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Using incentives to attract nurses to remote areas of Tanzania: a contingent valuation study
Michael A Munga1, Gaute Torsvik, Ottar Mæstad
1Health systems and Policy Reserach/Monitoring and Evaluation Departments, National Institute for Medical Research, P.O BOX 9653, DAR ES SALAAM 999, United Republic of Tanzania. michaelmunga@yahoo.com.
Abstract:
This article analyses (1) how financial incentives (salary top-ups) and non-financial incentives (housing and education) affect nurses' willingness to work in remote areas of Tanzania and (2) how the magnitude of the incentives needed to attract health workers varies with the nurses' geographic origin and their intrinsic motivation. A contingent valuation method was used to elicit the location preferences of 362 nursing students. Without any interventions, 19% of the nurses were willing to work in remote places. With the provision of free housing, this share increased by 15 percentage points. Better education opportunities increased the share by 28 percentage points from the baseline. For a salary top-up to have the same effect as provision of free housing, the top-up needs to be between 80 and 100% of the base salary. Similarly, for salary top-ups to have the same effect as provision of better education opportunities, the top-up should be between 120 and 140%. Our study confirms results from previous research, that those with a strong intrinsic motivation to provide health care are more motivated to work in a remote location. A more surprising finding is that students of older age are more prepared to take a job in remote areas. Several studies have found that individuals who grew up in a remote area are more willing to work in such locations. A novel finding of our analysis is that only nursing students with a 'very' remote origin (i.e. those who grew up farther from a district centre than the suggested remote working place) express a higher willingness to take the remote job. Although we do control for nursing school effects, our results could be biased due to omitted variables capturing individual characteristics.
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