Graziella Bonanno

Dr. Graziella Bonanno

Department of Economics and Statistics, University of Salerno, Via Giovanni Paolo II 132 – 84084, Fisciano (SA), Italy

Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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12 January 2022 in Business Strategy and the Environment
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Journal Article
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GBGiuliana Birindelli
Giuliana Birindelli
Graziella BonannoGraziella Bonanno
Graziella Bonanno
SDStefano Dell'ATti
Stefano Dell'ATti
Antonia Patrizia IannuzziAntonia Patrizia Iannuzzi
Antonia Patrizia Iannuzzi

11 January 2017 in Journal of Economic Surveys
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Journal Article
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FAFrancesco Aiello
Francesco Aiello
Graziella BonannoGraziella Bonanno
Graziella Bonanno

20 January 2017 in Journal of Productivity Analysis
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Graziella BonannoGraziella Bonanno
Graziella Bonanno
DGDomenico De Giovanni
Domenico De Giovanni
Domma FilippoDomma Filippo
Domma Filippo

28 January 2019 in Journal of Economic Surveys
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Journal Article
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FAFrancesco Aiello
Francesco Aiello
Graziella BonannoGraziella Bonanno
Graziella Bonanno

20 November 2020 in Current Issues in Tourism
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Journal Article
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FAFrancesco Aiello
Francesco Aiello
Graziella BonannoGraziella Bonanno
Graziella Bonanno
FFFrancesco Foglia
Francesco Foglia
This note focuses on the impact of coronavirus on Italian tourism. Using a sample of 1056 travellers, we find a positive relationship between the security of destination and the probability to accommodate in hotels and B&B. Furthermore, regional contagion is negatively associated to the willingness to pay for accommodation services. The policy implications are twofold. Firstly, hotels/B&B claim for financial support to ensure social distancing and, thus, security that will attract tourists. Secondly, public finance could sustain the demand of tourist services in hotels and B&B which is lowering because of coronavirus.

14 September 2015 in International Review of Applied Economics
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Journal Article
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FAFrancesco Aiello
Francesco Aiello
Graziella BonannoGraziella Bonanno
Graziella Bonanno
One learns two main lessons from studying the great quantity of banking efficiency literature. These lessons regard the heterogeneity in results and the absence of a comprehensive review aimed at understanding the reasons for this variability. Surprisingly, although this issue is well-known, it has not been systematically analyzed before. In order to fill this gap, we perform a Meta-Regression-Analysis (MRA) by examining 1661 efficiency scores retrieved from 120 papers published over the period 2000–2014. The meta-regression is estimated by using the Random Effects Multilevel Model (REML) because it controls for within- and between-study heterogeneity. The analysis yields four main results. First, parametric methods yield lower levels of banking efficiency than nonparametric studies. This holds true even after controlling for the approach used in selecting the inputs and outputs of the frontier. Secondly, we show that banking efficiency is highest when using the value-added approach, followed by estimates from studies based on the intermediation method, whereas those based on the hybrid approach are the lowest. Thirdly, efficiency scores are also determined by the quality of studies and the number of observations and variables used in the primary papers. As far as the effects of sample size, dimension and quality of papers are concerned, there are significant differences in sign and magnitude between parametric and nonparametric studies. Finally, cost efficiency is found to be higher than profit efficiency. Interestingly, MRA results are robust to the potential outliers in efficiency and sample size distributions.