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ABSTRACT

Across the OECD, healthcare spending has typically outpaced economic growth in recent decades. While such spending has improved health outcomes, there are concerns about the financial sustainability of this upward trend, particularly as healthcare systems are predominantly funded from public resources in most OECD countries. To better explore this financial sustainability challenge, many countries and international institutions have developed forecasting models to project growth in future healthcare expenditure.

Despite methodological differences between forecasting approaches, a common set of healthcare spending drivers can be identified. Demographic factors, rising incomes, technological progress, productivity in the healthcare sector compared to the general economy (Baumol's cost disease) and associated healthcare policies have all been shown to be key determinants of healthcare spending.

* For demographics, death-related costs has been shown to be the main factor behind increasing healthcare costs, with costs for people in their last year before death between 2 and 15 times higher than for people who survive. The impact of ageing on increased health expenditure, then, is predominantly in terms of the share of a country's population being close to death (non-survivors).

* For income, most cross-country econometric studies have found income elasticity (the relative share of GDP allocated to the health sector as a country becomes richer) in high-income countries to be less than one, after other spending drivers are accounted for. More specifically, the average elasticity estimate is 0.75. At the same time, there is evidence that low- and middle-income countries show higher elasticities.

* Low productivity in the health sector - commonly referred to as Baumol's cost disease - has been widely documented in high-income country settings. On average, the literature points to over half of productivity gains in the overall economy being translated to wage increases in the healthcare sector.

* Technology has also been shown to have, on aggregate, a positive impact on health spending. Estimates of its exact effect, and the methods used to derive such estimates, vary widely.

Based on this review, this paper sets out a theoretical framework for forecasting health expenditure trends that reflects the relative strengths of each of these drivers through panel regressions, while ensuring that modelling assumptions are transparent and internally consistent. Results from the literature offer plausible ranges for key model variables....