health workforce data, which are the result of concerted efforts by the WHO to gather cross‐ national data on workforce numbers since 1990. Because the demand model requires rich historical data on health worker densities, separate models for nurses/midwife and all other health professionals could not be estimated; data for these cadres are insufficient to produce demand projections. Thus, we first predicted number of physicians from the demand model, and then apply constant ratios to obtain estimates of nurses/midwives and all other health workers (AOWs) to obtain the total projected number of health workers.

In most health systems, spending on health workforce wages and benefits represents a significant share of total health expenditures (Hernandez et al 2006). Previous studies indicate that overall economic growth as measured by national income is the best predictor of health expenditures, from which the demand for health workers is derived (Cooper et al. 2003; Getzen 1990). In other words, spending on health care tends to increase as overall income increases, which in turn suggests that more workers can be employed to deliver health services (Cooper et al. 2003; Scheffler et al. 2008).

To our knowledge, few have previously projected future health workforce labor market demand. Owing to data requirements, early works largely focus on specific developed countries for which data on health workers are more readily available (e.g. Korch et al. 2012, Basu & Gupta 2004). Leveraging efforts to obtain cross‐national and longitudinal data on health workers, Scheffler et al. (2008) were the first to forecast the demand, need, and supply of physicians for 158 countries with suitable data. While notable in the scope of global coverage, their resulting model relied on only one model parameter input—gross national income—to generate projections.

Our demand model expands on previous methods developed by Scheffler et al. (2008). In addition to income (i.e. gross domestic product (GDP) per capita), we include measures for demographic and health coverage patterns that also drive demand for health workers (Getzen 1990; Cooper et al. 2002; Cooper et al., 2003). The size of the population aged 65 or over is used as an indicator of aging, which is known to increase the demand for health services (Cooper et al., 2002). We also include private per capita household out‐of‐pocket (OOP) spending on medical care as a proxy indicator for the extent of social protection for health care spending. While overall health care spending may trend upward with national income, the portion spent OOP is largely determined by the level of health coverage by health insurance, government subsidies and other forms of risk pooling and financial protection (Newhouse 1977). Less generous health coverage leaves individuals to pay more OOP, which is expected to lower the demand for and use of health services. Thus, we expect higher OOP health spending to be correlated with lower demand for health workers. We exclude additional structural factors affecting the labor market, such as attrition, training capacity, labor regulations, and migration, as these data are largely unavailable across countries or over time.

The following section provides a summary description of the estimation model. A detailed description of the demand projection methodology, model specification choice, and imputation for missing data is provided in Annex A. Due to the missing data problems for nurses/midwives 5