Determinants of health-system efficiency: evidence from OECD countries

Note that the literature on efficiency measurement in health production is enormous at the hospital level (e.g. Athanassopoulos et al. 1999; Dismuke and Sena 1999; Hofmarcher et al. 2002). Hollingsworth et al. (1999) and Hollingsworth (2003) provide an overview.

In any case, we still consider that our results should be interpreted with caution as emphasized in Spinks and Hollingsworth (2009).

Note that this latter finding is somehow in contrast with previous literature. For instance, gatekeeping has been traditionally associated with lower utilization (Bolin et al. 2009), lower costs (Gerdtham et al. 1998) and higher efficiency (Bhat 2005).

Moreover, the efficiency measures used in the literature may refer to different levels of the healthcare system. Generally, a distinction is made between those papers that measure gains in health status for each type of illness across the various sub-sectors of the healthcare system (hospitals, chemist’s, etc.) or the healthcare system as a whole (see Häkkinen and Joumard 2007, for a discussion of the advantages and drawbacks of these three approaches).

More detailed information on the definition of the indicators and their construction can be found in Paris et al. (2010) and OECD (2010).

For the sake of brevity we do not report the alternative SFA rankings but they are available upon request.

See Table 9. Additional information about the results using the alternative SFA efficiency index is available upon request.

Moreover, unreported Hausman tests clearly indicate that the fixed effects specification is more appropriate than the alternative random effects specification within the panel SFA approach.

Despite there is no consensus in the literature about the appropriateness of Tobit-type approaches in this setting due to the inexistence of a “latent efficiency”, Fried et al. (1999) and Rosko (1999) are examples of previous efficiency studies considering Tobit-based estimators. Note also that, if there are no censored observations, the Tobit approach is equivalent to the least squares approach; hence, given that the number of censored countries is very low in our sample, we expect the Tobit results to be similar to the least squares results.

Considering the random priors in Ley and Steel (2009), the prior inclusion probability for a given variable follows a Beta distribution so that it does not necessarily coincide for all the variables and a meaningful (and common) threshold is therefore not available.

Due to its single-case nature and as a result of masking, the employed deletion diagnostic can fail in the presence of multiple unusual countries jointly influencing the results.

Paris et al. (2010) provides detailed information on all the twenty indicators.

Moreno-Torres et al. (2010) analyze the effects of 16 regulatory policies in Catalonia between 1995 and 2006, classifying the policies in five groups: (i) those aimed at reducing the margins of drug distributors and retailers; (ii) those based on lists of drugs excluded from receiving public funding; (iii) those in which the public authorities unilaterally impose a reduction on drug manufacturers’ maximum selling prices; (iv) those based on reference prices, i.e. when there are several drugs with the same characteristics and end-use and Footnote 13 continued a reference price is set on the basis of the cheapest drug in the group, which will be the maximum amount that the public health system may reimburse for any drug in the group; (v) regulations whose purpose is to economically generic drugs. Given these five types of policies, the results in the paper indicate that, on one hand, 12 of the 16 regulations were not effective in reducing spending on drugs; and, on the other, of the four regulations that were effective in the short term; none had significant effects in the medium/long term. Sood et al. (2009) obtain the same result using different data and methodology.

Note that this finding provides support for the theoretical “excessive specialization” channel proposed by Brekke et al. (2007). However, it is in contrast with previous empirical literature, which has typically found that higher degrees of gate keeping are associated with lower utilization (Bolin et al. 2009), lower costs (Gerdtham et al. 1998) and higher efficiency (Bhat 2005).

The efficient country will be the one with the higher life expectancy given certain inputs. This may arise because life expectancy is higher for the same given level of expenditure, or because for the same life expectancy, there is a lower level of expenditure or of other inputs.

Note that dummy variables are also included for each year, reducing the potential correlation between each country’s error terms. This is a vital prerequisite for the consistency of the estimates.

In fact, the OECD (2010) also performs very similar panel regressions to that considered in this paper. However, it does not calculate SFA efficiency indices in the proper sense of the term; it rather uses estimated residuals as proxies of efficiency.

Even if the posterior inclusion probability is lower than the prior inclusion probability for a given variable, it might be the case that this particular variable is important to decision-makers under some circumstances. Therefore, although useful for presentation purposes, the mechanical application of a threshold, or a simple comparison between the prior and the posterior, should often be avoided.

While the ratio of posterior mean to posterior standard deviation is not distributed according to the usual t-distribution, having a ratio around two in absolute value indicates an approximate 95 % Bayesian coverage region that excludes zero (see Sala-i-Martin et al. 2004).