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Due to the lack of internal consistency across unit root and cointegration test methods for short time-series data, past research findings conflict on whether the OECD health expenditure data are stationary. Stationarity reasonably guarantees that the estimated OLS relationship is nonspurious. This paper departs from past investigations that applied asymptotic statistical tests of unit root to insufficient time-series lengths. Instead, data were calibrated in annual growth rates, in 5-year (1968-72, ..., 1993-97) partitions, for maximum likelihood estimation using flexible Box-Cox transformations model and bias-reducing jackknife resampling plan for data expansion. The drivers of OECD health care spending growth are economic and institutional. Findings from the growth convergence theory affirm that health care expenditure growth accords with conditional beta convergence. Statistical significance and optimal functional form models are not unique across the growth period models. Our findings exemplify the benefits of jackknife resampling plan for short data series, and caution researchers against imposing faulty functional forms and applying asymptotic statistical methods to short time-series regressions. Policy implications are discussed.