 03/05/2019; Strathmore University 04/12/2019|
|C10 Will the intervention be implemented by the researcher or a third party?||Researchers, Busara Center for Behavioral Economics|
|C11 Did any of the research team receive remuneration from the implementing agency for taking part in this research?||No|
|C12 If relevant, is there an advance agreement with the implementation group that all results can be published?||No|
|C13 JEL Classification(s)||D1|