Training, motivating and retaining human resources is crucial for the improvement of health outcomes, especially in low and middle-income countries (LMICs) where human resources availability and management have been recognized as one of the key health system’s barriers to the achievement of the Millennium Development Goals. In recognition of the limitations of current financial incentives and/or remuneration levels, Performance-Based Financing (PBF) mechanisms have been introduced in many LMICs in recent years.
This project took advantage of an existing field experiment funded by the World Bank (WB) that introduced PBF for some randomly selected primary care facilities in Benin in 8 pilot districts since March 2012. While the impact evaluation of the WB project focuses mainly on the effects of the financial incentives on the uptake of health services and health outcomes in the population, the current project complements this evaluation by collecting detailed information of individual health care provider performance and motivations, in order to test the impact and causal pathways through which financial incentives operate.
The objective of this mixed-methods survey is to explore the impact of PBF on health worker performance as well as to propose some explanations. It examines the impact of PBF on different dimensions of health worker performance, including clinical productivity (i.e. absenteeism, average time per patient, reduction of slack, etc.), quality of care and responsiveness to patients as well as reasons for change, namely the improvement of “can-do” skills and knowledge, working environment and/or “will-do” attitude (motivation/effort) or contextual factors (level of management autonomy).
We surveyed 434 qualified health care workers (doctors, nurses, midwives) in 135 primary care facilities located in three different groups of facilities: (1) those receiving quarterly financial bonuses linked to the facility performance, (2) those receiving an equivalent amount of financial resources independently of their own performance and (3) some “pure” control ones, receiving no additional resources. We collected information on health workers' characteristics using a standard questionnaire, and measured several dimensions of the performance of health workers thanks to direct observations, patient exit interviews and a time-and-motion survey.
Results from the study suggest that the PBF intervention in Benin improved some aspects of the performance of health workers: positive impact on quality of care and responsiveness towards patients but has no significant impact on clinical productivity. There was no difference on the volume of activities in facilities that received the conditional bonus compared to those that received additional resources unconditionally. However, there was a positive impact of the bonuses on a variety of measures of quality of care provided by the staff (observed and reported by patients), which provides some evidence of the successful emphasis on quality of the PBF intervention in Benin. The absence of impact on clinical productivity can be explained by the strong focus of the PBF scheme in Benin on the quality of care. The absence of negative impact on the number of patients seen also suggests that HWs were operating below their production constraint.
Comparing facilities that received the PBF bonuses to the facilities in the pure control group, we found relatively small and inconsistent effects of PBF. The PBF facilities were not associated with higher volume of activities, but there was a slight reduction in costs associated with PBF. There was no evidence of a difference in the resources of facilities available to facilities in the PBF group compared to the pure control group. For some of these outcomes, this lack of results might be partly due to the lack of power to detect differences in outcomes measured at the facility level. Besides, the lack of equivalence of the pure control group makes it harder to control for all potential factors that may explain differences (or lack thereof). Further analyses will use matching techniques to reduce differences between PBF and pure control facilities.