Dear Edgeworth--The BLS just released statistics showing how few jobs have been lost due to "outsourcing.' There is some debate as to what these statistics mean. I wish to understand these statistics better. You have spoken about economic theory at some length and have prided yourself on how irrelevant your models are to the flow of human events. But surely, empirical economics has more to do with real life than the abstract models you describe? How should I go about interpreting economc statistics so that my perspective on world events will be more informed? --Curious George
Your chosen nickname illustrates your first misconception: You are curious. A fatal flaw in the economics discipline is to exhibit intellectual curiosity. It is bad form! This is nearly as deadly, as rude, as uncalled for, and as counter to the ethos of the discipline as those horrid vices my misguided research assistant, P.S. Babcock, admires so: creativity and independent thought. Your fundamental mistake is to approach empirics with the idea that you will learn something about the world in which you live. The mistake is to look for something to "believe," to be curious about how people actually behave. Empirical work does not tell you this. It is not designed to tell you this. You have misunderstood in a way that could be fatal to your career!
As a theorist, I am no expert in empirical economics. But I was chatting with my good friend and colleague, Les Prattlemore, in the Oceanview Lunch Group, and Les recommended that I wander over to the office of the esteemed applied economist, Professor Emeritus, Dr. Seymour Efstatz. Dr. Eftatz has gratiously offered this list of dos and don'ts for the young economist or lay person:
1. You must never read an empirical sudy expecting to learn something about the world. Further you must never conduct an empirical study for that purpose. You will not be judged on that criterion. Methodology is what matters. You must do something complicated, methodoligically. It must be so complicated, so pointlessly innovative, as to leave no doubt whatsoever in anyone's mind that the results are complete bullshit.
2. Never pose a clear and simple question. Never answer it well. Never choose a topic for which there is a straightforward means of answering a straightforward question. Why? Because if the question is answerable in any clean way, if the question is well posed, if you can actually get some traction and say something that has a shred of meaning or believability, then the methodology will not be impressive. Methodological innovations take bad data and poorly posed questions, construct microscopic band-aids to paste over gaping bloody wounds, and then pretend to have accomplished something beyond puffing up the reputation of the researcher. Learning something about the world is not on the agenda.
3. You have a good dataset? You can actually answer a real question? Do not despair! There is no dataset that can't be screwed up, twisted, tortured, turned inside out, and reduced to irrelevance given enough statistical manipulation. If you are unfortunate enough to have obtained a strong and simple result from your data, don't give up hope! No dataset is perfect. Find a source of misspecification. This will always exist, as specifications always simplify. Then you can "correct" this imperfection with some completely bogus technique whose only purpose is to give you an excuse to pretend to be smart.
If you were a physician the technique would work like this: You heal the patient, but there remains an imperfection, a scar on his left finger. You then feign concern over this scar. It is not a successful operation because of this scar. (Our metaphorical scar is, say, a possible source of correlation between the error term and regressors in ordinary least squares). You propose a procedure that will eliminate scars, if all goes well. The procedure is complicated, relies on preposterous assumptions, is impossible to do correctly, and when applied in practice will cut off all blood flow so that the patient's arms, legs, head, and genitalia will have to be amputated. Thus, there is no scar on the patient's finger! You have succeeded! Whenever anyone else peforms the simpler procedure, criticize and berate the person. Condescend to him. Point out the scar on the patient's finger and boast that your procedure leaves no such scar. The profession will then embrace you with open arms.
4. Never discus policy. This, too, is bad form. It advertises your naivete. If you must discuss policy, make sure that your policy recommendations are shallow, terse, and poorly thought out. They are an afterthought. Demonstrate that you have put liitle or no effort into constructing an overarching argument. Your policy conclusions must not follow from your study. They must be an extension of whatever intellectual prejuduces you bring to the table. Any time you spend thinking about the meaning of what you have done is time wasted. It is time that could have been spent on pointless technicalities that would have signaled to your colleages that you belong with them, that you share their lack of intuition and their deliberate obtuseness about human behavior. Take your clue from Paul Krugman. Come up with positions so shallow and buffonish, so arrogantly vapid, that everyone will know you must have demonstrated prodigious technical skills somewhere along the way. (Otherwise, how could you ever have graduated third grade, let alone college?)
Thanks to Dr. Efstatz for his advice. I, Dr. Edgeworth Boks, can only echo what he has said above. The fact that you are curious, my dear George, tells me all I need to know. You must lose this curiousity and embrace the dicsipline in all its glorious irrelevance. Don't worry about the BLS statistics and what they mean. Don't worry about the true impact of outsourcing. Don't worry, my friend. Rejoice in a zen-like freedom from the hunger for knowledge. Worship at the altar of your own academic reputation. Worry about writing a dissertation, worry about getting published, worry about impressing other members of the profession. These are what matter in the long run.