iii LIST OF ACRONYMS AAR: Airport Access Road ATT: Average Treatment Effects on the Treated ASD: Airport System Development CBD: Central Business District CAAV: Civil Aviation Admin
INTRODUCTION
Overview
The aviation sector is crucial for global economic development, enhancing connectivity between cities and countries This connectivity facilitates international business by promoting the flow of freight, passengers, capital, and technology, making aviation a vital component of modern society.
Airports consist of various essential facilities, including operational areas, terminals, and runways, designed to meet specific safety requirements and accommodate transportation demand Due to their need for expansive space for future expansion, airports are typically situated away from the central business district (CBD) Consequently, efficient ground access, such as rail or road connections, is crucial for linking airports to the CBD and other regions.
The Global Aviation Sector
The aviation sector is experiencing rapid growth, with air passenger traffic projected to double to 8.2 billion by 2037, potentially generating 100 million jobs globally (IATA, 2018) Additionally, air cargo, which currently constitutes less than 1% of global trade by tonnage but over 35% by value, is anticipated to more than double in the next two decades, driven by the rise of e-commerce Expected demand for air cargo is set to reach 220 billion freight tons-km, reflecting a 60% increase over the next 20 years (ICAO, 2021; Boeing, 2018).
2021) By effect of COVID-19, air demand have been effected significantly however global air traffic is forecasted to reach pre-COVID-19 levels by around 202 (ACI,
Figure 1.1.General system of Airport
Figure 1.2 World Air Travel and Freight
The airport system development (ASD) is crucial for meeting the high demands of the aviation sector, as it involves renovations and expansions of runways, terminal facilities, and the construction of new airports, along with improvements to ground access through new roads and railways Global investment in ASD has been rising steadily, with projections indicating a need for over $2 trillion from 2017 to 2036 (Boeing, 2017), highlighting the importance of collaboration among governments and policymakers to foster economic growth and enhance trade.
The Vietnam Aviation Sector
According to the IATA, Vietnam is projected to become the world's fifth fastest-growing aviation market, with an average growth rate of 17.4% over the last decade, significantly surpassing the 7.9% growth in the Asia-Pacific region Air transport in Vietnam is expected to grow by 178% over the next 20 years, resulting in an additional 82.2 million passenger departures and a total of 128.2 million passengers by 2038 In the first half of 2022, Vietnam's airports served 40.7 million passengers and handled 765,000 tons of cargo, reflecting increases of 56.8% and 30.6% respectively compared to the previous year, according to the Civil Aviation Administration of Vietnam (CAAV).
Vietnam is home to 22 airports, including 10 international and 12 domestic facilities, designed to accommodate 91 million passengers annually However, in 2019, the passenger volume reached 116 million, leading to significant challenges in management, safety, and flight delays, as well as pressing needs for airport network development.
Figure 1.3 Current and planned airports in Viet Nam
In these scenes, The Ministry of
Transport (MOT) proposed a new draft national airport network development plan for the 2021-
2050 in Sep 2022 Following the plan, six airports will be invested, it raises up the capable of serving about 278 million passengers and
4.1 tons of cargoes annually To implement, the investment is required about $18 billion to build six airports (CAAV, 2023)
The Current Issues in Airport Investment
The Airport System Development (ASD) involves significant investments in constructing new airports or renovating existing infrastructure, such as runways, terminals, and airport access roads (AAR) This process demands substantial financial resources and a considerable amount of time to recoup these investments Additionally, airports can struggle to achieve optimal utilization without well-developed first- and last-mile transportation solutions (Murakami et al., 2016).
Vietnam's air transport demand is rapidly increasing, leading to capacity overload and highlighting the lack of high-quality airport ground access Traffic congestion has become a frequent issue, necessitating significant investments in both airports and ground access to meet service requirements However, the substantial costs associated with improving airport ground access must be justified by empirical evidence, as previous studies indicate that such investments can yield broader economic benefits through enhanced accessibility and agglomeration effects (Murakami et al., 2016).
Research Question
For the above issues, the research question is: How much does improvement of airport ground access affect the local economy?
Research Goals
This study quantitatively assesses the effects of airport access roads on Hanoi's local economy, explores the mechanisms driving these impacts, and discusses the policy implications for the development of airports and urban areas.
Case Study
Hanoi, the political and administrative heart of Vietnam, is also a key hub for economy, culture, science, education, and international trade The city is undergoing significant urban modernization, with its population and newly developed areas evolving to align with metropolitan trends Through strategic planning, funding, and management decisions, Hanoi aims to transform into a prominent international metropolitan city.
Hanoi's air transportation services connect the city to 32 international and 11 domestic destinations, with 76,986 annual departure movements recorded in 2018 Noi Bai International Airport, the country's largest for cargo transport, operates as a single-airport system catering to a moderate volume of international passengers and cargo In 2018, it ranked as the 34th busiest passenger airport and the 18th busiest cargo airport in Asia, with international traffic comprising 38.9% of total passenger traffic (25.9 million) and 72.7% of total cargo traffic (728,414 tons) Notably, passenger traffic surged by 139.9% from 2011 to 2018.
Noi Bai International Airport, located about 35 km north of Hanoi's city center, has undergone significant expansions to accommodate the increasing air passenger traffic over the years Originally a military airfield, the first terminal (T1) was converted into a civil international airport with an area of approximately 90,000 m², becoming operational in 2001 To further enhance its capacity, a second runway was added in 2006, addressing the immediate overload experienced at T1 due to the surge in air travelers.
Bai International Airport is currently facing connectivity challenges with Ha Noi city due to the outdated North Thang Long-Noi Bai road, built around 35 years ago This road has become overloaded, resulting in inefficient access between the city center in the south and the airport, as well as the surrounding industrial parks and newly developed northern areas.
In 2011, construction commenced on Terminal 2 (T2) next to the existing Terminal 1 (T1), with a total investment of JPY 76.1 billion, primarily financed by the Japan International Cooperation Agency (JICA) through its official development assistance (ODA) loan program, amounting to JPY 59.3 billion By early 2015, T2 became operational, covering approximately 140,000 m² and designed to accommodate 10 to 15 million international passengers each year.
Noi Bai International Airport has a maximum capacity of 50 million passengers annually, with potential for expansion on its 565-hectare site However, the airport is nearing its capacity limits, having recorded over 100,000 passengers per day in 2020 As Vietnam is projected to become the fifth fastest-growing aviation market globally, it faces significant urban challenges, particularly severe traffic congestion, primarily due to motorcycles, which constitute 70% of the modal share, followed by other private vehicles.
To address unreliable travel services caused by severe traffic congestion between Hanoi and the airport, significant ground transportation projects, including the Vo Nguyen Giap Expressway and Nhat Tan Bridge, were initiated alongside the T2 construction project These projects involved a total investment of JPY 27 billion and 80 billion, primarily funded by the JICA ODA loan scheme The Vo Nguyen Giap Expressway, which became operational in early 2015, features a nearly straight 12 km route with 6 lanes, connecting Noi Bai International Airport to the Nhat Tan Bridge The project's objectives included meeting rising traffic demands, alleviating congestion, and enhancing transportation and logistics capacity, ultimately promoting economic growth and boosting the country's international competitiveness.
The Nhat Tan Bridge has significantly shortened travel time between Hanoi City Center and the airport by approximately 30 to 45 minutes, yet its impact on regional economic activity remains inadequately defined (JICA, 2019).
Research Overview
This dissertation is structured into six chapters, with the first chapter providing a comprehensive background and a review of the research area, alongside an examination of current issues relevant to the study Subsequent chapters will build upon this foundation.
Chapter 2 literature reviews which overview previous research in the world and also review the status of research in Vietnam about AAR This chapter aims to understand the situation of researching about AAR in not only Vietnam but also in the world
Chapter 3 aims to develop the hypotheses in order to clarify the research objective and including evaluates and provide appropriate methodology for the research as well as introduce the formula that used for analysis (the approach)
Chapter 4 describes how to collect the data and data processing It also describes the descriptive statistic of the data
Figure 1.4 Airport Access Road Location in Ha Noi
Chapter 5 includes all the analysis with chosen approach on the data Based on the analysis results, whether the hypotheses are supported or not is clearly declared through the discussion
Chapter 6 concludes the key findings obtained from the hypotheses though analysis Finally, research limitations and future feasibility studies were discussed
LITERATURE REVIEW
Effects of Transportation Infrastructure Investment on Economy
Transportation infrastructure projects are designed to enhance user benefits by reducing travel time, lowering travel costs, improving safety, and minimizing maintenance expenses.
Investment in transportation infrastructure not only benefits users directly but also emphasizes the significance of indirect non-user benefits, particularly through wider economic impacts These impacts enhance spatial accessibility to economic resources and markets, shift location advantages within cities or regions, and ultimately lead to productivity gains through improved agglomeration economies Infrastructure projects consistently demonstrate a positive and significant developmental effect on the economy, boosting private sector output and fostering innovation processes both directly and indirectly.
2011), improves the capabilities of firms through enhanced communication efficiency, (Cieslik et al., 2004) increased labor productivity, widened product scale and extended geographical reach (Hulten et al., 2006; Straub, 2008; Murakami & Kato, 2020).
Effects of Airport Investment on Economy
Aviation has become a crucial component of the global economy, leading to numerous studies that explore the impact of airport investments on local economies Notable early research by Button et al (1999) and Button & Lall (1999) highlighted the economic benefits derived from airports Additionally, Button & Taylor (2000) examined the significance of international air services in the modern economy, simulating the relationship between air transport and economic growth.
The relationship between local employment and air passenger traffic in U.S metropolitan areas has been highlighted in research by Debbage and Delk (2001), which examined the correlation between administrative employment and air passenger volumes across the top 50 urban-airport complexes.
From 1973 to 1996, a significant study by Brueckner (2003) analyzed 91 US metropolitan areas, demonstrating the economic impact of airports The findings revealed that a 10% increase in airport passenger traffic correlates with a 1% rise in service-related employment, highlighting the crucial role of airports in boosting local economies.
A study by Mukkala and Tervo (2013) revealed a causal relationship between air passenger volumes and regional economic growth, specifically in suburban areas of Europe, highlighting the impact on employment and GDP In contrast, Neal (2012) examined the link between airport traffic volume and employment across 145 major commercial airports, focusing on sectors such as arts, design, entertainment, sports, and media.
From 2000 to 2008, research in US metropolitan areas indicated that attracting tourists and business flows significantly contributed to job growth, particularly in creative sectors Additionally, an analysis of sectoral employment across 290 metropolitan areas in 2007 revealed that increased airport capacity fosters industrial specialization and leads to substantial growth in tradable services employment.
Subsequent research has increasingly examined the direct impact of airport traffic on economic development Green (2007) investigated the relationship between various airport activities—such as passenger volumes, originations, hub status, and cargo volumes—in the US and their influence on urban employment and population growth The findings revealed that air passenger flows serve as a robust indicator of economic growth, while air cargo traffic does not demonstrate the same effect.
For the effect of airport passenger on economic in Asia, (Baker et al., 2015) show the causal links between air passenger and economic growth in Australia from 1985 to
Research by Hakim & Merkert (2016) and Mehmood et al (2015) established a unidirectional and bidirectional relationship between air traffic and economic growth in Asian countries from 1970 to 2014 Chang & Chang (2009) employed Granger causality analysis to demonstrate a long-term equilibrium and bidirectional relationship between air cargo and economic growth in Taipei, China Similarly, Gibbons & Wu (2020) conducted a survey of large industrial firms, further contributing to the understanding of this dynamic.
A recent analysis utilizing administrative data on county GDP indicates that the elasticity of productivity in relation to the expected air access index due to new airport construction is approximately 0.25 However, there appears to be no significant impact on GDP within the service sector in China.
In recent years, several aviation studies have summarized the economic effects of airport system development into four large categories (AVIATION-BENEFITS-2019-
Web.Pdf, n.d.; Europe’s Airports Economic Impact – the Theory and the Practice; ACI Europe Report Part 1 | CAPA, n.d.; The World of Air Transport in 2021, 2021;
Kato & Murakami, 2022b; Murakami et al., 2016; Murakami & Kato, 2020; Ronan,
- Direct Effects: Passengers service activities at the airport (e.g., check-in, security, boarding), cargo (e.g., loading and unloading), and aircraft (e.g., fueling, cleaning)
Indirect effects of airport operations stem from various inputs that support its functionality, leading to a significant influx of money into the local economy These inputs include jet fuel suppliers, electricity providers, and utilities, as well as fresh food sold in airport restaurants, all contributing to the economic vitality of the surrounding area.
Induced effects refer to the economic impact generated by consumption activities linked to airport services, involving both airport employees and traveling passengers These individuals allocate their earnings towards essential goods and services, which encompass retail shopping, housing, local transportation, dining at restaurants, and staying in hotels.
The catalytic effects of reduced travel costs, improved network connectivity, and enhanced agglomeration economies drive businesses like time-sensitive logistics, global trade, high-tech manufacturing, tourism, and producer services to relocate near airports and airport-linked industrial zones.
The "catalytic" effect of airport investment provides significant benefits, but its complex impacts are more challenging to measure compared to other effects While the causal link between airport investment and economic growth is widely supported, the extent and patterns of economic development related to airports remain a topic of debate.
Effects of Airport Ground Access Investment on Economy
Airport ground access is crucial for enhancing the competitiveness and sustainability of airport developments Case studies from the UK and Australia highlight the significance of ground access in addressing public transportation congestion and environmental concerns Key research by Humphreys et al (2005), Budd et al (2011a, 2011b), Ison, Merkert, and Mulley (2014), and Budd et al (2016) underscores the importance of effective ground access solutions for airports.
Research on the economic impact of airport ground access is limited Thomson (1995) highlights that integrating high-speed rail with motorway networks can foster new employment clusters and stimulate economic growth, as demonstrated by the Lyon Satolas International Airport/High-Speed Rail Hub in France J Kasarda and Appold (2014) emphasize the need to reduce first- and last-mile travel costs and enhance logistics efficiency by incorporating the airport city/aerotropolis model into ground transportation planning Additionally, Murakami, Matsui, and Kato (2016) found that shorter access times for airport rail links positively influence productivity, based on an analysis of 82 cities with the world's busiest airports.
Gibbons and Wu (2020) analyzed the impact of new airport construction in the 2000s on industrial productivity in counties across China, finding that improved ground accessibility to airports positively influenced productivity Similarly, Murakami and Kato (2020) examined the relationship between airport accessibility, employment density, and labor productivity in Tokyo, which features two major airports with multimodal ground transport Their findings indicate that municipalities with high accessibility to inner-city airports and a strong presence of firms benefit from increased labor productivity However, the causal relationships remain debatable, and there is still limited knowledge regarding the effects of airport ground transportation on employment location and business productivity.
Conclusion from literature review
Transportation infrastructure, such as airports, significantly influences economic activities by enhancing travel efficiency and reducing costs Previous studies have explored various aspects, including direct benefits like shorter travel times, spillover effects, economic agglomeration, labor productivity, and employment density at regional, city, and national levels These studies highlight both the positive spillover effects and negative externalities across different industries and geographic scales.
Limited research has been conducted on the effects of airport ground access, primarily concentrating on environmental concerns, employment density, and labor productivity associated with airport rail links in developed nations.
There are no research and evidence on the impacts of airport ground access investment on local economy in developing countries, especially the impact of airport ground access road development
METHODOLOGY
Research Framework
Follow up the literature review stage and to answer the above research question, the research framework for this study is implemented as figure below:
The improvement of airport access roads lead to some kinds of impact (e.g., direct impact, indirect impact, and catalytic impact) on the enhancement of economic activities
The implementation of construction projects significantly impacts the construction industry by creating new jobs and increasing labor demand, which boosts overall production These projects also drive the development of innovative technologies and processes, leading to higher material consumption that enhances production efficiency and reduces costs Ultimately, this results in increased profits and fosters a more competitive industry.
Indirect impact: most similarity with the impact on construction industry production
Investing in airport access roads significantly enhances the aviation industry surrounding the airport This development boosts airport transportation services, such as catering, taxi, and airline services, leading to an increased demand for labor.
Figure 3.1 Hypothetical Causal Relationship Framework
Access roads significantly enhance transportation efficiency to and from the airport, resulting in increased air traffic and a heightened demand for local services and goods Improved accessibility minimizes delays, boosting customer satisfaction and attracting more businesses to the area For tourists, easier and faster access makes the region more appealing, fostering a thriving environment for businesses This growth creates jobs and encourages the establishment of new firms, industrial zones, and residential communities, ultimately driving economic growth and development in the region.
Research Hypothesis Development
In order to clarify the effect of airport access road on local economic, some hypotheses are proposed which partly based on above the hypothetical causal relationship framework
The introduction of airport access roads significantly enhances economic impact in areas with improved airport accessibility compared to regions where such accessibility remains unchanged This improvement in transport infrastructure leads to increased business opportunities, higher property values, and greater tourism potential, ultimately driving economic growth in the connected areas.
Airport access road has positive economic impacts
• Treatment group: The zones that are located near the airport access road
• Control group: The zones that are located far from the airport access road
Sub-Hypothesis H2.1: The economic impacts from the introduction of airport access road in the areas at the end of
The accessibility to the airport has significantly improved in the AAR, located near the central business district (CBD), compared to areas farther from the city center.
The impact from airport access road is greater in the end
15 area of AAR (where near the CBD) than in other areas
• Treatment group: The zones that are located near the AAR and near CBD
• Control group: The zones that are located far from the AAR and near CBD
• Treatment group: The zones that are located near the AAR and not near CBD
The control group consists of areas situated far from the Airport Access Road (AAR) and the Central Business District (CBD) Sub-Hypothesis H2.2 posits that the economic benefits resulting from improved accessibility to the airport are more significant in regions near the AAR, particularly at its starting point close to the airport, compared to areas located farther away where accessibility has also been enhanced.
The impact from airport access road is greater in the start area of AAR (where near the airport) than in other areas
• Treatment group: The zones that areas located near the AAR and near the airport
• Control group: The zones that are located far from the AAR and near the airport
The introduction of the airport access road significantly influences economic growth, particularly in densely populated areas where improved airport accessibility is evident In contrast, the economic impacts in less populated regions, despite enhanced access to the airport, are comparatively minimal This disparity highlights the crucial role of population density in determining the economic benefits derived from improved transportation infrastructure.
The impact of the airport access road is greater where have high population density (PD)
Subgroup 2.1: Higher population density (Higher 50%)
• Treatment group: The zones that are located near the AAR with higher PD
• Control group: The zones that are located far from the AAR with higher PD
Subgroup 2.2: Lower population density (Lower 50%)
• Treatment group: The zones that are located near the AAR with lower PD
The control group consists of areas distant from the Airport Access Road (AAR) that exhibit lower population density (PD) Sub-Hypothesis H4 posits that the introduction of the airport access road will yield significant economic benefits in regions with higher economic performance, particularly where access to the airport is enhanced.
16 has been improved is greater than that in the areas with lower economic performance where the accessibility to airport has been improved
The impact from airport access road is greater where have higher economic performance (EP)
Subgroup 3.1: Higher economic performance (Higher 50%)
• Treatment group: The zones that are located near the AAR with higher EP
• Control group: The zones that are located far from the AAR with higher EP
Subgroup 3.2: Lower economic performance (Lower 50%)
• Treatment group: The zones that are located near the AAR with lower EP.
Research Assumption
While GDP per capita and household income are commonly used to gauge economic activities, detailed data may be lacking for Hanoi over multiple years To address this, remote sensing data emerges as a valuable alternative, offering numerous advantages such as the ability to monitor changes monthly and high accuracy at resolutions of 100 to 500 meters This technology has transformed scientific research across various fields Initially, the Defense Meteorological Satellite Program Operational Line Scanner (DMSP/OLS) was employed in the 1970s to assess global cloud distribution and temperature, and the datasets generated have since been extensively utilized by the scientific community, especially following the establishment of a digital archive by the NOAA/NGDC.
Since 1992, DMSP/OLS nighttime images have been utilized across various scientific fields, including the monitoring of human settlements, estimating urban populations and density, analyzing socio-economic activities, and assessing energy consumption and gas emissions These images have also been instrumental in measuring the environmental impacts of urban growth, detecting nocturnal fishing vessels, mapping nighttime sky brightness, and monitoring forest fires Furthermore, they play a crucial role in evaluating the effects of emissions on ecosystems and human health, as well as assessing damage from natural disasters and military conflicts Numerous studies have demonstrated a strong correlation between remote sensing of nighttime lights (NTL) and gross domestic product (GDP).
17 product and regional domestic product (Doll et al., 2000, 2006; Pérez-Sindín et al.,
Night-time light (NTL) serves as an effective predictor and proxy for economic activity, as supported by various studies (C D Elvidge et al., 1997; Forbes, 2013; Li et al., 2013) In my research, I utilize NTL to approximate economic activities.
Methodology
Difference-in-differences (DID) is an effective method for estimating causal effects by comparing changes in outcomes over time between a treatment group enrolled in a program and a control group not enrolled, under the assumption that both groups follow the same trend over time This method has been widely utilized in various fields of scientific research, including Labor Economics, Education, Food Economics, Environmental Studies, and the Financial Sector To implement DID, it is essential to observe outcomes for both treated and control groups before and after the intervention, ensuring a sufficiently long monitoring period In my research, the Net Trade and Labor Index (NTLI) serves as the indicator for economic activities, analyzed over multiple years from 2012 to 2020, with three years prior to the intervention (the ARR program starting in 2015) and six years following its implementation Therefore, DID estimation is selected to assess the impact of the ARR on the local economy in this study.
3.4.1 Difference-in-differences for Two-time Frame
With two-time period, the concept of difference-in-differences can explain as figure below:
Figure 3.3 Concept of DID in two-year framework
The impact can observe at two different periods, the pre-intervention (Y| T=0) and after (Y| T=1) The impact could be identified as:
The formula 𝛽 = (Y| T = 1) − (Y| T = 0) represents the difference in outcomes based on the presence or absence of an intervention This result can be estimated using a regression model that incorporates three key variables: (i) an intervention dummy variable indicating whether the intervention is present or absent, (ii) a time dummy variable reflecting the period before or after the intervention, and (iii) an interaction term that multiplies these two factors to capture their combined effect.
The equation of the difference-in-differences regression model is
−𝑇 𝑡 : 𝑇𝑖𝑚𝑒 𝑑𝑢𝑚𝑚𝑦 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒 𝑇 𝑡 = 1 if t is a year after intervention, others
−𝐼 𝑖 : 𝐼𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛 𝑑𝑢𝑚𝑚𝑦 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒 𝐼 𝑖 = 1 if zone i is belong to treatment group, others
Table 3.1 Estimation of the Effect of the Intervention in Regression Equation
The regression equation estimates the intervention effect through a difference-in-difference approach In the control group, the post-intervention outcome is represented as β0 + β1, while for the treatment group, it is β0 + β1 + β2 + β3 The change in outcome for the treatment group compared to the control group is calculated as (β0 + β1 + β2 + β3) - (β0 + β1), resulting in β2 + β3 Additionally, the pre-intervention outcomes are assessed as (β0 + β2) - β0, yielding β2 The difference between the treatment and control outcomes is (β2 + β3) - β2, leading to β3, which signifies the intervention effect as indicated by the coefficient of the interaction term.
Apply to case study in my search, table 1 can re-write into the new form as table 3.2 below:
Table 3.1 Estimation of the Impact of ARR in the NTLI
Pre-Intervention (T=0) After-Intervention (T=1) Treated (𝑇𝑟𝑒𝑎𝑡 𝑖 = 1) 𝐸(𝑌 𝑖1𝑇 (0)|𝑇𝑟𝑒𝑎𝑡 𝑖 = 1) 𝐸(𝑌 𝑖2𝑇 (1)|𝑇𝑟𝑒𝑎𝑡 𝑖 = 1) Control (𝑇𝑟𝑒𝑎𝑡 𝑖 = 0) 𝐸(𝑌 𝑖1𝐶 (0)|𝑇𝑟𝑒𝑎𝑡 𝑖 = 0) 𝐸(𝑌 𝑖2𝐶 (1)|𝑇𝑟𝑒𝑎𝑡 𝑖 = 0) The impact of the ARR could be estimate based on average treatment effect on treated group ATT, which is forming as follows:
−𝐸(): mean NTL in the zone i with the treatment after intervention
−𝑦 𝑖2𝑇 : mean NTL in the zone i with the treatment after intervention
−𝑦 𝑖2𝑇 : mean NTL in the zone i with the treatment before intervention
−𝑦 𝑖2𝐶 : mean NTL in the zone i without the treatment after intervention
−𝑦 𝑖2𝐶 : mean NTL in the zone i without the treatment before intervention
3.4.2 Difference-in-differences for Multi-time Frame
Similarity, when we observe the treated and control units’ multiple times before and after treatment We can calculate the ATT at any time of the post-treatment time points
In this case, the most common approach to trying to estimate the effect of a binary treatment in this setup is the TWFE linear regression This is a regression like
−𝑌 𝑖𝑡 :Night-time light intensity at the year t at the zone i
−𝑇 𝑡 = 0 if t is a year t before intervention and 1 if the year t is a year after intervention
−𝐼 𝑖 : 𝐼𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛 𝑑𝑢𝑚𝑚𝑦 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒 𝐼 𝑖 = 1if zone i is belong to treatment group, others
− 𝜀 𝑖𝑡 : Error component at a year t at a zone i.
To obtain unbiased estimated effects of treatments in an observational study, it is essential for both the treatment and control groups to possess similar characteristics and distributions This necessitates the application of matching methods to select well-matched individuals from the original treatment group for the control group.
To achieve a similar distribution between treatment and control groups, two primary matching methods can be employed: Propensity Score Matching (PSM) and Mahalanobis Distance Matching (MDM) These techniques help select control groups that closely resemble the treatment group within specific distance categories Propensity scores condense multiple covariates into a single scalar value that indicates the likelihood of receiving treatment The estimation of these scores is crucial for effective matching and ensuring comparability between groups (Stuart, 2010).
- 𝑃𝑆( ) represents a propensity score, which is typically calculated as a logit model
- 𝑇 𝑖 = 1 if the zone is in the treatment group, and 0 if in the control group
The distance between two individuals is defined as follows:
- 𝐷 𝑖𝑗 :the distance between zone i and j for matching
- 𝑃𝑆 𝑖/𝑗 : propensity score for the zone i/j
The Mahalanobis distance between covariates of the treated unit X i and covariates of the control unit Y j is defined as follow:
- 𝑀𝐷 𝑖𝑗 :the distance between covariates for the zone i and j for matching
- 𝑋 𝑖 𝑣𝑠 𝑌 𝑗 the matching variable values including the propensity score
- S −1 : is the sample covariance matrix of matching variables from the control subjects.
Propensity Score Matching (PSM) and Mahalanobis Distance Matching (MDM) each have their strengths and weaknesses MDM focuses on creating pairs of units that are close in covariate values, while PSM pairs units based on similar propensity scores, which condenses the entire covariate distribution into a single dimension This means that units with similar propensity scores may not have comparable covariate values, leading PSM to ensure well-balanced samples but not necessarily closely matched pairs In contrast, MDM provides matched samples that are both well-balanced and closely paired However, MDM is more effective with a limited number of covariates and those that are normally distributed, while PSM performs well under a broader range of conditions.
21 the propensity score is reasonably well estimated (King & Nielsen, 2019; Ripollone et al., 2018)
This study utilized Genetic Matching, specifically the "genetic" method with Mahalanobis distance and replacement in MatchIt, to effectively address the identified issues This approach merges Propensity Score Matching (PSM) and Mahalanobis Distance Matching (MDM), employing optimization to identify the most effective distance measure for achieving balance in the matched dataset The results are expected to be consistently improved due to the multifaceted role of covariates, which include optimizing balance, contributing to the generalized Mahalanobis distance, and aiding in propensity score estimation Genetic Matching is endorsed by King & Nielsen (2019) as a reliable method.
In chapter 5, this study will use the Structural Equation Modeling and this Matching method in R Software to create the model of this DID regression framework to acquire the results
DATA PROCESSING AND STATISTIC
Data Collection
This study employed nighttime light data from the Visible Infrared Imaging Radiometer Suite (VIIRS DNB), which is sourced from the Suomi National Polar-Orbiting Partnership (S-NPP) satellite launched by NASA and NOAA in 2011.
Early research utilized the Nighttime Lights (NTL) data from the Defense Meteorological Satellite Program’s Operational Line Scanner (DMSP-OLS), released in 1992 Despite its advantages, the DMSP-OLS has several limitations, including low spatial resolution, lack of onboard and inter-satellite calibration, absence of records for in-flight gain changes, limited 6-bit quantization of digital numbers, light saturation in urban areas due to high gain settings, the blooming effect which overestimates lit areas, and restricted access to high-quality daily OLS data.
The advancement of technology has led to significant improvements in VIIRS DNB compared to DMSP-OLS data, featuring a much smaller pixel footprint, enhanced detection capabilities, an expanded dynamic range, increased quantization precision, and in-flight calibration (C Elvidge et al., 2013; Johnson et al., 2013).
V2.1 VNL is one of VIIRS DNB product from Earth Observation Group, which annual global NTL dataset made by using monthly cloud-free radiance averages from NASA/NOAA (https://eogdata.mines.edu/products/vnl/) Amount of VIIRS DNB, V2.1 VNL has key importation point compare with others including (1) dataset constructed from the monthly averages with filtering to remove sunlit, moonlit, and cloudy pixels, other extraneous features such as biomass burning leading to rough composites that contain lights, fires, aurora, and background; (2) Product applied method to extends temporal leverage in the noise filtering by developing the data range threshold from a multiyear maximum data range and a multiyear percent cloud- free grid; (3) the outlier uses the twelve-month median radiance to discard high and
This study employed the V2.1 VNL VIIRS DNB nighttime light dataset, which features high-resolution 15 arc-second grid cells (approximately 500m x 500m), covering the years 2012 to 2020 The analysis focused on filtering out 23 low radiance outliers, effectively isolating background data while minimizing the impact of most fire events (C D Elvidge et al., 2021; Zhao et al., 2019).
Data Processing
Step 1: Download the raw annual NTL from EOG (raster file-band 1 gray)
Figure 4.1 Global Nighttime Light in 2020
Step 2: Clip and Enlarge Research Area before applies the zonal statistics algorithm in QGIS (https://www.qgis.org/en/site/) to extract the value of NTL intensity value for each cell of grid 500mx500m
Step 3: Geospatial data and Transportation Network is collected from OpenStreetMap application Airport access road and influence zone of access road is also computed with every 500m
Figure 4.3 Airport Access Road and
Step 4: Assigning data area to the influence zone/distance group was defined based on the same distance offset from the road
Treated group around 1.0km from AAR
Treated group around 1.5km from AAR
Treated group around 2.0km from AAR
Treated group around 2.5km from start of AAR (near airport)
The observed zone near Airpor/ near CBD
Figure 4.5 Group Data of Specic Distance
Data Statistics
The statistical analysis of various distances from the AAR of Nighttime Night over the years enables a quantitative assessment of the data This process simplifies extensive datasets, making it easier to observe trends and evaluate data consistency, while also determining whether the data follows a normal distribution.
It is also a necessary step to assess whether data can be used for further analysis
Each distance group descriptive statistic is shown in the tables below, respectively:
Table 4.1 Descriptive Statistic Results of 12km around AAR
112km Observed Data around AAR (N &49)
Mean SD Min 25% 50% 75% Max Mean SD Min 25% 50% 75% Max
Table 2.2 Descriptive Statistic Results of 500m Group
Mean SD Min 25% 50% 75% Max Mean SD Min 25% 50% 75% Max
Table 4.3 Descriptive Statistic Results of 1000m Group
Mean SD Min 25% 50% 75% Max Mean SD Min 25% 50% 75% Max
Table 4.4 Descriptive Statistic Results of 1500m Group
Mean SD Min 25% 50% 75% Max Mean SD Min 25% 50% 75% Max
Table 4.5 Descriptive Statistic Results of 2000m Group
Mean SD Min 25% 50% 75% Max Mean SD Min 25% 50% 75% Max
Table 4.6 Descriptive Statistic Results of 2500m Group
Mean SD Min 25% 50% 75% Max Mean SD Min 25% 50% 75% Max
Cross summary in both changing in years and the distance of these above tables we can have a short stated on the dataset as bellow:
• There were significant differences between the NTLs max and 75% of the dataset while the mean and 75% have slightly changed, the value around 10 units of NTL
• The NTLs were quite stable from 2012 to 2014 and start to increase from 2015
Figure 4.6 Data Statistically for NTL around 12km of AAR Areas
• The NTLs were quite low, and stable, and seem there was no difference between each distance group from 2012 to 2014
• The NTLs start to increase significantly from 2015 The changing magnitude is different for each distance group, the highest change is 0.5km and declines with distance from AAR
Figure 4.7 NTL Mean Value Around AAR Areas
Figure 4.8 NTL Mean Value of Areas at End Poit of AAR (Near the CBD)
Figure 4.9 NTL Mean Value of Areas at Start Poit of AAR (Near the Airport)
• In general, both figures have a seminal trend In both figures, NTLs were the lowest, stable from 2012 to 2014, and start to increase significantly from 2015
• In both figures, the change in magnitude is different for each distance group, the highest change is 0.5km and declines with distance from AAR
• NTLs have a major and continuous change from 2015 in areas near CBD while they have significant change just from 2017 in areas near the airport
DATA ANALYSIS AND DISCUSSION
Matching Result before DID Analysis
Genetic matching methods are applied to datasets to meet specific requirements, focusing on standardized mean differences (SMD) and variance ratios (VR) of covariates in treatment and control groups (Zhang et al., 2019) According to Stuart (2010), a covariate is considered balanced when the absolute SMD value is less than 0.25, while Austin (2011) suggests a stricter threshold of less than 0.1 For variance ratios, a balance is indicated when the ratio is close to 1.0, with values below 0.5 or above 2.0 deemed "too extreme" (Rubin, 2001) The results of the matching process are presented in Table 5.1, with additional details available in the attached appendix.
Table 5.1:Balance Diagnostics Before and After Matching
Within 2.0 km from AAR Before
Number of samples in treatment group 1,071 1,071 2,637 2,637 4,347 4,347 6,246 6,246
Number of samples in control group 68,337 907 667,71 2,115 65,061 3,218 63,162 4,273
Analysis Result
As mentioned in chapter 3, the DID regression model with two-way fixed effects in R will be applied to estimate the impact of ARR on local economy
Recall the DID regression formula:
−𝑌 𝑖𝑡 :Night-time light intensity (NTLI) at the year t at the zone i
−𝑇 𝑡 = 0 if t is a year t before intervention (before 2015) and 1 if the year t is a year after intervention (after 2015)
−𝐼 𝑖 : 𝐼𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛 𝑑𝑢𝑚𝑚𝑦 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒 𝐼 𝑖 = 1if zone i is belong to treatment group (as hypothesis), otherwise
−𝑋 𝑖 is the population density present for the control variable
− 𝜀 𝑖𝑡 : Error component at a year t at a zone i.
Please to take noted: the airport access road started operation from Jan 2015
After defining the parameters, the dataset is imported into R software for estimation The impact of ARR is determined through regression calculations based on each hypothesis.
The introduction of airport access roads significantly enhances economic growth in areas with improved airport accessibility compared to regions where access remains unchanged This increased connectivity stimulates local economies by attracting businesses, boosting tourism, and creating job opportunities, ultimately leading to a more prosperous community.
Airport access road has positive economic impacts
• Treatment group: The zones that are located near the airport access road
• Control group: The zones that are located far from the airport access road
Table 5.2 presents the estimation results from baseline models analyzed using ordinary least squares (OLS), random effects, and fixed effect regression models across four datasets, with treatment groups defined by distances of 0.5, 1.0, 1.5, and 2.0 km from the AAR The findings from both the F-test and Hausman test indicated that the fixed effects model was the most suitable choice for this analysis.
The opening of the AAR significantly boosted NTL intensity, indicating that its introduction enhances local economic activities Additionally, the analysis revealed that the effects of the AAR diminished with increased distance, suggesting that areas closer to the AAR experienced greater impacts.
Table 5.2 DID Analysis Results on Main Hypothesis H1
Treatment group Within 0.5 km from AAR
Year FE No No Yes No No Yes
Zone FE No No Yes No No Yes
Effects Fixed Effects OLS Random
Treatment group Within 1.5 km from AAR
Year FE No No Yes No No Yes
Zon FE No No Yes No No Yes
Effects Fixed Effects OLS Random
The introduction of an airport access road significantly enhances economic impacts in areas near the central business district (CBD) compared to regions farther from the city center Improved accessibility to the airport in these central locations leads to greater economic benefits, highlighting the importance of strategic infrastructure development in urban planning.
The impact from airport access road is greater in the end area of AAR (where near the CBD) than in other areas
• Treatment group: The zones that are located near the AAR and near CBD
• Control group: The zones that are located far from the AAR and near CBD
• Treatment group: The zones that are located near the AAR and not near CBD
The control group consists of zones situated far from the Airport Access Road (AAR) and the Central Business District (CBD) Sub-Hypothesis H2.2 posits that the economic benefits stemming from the introduction of the AAR are more significant in the areas closest to the airport, where accessibility has been enhanced, compared to those further away Therefore, the impact of the airport access road is notably greater in the initial areas of the AAR, particularly those near the airport, than in the more distant regions.
• Treatment group: The zones that areas located near the AAR and near the airport
• Control group: The zones that are located far from the AAR and near the airport
Table 5.3 DID Analysis Results on Sub-Hypothesis H2
𝑇 𝑡 𝐼 𝑖 (AAR) 6.634*** 4.134*** 3.833*** 4.225*** 2.823*** 2.346*** Treatment group Within 0.5 km Within 0.5 km Within 0.5 km Within 1.0 km Within 1.0 km Within 1.0 km
Area Near city center Near airport Other areas Near city center Near airport Other areas
Year FE Yes Yes Yes Yes Yes Yes
Zone FE Yes Yes Yes Yes Yes Yes
𝑇 𝑡 𝐼 𝑖 (AAR) 3.852*** 2.318*** 1.427*** 3.213*** 1.878*** 0.909*** Treatment group Within 1.5 km Within 1.5 km Within 1.5 km Within 2.0 km Within 2.0 km Within 2.0 km
Area Near city center Near airport Other areas Near city center Near airport Other areas
Year FE Yes Yes Yes Yes Yes Yes
Zone FE Yes Yes Yes Yes Yes Yes
As the impacts of an AAR on local economies may vary across areas, DID estimations were performed for three different metropolitan areas: near Hanoi City Center, near the
The analysis of the AAR's impact revealed that areas near the city center experienced the strongest effects, as shown in Table 5.3, compared to regions near the airport and other locations This indicates that the proximity to the city center significantly enhances the effectiveness of the AAR implementation.
The introduction of the airport access road significantly influences economic growth, particularly in densely populated areas where improved airport accessibility is observed In contrast, the economic impacts in regions with lower population densities are less pronounced despite similar enhancements in airport access.
The impact of the airport access road is greater where have high population density (PD)
Subgroup 3.1: Higher population density (Higher 50%)
• Treatment group: The zones that are located near the AAR with higher PD
• Control group: The zones that are located far from the AAR with higher PD
Subgroup 3.2: Lower population density (Lower 50%)
• Treatment group: The zones that are located near the AAR with lower PD
• Control group: The zones that are located far from the AAR with lower PD
The introduction of airport access roads significantly enhances economic performance in areas with already high economic activity, resulting in greater economic impacts compared to regions with lower economic performance This improvement in accessibility to airports fosters economic growth more effectively in thriving areas.
The impact from airport access road is greater where have higher economic performance (EP)
Subgroup 4.1: Higher economic performance (Higher 50%)
• Treatment group: The zones that are located near the AAR with higher EP
• Control group: The zones that are located far from the AAR with higher EP
Subgroup 4.2: Lower economic performance (Lower 50%)
• Treatment group: The zones that are located near the AAR with lower EP
• Control group: The zones that are located far from the AAR with lower EP
Table 5.4 DID Analysis Results on Sub-Hypothesis H3&4
Year FE Yes Yes Yes Yes Yes Yes Yes Yes
Zone FE Yes Yes Yes Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes Yes Yes Yes
Zone FE Yes Yes Yes Yes Yes Yes Yes Yes
Table 12 presents the estimation results for two subgroups based on population density and NTL levels across four treatment groups The findings reveal that the DID estimators in both high and low population density areas showed significantly positive impacts, indicating that the AAR positively influenced the local economy irrespective of population density Furthermore, the estimated coefficients for the higher 50% population density subgroup were notably greater than those for the lower 50%, suggesting a stronger impact of the AAR in densely populated regions Additionally, the results highlighted that DID estimators in higher NTL areas were significantly positive, while those in lower NTL areas were insignificant, implying that the AAR's positive effects were more pronounced in regions with higher existing economic activities.
Parallel Trend Test
The parallel trend test is a necessary step prior to concluding the results of a DID estimation The model for this test is formulated as follows:
The model equation \( Y_{it} = \tau_t + \pi_i + \sum_{3\tau=1} \beta B_{\tau} D_{it} B_{\tau} + \sum_{5\tau=1} \beta A_{\tau} D_{it} A_{\tau} + \epsilon_{it} \) incorporates dummy variables \( D_{it} B_{\tau} \) and \( D_{it} A_{\tau} \) to differentiate between treatment groups before and after the introduction of the AAR Specifically, \( D_{it} B_{\tau} \) is set to 1 if zone i in year t is part of the treatment group in the three years leading up to the AAR's implementation, while \( D_{it} A_{\tau} \) is 1 for the five years following its introduction This analysis spans from 2012 to 2020, marking the operational start of the earliest AAR road section.
The analysis covers two periods: 𝐷 𝑖𝑡 𝐵𝜏 spans three years prior to 2015, while 𝐷 𝑖𝑡 𝐴𝜏 includes five years after 2015 Figure 17 illustrates the results of the parallel trend test across four treatment groups at varying distances from the AAR, specifically 0.5 km, 1.0 km, 1.5 km, and 2.0 km The findings indicate that the estimated coefficients were statistically insignificant before the AAR's opening, but showed significant positive values afterward, confirming the presence of a parallel trend.
Table 5.5 DID Analysis Results on Parallel Trend Test
Group Term Coef SE Sign Treatment
Group Term Coef SE Sign
The areas within 1,5 km around the AAR
The areas within 2,0 km around the AAR
Placebo Test
The local economies near the AAR may also be affected by unobserved factors other than AAR introduction during the study period
To address the problem of spurious regression, this study conducted a placebo test using randomized simulations, where sample data were assigned to various groups to create artificial treated groups The results, illustrated in Figure 3, display the distribution of p-values and estimator coefficients from 500 simulations, predominantly clustering around 0, with most coefficients yielding p-values exceeding 0.1 Notably, instances with p-values less than 0.1 revealed coefficients significantly diverging from the baseline regression estimate of +3.098, indicating that the impact of omitted factors on the estimation results can be disregarded.
Figure 5.1 Parallel Trend Test with Estimated Dynamic Effects
Endogeneity Test with Instrumental Variable Method
The construction of an AAR involves significant capital investment, which may lead to endogeneity issues in site selection To mitigate bias in our regression analysis, we utilized instrumental variables based on average slope and altitude The two-stage estimation results, presented in Table 5.6, indicate that the Kleibergen-Paap rk Wald F statistic in the first stage exceeded 10, confirming the validity of our instrumental variables Furthermore, the second-stage DID estimators were significantly positive and showed a tendency to decrease with distance from the AAR, reinforcing the reliability of our baseline regression findings despite the potential endogeneity concerns.
Table 5.6 Estimation Results with Instrumental Variable Methods
Models Model 2-1-1 Model 2-1-2 Model 2-2-1 Model 2-2-2
Stage First Second First Second First Second First Second
Treatment group Within 0.5km Within 0.5km Within 1.0 km Within 1.0 km
Year FE Yes Yes Yes Yes
Zone FE Yes Yes Yes Yes
Models Model 2-3-1 Model 2-3-2 Model 2-4-1 Model 2-4-2
Stage First Second First Second First Second First Second
Treatment group Within 1.5 km Within 1.5 km Within 2.0 km Within 2.0 km
Year FE Yes Yes Yes Yes
Zone FE Yes Yes Yes Yes
Further Discussion
An airport access road enhances local economic growth by facilitating efficient transportation between the airport and surrounding areas, including the city center and nearby regions The construction of such roads leads to increased accessibility, which can attract businesses, boost tourism, and create job opportunities, ultimately benefiting the local economy.
The improved access road significantly enhances connectivity between the airport and key areas of the city, making it more accessible for both businesses and travelers This development is poised to boost tourism, create new business opportunities, and facilitate increased trade.
• Economic Development: The improved accessibility can attract businesses and industries to set up their operations near the airport, leading to increased investments and job opportunities in the area
The presence of a well-connected airport access road significantly enhances the value of nearby real estate, prompting developers to invest in hotels, office complexes, and various commercial properties to meet the rising demand.
• Trade and Cargo: Efficient airport access can facilitate the movement of goods and cargo, boosting local trade and exports
• Revenue Generation: Increased tourism and business activity can lead to higher tax revenues for the local government, enabling them to invest in infrastructure and public services
The influence of an airport access road is significantly more pronounced in areas close to the city center compared to other locations This increased impact can be attributed to several factors, including higher traffic volumes, greater economic activity, and enhanced connectivity to urban amenities, all of which contribute to a more substantial effect on the surrounding environment and community dynamics.
The city center is home to a large concentration of businesses and commercial establishments, making it a vital economic hub Direct airport access via a dedicated road enhances connectivity to this central business district, facilitating improved accessibility and fostering increased economic activity.
Many cities boast popular tourist attractions in or near their centers, and having efficient airport access can significantly enhance tourist influx Improved transportation routes not only make it easier for visitors to reach these destinations but also lead to increased tourism revenue for the local economy.
The city center serves as a vital transportation hub, offering a range of public transportation options By adding an airport access road to this hub, the overall transportation network is improved, facilitating easier travel for passengers between the airport and their final destinations.
The city center, being a vital economic hub, attracts significant investment in infrastructure development, enhancing the positive effects of the airport access road.
Sub-Hypothesis 3&4 suggest that the influence of an airport access road is more pronounced in regions with high population density and strong economic performance This correlation indicates that areas with greater population density and robust economic activity experience a heightened impact from airport access roads, enhancing connectivity and accessibility.
• Higher Demand for Connectivity: Areas with high population density and economic activity typically have more people traveling to and from the airport
An access road that efficiently serves this demand can lead to increased usage and economic benefits
High population density and strong economic performance make locations more appealing to businesses, as they offer a larger customer base and greater market potential The presence of an airport access road enhances the attractiveness of these areas, ultimately driving economic growth.
Improved airport access can lead to enhanced economic performance, resulting in increased demand for skilled workers This influx of talent from other regions will strengthen the local workforce and foster economic development.
High population density and robust economic performance significantly enhance the likelihood of attracting investment and development projects In this context, an airport access road serves as a crucial catalyst for such investments, fostering the establishment of new ventures and industries.
In conclusion, the findings support previous research, indicating that enhancing accessibility to airports and related developments significantly increases demand for both freight and passenger services This improvement not only promotes population density and urban growth but also drives the creation of new economic zones, ultimately leading to extensive economic development.
43 local areas as well as the country level This provides extremely important evidence for the benefit of economic development in areas related to airport investment
Figure 5.3 (23) Changes in NTL Around the AAR (2014-2020)
CONCLUSION AND POLICY RECOMMENDATION
This study analyzed the effects of the introduction of an AAR on the local economy in Hanoi, Vietnam, utilizing a quasi-experimental design to assess its impact on nighttime light (NTL) at a microgeographic level The results from a difference-in-differences (DID) estimation, based on matched panel data from 2012 to 2020, revealed that the AAR investment significantly boosted local NTL, with the most pronounced effects observed in areas nearest to the AAR, diminishing as the distance increased.
The AAR's effects were most pronounced near the city center, with minimal impacts observed in the region between the airport and the city Additionally, significant positive impacts were noted across all population densities, although these effects were primarily evident in areas with higher Nighttime Lights (NTL) levels.
Our findings underscore the importance of airport-related investments for policymakers, indicating that such multi-billion-dollar initiatives can significantly enhance economic performance and population density in the surrounding areas This aligns with the concept of aerotropolis, where airports serve as vital infrastructure for urban regeneration and improved accessibility Additionally, our results suggest that AAR investments boost local accessibility and agglomeration economies, particularly near central business districts, as air travelers are willing to incur costs for convenient transport between city centers and airports Therefore, urban development strategies should prioritize urban areas to maximize economic benefits Lastly, to enhance the global competitiveness of cities benefiting from airport investments, integrating new economic zones, urban regeneration, and real estate redevelopment into the urban development framework is essential.
This study is the first to explore the impact of the AAR on local economies in developing cities, utilizing a cutting-edge methodology for causal inference in econometric evaluation, recognized with a Nobel Prize in 2021 By employing a comprehensive dataset from satellite sources, the research ensures a high level of reliability The study also emphasizes a consistent process through advanced data matching and a variety of analytical models, conducting numerous robustness tests such as robust and cluster standard errors, pre-parallel trend assessments, placebo tests, and instrumental variable analysis in 2SLS These approaches enhance the reliability and scientific value of the findings, benefiting not only this study but also other developing cities.
This study identifies three key analytical challenges for future research: First, it acknowledges the limitations of the dataset, which only covers a few years post-project and does not account for the significant impact of the COVID-19 pandemic on the aviation sector and the economy beyond 2020 Second, the research is confined to local economic development in Hanoi, highlighting the need for additional case studies to validate the findings and explore their applicability in other developing cities Lastly, the analysis does not consider the effects of various airport capital project capacities, such as the number of terminals or runways, on the Airport Accessibility Ratio (AAR) and landside capacity expansion amid rapid urban transformation These areas present opportunities for further investigation.
1 Common support and distribution of balance of Treatment and Control group for 500m matching
2 Common support and distribution of balance of Treatment and Control group for 1000m matching
3 Common support and distribution of balance of Treatment and Control group for 1500m matching
4 Common support and distribution of balance of Treatment and Control group for 2000m matching
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