Data and Doing Empirical Work

Data Often its own section of may be part of the analysis section. If there are a lot of variables or other things, put most detail in an appendix. Purpose is to verify that you apply the analysis to reputable and reasonable data, – Increase the believability of what you find – Allow others to be able to replicate your results. Unless the data are proprietary, you should make it available and clearly indicate how it can be acquired. Many journals now require this for empirical work.

Components of the data section General introduction about the data – Source and how it was collected – Time period and frequency – Geographical location where the data were collected – Relevant groupings – Describe whey it is interesting Explain any transformations you (or others) made to the data. Discuss summary statistics – Definitions – Table of, at least, the mean, variance, minimum and maximum – Disaggregate by groups Special features of your data. Limitations of your data.

Survey Data Discussion of the data collection process Explain – sample frame – response rate – The actual questions used to yield data for key (and unusual) responses, (perhaps in an appendix) Tell if it is available to others, and how

Strategy for Empirical Work Convey how you will discover your answer – model(s) you are going to estimate – how you will estimate it (i.e., what econometric procedure you will use – how the estimated coefficients will produce an answer to your question. You must understand your work! – Never use a technique you don’t understand yourself. – Empirical work boils down to a claim that "A causes B," usually documented by some sort of regression. Explain how the causal effect is identified.

Specifics on Empirical Models Describe what economic mechanism caused the dispersion in your right hand variables. Natural experiments are rare. If you think you have one, be able to explain it. Describe what economic mechanism constitutes the error term. Explain what is causing variation in the dependent variable that is not covered by your predetermined variables. – Explain why it is uncorrelated with RHS variables, or how you will fix it – Explain the economic as well as statistical reasons. Explain the economics of any instrumental variables used, – why they are good instruments and uncorrelated with the errors – Understand the difference between an instrument and a control. Should it be an additional variable not an instrument?

Describe the source of variation in the data – For example, fixed effects vs random effects Specify whose behavior you are modeling. A – How do you know it is a demand curve, not a supply curve? – How is it not an equilibrium? Be cautious of bidirectional causality. – Think of the obvious reverse-causality stories. – Explain what is truly exogenous, and why. Defend the specification – High R2 can be bad (left shoes = b0 + b1 right shoes) – What out for estimating identities

Give the stylized facts in the data that drive your result – Don’t focus only on estimates and p-values. – A stylized fact is a broad generalization that summarizes some complicated statistical relationships – A prominent example of a stylized fact is: "Education significantly raises lifetime income.“ – Another stylized fact in economics is: "In advanced economies, real GDP growth fluctuates in a recurrent but irregular fashion, with an average cycle length of five to eight years". But not all stylized facts are true – Discuss counter examples. – In the case given above, holding a PhD may lower lifetime income,

Explain the economic significance of your results. – Discuss the magnitude of the central numbers in the context of an economic argument, not just their statistical significance. – STATISTICAL SIGNIFICANCE IS NOT THE SAME AS ECONOMIC SIGNIFICANCE. tatistically_significant_result.pdf tatistically_significant_result.pdf Every important number should include a standard error.