** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** THIS PROPOSAL RESPONDS TO THE ECONOMICS, MARKETS, AND TRADE PROGRAM PRIORITY OF DEVELOPMENT OF INNOVATIVE EMPIRICAL METHODS FOR ADDRESSING ECONOMIC ANALYSIS USING BIG DATA, MACHINE LEARNING, AND NATURAL LANGUAGE PROCESSING TECHNIQUES. WE COMBINE PRINCIPAL-AGENT INCENTIVE DESIGN MODELS, MACHINE LEARNING, AND EXPERIMENTAL ECONOMICS TO GENERATE SYSTEMATIC KNOWLEDGE ABOUT HOW BEHAVIORAL NUDGE OUTCOMES ARE AFFECTED BY HETEROGENEITY AND CONTEXTUAL FACTORS. IMPROVING THE METHODOLOGY OF BEHAVIORAL NUDGING CAN SUPPORT IMPROVED POLICY IN THE AREAS OF HEALTH AND NUTRITION, AND SUSTAINABLE AGRICULTURAL, WHICH ARE BROAD PRIORITIES FOR AFRI FUNDING.RECENT ACADEMIC PAPERS HAVE SUGGESTED THAT PUBLICATION BIAS HAS OVERSTATED THE IMPACT OF NUDGES AND THAT NUDGE OUTCOMES ARE HIGHLY SENSITIVE TO HETEROGENEITY AND CONTEXT. WE KNOW OF NO STUDIES THAT ATTEMPT TO CAPTURE THIS SENSITIVITY IN A SYSTEMATIC WAY. THE OBJECTIVE OF THIS GRANT PROPOSAL IS TO FILL THIS KNOWLEDGE GAP.WE PROPOSE THAT NUDGE DESIGN CAN BE FRAMED AS A MODIFIED PRINCIPAL-AGENT PROBLEM WHERE THE PRINCIPAL CHOOSES DISCRETE NUDGE STRATEGIES RATHER THAN MONETARY INCENTIVES. BY REFRAMING NUDGES AS AN INCENTIVE PROBLEM, WE HAVE A SYSTEMATIC FRAMEWORK FOR EVALUATING THE UNDERLYING MECHANISMS THAT DRIVE UNEVEN RESPONSES TO NUDGES. HOWEVER, GIVEN THE HUGE VARIETY OF NUDGE ARCHITECTURES, WE MUST ALSO USE THE TEXT PROCESSING CAPABILITIES OF MACHINE LEARNING IN COMBINATION WITH EXISTING DATA ON NUDGE INTERVENTIONS TO REDUCE THE DIMENSIONALITY OF NUDGE TYPES AND TO IDENTIFY IMPORTANT CONTEXTUAL FACTORS. EXPERIMENTAL ECONOMICS WILL BE USED TO TEST WHETHER THE UNEVENNESS OF RESPONSES TO NUDGES CAN BE ATTRIBUTED TO FAILURES OF INCENTIVE COMPATIBILITY ACROSS DOMAINS.