Application of the markup pricing method as an alternative to the non-cost-based model for airport charges

Fiaco, Reinaldo Moreira Del; Silva, Paulo Afonso Lopes da

doi:10.1590/1806-9649.2020v28e4788

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ORIGINAL ARTICLE • Gest. Prod. 28 (1) • 2021 • https://doi.org/10.1590/1806-9649.2020v28e4788 copy

Application of the markup pricing method as an alternative to the non-cost-based model for airport charges

Aplicação do método markup de preceificação como alternativa do modelo não baseado em custo para as tarifas aeroportuárias

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

The experience of developed countries shows that airport managers seek to increase their profitability, while regulatory agencies aim to guarantee quality, investments and fair charges. This study presents an application of the Tornqvist Index and the markup pricing method to find the market price opportunity for airfares and one of the specific objectives is to present a methodology for comparing the financial results of airports based on ANAC Resolution No. 350/14, which allows a 100% increase in aeronautical charges concerning the tariff ceiling. To predict passenger demand with new airport fares, the least-squares method was used based on empirical equations and the concept of elasticity. The scenario used was the Campinas / Viracopos Airport from January 2014 to March 2017. The results showed that it is possible to increase the price of the domestic aeronautical charges and the revenue from the airport, with the counterpoint of falling demand. This method, based on cost and market opportunity, but on which the financial statement should be audited, is shown to be promising to increase airport revenue.

Keywords:
Pricing; Charging; Concessions; Airports; Aeronautics

Acronyms		Settings
Input	Output	Settings
$R E D B R U T_{i j}$	$r e d b r u t_{i j}$	Gross operating revenue^* * Values in real.
$R E D_{i j}$	$r e d_{i j}$	Net operating revenue*
$C T_{i j}$	$c t_{i j}$	Total airport cost or total cost of ij* product
$C S P$	$-$	Cost of services provided*
$D G A$	$-$	General and administrative expenses*
$C T M_{i j}$	$-$	Average total cost*
$-$	$p t f_{i j}$	Total factor yield
$P A X P G_{i j}$	$p a x p g_{i j}$	Sum of pax paid
$P A X G R_{i j}$	$-$	Free pax sum
$Q P A X_{i j}$	$q p a x_{i j}$	Paxpg and PAXGR sum
$A S S$	$-$	Number of seats offered by airlines
$V M P T$	$-$	Weighted average fare value*
$T E M B_{i j}$	$t e m b_{i j}$	Boarding Fee*
$V M P T T_{i j}$	$v m p t t_{i i j}$	Weighted average value of the total fare*
$-$	$E a s s_{i j}$	Elasticity of seats relating to $V M P T T_{i i j} A S S_{i j}$
$-$	$E p a x p g_{i j}$	Elasticity of passengers paid relating to the $V M P T T_{i i j}$

Variables	Coefficients	Standard Error	Test-t	P-Value
C	${(x' x)}^{- 1} x' y$	$\sqrt{\frac{\sum_{i}^{n} (x - \bar{x})}{n - 1}}$	$\frac{(b_{k} - β_{k})}{e p (b_{k)}}$ * or $\frac{(b_{k} - β_{k})}{\sqrt{v â r (b_{k)}}}$ **r	$\frac{\bar{x} - μ_{0}}{\frac{σ}{\sqrt{n}}}$
B				$\frac{\bar{x} - μ_{0}}{\frac{σ}{\sqrt{n}}}$
R2	$\frac{\sum_{i = 1}^{n} (y_{i}^{e} - \bar{y}) ²}{\sum_{i = 1}^{n} (y_{1} - \bar{y}) ²}$	Standard dependent var		$\sqrt{v a r (y)}$
${\bar{R ¨}}^{2}$	$1 - \frac{(1 - R^{2}) x (n - 1)}{n - k - 1}$	Var dependent on Standard Deviation		$\sqrt{\sum_{i = 1}^{n} (} y_{n} - \bar{y}) / (n - 1)$
SQR	$\sqrt{\frac{\hat{ε}' ε}{(n - k)}}$	Akaike Information Criterion		$\frac{2 (k + 1)}{n} - \frac{2 ln (l)}{n}$
SQE	$\sum_{i}^{n} (y_{i} - x_{i}' b) ²$	Schwarz Criterion		$\frac{(k + 1) ln (n)}{n} - \frac{2 ln (l)}{n}$
Log Verossimi-lhança	$- \frac{n}{2} \ln (2 π) - \frac{n}{2} \ln (\hat{σ}) - \frac{(y - x \hat{β})' (y - x \hat{β})}{2 \hat{σ} ²}$	Hannan-Quinn Criterion		$\frac{2 (k + 1) ln (\ln (n))}{n} - \frac{2 ln (l)}{n}$
F-test	$\frac{(\frac{R S S_{1} - R S S_{2}}{p_{2} - p_{1}})}{(\frac{R S S_{2}}{n - p_{2}})}$	Durbin-Watson Statistics		$\frac{\sum_{i}^{n} {({\hat{ε}}_{n} - {\hat{ε}}_{n - 1})}^{2}}{\sum_{i}^{n} {\hat{ε}}_{n} ²}$
Prob (test-F)	$\frac{\frac{S Q R}{p}}{\frac{S Q E}{n - p - 1}}$

Variables	Extension	Freq.	Database	References
RED_ij; CT_ij;	Jan./2014-Mar./2017	Quarterly	Corporate Governance	VIRACOPOS (2017)Aeroporto Internacional de Viracopos – VIRACOPOS. (2017). Corporate Governance. Financial Statements. Institutional. Retrieved in October 20, 2017, from https://www.viracopos.com/pt_br/institucional/governanca-corporativa.htm / https://www.viracopos.com/pt_br/instituc...
PAXPG_ij; PAXGR_ij; ASS_ij;	Jan./2000-2017	Month	Statistical Air Transportation Database	ANAC (2017c)Agência Nacional de Aviação Civil - ANAC (2017c). Demanda e Oferta do Transporte Aéreo – Empresas brasileiras. Retrieved in April 02, 2017, from https://goo.gl/q3ayVB. https://goo.gl/q3ayVB...
QEAE_ij; VMPT_ij;	Jan./2002-2017	Month	Commercialized AirFare Microdados	ANAC (2017c)Agência Nacional de Aviação Civil - ANAC (2017c). Demanda e Oferta do Transporte Aéreo – Empresas brasileiras. Retrieved in April 02, 2017, from https://goo.gl/q3ayVB. https://goo.gl/q3ayVB...
TEMB_ij;	Jan./2010-Dec./2011	Month	Tariff	ANAC (2017c)Agência Nacional de Aviação Civil - ANAC (2017c). Demanda e Oferta do Transporte Aéreo – Empresas brasileiras. Retrieved in April 02, 2017, from https://goo.gl/q3ayVB. https://goo.gl/q3ayVB...
CRPF; CRPJ;	Mar./2007-2017	Month	Credit Indicators	Banco Central do Brasil (BCB, 2017Banco Central do Brasil – BCB. (2017). Time Series Manager System - SGST. v. 2.1 Public module. Retrieved in September 20, 2017, from https://goo.gl/tgVBda https://goo.gl/tgVBda... )
DOLLAR;	Nov./1984-2017	Day	Exchange Rate	Banco Central do Brasil (BCB, 2017Banco Central do Brasil – BCB. (2017). Time Series Manager System - SGST. v. 2.1 Public module. Retrieved in September 20, 2017, from https://goo.gl/tgVBda https://goo.gl/tgVBda... )
IPCA; INPC; PIB;	Feb./1992-2017	Month	Monetary Indicators	Banco Central do Brasil (BCB, 2017Banco Central do Brasil – BCB. (2017). Time Series Manager System - SGST. v. 2.1 Public module. Retrieved in September 20, 2017, from https://goo.gl/tgVBda https://goo.gl/tgVBda... )
POUP;	Jul./1994-2017	Month	Financial and capital markets	Banco Central do Brasil (BCB, 2017Banco Central do Brasil – BCB. (2017). Time Series Manager System - SGST. v. 2.1 Public module. Retrieved in September 20, 2017, from https://goo.gl/tgVBda https://goo.gl/tgVBda... )
BRENT.	Aug./2002-2017	Month	Commodity Price Data	World Bank (2017)World Bank. (2017). Commodity Markets. Archives (data). Retrieved in October 20, 2017, from https://www.worldbank.org/en/research/commodity-markets#3. https://www.worldbank.org/en/research/co...

Period	$P A X P G_{11}$	$P A X G R_{11}$	$Q P A X_{11}$	ASS*	VMT^**	VMTP***	$T E M B_{11} *$
2014.1	1.172.437	49.069	1.221.506	1.609.627	455,41	324,17	16,49
2014.2	1.179.020	47.509	1.226.529	1.572.685	441,05	323,43	16,49
2014.3	1.221.422	51.323	1.272.745	1.518.275	474,95	323,98	16,49
2014.4	1.247.247	51.610	1.298.857	1.646.262	502,26	360,15	17,68
2015.1	1.204.579	47.088	1.251.667	1.645.790	443,46	307,88	17,68
2015.2	1.106.233	45.109	1.151.342	1.624.014	460,34	337,06	17,68
2015.3	1.173.601	45.976	1.219.577	1.482.040	524,45	397,57	19,02
2015.4	1.125.890	49.623	1.175.513	1.564.928	551,38	415,56	19,02
2016.1	1.067.199	44.249	1.111.448	1.528.946	508,93	383,39	19,02
2016.2	971.811	43.628	1.015.439	1.468.110	534,14	407,31	19,02
2016.3	1.072.785	46.507	1.119.292	1.311.289	568,72	414,62	20,36
2016.4	1.083.976	53.057	1.137.033	1.394.242	581,04	442,90	20,36
2017.1	1.083.724	47.779	1.131.503	1.439.904	537,63	381,23	27,67

Period	$p t f_{11}$	$c t_{11}$ ^* * Values in real.	Period	$p t f_{11}$	$c t_{11}$ *
2013.4	-	-	2015.3	0.164181	-31,982,546
2014.1	0.173761	-53,179,305	2015.4	0.158906	-42,201,807
2014.2	0.180207	-54,820,041	2016.1	0.145267	-35,090,874
2014.3	0.178204	-52,919,506	2016.2	0.094217	-3,808,536
2014.4	0.177955	-118,388,518	2016.3	0.124462	-30,048,157
2015.1	0.169242	-29,782,498	2016.4	0.152058	-14,221,861
2015.2	0.146245	-28,424,972	2017.1	0.165661	-2,262,291

Period	Ep	$c t m_{11} *$	$t e m b_{11} *$	$v m t p_{11}$ *	Period	Ep	$c t m_{11} *$	$t e m b_{11} *$	$v m t p_{11}$ ^* * Values in real.
2013.4	-	-	-	-	2015.3	0.981	26.22	39.78	437.35
2014.1	0.834	43.54	63.57	387.74	2015.4	1.069	35.90	54.58	470.15
2014.2	0.852	44.70	65.00	388.43	2016.1	1.084	31.57	48.51	431.90
2014.3	0.832	41.58	60.63	384.61	2016.2	1.157	3.75	5.82	413.14
2014.4	0.845	91.15	132.53	492.68	2016.3	1.236	26.85	41.71	456.33
2015.1	0.831	23.79	34.69	342.57	2016.4	1.240	12.51	19.26	462.16
2015.2	0.845	24.69	36.92	373.97	2017.1	1.173	18.79	28.94	410.17

Variables	Coefficients	Standard Error	Test-T	P-Value
C	439178.5	1058144	0.415046	0.6925
VMPTT	-243.3219	684.3448	-0.355555	0.7343
PF	23.27758	47.30421	0.492083	0.6401
PJ	-8.260585	19.68553	-0.419627	0.6894
IPCA	2544.177	2639.032	0.964057	0.3722
INPC	-2705.325	2396.309	-1.128013	0.3024
PIB	2.376763	1.715570	1.385408	0.2152
R²	0.834272	Standard dependent var		1131533
${\bar{R}}^{2}$	0.668545	Var dependent S.D.*		76824.45
SQR	44229.49	Inf. Akaike's Criterion		24.53590
SQE	1.17E-10	Schwarz Criterion		24.84011
Log-Likelihood	-152.4834	Hannan-Quinn Criterion		24.47338
F-test	5.033996	Durbin-Watson stat		3.331864
Prob (Teste-F)	0.034953

Period	$p a x p g_{11}$ *	$r e d b r u t a d_{11}$ *	$r e d_{11}$	Period	$p a x p g_{11}$	$r e d b r u t a d_{11}$ *	$r e d_{11}$ *
2013.4	-	-	-	2015.3	1,133,723	45,097,218	22,120,185
2014.1	1,171,821	74,496,761	36,540,661	2015.4	1,132,801	61,831,444	30,328,323
2014.2	1,164,195	75,675,370	37,118,769	2016.1	1,040,626	50,484,110	24,762,456
2014.3	1,192,682	72,314,429	35,470,228	2016.2	1,046,810	6,096,859	2,990,509
2014.4	1,226,028	162,485,444	79,699,110	2016.3	1,022,785	42,662,265	20,925,841
2015.1	1,181,780	40,999,021	20,110,020	2016.4	1,082,962	20,859,279	10,231,477
2015.2	1,144,380	42,246,276	20,721,798	2017.1	1,075,992	31,136,188	26,450,192

Universidade Federal de São Carlos Departamento de Engenharia de Produção , Caixa Postal 676 , 13.565-905 São Carlos SP Brazil, Tel.: +55 16 3351 8471 - São Carlos - SP - Brazil
E-mail: gp@dep.ufscar.br

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[TFN1] ^*
Values in real.