The effects of sector reforms on the productivity of Greek banks: a step-by-step analysis of the pre-Euro era


The paper analyses the effects on the productivity of Greek commercial banks of sector regulatory reforms in the pre-Euro era, using the Global Malmquist Index. In a bootstrap Data Envelopment Analysis framework, we propose an alternative to smoothing that utilises the Pearson system random number generator, offering greater flexibility in the choice of the fitting distribution. In the context of a step-by-step approach, we demonstrate the contribution of deregulatory commercial freedoms to greater productivity and the negative effect of prudential controls. Our findings offer insights into the current state of the Greek banking sector, suggesting that the imposition of additional prudential controls may have a detrimental impact on the productivity of Greek banks, given the adverse business conditions.

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  1. 1.

    Their results also indicate a small decline in the average technical, allocative and cost efficiency after the first year of (de)regulation. Although the authors do not comment on this, it could be attributed to the fact that, in 1995, Chinese commercial banks were banned from participating in non-traditional banking activities such as insurance or securities trading.

  2. 2.

    The early literature provides evidence in support of CRS in the form of flat, U-shaped cost curves (Berger et al. 1993), while later studies turn their attention to unexploited economies of scale by small banks that provide arguments in support of variable returns to scale (VRS) (Berger and Mester 1997). However, Matthews and Thompson (2014) argue that studies of scale economies exist in mature banking markets that accommodate small banks alongside large multinational ones, and conclude that the potential for scale economies is left open in the literature. The choice of CRS in this paper is justified by the fact that CRS are associated with the minimisation of long run average costs and the exploitation of economies of scale, which we deem to be one of the desirable effects of deregulation. The CRS assumption is also consistent with a service industry. In contrast, the VRS assumption that may be appropriate for short periods is questionable in a dataset covering 13 years. Another reason for the choice of the CRS assumption is that the median scale efficiency is 0.989, suggesting that half of the DMUs in the sample are associated with a scale efficiency between 0.99 and 1.

  3. 3.

    Note that in the computation of the first four moments (sample mean, variance, skewness and kurtosis) the Pearson system uses the standard deviation.

  4. 4.

    The sufficient criteria for the characterisation of a distribution type are: Type 0: \(c_1 =0,\,\beta _2 =3\); Type I: \(\kappa <0\); Type II: \(\beta _1 =0,\,\beta _2 <3\); Type III: \(2\beta _2 -3\beta _1 -6=0\); Type IV: \(0<\kappa <1\); Type V: \(\kappa =1\); Type VI: \(\kappa >1\); Type VII: \(\beta _1 =0,\,\beta _2 >3\).

  5. 5.

    Although this could be considered as a limitation of the proposed approach, simulations show that the practice of truncating the distribution has a negligible effect on the results, especially as the sample size increases. In particular, we conducted separate Monte Carlo simulations and computed the median absolute differences (MADs) between the moments of the truncated “pseudo-populations” and the moments that would result without truncation. The resulting MADs are too small to affect the position or shape of the distribution, while for samples larger than 100 units the differences become negligible. The results of these tests are available upon request.

  6. 6.

    We use \(1,2,\ldots N\) to denote all DMUs included in the unbalanced panel of DMUs that comprise the global reference set. We use the time subscripts for the DMU under evaluation to ease the exposition, as we find this presentation more straightforward when it comes to the computation of productivity change over time.

  7. 7.

    We should note that, given that GMI is circular in the sense that \(M^{G}\left( {x_k^t ,y_k^t ,x_k^{t+\kappa } ,y_k^{t+k} } \right) =M^{G}\left( {x_k^t ,y_k^t ,x_k^{t+1} ,y_k^{t+1} } \right) \cdot \ldots \,\, M^{G}\left( {x_k^{t+k-1} ,y_k^{t+k-1} ,x_k^{t+k} ,y_k^{t+k} } \right) \), etc., the same test can be applied between any two periods t and \(t+\kappa \).

  8. 8.

    We also include a second artificial bank, acting as a representative large bank, using the weighted averages (weighted each year by total assets) of the variables of interest, to examine the extent to which the market is driven by large banks. The qualitative results do not change and we therefore discuss the results for the Average Bank for concision. Hence, the sample comprises 216 DMUs, of which 26 correspond to the aforementioned artificial observations.

  9. 9.

    Bankscope defines other securities as the sum of investments of banks to associates through equity and other securities, which in turn includes bonds, equity derivatives and any other type of security.

  10. 10.

    The same information for each bank can be found in the supplementary file, and we use this in our discussion.

  11. 11.

    As a robustness check, we have applied the same methodology but using Simar and Wilson’s (1998) bootstrap DEA and under two different smoothing methods: least squares cross-validation and Sheather and Jones’ (1991) plug-in estimator. The position and width of the confidence intervals changes slightly, which relates to differences in the performance in the relevant simulations conducted by the authors. The results are available from the authors upon request.


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Correspondence to Panagiotis Tziogkidis.

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Tziogkidis, P., Matthews, K. & Philippas, D. The effects of sector reforms on the productivity of Greek banks: a step-by-step analysis of the pre-Euro era. Ann Oper Res 266, 531–549 (2018).

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  • Bank productivity
  • Bank regulation
  • Global Malmquist Index
  • Moments bootstrap DEA

JEL Classification

  • C14
  • G21
  • G28