Airports Managerial Practices and Efficiency Study
The 2019 Spanish STATA Conference
Madrid, 17th October 2019
A
irports’
managerial practices and efficiency
Ane
Elixabete Ripoll-Zarraga
(Universidad Pontificia de Comillas)
Sonia Huderek-Glapska
(Poznan University of Economics and Business)
R
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o
n
a
l
e
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Airports are companies
perceived both as public utility infrastructure and
market driven business (Doganis 1992, Jarach 2001, Gillen 2011, Graham 2009,
2013).
•
A growing literature emphasizes
differences in management practices as a
source of companies productivity differences
(e.g. Bloom and Van Reenen, 2011
and Bloom et al., 2016)
;
better management practices are associated with better
performance (total factor productivity, profitability, survival etc.) (Guner at al.
2018).
•
Different path of growth of airports
is observed even those located within the
area of similar environmental characteristics.
•
Search for
internal factors
affecting airport performance and development.
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Impact of managerial qualification and experience on airports’ technical efficiency
•
SFA Output distance function: Environmental variables (Battese and Coelli, 1995)
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•
The results conclude that aircraft movements and non-aeronautical
income are relevant outputs enhancing passengers.
•
Costs such as depreciation, operating and non-operating costs have a
negative impact.
•
Airports experimenting changes in management drop their efficiency,
as well as airports with competitors (medium and small) in their
catchment area.
•
The field of specialisation in air transport is not relevant or in some
cases may increase inefficiency.
•
Airports are individual business with specific organisational and
corporate governance that is essential to take into account even
though the manager has relevant experience managing other
airports.
Thank you for your attention!!!
Discussion
Insight into the importance of managerial knowledge and experience for the technical efficiency of Polish regional airports
The airports‘ operators competences and skills are considered as environmental variables meaning that are not controllable or changeable at least in the short-term. However it is changeable since the CEOs are changing! In sesthe short term you can graduate cour
in the shortermi, in the period of analysis there were 3
This study delves into the impact of managerial qualification and experience on airports' technical efficiency. It analyzes a range of variables from 2009-2017 at Polish airports, exploring factors influencing productivity disparities in airport management practices and their implications. Through a strategic management lens, the research investigates how internal and external factors shape airport performance and development.
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Presentation Transcript
The 2019 Spanish STATA Conference Madrid, 17th October 2019 Airports managerial practices and efficiency Ane Elixabete Ripoll-Zarraga (Universidad Pontificia de Comillas) Sonia Huderek-Glapska (Poznan University of Economics and Business)
Rationale Rationale Airports are companies perceived both as public utility infrastructure and market driven business (Doganis 1992, Jarach 2001, Gillen 2011, Graham 2009, 2013). A growing literature emphasizes differences in management practices as a source of companies productivity differences (e.g. Bloom and Van Reenen, 2011 and Bloom et al., 2016); better management practices are associated with better performance (total factor productivity, profitability, survival etc.) (Guner at al. 2018). Different path of growth of airports is observed even those located within the area of similar environmental characteristics. Search for internal factors affecting airport performance and development.
Rationale Rationale Level of analysis Factors (strategic management perspective) Associated with the manager Structure of the company Micro Company s financial situation Company's competitive position Related to the industry Medium Related to the region Economic situation Technological and Educational environment Macro Political and legal situation Socio-cultural environment
Aim of research Aim of research Impact of managerial qualification and experience on airports technical efficiency SFA Output distance function: Environmental variables (Battese and Coelli, 1995) ? ???= ??????+ ??? ?=1
V Variables ariables: Inputs : Inputs- -Outputs (2009 Outputs (2009- -2017, Polish airports, n=12) 2017, Polish airports, n=12) Variable Observations Mean Standard Dev. Minimum Maximum Passengers 102 2,238,245 2,959,325.93 5,697 15,730,330 Air Traffic Movements 102 22,777 33,516.49 42 157,044 Non-Aeronautical Income (th ) 104 2,264 2,635.14 0.79 15,426 Labour Costs (th ) 104 9,513 18,401.35 256 115,621 Depreciation Assets (th ) 104 5,370 6,832.60 6.75 29,825 Operating Costs (th ) 104 9,882 10,680.12 339 42,027 Non-Operating Costs (th ) 104 1,840 7,556.61 2.32 74,593 Land (th ) 104 82,939 72,069.55 6,102 340,921 Equity Shares (th ) 104 48,624 26,941.79 1.02 111,036
V Variables ariables: Environmental (2009 : Environmental (2009- -2017, Polish airports, n=12) 2017, Polish airports, n=12) Variable Observations Mean Standard Dev. Minimum Maximum Terminal PAX 105 1.41 0.55 1 3 Number of Runways 105 1.48 0.88 1 4 Catchment 105 1.34 0.96 0 3 Train 105 0.33 0.47 0 1 Qualifications 105 0.50 0.71 0 1 Experience 102 4.74 3.39 0 13 Change in CEO 102 0.12 0.32 0 1
Results: Stochastic Frontier Analysis ( Results: Stochastic Frontier Analysis (Battese Variable Constant LnATM LnNon-Aeronautical Income LnWages LnDepreciation LnOperating Costs LnNon Operating Costs LnLand LnEquity Shares Constant Qualifications Change in CEO Number of Runways Catchment Area Train Type of Airport Terminal PAX Battese and Coefficient -0.1212 0.7432 0.5430* 0.4219 -0.7785* -0.4798* -0.1629* -0.0157 0.3803* 6.0848 0.6428 0.8514* 0.5864* 0.8858* 0.8866 0.7101 -9.7933* and Coelli Coelli, 1995) Std. Error 0.0793 0.3846 0.0615 0.2307 0.2077 0.1519 0.0274 0.1602 0.1203 4.1649 0.3421 0.3303 0.2248 0.3150 0.5100 0.4491 4.2158 , 1995) Prob > |z| 0.126 0.053 0.000 0.067 0.000 0.002 0.000 0.922 0.002 0.144 0.060 0.010 0.009 0.005 0.082 0.114 0.020 Parameter
Results: Stochastic Frontier Analysis ( Results: Stochastic Frontier Analysis (Battese Battese and and Coelli Coelli, 1995) , 1995) Inefficiency Model Parameter Coefficient Prob > |z| Inefficiency Model Parameter Coefficient Prob > |z| Constant Qualifications Change in CEO Number of Runways Catchment Area Train Terminal PAX Type of Airport Sigma-U Sigma-V Lambda Log Likelihood 6.0848 0.6428 0.8514* 0.5864* 0.144 0.060 0.010 0.009 Constant Qualifications Experience Number of Runways Catchment Area 5.8551 0.8201* -0.0937* 0.4761* 0.168 0.044 0.038 0.060 0.8858* 0.005 1.0003* 0.006 0.8866 -9.7933* 0.7101 0.40117* 0.04381* 9.15685* 81.83 0.082 0.020 0.114 0.000 0.000 0.000 Train Terminal PAX Type of Airport Sigma-U Sigma-V Lambda Log Likelihood 0.5096 -8.5237 0.265 0.060 0.41496* 0.04402* 9.42640* 79.77 0.000 0.000 0.000
Results: Technical Efficiency ( Results: Technical Efficiency (Battese Airports 2009- 2017 BZG (CEO) 79.00% 94.75% Batteseand 2012 and Coelli Coelli, 1995) 2013 2014 , 1995) 2009 2010 2011 2015 2016 2017 55.07% 56.63% 54.43% 94.74% 91.83% 96.24% 76.22% 91.11% GDN 92.92% 76.82% 72.84% 95.59% 98.58% 98.55% 97.79% 98.67% 98.92% 98.56% KRK (CEO) 88.04% 95.33% 91.59% 96.10% 90.94% 66.18% 97.60% 91.42% 64.25% 98.96% KTW 98.55% 99.02% 98.64% 98.50% 96.94% 98.96% 97.47% 98.95% 99.28% 99.21% LCJ (small) 98.50% 98.80% 98.99% 98.20% 98.46% 98.43% 98.04% 98.24% 98.42% 98.92% LUZ 60.39% 45.76% 84.13% 85.48% 54.99% 44.35% 47.64% 72.56% (missing) (missing) (missing) POZ 57.38% 74.04% 94.65% 73.06% 62.93% 73.28% RZE (CEO) 13 years SZZ 96.74% 94.85% 97.53% 87.34% 98.52% 98.12% 98.80% 99.25% 97.58% 98.66% 73.11% 93.67% 93.86% 70.69% 93.96% 43.91% 39.88% 67.90% 60.59% 93.57% WAW 98.48% 98.17% 98.91% 98.75% 98.47% 98.51% 98.40% 98.68% 98.49% 97.89% WMI 40.37% 48.44% 81.84% 27.55% 17.17% 19.15% 48.07% WRO 87.54% 77.51% 67.78% 95.39% 86.76% 91.22% 98.32% 93.63% 85.94% 91.29%
Conclusions Conclusions The results conclude that aircraft movements and non-aeronautical income are relevant outputs enhancing passengers. Costs such as depreciation, operating and non-operating costs have a negative impact. Airports experimenting changes in management drop their efficiency, as well as airports with competitors (medium and small) in their catchment area. The field of specialisation in air transport is not relevant or in some cases may increase inefficiency. Airports are individual business with specific organisational and corporate governance that is essential to take into account even though the manager has relevant experience managing other airports.
Thank you for your attention!!! Discussion
Number of CEOs (2009- 2017) Experience (CEO s airport) Work experience in Aviation Managerial previous Experience Airport Sex Further Education Field of study Yes Airline and ground handling NO Warszawa M English philology 3 2016 Financial sector Krak w M Yes Management 2 2016 Several sectors Gda sk M Yes Land Transportation 2 2010 Airport - Financial, banking sectors - Financial, banking sectors Financial sector Katowice M Yes Engineering 1 2006 - Modlin M Yes Law Law, management, finance Law Political science, journalism Transportation 2 2016 Airline Wroc aw M Yes 1 2007 NO Pozna M Yes 1 2006 NO Rzesz w M Yes 2 2016 Aircraft production Marshall Office Szczecin Bydgoszcz M Yes 2 2011 Airport Operator Airport operator M Yes Law 2 2010 NO Marshall Office Lublin M Yes Engineering 1 2012 NO Financial Director International economy Transportation PANSA, Ministry of Infrastructures Marshall Office d F Yes 4 2017 ATM Zielona G ra Radom Olsztyn M Yes 2010 NO 1 M Yes Several 2018 Airport Operator Airport Operator 2 M Yes - 2018 CAA and Pilot CAA 2
