Behavioral economics - generating evidence for programming

Adolescents and young people (AYP) living in poverty face persistent barriers to accessing sexual and reproductive health (SRH) and HIV services, including low perceived risk, stigma, and limited decision-making power. Tiko, a non-profit organisation that leverages technology and behavioural insights to scale access to SRH services among AYP aged 15–24 years, integrates behavioral economics into its SBCC model to address these barriers. Tiko aims to improve SRH access among these AYP, focusing on those with low-risk perception and most affected by multidimensional poverty.
Tiko connects AYP to verified SRH and HIV providers via a mobile-based digital platform that rewards positive health actions with loyalty points ("Tiko Miles"), redeemable for essential goods. Behavioral nudges (reminders, incentives, and social proof) encourage uptake, adherence, and repeat visits. The platform integrates the Multidimensional Poverty Index and other vulnerability metrics, collected through periodic Client Exit Interview surveys (CEIs), to guide targeting and equity monitoring.
Across Uganda and Kenya, behavioral nudges significantly improved service uptake and engagement. Confidence to seek HIV testing rose from 91.4% to 96.3%, ART initiation increased from 1 in 2022 to 401 in 2024, and confidence in discussing condom use rose from 58.3% to 71.6%. In Kenya, PrEP adherence, mental health empowerment, and self-esteem improved significantly. Over 70% of respondents credited digital incentives and reminders for motivating at least one SRH or HIV visit. Integrating behavioral economics with poverty analytics demonstrates that targeted digital engagement can effectively improve health-seeking behavior and reduce inequities among adolescents in multidimensional poverty.

Lafia is a telemedicine application offering a concierge platform that transforms how individuals access and experience healthcare. At the core of Lafia's model is the belief that meaningful care begins with a deep understanding of the patient, who they are, what they need, and how best to support their health journey. Through a private, user-centric system powered by artificial intelligence, Lafia bridges critical gaps in the healthcare system by personalizing service delivery, optimizing the allocation of health workers, and supporting wellness both within and beyond the health facility.
Lafia's solutions are designed to support any health provider who is willing to integrate technology into their practice, whether they work in a national referral hospital, a peri-urban health center, a private clinic, or a community drug shop. We do not replace existing systems; we enhance them by assisting providers in delivering more personalized, responsive, and supportive care.

High-quality data can help governments address persistent health changes such as maternal and neonatal deaths through targeted, evidence-based policies and interventions. Data from communities can complement facility-based data and offer a more complete picture of the situation and needs, especially in settings such as Nigeria where utilization of services in facilities is low. However, making this data available does not guarantee that it will be used for decision making and action. Traditional interventions focus on data availability and technical skills, but overlook the behavioral barriers that prevent data use. This study applied behavioral design to address how upstream government officials engage with maternal and newborn health data from communities.
Formative research identified behavioral barriers, including narrow mental models of what constitutes data-informed decisions, low standards for data use, concerns about data quality, and insufficient soft skills to communicate and advocate with data. We collaborated with government stakeholders to develop two complementary interventions: an integrated data report that combines community and facility data with decision guidance, and a two-day interactive workshop that builds both technical and soft skills for data use and communication.
A pilot demonstrated strong feasibility and acceptability and significant improvements in participants' confidence using and communicating about data, as well as meaningful implementation progress in the following weeks. This work illustrates how applying behavioral science to upstream actors can drive system-level change by supporting those who set priorities and allocate resources.

Understanding outcome, normative and behavioural control beliefs is an essential part of the formative research for any SBC campaign. Most often, this research is conducted using exclusively qualitative methods, as these are ideal for exploring the depth and nuance of the relationships between beliefs, intentions and action. Though conventional qualitative methods are helpful for identifying which beliefs exist in relation to a given behaviour, they cannot quantifiably tell us which beliefs have the strongest relationship to desirable intentions and actions. Moreover, the relatively small sample sizes recruited in qualitative research make many findings difficult to generalise across the breadth of population that a media campaign may aim to reach. We present a novel quantitative methodology to identify priority beliefs for SBC campaigns in a development context.
Stage 1 of this methodology ('Elicitation'), involves a rapid assessment of beliefs held by the target audience in relation to a desired behaviour. Stage 2 ('Belief Prioritisation') quantifies the relationships between belief strength and behavioural intentions, thus calculating: 1) the proportion of people not currently endorsing critical beliefs, and 2) the association between critical beliefs and behavioural intentions. These are then combined to estimate 'potential to gain': the potential change in intention achieved if everyone endorsed the belief. This novel method of message prioritization provides multiple benefits when planning and designing SBCC. It enables a logical and evidence-based prioritization of messaging, based on a theory-driven understanding of how beliefs drive intentions and subsequent behaviours, and is relatively resource efficient.