اقتصاد پذیرش فناوری های خانوار در کشورهای در حال توسعه: فناوری های خورشیدی Economics of household technology adoption in developing countries: Evidence from solar technology adoption in rural India

1. Introduction How do households decide on the adoption of new technology? Innovation is one of the most important forces of development (Solow, 1956), and even very poor households now have access to a wide array of new technologies in local markets. However, it is far from clear what makes some households adopt a given technology, but not others. Even when improved household technologies do not directly enhance productivity, they may contribute to higher labor market participation, improved health outcomes and educational attainment (Dinkelman, 2011; World Bank, 2008; Samad et al., 2013). To understand the economics of household technology adoption, we focus on the case of improved household lighting through solar power. Households that do have an electricity connection often have limited hours of access because of poor quality of supply (Aklin et al., 2016). In the absence of grid electricity, households in developing countries often rely on unhealthy and costly alternatives such as kerosene (Lam et al., 2012). When a household decides on adopting new lighting technology, it must compare the costs of the new technology to the benefits from fuel savings and improved lighting quality. We present a decision-theoretic model that offers an analytical framework to explain household adoption, and generates testable hypotheses. We test these hypotheses against technology adoption data from an impact evaluation study of solar microgrids in rural Uttar Pradesh, India (Aklin et al., 2017). In a random sample of 49 unelectrified habitations (Indian equivalent for hamlets), households were provided an opportunity to subscribe to a solar microgrid service for lighting and mobile charging in exchange for a monthly fee of 100 rupees (∼US$ 1.5).1 Importantly, the technology offered to rural villagers was identical for everyone and the fee-based business model of the technology provider means that we can rule out credit constraints as an explanation for non-adoption by households.