Most professionals, from doctors to policy makers, are subject to biases that can prevent them from helping the people they are actually seeking to assist. Previous beliefs or personal culture can prevent them from objectively analyzing the information they have in front of them. This can be particularly serious in professions where data analysis plays a major role, like development professionals. Fortunately, the World Bank - an institution full of such people - decided that instead of pretending such issues were not present, they would release a report where they collected data examining how their staff behaved, testing if the biases were actually there.
Decisions can be very complex. Suppose you have a problem at hand, which is particularly messy. How do you compare alternatives in such situations? And what if alternatives are almost infinite? Even in simpler cases, going from two to three alternatives has been suggested to "irrationally" change professionals actions: doctors were outlined a situation where a patient had two alternatives: 1) Ibuprofen + Referral (to specialist); or 2) Just referral. In such a case 53% of doctors chose option 2. Another set of doctors was given a third alternative: 3) Piroxicam + Referral. Paradoxically, now many more doctors (72%) chose the previously available option of just Referral. Complexity modifies their behavior, leading them more to the simpler solution of just referral. Since the only difference between the two cases was that a new (possibly "irrelevant") alternative was added, we should expect less doctors choosing any of the previously available ones. But this did not happen. And this are highly educated and experienced professionals!
Framing (basically how something is described) is also unbelievably important: people seem to have much negative views of a policy that says 1/3 of people will die versus one that says 2/3 of them will survive. Obviously, the two are exactly the same. Moreover, when framed in terms of gains, people prefer certainty ("1/3 of people survive"). However, when framed in terms of losses ("2/3 of people die"), people are more willing to take a gamble and try to save more people (1/3 chance no one dies and 2/3 chance everyone dies"). Does this happen at the World Bank as well? Certainly.
Confirmation Bias means that people gather or value information selectively in order to support their previous beliefs. We all have cultural and ideological priors, which leaves us susceptible to analyze or interpret information with motivated reasoning, so as to arrive to the conclusions we like. A very nice experiment was held where professionals were given the same data to analyze. To some people they told it referred to the effectiveness of a skin cream (something for which we don't have priors or political beliefs) and others were told it was about the impact of the minimum wage laws on poverty. Figure 1 shows that World Bank professionals were more likely to identify the answer supported by the data (i.e. the "correct" one), when talking about skin cream. (I am also shocked by how low the percentage of right answers is when talking about skin cream, but I guess that's another issue...). When dealing with the minimum wage they were more likely to get the right answer when this matched their cultural views. And differences in seniority or cognitive ability did not improve the interpretation of the data. This was also done with people outside the World Bank and they actually found that skill helps only when the right answer matches your ideology...
Figure 1: Subjective interpretation of data.
We could take all this information in a negative way: some of the best people in charge of world development are not doing a good job. However, I believe that's not correct. This bias is present in all of us. And the positive thing is that the people at the World Bank now seem to be willing to recognize it and hopefully move forward to try to make progress. Identifying the problem is the first step, or as John Dewey said a problem well put is half solved.
After a long long time devoted to education, economists do need to look for a job. But they (generally) do not do it in the standard way: calling, sending CVs and so on. There is something called the Job Market that takes place every year early in January. Obsessed with efficiency, the Economics Job Market has a particular advantage: applications and initial interviews are centralized. Most of them take place at the American Economic Association annual meeting. And after some very stressful days, interested employers call back and schedule fly-outs for February-March. After meeting them, going for drinks and dinner, and also presenting your research, job offers are determined. But the question I have is what determines the outcome of this stressful process? As a person that will hopefully eventually go through this ordeal, I wondered if there was any data about it.
Even though all economists have experienced this, I wasn't able to find much research about the job market unfortunately. But I found one paper where they asked what aspects of education are associated with good outcomes in the job market. They collected data on graduates from Top departments (Harvard, MIT, Princeton, Stanford and Chicago) and checked what was associated with the best job outcomes. Obviously, the sample of graduates coming from those departments is not representative of all economics graduates. Since they were accepted in such departments, they are most likely representative of the very top of the distribution of applicants to PhDs. Studied by academics, another caveat is that job placements in the business sector were generally assigned a much lower ranking than university ones (a good business sector job was similar to a university in the 200-250 rank). But well, the questions are:
0) What is the typical PhD graduate? 1990-1999.
He (only 25% female) is a foreigner (63% non-US) who might come from a foreign undergrad school (49%). Most likely he does not come from a top undergrad school though (22% coming from top-15) nor does he have a masters degree (24% with masters). 3 out 4 admitted students do graduate the PhD. And around 26% of (this very selective group of) graduates end up in a Top-20 school. The sample is a bit old and selective unfortunately and some things might have changed. Unfortunately, one has definitely not. It is still mainly male students.
1) Do admissions requirements matter for grades?
Before entering, a standardized exam called GRE is required. This has three parts: math, verbal and analytical. GRE math and analytical grades - even within this group of people with really high ones - are highly positively associated with good core grades in the PhD program. I always thought this was more of a filter requirement: once above it, all students would be pretty similar. But it seems not.
Coming from a Top-15 US university is not associated with better grades. A masters degree helps slightly. And coming from a foreign school is correlated with better grades. But this may be due to a much more selective procedure for students coming from abroad. Or from them being more devoted since they are willing to leave their home countries.
2) Do grades matter for graduation?
First, grades are highly correlated: if you do well in one, you also do well in others. I find this very interesting since we are looking at people who will later on focus on a very very tiny part of the world of economist, so we could have expected that people doing great in one Micro would not do well in Macro, or viceversa. Let me clarify that grades are only a small part of the PhD. Most of it is actually doing research, which is what most graduates will do afterwards in their careers. But core micro and macro - sorry econometrics! - grades certainly seem to matter for graduation. Even (sort of) when restricting to those who passed the courses requirement, goods grades were associated with graduation.
3) And finally, what matters for job placement?
A) Observable before starting the PhD.
Once again, coming from a foreign university is positively associated with landing a Top 20 job. Coming from a Top school in the US is also good. GRE not so much anymore. (Being a man or a woman does not seem important either, so maybe we have a hope.)
B) Observable after starting the PhD.
Micro and Macro core grades are good predictors of job placement - sorry econometrics again. Admissions rank does not seem relevant, which might question the capacity of departments to rank students. Conditional on grades, coming from a foreign school does not seem to matter as much. But coming from a Top US school still does. I wonder if a language or culture bias could be behind this...
The questions that remains are why are some characteristics much stronger predictors of grades than of job placements? If what really matters for the outcome of PhD students or the evaluation of the department is the placement, why does the admission procedure seem quite ineffective in predicting it? And, finally, what's wrong with econometrics?
Based on article from AEA.
In the US people tip waiters almost 20% while in Argentina this number is reduced to 10%. In other countries like Japan, tipping is insulting. And then in Spain, the customer might even be considered to be tipped: if you are with a big group and brought enough business, you will probably get some digestive "chupitos" (drink shots) for free at the end of your meal. Secondly, why are servers tipped, but not doctors nor salesmen? (Actually, in Japan doctors are tipped, but not servers...) These and many other questions were very well posed by Mr. Pink in Reservoir Dogs.
This begs the first question: why tip at all? History suggests that originally 16th century Europeans tipped in advance to obtain faster service. If you were in a hurry, you would put a few coins up front, making sure you are noticed so that you get better service. Some suggest there was a sign saying T.I.P., "To insure promptitude," which originated the word "tip". Others suggest it was actually a slang word that spread around. According to Michael Lynn, a professor at the Cornell University with over 50 academic papers on the topic, tipping in the US began in the late 1800’s, when wealthy Americans traveling abroad to Europe witnessed tipping and brought the aristocratic custom back with them to “show off.”
But nowadays the social norm is that we tip afterwards. Waiters are supposed to provide good service in advance with the hope of getting tip as a reward. Nevertheless, it seems that people tip almost automatically, a rather fixed percentage (which might depend on the country). A study by Cornell University found that quality of service did not correlate much with service. So it seems we do not tip for good service. One thing that did correlate with tips was how attractive the waitress was (not for waiters though). Touching the patron's shoulder when delivering the check also seems to increase tips. So beware of touchy good-looking waitresses next time you are in a restaurant.
Second, why do you tip servers but not dry-cleaners? Why tip the hotel doorman, but not the person behind the reception desk? Why tip a baggage handler at the airport, but not the flight attendant? There really seems to be no logical explanation for this. The U.S. is empirically tip-addicted, with 31 different services being tipped. On the other hand, Canada has around 26, Scandinavian countries between 5 and 10, Japan 4 and Iceland 0. Most of the world operates on the simple premise of a service charge or a fixed price, no tip expected. But not the US.
Being now in Japan, I can see that servers deliver food promptly even without a tip. The restaurant business does not run into chaos without tips. It seems that tipping is more of a social norm nowadays, rather unrelated to service, where some countries tip and some do not. Some services are tipped and others are not. But this social norm also seems to depend to the racial group of the customer. How much is enough for a tip? How much is too much? In the US, only about a third of blacks say they tip in 15-20% range, compared to two-thirds of white. This might be mediated by their socioeconomic status (lower average education and income), but it does not explain it completely.
Let me end with an open question I recently read. Suppose tipping had never been invented and you were starting a restaurant, would you use tipping as the way to compensate your best employees? Or all your employees? Would that be the system that you would pick in a vacuum to compensate your team? I guess not. But this rather odd system can become gigantic. For example, tips have been estimated to account for around for 40 billion dollars in the US, bigger than the GDP of almost half the countries in the world.
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