Monthly Archives: August 2016

The economics of kidney transplantations

Have you ever found yourself wondering whether economists and their theories are any good to society? Nobel Laureate Alvin Elliot Roth and several of his colleagues were particularly successful as they made one of the most unquestionable contributions to the real world. As a part of their aim to fix some of the market failures, they studied a “market” of kidney donations. It is not a typical market as it is illegal to sell one’s organs and therefore money and price have no allocation power. Therefore, even though everyone has one extra kidney which he or she can live without, there is no matching mechanism which would bring together supply and demand for kidneys. To make the problem even more complicated, once a man in need of a kidney finds a relative or a friend who would be willing to become his donor, it is quite likely that their blood types do not match and thus the transplantation is not possible.

Having the problem analyzed, Roth suggested to create a centralized authority that keeps track of those who would be willing to help someone by donating his or her kidney. Having such a database, it became easier to find pairs of patients and potential donors for whom the blood type fits. Hence, it has rapidly increased the likelihood of a successful donation and consequently transplantation. Moreover, they also proposed a new matching algorithm for kids to find a public school in big cities or to allocate medics into hospitals. All of these interventions are meant to fix market failures and increase efficiency of the particular market and most of them have a significant positive impact on day-to-day life of thousands of people.

Reference: Roth, A. E. (2008). What have we learned from market design?. The Economic Journal, 118(527), 285-310. Available here.

Why are we still not making 18-cent coins?

In the US, commonly used coins are worth 1, 5, 10 and 25 cents. Have you ever wondered whether the current system of coin denominations is efficient? Well, turns out it’s not. Actually, as Jeffrey Shallit (2003) shows in his short note, the US system could be improved by 17% just by changing the dime to an 18-cent coin!

The author’s aim is to show which denominations are optimal (in that they minimize the number of coins needed in an average change-making) for systems made of different numbers of coins. His results are shown in the figure below. For a 4-coin system, the most efficient combination would be any of the two sets (1,5,18,29) and (1,5,18,25), which would bring about a decrease in the cost of change-making by 17%. If we wanted to add one coin and not take away any of the beloved coins that are currently used, a 32-cent coin is the best option. Well, politicians – the ball’s in your court!

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Reference: Shallit, J., 2003. What this country needs is an 18-cent piece, Math. Intelligencer 25 (2), 20-23. Available here.

Taxi vs. Uber

In their research, Cramer and Krueger managed to compile a unique dataset on the utilization of taxi and UberX drivers in 5 cities in the U.S. From the comparison they conducted, it seems that UberX driver are able to operate with higher efficiency (with an exception for New York City, where both taxi and UberX drivers achieve similar results) than taxi drivers. In particular, the authors created two different measures to assess the efficiency and both measures indicate a similar pattern and conclusion. UberX drivers have a passenger in their car for a larger fraction of driven miles than taxi drivers.

Apart from a more efficient driver-­passenger matching technology brought about by using Uber’s internet ­based system, the reasons lie, as they argued, also in inefficient taxi regulations. Moreover, the authors mentioned two more potential reasons – a more flexible labor supply model and a larger scale of Uber. Even without knowing what the actual reason for the differences in performance is, the resulting effect is significant and questions the current taxi system.

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Source: Cramer, J., & Krueger, A. B. (2016). Disruptive change in the taxi business: The case of Uber. The American Economic Review, 106(5), 177-182. Available here.

Is it easier for children of rich parents?

Is it easier to have a great career for children of rich parents than for their peers with poorer parents? How does the parents’ income affect their offspring’s income? Or more generally, does income inequality enhance or prevent inter­generational mobility? Without drawing any causal conclusion, Corak examined the so ­called Great Gatsby Curve – a relationship between income inequality, measured by the Gini coefficient, and intergenerational earnings elasticity used as a proxy for intergenerational mobility. In order to avoid problems associated with women’s positions on the labor market, the author considered only the father­-son path.

While more egalitarian countries (Finland, Norway, and Denmark) exhibit more mobility, in countries with (relatively) higher Gini coefficient such the U.S., Italy, and the United Kingdom, people are less likely to move up or down in income distribution across generations. This, however, does not mean that other countries offer more of the “American dream” than the U.S. itself as the intergenerational earnings elasticity does not take into account the differences in opportunities.

The author also discussed the increasing chasm in enrichment expenditures between high income families and bottom income families, indicating that money indeed matters when it comes to career.

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Reference: Corak, M. (2013). Income inequality, equality of opportunity, and intergenerational mobility. The Journal of Economic Perspectives, 27(3), 79-102. Available here.

How to get your research cited

One of the main problems in academia is to evaluate a researcher or a particular research project. The best method that scientists have come up with so far is to assess the impact factor of journals in which author publishes and/or to measure the number of citations i.e., how many times an academic paper was mentioned in other research. It naturally evokes several questions and potential issues; reciprocal citations, purely personal and/or professional reasons (beyond the quality of the research) for publishing in a journal, paid or free access to publication for readers and so on. Note that free access to a publication is ensured by the authors themselves who pay a fee to the publishing company.

Gaule and Maystre (2011) examined whether free access to a publication increases its chances of being cited more often. The data and previous studies show that open access articles achieve a higher number of citations, however, Gaule and Maystre set up a model which explains why, when choosing between open access and restricted access articles, researchers may tend to prefer an open access article if the research project is of higher quality. Their results suggest that a higher number of citations of open access articles might not be a consequence of a diffusion effect, but rather a self-selection effect.

In particular, the authors studied more than 4000 articles issued in Proceedings of the National Academy of Sciences (PNAS), where authors can choose whether to publish their article as open access or restricted access (there are some exceptions for developing countries etc.). To distinguish between a diffusion effect and a self-selection effect, the authors also controlled for the quality of the research by creating two proxy variables – the quality of authors and the quality of the article. The figure clearly shows that the number of citations achieved in the 2 years following the publication of the article is higher for open access articles, however, as the study concludes, this fact is most likely driven by the self-selection effect i.e., open access articles tend to be of higher quality. The authors argue that the diffusion effect of open access is, if any, relatively small.

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Reference: Gaule, P., & Maystre, N. (2011). Getting cited: does open access help? Research Policy, 40(10), 1332-1338. Available here.

The best way to get your theory to succeed is to kill its opponents

While progress in technology development is driven by demand and supply forces, progress in academia and research does not reflect market signals (at least not as sensitively). Therefore the path of evolution is not paved in such a straightforward way and progress is more chaotic and long-winded.  Max Planck argued that progress in research, which is given by accepting a new theory, is reached when its opponents die rather than when they are convinced by proofs. To explore this thought, Azoulay, Fons-Rosen and Zivin (2015) tested how the death of excellent scientists affects the subsequent academic output. The authors analyzed more than 31 000 distinct sub-fields and defined more than 12 000 scientists as “stars of a sub-field”. Moreover, they managed to distinguish who, from contemporary scientists, were collaborators and non-collaborators with the passed star scientist.

The results show that when a star scientist dies, her collaborators suffer a drop in output, whereas the output of non-collaborators tends to increase. The overall effect is rather increasing, but nearly insignificant, as the decrease of collaborators’ output is offset and overcome by the increase of non-collaborators’ ones. Furthermore, as authors pointed out, it means that current scientists restrict the entry of new and innovative thoughts, opinions, and methods.

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Reference: Azoulay, P., Fons-Rosen, C., & Zivin, J. S. G. (2015). Does science advance one funeral at a time? (No. w21788). National Bureau of Economic Research. Available here.

Where is it worth it not to work?

Measuring and comparing the generosity of social programs (pensions, unemployment benefits, sickness benefits) across countries is not an easy task. There are several aspects which affect the actual level of generosity: eligibility, duration, waiting period, a level of the payment itself etc. Moreover, to be precise, when comparing different countries, one would need to account for economic conditions as well. The CWED project took a first step towards this goal and created a one-dimensional index which allows us to compare the generosity of social programs among different economies.

Kuitto, Jahn and Düpont took an advantage of the CWED dataset and analyzed welfare policy institutions in Europe between 1995 and 2007. In particular, they studied whether welfare policies across Europe converge. To do so, the authors divided European countries into 5 groups according to their historical-cultural background: Anglo-Saxon, Bismarckian, Scandinavian, Southern European, and CEE and plotted their averaged indices over time.

In case of unemployment benefits, the duration when the unemployed are eligible for benefits differ significantly. Apart from Belgium which, as they claimed, has unlimited duration, the longest durations are observed in the Scandinavian countries, providing unemployment benefits for more than 3 times longer than the CEE or Anglo-Saxon countries. When it comes to replacement ratio, which measures how much of the average income is paid as unemployment benefits, a similar conclusion may be drawn; the Anglo-Saxon as well as the CEE countries are below the average of 60%, whereas Scandinavian, Southern, and Bismarckian are above.

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Reference: Kuitto, K., Jahn, D., & Düpont, N. (2012). Welfare Policy Institutions in the enlarged European Union: Convergence, Divergence or Persistence (No. 1). Greifswald Comparative Politics Working Paper. Available here.

Markets with and without money

Heyman and Ariely conducted three experiments verifying several hypotheses about the relationship between the effort one makes and rewards paid back in return. In particular, they distinguished between behavior on two different markets: (i) social market (market with no money), and (ii) monetary market; arguing that once we introduce money, people abound their willingness to help (as much as they can) and condition their effort by a level of payments. Thus, not paying the subjects at all led to high exhibited effort, probably motivated by altruistic incentives, whereas offering them a low amount of money caused significantly lower performance. However, as the payment increased the willingness to help increased as well.

The figure captures the results of an experiment in which students were asked to solve mathematical problems which had no solution and researchers measured the length of time before giving up. It clearly shows that not being paid, denoted as a control group, was associated with the highest effort, whereas students motivated by a low payment have searched for a solution for only half the time expended by the control group.  However, when the subjects were offered higher rewards, their effort level raised up to the control group level. Moreover, this experiment also revealed that the presence of money is not crucial and to obtain the same results, it is enough to let the subjects know about the monetary price of the rewards.

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Reference: Heyman, J., & Ariely, D. (2004). Effort for payment: a tale of two markets. Psychological science, 15(11), 787-793. Available here.