Intern, Demand Impact Modeling

Employment Type:

Internship

Contract Duration: 6 months

At IATA, we represent over 350 airlines worldwide, striving to makeaviation safer, smarter, more sustainable, and inclusive.

• Our Values are not just words on a page - they are the energy behind everything we do:
  ONE IATA - We collaborate across teams,
  TRUSTED -We do the right thing,
  INNOVATIVE -We make tomorrow better,
  INCLUSIVE -We embrace diverse perspectives.
• With over 30,000 courses available, we believe in continuous learning and support your growth in an ever-changing industry.
• Diversity, equity, and inclusion are our priorities. We are certified by the Equal Salary Foundation, offering equal pay and family-friendly policies.
• We encourage community involvement through volunteering and strive to make tomorrow better for aviation and our communities. We offer time off so you can support causes important to you.
• We promote work-life balance with flexible work options, including remote and hybrid work, a generous 'work from abroad' policy, and you get your birthday off!

IATA is committed to achieving net zero CO2 emissions by 2050. In the Sustainability and Economics Division, we take a holistic approach to this challenge and seek to combine subject matter expertise with a cross‑functional approach to leverage on all knowledge within the team to facilitate environmental and financial sustainability of the airline industry. The Industry Analysis team conducts research about the airline industry and produces key forecasts regarding passenger and cargo volumes, as well as airlines’ financial performance.

The team is also responsible for producing ad‑hoc analysis, maintaining regular publications, and producing and delivering presentations in support of the Senior Vice President Sustainability and Chief Economist and IATA’s internal and external stakeholders.

You will work as an integral member of the Industry Analysis team within the Sustainability and Economics Division, performing research regarding the empirical price impact on air travel demand. You will support advancing a research project that estimates demand elasticities at various granularity levels, using IATA’s unique air travel demand and price data. Your work will contribute across all stages of the research cycle of the project: from literature review, data preparation, and econometric modeling, to robustness testing, and final documentation.

Your work will directly feed into publishable research outputs for a key flagship report by IATA Sustainability and Economics, with scope for meaningful intellectual contributions to the methodology, and to the findings.

The role is expected to include, but not be limited to, the following aspects:

• Conduct a structured literature review spanning transport, energy, trade, and tourism economics to compare price elasticity estimation methods, instrument validity, and demand modeling best practices.
• Estimate price elasticities of air travel demand at various levels using basic panel econometric methods (e.g., OLS, fixed effects), working within an established econometric modeling framework.
• Design and implement robustness and sensitivity checks, sub‑sample analysis, and model diagnostics to stress‑test key elasticity estimates.
• Produce well‑documented, reproducible code in R hosted on Bitbucket, and write clear analytical memos and a final project report summarizing methodology, results, and limitations.
• Present interim findings to the project team, contribute to internal knowledge‑sharing, and support the preparation of results for external publication or stakeholder communication.

• Master in Economics, Econometrics, Transport Economics, or a closely related quantitative field; candidates with a background in transport modeling with experience in researching price elasticities are particularly encouraged to apply.
• Demonstrated ability to conduct high‑quality research independently, with a systematic approach of reviewing academic literature with critical thinking.
• Good understanding of econometrics and causal inference, ideally regarding…