Author Archives: Sander Heinsalu

Claims that the economics job market is tough this year

It seems that every year since I started grad school, I hear someone say that the economics job market is tough (for candidates) that year. Usually it is in connection with some graduate student on the market getting a less good job than one anticipated. But the toughness of the market is a relative measure, so relative to what year is this year tough? Relative to 1950? After the Second World War, the US expanded its university sector with the GI Bill, which created a large demand for new faculty members. This made the market easy for candidates and as the effect gradually faded, the market got tougher. This is probably not what people have in mind when they claim a tough market.
As computing power becomes cheaper, the demand for people who are substitutes of computers (theorists) falls and the demand for complements of computers (empirical and computational researchers) rises. So the theory market may get tougher for candidates over time, but the empirical market should get easier.
There are other long term trends, like the fraction of the population getting a university degree increasing, but at a decreasing rate. If the university sector expands to cater to the increased demand, the market should get easier for candidates. But this also depends on the expectations of the universities. Hiring responds to anticipated future enrollment, not just the current number of students.  So if demand for university education rises less than expected (it does not have to fall), the demand for new faculty members falls.
Lengthening lifespans mean older faculty members free up fewer spots in universities, which reduces demand for new faculty members, but this effect is tiny, because lifespans lengthen very slowly.
A short term effect on hiring was the financial crisis, which reduced university hiring budgets. This made 2009 a tough year for candidates relative to the surrounding years.
A study on how tough the market really is would be interesting, but hard to do, because it requires a measure of the quality of candidates that is independent of the jobs they get or papers they publish. Both jobs and papers are subject to a congestion effect, so the toughness of the job market or publication market affects these measures. The definition of toughness is that the tougher the market, the worse the results for a graduating student of a given quality.
The market for economists is worldwide, so it would be easier to study academics in some field that is country-specific and thus has barriers to trade, say law.

Claims that placement officers do a great job

Those on the economics job market have probably heard statements in their department like “our placement officers do a great job” and “we place our students very well”. First, no university would say they place students badly, because then students would not apply there. Second, faculty members don’t want to be in committees, including placement, so if one faculty member said that another does a bad job in placement, then the immediate response would be: “You do it then, and do better.” Anticipating this, no faculty member will criticize another’s committee work quality.
Hence, an empirical project idea: how does the placement outcome (e.g. rank of institution making job offer) depend on student quality (e.g. papers published before graduating) and the placement committee and university fixed effects? The measures of quality and outcome are of course noisy, but the sample size (people on the job market) is fairly large.

Inept Australian banks

aving experienced banks in the US (I was a customer of Wachovia, Wells Fargo and Bank of America), I thought that those were the lower bound on the competence level of financial institutions. I was wrong.

In Australia I first opened an account in the Bank of Queensland in the shopping centre Toowong Village. That evening I tried to access their online banking and, experiencing difficulties, called their customer service. After some conversation they told me that the bank employee opening my account had neglected to enter some ID details into their computer system. Next day I opened an account at the Commonwealth Bank office in the University of Queensland.

A few days later I received a debit card with the wrong name on it, despite the employee opening my account having looked at my passport while typing in the details. I tried to complain through their online banking system and received a reply that I should call. When I called, they told me I should go to the bank branch. At the branch they agreed to send me a new debit card with the correct name. A few days later I received a debit card with the same wrong name. Then I tried to open an account at the credit union called bankmecu at their office in the university. They asked for my employment contract, which no other bank had asked for. When I said I did not have it with me, they didn’t ask me to come back later with the contract, but suggested I open an account at the ANZ bank next door. Obviously, bankmecu is a nonprofit and does not want customers. So I went to ANZ and opened an account there.

After about three weeks of waiting, I received a letter with the PIN for the ANZ debit card. The letter had been sent to the wrong city district and post code, but the right street address, so it somehow found its way to me. After a month, the debit card still had not reached me. When it finally arrived, my name was spelled wrong.

I did not receive the dividends of one stock I bought through ANZ Etrade. I contacted ANZ through their online banking. The customer service told me to contact Etrade, who told me to contact Computershare. A week later Computershare told me to contact their New Zealand office. A week after that the New Zealand office replied that they had sent a dividend cheque to my address. I replied that no cheque had reached me and asked them to deposit the dividends to my bank account. Some days later they agreed. A week later the money has not reached me.

Distributed Facebook

To avoid the privacy violations of Facebook (and other big internet companies), an online social network could be created that is distributed on the computers of the users. Every user has their own profile and the shared posts of their friends on their computer. When a user posts something new, it is put in a queue, to be shared with each designated friend when both the poster’s and the friend’s computers are online. Each pair of friends could have a different password for sharing data.

This would also make it difficult for dictatorships to track the activity of protesters and dissidents. Everyone would only know their own and their friends’ data, so there is no single database through which to easily spy on many people.

The distributed network may not need coding from scratch. Some existing programs can perhaps be modified to serve, e.g. torrents or version control software. The posts of a user are like a torrent updated from time to time and shared only with some people (or publicly if the user chooses). Or the posts and comments are like a collaboratively edited document (think Google Docs).

I am unlikely to be the first with this idea, but I didn’t search for similar ones.

Triangulating translations

If a text has already been translated to a couple of languages with high quality, then it may be possible to improve the quality of machine translation to another language by translating separately from each original language and averaging in some sense. I do not know whether a program currently exists that is able to take into account multiple starting languages – Google Translate and other online automatic translation services I have seen only use one. Several different translations should contain more information than one, so by comparing them, some errors may be eliminated. At least inconsistencies can be discovered by computer and then checked by a human, saving labour.

Recursive definition of fitness

Evolutionary theory predicts fitness maximization by organisms over a large enough number of generations. Fitness is described as the ability to survive and reproduce given the environment, but I have not seen a formal definition of fitness even in mathematical models of adaptation.

A direct definition of fitness is difficult to give. Fitness is not the number of offspring, because then a fitness-maximizing organism would accept the following trade: add one child and remove the reproductive ability of all your children. Similarly, fitness is not the number of grandchildren or grand-to-the-n-children, because if it was, the reproductive ability of the grandchildren would be traded away for one more grandchild. If fitness was the number of fertile offspring surviving to adulthood, this trade could be shifted by one generation: add one adult fertile child and remove the reproductive ability of grandchildren or all descendants in a more distant generation. Clearly one has to take into account all descendants in the infinite future in some way.

Fitness can be defined recursively: it is the sum of the fitnesses of the offspring (multiplied by some positive constant). The fitness of each child in turn is the sum of the fitnesses of that child’s offspring, their fitnesses are the sums of the fitnesses of their offspring, and so on to infinity.

Under this definition, the trade described initially would not be made: changing the fitnesses of all descendants in some generation to zero would not be accepted, no matter what is offered in return (increasing the fitness of more distant descendants cannot be part of the same trade). Increasing the summary fitness of descendants in some more distant generation, other things equal, is inconsistent with reducing the fitness of descendants in a nearer generation. This is because if the fitness of an organism increases, other things equal, then the fitness of all that organism’s ancestors increases.

Under multilevel selection, as in Reeve and Holldöbler 2007, there is more than one fitness concept. There is individual fitness and group fitness, if both individuals and groups reproduce. The definitions of these fitnesses are also recursive, but more complicated, since fitness at one level of reproduction will interact with the fitness at another.

Who discriminates whom?

In social networks with multiple races, ethnic or religious groups involved it is generally the case that there are fewer links between groups and more within groups than would be expected from uniform random matching. One piece of research exploring this is Currarini, Jackson, Pin (2009).

When observing fewer intergroup links than equal-probability matching predicts, the natural question is who discriminates whom. If group A and group B don’t form links, then is it because group A does not want to link to B or because B does not link to A? If we observe more couples where the man is white and the woman is Asian than expected from uniform random matching, is this due to the `yellow fever’ of white men or a preference of Asian women for white men? It could also be caused by white men and Asian women meeting more frequently than other groups, but this particular kind of biased matching seems unlikely.

Assume both sides’ consent is needed for a link to form. Then the probability that a member of A and a member of B form a link is the product of the probabilities of A accepting B and B accepting A. We can interpret these probabilities as the preference of A for B and B for A and say that if the preference of A for A is stronger than the preference of A for B, then A discriminates against B. From data on undirected links alone, only the product of the probabilities can be calculated, not the separate probabilities. So based only on this data it is impossible to tell who discriminates whom.

If there are more than two groups in the society, then for each pair of groups the same problem occurs. Under the additional assumption that a person treats all other groups the same, only his own group possibly differently from the other groups, the preference of each group for each group can be calculated. This assumption is unlikely to hold in practice though.

If only one side’s consent is needed for a link to form, then from data on these directed links, the preference of each group for each group can again be calculated. The preference of A for B is just the fraction of A’s links that are to B, divided by the fraction of B in the population.

With additional data on who initiated a link or how much effort each side is putting into a link, the preference parameters may be identifiable. The online dating website OKCupid has some statistics on how likely each race is to initiate contact with each other race and how likely each race is to respond to an initial message by another race. If these statistics covered the whole population, then it would be easy to calculate who discriminates whom. In the case of a dating website however, the set of people using it is unlikely to be a representative sample of the population. This may change the results in a major way.

If the average attractiveness of group A in just the dating website (not in the whole population) is higher than that of other groups, then group A is likely to receive more initial contact attempts just because they are attractive. They can also afford to respond to fewer contact attempts since, being attractive, they can be pickier and make less effort to form links. If we disregard the nonrepresentative sample problem and just calculate the preferences of all groups for all other groups, then all groups will be found discriminating in favour of group A, and group A will be found discriminating against all others. But in the general population this may not be the case.

The attractiveness of group A in the dating website can differ from their average attractiveness if the website is more popular with group A and there is adverse selection into using the website. Adverse selection here means that only the people sufficiently unattractive to find a match by chance during their everyday life make the extra effort of starting to use the website to look for matches. So the average attractiveness of all groups using the website is lower than the population’s average attractiveness.

If a larger fraction of group A prefers to use the website and the users from all groups are drawn from the bottom end of the attractiveness distribution, then the website is relatively more popular with attractive members of A than with attractive members of other groups. Therefore the average attractiveness of those members of A using the website is higher than the average attractiveness of those members of other groups using the website. The higher preference of group A for using the website must be exogeneous, i.e. due to something other than A’s lower average attractiveness, otherwise this preference does not cause A’s attractiveness on the website to rise. It could be that members of A are more familiar with the internet, so have a lower effort cost of using any website. Or there may be a social stigma against using online dating sites, which could be smaller in group A than in other groups.

If statistics from a nonrandom sample show discrimination, there may or may not be actual discrimination in the population, depending on the bias of the sample. It could also be that the actual discrimination is larger than the sample shows, if the sample bias goes in the opposite direction from the one described above.

Trust of tourists and the tragedy of commons

The tragedy of commons is a situation where a common resource has many users whose property rights are not precisely defined. Each user benefits from taking more of the resource, but all other users lose if one takes more. At the equilibrium level of resource use, if one user takes more, then the combined loss of the other users exceeds the gain of the one user.

The trust of tourists is like a common resource for the tourism businesses in one area. Each firm can use aggressive business practices that yield higher immediate profits, but leave tourists with a bad experience. This erodes the goodwill of tourists towards all firms in the area, so all other firms suffer because one firm cheats tourists. The cheating firm’s future profits will also be lower, but usually not by enough to deter cheating at present.

It may be an equilibrium for all firms to get as much immediate profit as they can and for tourists not to trust any firm in that area. The sum of the firms’ profits is then probably lower than in the case where they all follow good practices and the tourists trust them.

If the firms agree to use good practices and this agreement is enforceable, for example via a trade association, everyone might win. The tourists get better quality, better value for money, and the firms get higher profits. On the other hand, if the firms can agree on one thing, they can agree on another, for example a cartel. This will further increase firm profits, but will lower value for money of tourists. The overall effect of an agreement among firms on the benefit tourists get is uncertain.

Why politics is as it is and how to change it

Politics in all democratic countries is dishonest, propagandistic, riven by special interests etc. From time to time politicians who promise to change this arise. Mostly these politicians fall into the old ways and create no change but sometimes they turn their countries into dictatorships.

It is very difficult to change the way politics is done because there is a reason why politics is the way it is. Not many people set out to lie and cheat their way to the top. Mostly they start with good intentions but gradually adopt the tactics generally used.

The reason for dishonesty is that politics is an evolutionary process (mutation, selection, reproduction). People invent new ways to manipulate others all the time (mutation). Those who use the kind of tactics generally used in modern politics are likely to get elected (selection). Their tactics are then copied by the next generation of politicians (reproduction). The end result is a thoroughly dishonest political class because lying and cheating work as ways to get to the top. There is no lack of idealists trying to do honest politics but mostly they won’t get elected because their restriction to honest methods severely limits the crowd-manipulation tools available to them. If they do get elected, they will be outnumbered by the dishonest ones.


Proposed method of change

Trying to get enough honest politicians elected to change the system just won’t work because honesty limits their tools of making people elect them. Politicians use dishonesty because it works and gets them power.

In a democracy the power ultimately rests with the people. If all people or even just a bare majority were rational and perfectly informed, there would be no room for manipulation and dishonesty. The present political situation is only possible because people are stupid enough to be manipulated into electing the people who create such a political situation. Every nation deserves its leaders.

The way to lessen dishonesty in politics is to make people recognize and dislike it. Most people are not clever enough to see through the manipulation themselves, so the media and perhaps scientists should help them.

When televising speeches of politicians the news agencies could place a running commentary on the speech in the subtitles, pointing out logically or factually wrong statements, demagogy and meaningless phrases, giving examples of the politician’s possible motives for saying certain things, pointing out the interest group to whom a promise is aimed.

The news agencies could keep a file on every politician of sufficient influence. The file should contain their earlier promises, statements, voting record and press releases. Every time the news agency runs a story containing that politician the online version of the story should have a link to that politician’s file. If the politician contradicts his or her earlier talk, it should be pointed out by the news agency and a link to the appropriate place in that politician’s file placed next to the reference.

People could be educated in basic mathematical logic so they could notice some logically false statements (one can never teach most people enough to make them recognize factually wrong claims, that is what the politician’s file would be for).

In countries where a certain number of citizens can initiate laws, those interested in honest politics could campaign for a law recalling a politician who has lied. Then a referendum can be organized to pass that law because politicians themselves certainly would not do it. Lying would need to be clearly defined in the law so that uncertain statements and slips of the tongue would not empty all government institutions. In some cases, however, it can certainly be proved that what the politician said contradicts the facts or is logically false.

Suggested questions for admitted graduate students

For people admitted to PhD programs in economics, here are some suggested questions to get answers to before choosing the program.

Answers from online search and other sources

What is the ratio of faculty members to students?

How many faculty members does the department have in the field you are interested in? Count only those whose primary field that is, not the ones who once wrote one paper in it or who hang out at the seminars for the free food.

How many years and how many hours a week are students expected to TA during their program?

What is the ratio of students on the job market to the size of the incoming class?  Equivalently how many students were mysteriously lost in the grad school process? Average across multiple years if possible.

Answers from faculty members

How many hours a week do you spend advising graduate students (reading their work, talking to them about their research)?

How many hours a week are graduate students expected to spend on TAing?

Answers from graduate students

How many hours per week or month do faculty members advise you (read your work, talk to you about your research)? Use the number of graduate students (perhaps exclude first and second years) and faculty members in the department to compare answers to this question from faculty members and graduate students.

How many hours per week are you supposed to spend on TAing and how many do you actually spend? Compare the student answer to the faculty answer.

What is the average number of years students take to reach the job market? What is the length of the stipend? How hard is it to get campus jobs (TAing, RAing) that pay for living costs after the stipend ends?

How many students are kicked out after the first year? How many in each year leave without a PhD after passing the first year exams? Divide by the average size of the incoming class for cross-university comparison.

How much time do star faculty actually spend in the department? Some professors are on the faculty at multiple universities (Dekel, Phillips) and may spend between two weeks to six months per year at any one place.

General comments

You can ask a question and look stupid, or not ask a question and be stupid.

Take into account that the graduate students who come to the visit day events and talk to admitted students are a biased sample – those who care the most about the department and those who have the most extreme opinions to share.

The field of interest may change during the graduate program, but for most people it does not.

Ask the same question from multiple people and compare answers. This gives an indication of honesty, or at least preparation and coordination of lying.

The PhD comics, especially the earlier ones, are a very accurate description of the lives of graduate students. Note the absence of smileys in this sentence.

The questions admitted graduate students should be asking have been discussed in academia.stackexchange and in forums.