“…See also the comprehensive review of DEA studies in education by Fuentes, Fuster, and Lillo-Banuls [99] (pp. [91][92][93] regarding the applied inputs and outputs which are highly influential for the calculated efficiency results (in comparison with the number of observed DMU). This baseline is further analyzed regarding time-series changes in the following years until 2016.…”
Section: Discussionmentioning
confidence: 99%
“…This connects to the fact known widely in management science, and especially with accounting and controlling, that achieving short-term improvements is a totally different game than sustaining long-term enhancements, in this case a six-year period [90][91][92]. The success of German institutions in the sample may be connected to the experience that these institutions had to adapt to the international ranking system, regarding the specific data gathering in this arena, within the analyzed timeframe.…”
“…In addition, a standardization approach for each indicator is used based on the distribution of data within a particular indicator-a cumulative probability function using a version of Z-scoring (see [57] (pp. [91][92][93] and [58]). Within the applied dataset from THE, not all out of the five indicator values reached a maximum of 100 among the selected 70 European universities, as in some cases the 100 maximum value was attained by a non-European university (Australia, Canada, China, the United States, etc.…”
An increasing effort has been put into dealing with the question of time-series analysis regarding institutional efficiency, including in the area of higher education. Universities are important institutions for economies and societies and are expected to provide excellence as well as efficiency in their processes and outputs. This is reflected in the context of an increased global competitive environment by more refined international university rankings. Combining the two areas, this paper points towards a methodological challenge in comparing different ranking datasets for their use in a data envelopment analysis (DEA) Malmquist index time-series efficiency analysis, namely, index-based data compared to additive data. The problem is discussed in a theoretical framework and complemented with an empirical application: calculations for 70 European universities with budget and staff input data and different ranking output data for the timeframe of 2011-2016 show that there is no evidence for a specific index data problem. Important implications regarding university management and higher education policies are outlined. Efficiency improvements among the analyzed universities are significant but also unevenly distributed and not easy to obtain for individual institutions.
“…See also the comprehensive review of DEA studies in education by Fuentes, Fuster, and Lillo-Banuls [99] (pp. [91][92][93] regarding the applied inputs and outputs which are highly influential for the calculated efficiency results (in comparison with the number of observed DMU). This baseline is further analyzed regarding time-series changes in the following years until 2016.…”
Section: Discussionmentioning
confidence: 99%
“…This connects to the fact known widely in management science, and especially with accounting and controlling, that achieving short-term improvements is a totally different game than sustaining long-term enhancements, in this case a six-year period [90][91][92]. The success of German institutions in the sample may be connected to the experience that these institutions had to adapt to the international ranking system, regarding the specific data gathering in this arena, within the analyzed timeframe.…”
“…In addition, a standardization approach for each indicator is used based on the distribution of data within a particular indicator-a cumulative probability function using a version of Z-scoring (see [57] (pp. [91][92][93] and [58]). Within the applied dataset from THE, not all out of the five indicator values reached a maximum of 100 among the selected 70 European universities, as in some cases the 100 maximum value was attained by a non-European university (Australia, Canada, China, the United States, etc.…”
An increasing effort has been put into dealing with the question of time-series analysis regarding institutional efficiency, including in the area of higher education. Universities are important institutions for economies and societies and are expected to provide excellence as well as efficiency in their processes and outputs. This is reflected in the context of an increased global competitive environment by more refined international university rankings. Combining the two areas, this paper points towards a methodological challenge in comparing different ranking datasets for their use in a data envelopment analysis (DEA) Malmquist index time-series efficiency analysis, namely, index-based data compared to additive data. The problem is discussed in a theoretical framework and complemented with an empirical application: calculations for 70 European universities with budget and staff input data and different ranking output data for the timeframe of 2011-2016 show that there is no evidence for a specific index data problem. Important implications regarding university management and higher education policies are outlined. Efficiency improvements among the analyzed universities are significant but also unevenly distributed and not easy to obtain for individual institutions.
“…In China, Berger, Hasan and Zhou (2009) conclude that foreign banks are more cost and profit efficient than all types of domestic banks. In India, Gulati and Kumar (2016) observe that foreign banks are more profit efficient than domestic banks. In Pakistan, Bonaccorsi di Patti and Hardy (2005) find that domestic state-owned banks are less efficient than domestic private banks and foreign banks, while privatized domestic banks can outperform foreign banks.…”
There has been a large expansion of foreign banks in Africa over the two decades with Pan-African banks playing a key role in this phenomenon. This paper questions if this development is beneficial for bank efficiency in African countries by investigating if Pan-African banks are more efficient than other types of foreign banks and domestic banks. We analyze the relation between ownership type and bank efficiency on a large sample of African banks covering 39 African countries over the period 2002-2015. We find that Pan-African banks are the most efficient banks in African banking industries. We explain this finding by the fact that these banks combine the best of both worlds: they have the global advantages of foreign banks and the home field advantages of domestic banks. They are then able to be more efficient than foreign banks from developed countries but also than domestic banks. This suggests that favoring entry of Pan-African banks would be beneficial to bank efficiency in Africa.JEL Codes: G21, G32, N27.
“…Scholars also compared SFA with DEA such as Bhattacharyya, Lovell, and Sahary () and Huang and Wang (). Isik (); Staub, Souza, and Tabak (); Paradi, Zhu, and Edelstein (); Chiu, Luo, Chen, Wang, and Tsai (); Ayadi (); Gulati and Kumar ();…”
Previous bank efficiency studies assumed that the same efficient frontier system exists in different types of banks, but the conventional radial data envelopment analysis (DEA) model slacks are not counted in efficiency scores, and nonradial DEA models do not consider radial features. This paper develops an Epsilou‐based measure meta‐DEA approach for measuring the efficiencies and technology gaps of different bank types. The results are as the follows: The average efficiency and technology gaps of nonfinancial holding banks are better than those of financial holding banks. The nonfinancial holding banks are more efficient than financial holding banks in investment and other income.
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