Viva Questions for Economics

Economics vivas demand a strong command of your empirical strategy, identification approach, and the assumptions underlying your models. Examiners will probe whether your causal claims are well identified, whether your data and methods are appropriate, and how your findings contribute to the economic literature. Whether your work is microeconomic, macroeconomic, or applied, expect detailed technical questioning alongside broader questions about policy relevance and real-world implications.

Economics vivas tend to be technically rigorous and direct. Your examiners will often be economists who work in similar areas and will quickly focus on the credibility of your identification strategy and the robustness of your results. The discipline has high standards for causal inference, and examiners will push hard on whether your results can support the claims you're making. If you've used quasi-experimental methods, expect detailed questioning about the plausibility of your identifying assumptions.

Questions about your research

Economics examiners will focus on whether your empirical approach is credible and whether your data is suitable for answering the question you've posed. They'll scrutinise your identification strategy – whether you're using instrumental variables, regression discontinuity, difference-in-differences, or another approach – and test whether the assumptions it requires are plausible. They'll also be interested in how you've handled practical data issues and whether your results are robust to alternative specifications.

• What is the central economic question your thesis addresses, and why is it important?
• Can you explain your identification strategy in detail and make the case that it's credible?
• What data sources did you use, and what are their strengths and limitations for your research question?
• How did you handle endogeneity concerns – and how confident are you that you've addressed them adequately?
• What econometric methods did you use, and why did you choose them over the alternatives?
• How robust are your results to different model specifications, sample restrictions, or variable definitions?
• Can you walk us through your key regression table and interpret the coefficients?
• How did you deal with missing data, measurement error, attrition, or sample selection?
• What robustness and sensitivity checks did you perform, and what did they reveal?
• Did you consider any natural experiments, policy changes, or institutional features as sources of variation?
• How did you construct your key variables, and are there alternative operationalisations that might give different results?

Questions about theory and literature

Economics examiners will want to see that your empirical work is motivated by economic theory and situated within the relevant literature. They'll ask how your results relate to existing findings, whether your estimates are consistent with theoretical predictions, and how your work advances the debate. If your thesis includes a theoretical model, expect questions about its assumptions, tractability, and testable predictions.

• What economic theory motivates your research question, and how does it generate testable predictions?
• How does your work build on, challenge, or extend existing findings in the literature?
• What are the key empirical papers your work relates to, and where specifically does yours differ in approach or findings?
• Are there competing theoretical explanations for the relationships you observe in your data?
• How does your model relate to the canonical models in your subfield – and how does it depart from them?
• What assumptions does your theoretical framework make, and are they empirically testable?
• How does your work relate to the broader evolution of your subfield over the past decade?

Questions about contribution and impact

In economics, contribution is typically measured by whether your paper provides new credible evidence on an important question. Examiners will want to know whether your estimates are useful for policymakers, whether they change our understanding of an economic mechanism, or whether they resolve an empirical puzzle. Be precise about what your work adds – incremental improvements in estimation precision are less compelling than genuinely new findings or approaches.

• What is your thesis's primary contribution to the economic literature?
• What are the specific policy implications of your findings – and how precisely can you estimate the relevant magnitudes?
• How would a policymaker or central bank use your results in practice?
• Do your findings have implications for welfare, inequality, efficiency, or market design?
• What further research – whether a field experiment, a structural estimation, or new data – would be needed to strengthen the case for policy action?
• How do your estimates compare with those in the existing literature, and what accounts for any differences?

Tough follow-ups your examiners might ask

Economics examiners will test the credibility of your causal claims with precision. They'll push on your exclusion restriction, ask about threats to internal and external validity, and probe whether your results are economically meaningful as well as statistically significant. The distinction between statistical and economic significance is one that economics takes seriously, and you should be prepared to discuss it.

• How confident are you that you've identified a causal effect rather than a spurious correlation?
• What would happen to your results if you used a different instrument, running variable, or identification strategy?
• Your results are statistically significant – but are they economically meaningful? How large is the effect in practical terms?
• How do your findings hold up out of sample, in a different time period, or in a different institutional context?
• A referee argues that your exclusion restriction or parallel trends assumption doesn't hold – how do you respond?
• What is the most plausible threat to your identification strategy, and what evidence can you offer against it?
• If you had access to better data – administrative records, linked datasets, experimental variation – how would your approach change?

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