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Is health care expenditure across Europe converging? Findings from the application of a nonlinear panel unit root test

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Uneven patterns of health care expenditure are a prominent feature of late capitalist society. Across Europe, spearheaded by European Union (EU) economic integration, there continues to be debate concerning health care expenditure and, more specifically, to what extent there has been an apparent convergence or divergence. The extant literature is contradictory, inconclusive and potentially misleading, characterised as a ‘mixed bag’. Therefore, as a means of resolving some of these tensions, this paper tests the hypothesis that health care expenditure per capita has converged. Departing from a conceptual review of key factors influencing health care expenditure, this paper applies a non linear time series test to longitudinal data for 14 EU countries for the period 1970–2008. This paper fills a notable research gap by better accounting for the existence of nonlinearity in the growth dynamics of health care expenditure by utilising the nonlinear panel unit root test. Using different reference countries, we cannot reject the null hypothesis of unit root-evidence against the notion of convergence. This generates some notable policy implications and raises issues for those researching this topic.

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Fig. 1
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  1. For a more recent study, see Batalgi and Moscone (2010).

  2. For a comparative study of the EU member states’ health care system comparison, see Jakubowski and Busse (1998).

  3. In the first draft, \( \bar{g}_{t} \) was the average of all 14 EU countries. We received comments from anonymous reviewers who suggested excluding the country under consideration. The results remain the same.

  4. Moon et al. (2006), however, contend that the power of Breitung (2000) test is slower than claimed.

  5. Im et al. (2003) contend that the finite sample properties of the third scenario are better than that of Lein and Lin (1993).

  6. Im et al. (2003) contend that the LM-bar statistic requires less strict convergence criteria \( \left(\frac{{N_{1} }}{N} \to k\right) \) than that of Levin and Lih (1993).

  7. For the case of panel with unobservable common time specific components, see Sect. 4.1 of Im et al. (2003).

  8. For a more general case where the errors are serially correlated, Eq. (9) is extended to: \( \Delta y_{i,t} = a_{i} + \delta y_{i,t - 1}^{3} + \sum\limits_{h = 1}^{h - 1} \vartheta_{ih} \Delta y_{i,t - h} + \gamma_{i} f_{t} + \varepsilon_{i,t}. \)

  9. The trend is assumed to have a constant and linear trend.

  10. Our analysis is ‘heuristic’ since the minimum sample size for BDS statistic to have reasonable performance is 500.

  11. Due to small sample size, the BDS test is limited to ten countries.

  12. The choice of benchmark countries is dictated by data availability. The panel of 14 EU countries is unbalanced.


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Correspondence to Chi Keung Marco Lau.



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Lau, C.K.M., Fung, K.W.T. & Pugalis, L. Is health care expenditure across Europe converging? Findings from the application of a nonlinear panel unit root test. Eurasian Bus Rev 4, 137–156 (2014).

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