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Eliminating for-profit academic publishing

Much has been written about the high profits academic publishers get from the volunteer labour of their referees and editors, and how high subscription costs reduce funds available for actual research. The opinion pieces and blog posts I have seen do not suggest a concrete way to change the system. They only express hope that with more researchers putting their work on the web, the for-profit publishing industry will eventually disappear. I think this disappearance can and should be hastened. The obvious way is to boycott for-profit journals as an author, referee, editor and librarian.
The obvious objection is that one’s career depends on publishing in certain journals that often happen to be for-profit, and that “service to the profession” (refereeing and editing) is one’s duty and also helps the career a bit. A moral counterargument is that while boycotting may impose some personal costs, it benefits other researchers and the increase in research benefits everyone, so as a favour to the rest of humanity, boycott is the right thing. After all, why do people become academic researchers when the private sector pays more?
Game theoretically, the academic system (including publishing) is a coordination game, like many social norms. As long as everyone else conforms to the system, it is costly to depart from it. Thus self-interested people choose to conform to the system. This keeps the system stable. Individual deviations are costly, but a collective (coalitional) deviation may be costless or at least cheaper. An example is the whole editorial board of a for-profit journal deciding to resign and start a nonprofit copy of this journal. They announce publicly that all articles that researchers were planning to submit to the for-profit journal should now be submitted to the nonprofit copy. The refereeing and editing process goes on as before, only the library subscriptions to the new journal are cheaper. There should be no loss of prestige for the editors or loss of publishing opportunity for the authors.
A journal is not complicated – it only requires an online system to let authors upload relatively small text files, let the editors forward these files (with author identity removed) to referees, referees to upload their text files and the editors to forward these (deidentified) files to authors. Such programs surely exist, free and open-source as well.
Perhaps a proofreader could be hired for the journal and paid out of subscription fees. But the total cost of running a journal (with volunteer labour like now) is very low.

Of beauty

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What is beauty in human beings? Poets have written a lot about it. I will take a different approach. Beauty is the traits that humans have evolved consider attractive. The characteristics of a good mate, in other words. A good mate is someone with whom one would expect to have numerous fit offspring. A healthy and fertile person. Beauty consists of the outward signs of health and fertility.
Health signals are similar for men and women – good posture, energetic movement, strength and speed, neither excessive fat nor skeletal thinness, smooth skin without patches of different colour, clear voice, bright eyes, no strange smells, thick lustrous hair etc. Posture, movement and strength signal that there is no internal disease that causes weakness or fatigue. Excessive fat or thinness suggest metabolic problems or malnutrition. Skin diseases manifest as roughness and redness. Clear voice signals absence of throat or lung diseases, bright eyes show no eye infection etc.
Fertility signals differ somewhat across genders. Youth and health are associated with fertility in both genders, but a masculine or feminine figure is a fertility signal only for the appropriate gender. Broad shoulders, big muscles, wide angular face and hairyness are all caused by high testosterone levels. These suggest a fertile male, but if observed in a female, are signs of some endocrine disease. Smooth curves, large breasts, narrow waist and wide hips are signs of high oestrogen levels and a fertile female. The narrow waist further suggests the woman is not pregnant already (pregnancy means current sex is unlikely to lead to offspring, which can be interpreted as temporary infertility as far as the current partner is concerned).

Of rankings

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Many universities advertise themselves as being among the top n in the world (or region, country etc). Many more than n in fact, for any n small enough (1000, 500, 100 etc). How can this be? There are many different rankings of universities, each university picks the ranking in which it is the highest and advertises that. So if there are 10 rankings, each with a different university as number one, then there are at least 10 universities claiming to be number one.
There are many reasonable ways to rank a scientist, a journal, a university or a department. For a scientist, one can count all research articles published, or only those in the last 10 or 5 years, or only those in the top 100 journals in the field, or any of the previous weighted by some measure of quality, etc. Quality can be the number of citations, or citations in the last 5 years or from papers in the top 50 journals or quality-weighted citations (for some measure of quality)…
What are the characteristics of a good ranking? Partly depends on what one cares about. If a fresh PhD student is looking for an advisor, it is good to have an influential person who can pull strings to get the student a job. Influence is positively related to total citations or quality-weighted publications, and older publications may be better known than newer. If a department is looking to hire a professor, they would like someone who is active in research, not resting on past glory. So the department looks at recent publications, not total lifetime ones. Or at least divides the number of publications by the number of years the author has been a researcher.
Partly the characteristics of a good ranking are objective. It is the quality-weighted publications that matter, not just total publications. Similarly for citations. Coauthored publications should matter less than solo-authored. The ranking should not be manipulable by splitting one’s paper into two or more, or merging several papers into one. It should not increase if two authors with solo papers agree to add each other as the author, or if two coauthors having two papers together agree to make both papers single-authored, one under each of their names. Therefore credit for coauthored papers should be split between authors so that the shares sum to one.
How to measure the quality of a publication? One never knows the true impact that a discovery will have over the infinite future. Only noisy signals about this can be observed. There currently is no better measure than the opinion of other scientists, but how to transform vague opinions into numerical rankings? The process seems to start with peer review.
Peer review is not a zero-one thing that a journal either has or not. There are a continuum of quality levels of it, from the top journals with very stringent requirements to middle ones where the referees only partly read or understand the paper, to fake journals that claim to have peer review but really don’t. There have been plenty of experiments where someone has submitted a clearly wrong or joke article to (ostensibly peer-reviewed) journals and got it accepted. Even top journals are not perfect, as evidenced by corrigenda published by authors and critical comments on published articles by other researchers. Even fake journals are unlikely to accept a paper where every word is “blah” – it would make their lack of review obvious and reduce revenue from other authors.
The rankings (of journals, researchers, universities) I have seen distinguish peer-reviewed journals from other publications in a zero-one way, not acknowledging the shades of grey between lack of review and competent review.
How to measure the quality of peer-review in a journal? One could look at the ranking of the researchers who are the reviewers and editors, but then how to rank the researchers? One could look at the quality-weighted citations per year to papers in the journal, but then what is the q uality of a citation?
Explicit measurement of the quality of peer-review is possible: each author submitting a paper is asked to introduce a hard-to-notice mistake into the paper deliberately, report that mistake to an independent database and the referees are asked to report all mistakes they find to the same database. The author can dispute claimed mistakes and some editor has to have final judgement. It is then easy to compare the quality of review across journals and reviewers by the fraction of introduced mistakes they find. This is the who-watches-the-watchmen situation studied in Rahman (2010) “But who will monitor the monitor?” (http://www.aeaweb.org/articles.php?doi=10.1257/aer.102.6.2767).
One could disregard the journal altogether and focus on quality-weighted citations of the papers, but there is useful information contained in the reputation of a journal. The question is measuring that reputation explicitly.
If there is no objective measure of paper quality (does the chemical process described in it work, the algorithm give a correct solution, the material have the claimed properties etc), then a ranking of papers must be based on people’s opinions. This is like voting. Each alternative to be voted on is a ranking of papers, or there may simply be voting for the best paper. Arrow’s impossibility theorem applies – it is not possible to establish an overall ranking of papers (that is Pareto efficient, independent of irrelevant alternatives, non-dictatorial) using people’s individual rankings.
Theorists have weakened independence of irrelevant alternatives (ranking of A and B does not depend on preferences about other options). If preferences are cardinal (utility values have meaning beyond their ordering), then some reformulations of Arrow’s criteria can be simultaneously satisfied and a cardinal ordering of papers may be derivable from individual orderings.
If the weight of a person’s vote on the ranking of papers or researchers depends on the rank this person or their papers get, then the preference aggregation becomes a fixed point problem even with truthful (nonstrategic) voting. (This is the website relevance ranking problem, addressed by Google’s PageRank and similar algorithms.) There may be multiple fixed points, i.e. multiple different rankings that weight the votes of individuals by their rank and derive their rank from everyone’s votes.
For example, A, B and C are voting on their ranking. Whoever is top-ranked by voting gets to be the dictator and determines everyone’s ranking. A, B, C would most prefer the ranking of themselves to be ABC, BCA and CAB respectively. Each of these rankings is a fixed point, because each person votes themselves as the dictator if they should become the dictator, and the dictator’s vote determines who becomes the dictator.
A fixed point may not exist: with voters A and B, if A thinks B should be the dictator and B thinks A should, and the dictator’s vote determines who becomes the dictator, then a contradiction results no matter whether A or B is the dictator.
If voting is strategic and more votes for you gives a greater weight to your vote, then the situation is the one studied in Acemoglu, Egorov, Sonin (2012) “Dynamics and stability of constitutions, coalitions, and clubs.” (http://www.aeaweb.org/articles.php?f=s&doi=10.1257/aer.102.4.1446). Again, multiple fixed points are possible, depending on the starting state.
Suppose now that the weights of votes are fixed in advance (they are not a fixed point of the voting process). An objective signal that ranks some alternatives, but not all, does not change the problem coming from Arrow’s impossibility theorem. An objective signal that gives some noisy information about the rank or quality of each alternative can be used to prevent strategic manipulation in voting, but does not change the outcome under truthful voting much (outcome is probably continuous in the amount of noise).

Health insurance insures not health, but wealth

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If health insurance really insured health, it would offer a small constant health loss in exchange for reducing the probability of a big health loss. For example, it would offer a constant low-level headache but take away the chance of heart attack.
In reality, health insurance constantly takes away a small amount of money and (hopefully) in return removes a big monetary loss which may result from healthcare costs in the case of a serious health problem. This is wealth insurance, not health insurance.

Claims that the economics job market is tough this year

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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

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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

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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.

Triangulating translations

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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

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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?

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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.