# Comparing Enhanced Models for Evaluating the Economic Impact of Airports

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Methodology

#### 3.1. Input–Output Method

**L**-table is obtained by Equation (1). This matrix contains multipliers for calculating the indirect effects (

**L**) of each sector in the economic system.

**I**is the identity matrix and the matrix

**L**is the inverse of the difference between the identity matrix

**I**and table

**A**. It is called the

**L**-table or Leontief matrix, simplifying the linear system notation according to Equation (1):

**Y**vector consolidates the exogenous demand (consumption) data to the sectors that build the A-table to calculate the vector

**X**of production needed to meet such a demand.

_{j}= 1.

- IO Tables System for the National Economy: The Center for Regional and Urban Economics of the University of São Paulo (NEREUS) annually publishes good approximations of the IBGE matrices [24], which in turn publishes them every five years. These matrices are calculated according to the recommendations of the Brazilian System of National Accounts;
- IO Tables System for the Region of Study: In the Brazilian case, the primary source of information for constructing the location quotient vector is the data referring to the work factor [16]. The federal government provides detailed microdata on employment and wages, described individually by workers and indexed by the sector of economic activity and municipality. Following the methodology described by [25], it was possible to generate the location quotient vector for the S-table of the 68 industries and obtain the regionalized IO tables. The total remuneration of the labor factor provides good quality regional specialization indexes that are better than if they were obtained only by the number of jobs, especially in a country with solid regional inequalities, such as Brazil [25].

#### 3.2. Distribution Models for the Input–Output

#### 3.3. Circular Buffer Model

^{BC}is the weight of the circular buffer model relative to the straight-line distance from airport i to city k, n is the number of airports impacting the city’s economy, and D is the distance to the airport. For better definition, the circular buffer model will analyze two airports in which the operating radius is estimated to be 200 km. In this case, the savings from air transport will be split between both airports.

#### 3.4. Travel Time Model

^{TD}represents the weight assigned to the travel time models. The variable n denotes the number of airports that have an impact the city’s economy, whereas T represents the minimum displacement time from a user located at the center of downtown city k to the airport i.

#### 3.5. Huff’s Gravitational Model

_{ij}in this model Equation (11):

_{ij}is the distance from i to j, α is the parameter of beauty estimated from empirical observations, β is the decay parameter of the distance calculated from empirical observations, and n is the total number of attractive points j.

## 4. Results and Discussion

^{TM}, which resulted in substantial investments in the transport and infrastructure sectors. Moreover, Brazil attracted a large number of tourists in 2014 to attend the event, leading to a surge in air transport passenger movements. The second significant event was the Brazilian economic crisis, which ultimately led to the president’s impeachment process in 2016. This crisis had a profound impact on various industries, including air transport.

#### 4.1. Study of Attractiveness Coefficients for the Region of São Paulo

#### 4.2. Income Variable

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 4.**Total passenger movement and air cargo transport at SBSP, SBGR, and SBKP. Source: ANAC [31].

Author/ Year | Data | Models | Descriptions |
---|---|---|---|

Tveter (2017) [12] | - Population size - Jobs | Difference-in-difference model. | The author studied the impact on population growth due to regional airports. |

Nasution et al. (2018) [11] | -GDP—Gross domestic product - Movement of passengers - Cargo handling | Input–output vector error correction method. | The article shows a relationship between air transport and economic growth in Indonesia. Relating data on passenger/air cargo movement and GDP. |

Yu (2018) [13] | - Economic impact models. - Econometric models | Input–output CGE—Equilibrium general computable. | The article describes IO modeling and IO model applications. Economic modeling includes single regions and multi-regions. |

Aden (2020) [14] | - GDP—Gross domestic product - Passenger movement (domestic and international - Product and mobilities (2016) | Growth model based on GDP and passenger movement. | The authors correlate economic growth and the contributions of air transport. And they point out two critical factors for development: Tourism and transport logistics. |

Bagoulla and Guillotreau (2020) [15] | - Employment-inducing effects -Environmental effects | Circular buffer model, travel time, Thiessen polygons, and Huff. | Maritime transport in France. Using input–output to measure the effects of production and induced jobs. |

Subanti et al. (2020) [4] | - Indonesia air transport matrix data | Inter-regional input–output model. | The article identified which sectors of the Indonesian economy have the most interregional effects. |

Bandeira et al. (2019) [16] | - Brazilian IO tables from IBGE | Input–output | The authors presented an input–output model to analyze the metropolitan region of Campinas, São Paulo, Brazil. |

Keček et al. (2022) [17] | - Production - Product aggregates - Job multipliers | Input–output | The article shows a macroeconomic analysis. The results show that the air, land, and maritime transport sectors have a significant growth in the job multiplier. |

Mishra et al. (2021) [18] | - GDP—Gross domestic product | Pedroni’s method ordinary squares model vector error correction | The paper presents a study of 15 states in India to assess economic links with air traffic. |

Njoya and Nikitas (2020) [19] | - 60 activities, 104 commodities, 5 factors of production, 14 households, 4 types of taxes | Social accounting matrix and computable general equilibrium. | The authors studied the impact of the aviation sector on the economic growth of South Africa. |

Hakfoort et al. (2001) [20] | - Economic development | Input–output | The study examined the economic impact of Amsterdam’s Schiphol Airport, specifically focusing on job creation and the educational qualifications of workers in relation to the investments made. |

Hess and Polak (2005) [21] | - BACK aviation solutions | Multinomial logit | Analysis of airport choice among air travelers departing from the San Francisco Bay Area, using the mixed multinomial logit model, which allows for random distribution among decisionmakers. |

Hujer and Kokot (2000) [22] | - Investment, operating expenses, employment, income | Extended input–output | The authors presented an extended input–output model to account for an inter-regional scenario. |

Button et al. (2009) [23] | - 66 Virginia, USA | Input–output | The work also noted that regional economic development drives other factors, such as increased air traffic. However, it can be noted that airports generate air traffic as catalysts for local investments. Proposed the regional purchase coefficient (RPC). |

Zhao et al. (2022) [8] | - Input–output table | Input–output | They analyzed the effects of five different transport modes on China’s national economy using input–output analysis. |

Airport | SBSP | SBKP | SBGR |
---|---|---|---|

T(P, A) | 0.264 | 0.424 | 0.312 |

**Table 3.**Attractiveness coefficient according to the circular buffer model for SBSP, SBKP, and SBGR.

Airport | SBSP | SBKP | SBGR |
---|---|---|---|

D(P, A) | 0.237 | 0.521 | 0.242 |

Year | SBSP | SBKP | SBGR |
---|---|---|---|

2010 | 0.287 | 0.266 | 0.446 |

2011 | 0.276 | 0.292 | 0.431 |

2012 | 0.265 | 0.304 | 0.431 |

2013 | 0.257 | 0.307 | 0.436 |

2014 | 0.252 | 0.306 | 0.442 |

2015 | 0.259 | 0.306 | 0.435 |

2016 | 0.274 | 0.296 | 0.43 |

2017 | 0.277 | 0.291 | 0.432 |

2018 | 0.273 | 0.282 | 0.445 |

Airport | Average | Standard Deviation | Margin of Error (~95% I.C.) |
---|---|---|---|

SBSP | 1.2435 | 0.067 | 0.0438 |

SBKP | 1.5241 | 0.027 | 0.027 |

SBGR | 2.1218 | 0.0466 | 0.0466 |

Airport | Average | Standard Deviation | Margin of Error (~95% I.C.) |
---|---|---|---|

SBSP | 0.1918 | 0.0100 | 0.3273 |

SBKP | 0.2353 | 0.0123 | 0.0117 |

SBGR | 0.3273 | 0.0082 | 0.0077 |

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**MDPI and ACS Style**

de Quadra Magalhães, I.; Correia, A.R.; da Silva Pinto Bandeira, M.C.G.; Zackiewicz, M.; Tozi, L.A.
Comparing Enhanced Models for Evaluating the Economic Impact of Airports. *Future Transp.* **2023**, *3*, 1124-1146.
https://doi.org/10.3390/futuretransp3030062

**AMA Style**

de Quadra Magalhães I, Correia AR, da Silva Pinto Bandeira MCG, Zackiewicz M, Tozi LA.
Comparing Enhanced Models for Evaluating the Economic Impact of Airports. *Future Transportation*. 2023; 3(3):1124-1146.
https://doi.org/10.3390/futuretransp3030062

**Chicago/Turabian Style**

de Quadra Magalhães, Ivaciane, Anderson Ribeiro Correia, Michelle Carvalho Galvão da Silva Pinto Bandeira, Mauro Zackiewicz, and Luiz Antonio Tozi.
2023. "Comparing Enhanced Models for Evaluating the Economic Impact of Airports" *Future Transportation* 3, no. 3: 1124-1146.
https://doi.org/10.3390/futuretransp3030062