Abstract
A child’s health is an important determinant of their educational attainment and their subsequent lifetime earnings (Dercon and Porter, 2014; Bhalotra et al., 2022). In LMICs, poor health is a major cause of absenteeism in schools (Banda, 2017; Kabanga and Mulauzi, 2020) and is known to lower cognitive abilities (Clarke et al., 2017). A key determinant of child health are parental decisions to seek appropriate care in a timely manner or to invest in preventative measures. However, these decisions may not be optimal: for example, 40% of severe malaria cases could be averted if treatment was sought within the first day of symptoms (Mousa et al., 2020). In addition, there is evidence of differential decision making between younger and older children, which matches policy prioritisation of children under 5. Children over 5 are less likely to sleep under a bed net (Olapeju et al., 2018; Walldorf et al., 2015), parents are less likely to seek treatment when older children are ill (Walldorf et al., 2015), and when they do, they are less likely to seek care from formal providers (Coalson et al., 2019). As a result, this age group faces a considerable burden of disease (Hill, Zimmerman and Jamison, 2017). In Zambia, the setting for the study, the prevalence of malaria is highest in children aged 5-17, with 40% of children testing positive in endemic areas (Pinchoff et al., 2016).
In this study, I set out to measure parental beliefs regarding the costs of and returns to health-seeking behaviour in children of different age and gender in order to capture whether inaccurate beliefs lead to the observed inefficiencies. I will elicit beliefs and preferences using hypothetical vignettes, which will provide a detailed and novel dataset on parental beliefs by child characteristics. The use of vignettes enables me to vary these characteristics (age and gender) which could not otherwise be randomised. Using three vignettes, about diarrhoea, malaria, and a common cold, I will estimate the following parameters of parental beliefs: (1) expectations regarding costs of treatment and prevention measures, (2) expected returns to prevention measures, captured by changes in the expected likelihood of an illness episode with and without the measure, and (3) expected returns to acute care, or the difference between expected illness duration for the child with and without any treatment.