Green energy solutions, such as solar power, offer decentralized electricity generation, low variable cost, and the ability to mitigate the adverse effects of frequent blackouts. These alternative energy sources can contribute to the resilience of small firms, allowing them to continue operations through periods of traditional power unavailability.
However, adoption of solar among small firms in developing countries remains very low. Existing literature has documented that willingness-to-pay for green technology remains below market prices, possibly due to credit constraints. Traditional willingness-to-pay (WTP) assessments do not adequately capture the complexities of credit-based purchases, where payments are distributed over time and agents are time-inconsistent and credit constrained.
This project evaluates WTP for credit purchase contracts from two dimensions jointly, i.e. down payment and repayment, by acknowledging potential behavioral biases and nonstandard discounting. This approach allows the researchers to map a WTP frontier over these two dimensions, providing a more comprehensive understanding of demand for credit purchases. To achieve this, they first apply the Bayesian Adaptive Choice Experiment (BACE) method (Drake et al., 2022), a structural estimation approach that can efficiently estimate utility function parameters by updating choice menus based on previous choices. They then randomly implement a choice for one of the menus to ensure incentive compatibility. The intervention is a random subsidy embedded in the implementable menu offered to firms in Nakuru, Kenya. Each respondent is randomly assigned into one of three groups with equal chance: (1) a down payment subsidy, (2) a tailored subsidy, or (3) a control group. In both subsidy groups, the subsidy amount for each respondent is randomly drawn from 5 different levels, ranging from 12% to 60% of the down payment under the market contract.
