Disentangling risk from stability preferences: A new perspective to analyze technology adoption decisions

This research investigates the intertemporal decision-making process under risk by exploring technology adoption. Previous studies may have attributed too much importance to risk by failing to account separately for variability in expected income. My view of the problem is that adoption is influenced not only by risk and risk aversion, but also by the variability of the expected income and a preference for income stability. The approach used to construct the adoption model is a generalized version of the Expected Utility model, the Generalized Expected Utility (GEU) approach, in which the coefficient of risk aversion is disentangled from the Elasticity of Intertemporal Substitution (EIS). The EIS accounts for stability preferences as it measures the willingness to trade between the consumption/income of one period for another.; A two-period and a multi-period adoption model were developed. The two-period model was used to perform an exhaustive sensitivity analysis form which important conclusions with respect to the individual's behavior under different conditions and preferences parameter were obtained. The multi-period model was used to examine the impact of different stability levels and minimum income on stability premiums. The stability premium is the amount that an individual would be willing to pay to trade an uneven income path for an even path. The results of this analysis were used to investigate the implications of considering preferences for stability on policy and financial tools by doing an application of multi-period model in forestry adoption. The results indicate that there is a good opportunity to increase the adoption rate in tree plantation if stability is considered and incorporated in designing financial instruments that will meet the preferences of the farmers.; The approach developed in this dissertation to analyze and explain technology adoption decisions opens a new line for research in intertemporal decision-making. The findings generated also can be applied to other areas of decisions such as inventory management, management of the farm annual production, or investment.