One Size Does Not Fit All: Investigating Doctors' Stated Preference Heterogeneity for Job Incentives to Inform Policy in Thailand

Abstract

This study investigates heterogeneity in Thai doctors' job preferences at the beginning of their career, with a view to inform the design of effective policies to retain them in rural areas. A discrete choice experiment was designed and administered to 198 young doctors. We analysed the data using several specifications of a random parameter model to account for various sources of preference heterogeneity. By modelling preference heterogeneity, we showed how sensitivity to different incentives varied in different sections of the population. In particular, doctors from rural backgrounds were more sensitive than others to a 45% salary increase and having a post near their home province, but they were less sensitive to a reduction in the number of on-call nights. On the basis of the model results, the effects of two types of interventions were simulated: introducing various incentives and modifying the population structure. The results of the simulations provide multiple elements for consideration for policy-makers interested in designing effective interventions. They also underline the interest of modelling preference heterogeneity carefully.

The use of discrete choice experiments to inform health workforce policy: a systematic review

“…“Rural clinic” versus “Urban hospital” (Figure 1). Generic designs were used by the majority of studies (74.1%, 20/27), although seven studies featuring a labelled design in the last three years [4, 52–54, 57, 65, 69]. All of these studies presented rural versus urban alternatives, except the above study by Lagarde et al that also included jobs overseas and in private facilities [65].…”

“…All of these studies presented rural versus urban alternatives, except the above study by Lagarde et al that also included jobs overseas and in private facilities [65]. The use of labelled designs in this way can enhance realism for participants by allowing alternative-specific attributes to be defined in order to avoid unrealistic combinations that might lead to participant confusion and/or disengagement with the questionnaire (for example, the availability of private practice in rural posts) [4, 54, 56, 75]. Labelled designs can also provide choices between additional qualities associated with the labels by participants, but not captured by the limited number of attributes [75].…”

“…As described earlier, quite heterogenous populations are typically included in health DCEs, for which latent class models may be able to separate into subgroups with more similar (and accurate) preferences depending on characteristics, for example years of work experience or growing up in a rural area. Four studies (14.8%) used an extension of mixed logit, generalised multinomial logit models, with three of these finding a better fit to data than comparator mixed logit or logit models [51, 54, 58, 62]. Generalised multinomial logit models are able to account for scale heterogeneity of preferences as well as taste heterogeneity, i.e.…”

“…Lagarde et al [54] went further by examining the uptake of rural jobs by Thai doctors under different incentive policies for i) the original population; ii) three hypothetical populations with differing proportions of doctors with rural/urban backgrounds; iii) undergraduate training in Bangkok as opposed to outside the capital. Sivey et al [51] investigated specialty choice for junior doctors in Australia with an unlabelled design consisting of attributes describing various job aspects, but then used data from the accompanying survey sent to all Australian doctors to set typical levels for the same attributes for specialist doctors versus general practitioner (e.g.…”

Quality of care provided by mid-level health workers: systematic review and meta-analysis

“…Any task-sharing strategy should be implemented alongside other strategies designed to increase the total number of health-care workers. [79][80][81][82] The main obstacle to ensuring that mid-level health workers can help improve health outcomes is that they are often ignored by government policies, health workforce strategies and health system support measures, despite their widespread use. Until these workers are more comprehensively taken into account and supported, their potential contribution will not be fully realized.…”