Was Sheldon Cooper right?

In one episode of The Big Bang Theory, Sheldon Cooper let dice to make decisions for him. A similar experiment was conducted by Steven Levitt. However, instead of studying decisions that affected him and his life, he investigated the decision process of people in a field experiment. In particular, he collected data about more than 20 000 people who faced an important decision (quitting a job, leaving a spouse or going back to school) and let them toss a coin. As a rule, when a head appeared the subject was asked to make the change, whereas a tail stands for maintaining the status quo for at least two months. After 2 and then also 6 months the participants were given a survey asking, among others, about their happiness.

As the author claims himself, there are two main research questions: (i) do the participants obey the coin and do as it says; and (ii) is there any impact on their reported level of happiness? Assuming that only the marginal agents join this experiment and thus a half of them are expected to take an action if there were no coin toss and the coin is fair, means that if there is no real effect of the coin then actions of 50% the participant should coincide with the coin suggestions. However, in this case, as the author reports, significantly more than 50% of participants follow the coin recommendation. In particular, while in more important issues it is around 55%, the ratio is even higher for the less important issues – 67%.

When it comes to the causal effect on happiness, using slightly more advanced statistical methods to combat the endogeneity problem, Steven Levitt argues that those individuals whose coin came up heads (take a change) report being happier than those with tails (status quo). It can be because having a head motivates to take a change which he/she would rather postpone or do not take at all (status quo biased observed in normal life). Overall, even though the conclusion may be biased for several reasons, it plants a seed of doubt if we would not be better off letting (at least the less important) decision were taken randomly.




Reference: Levitt, S. D. (2016). Heads or Tails: The Impact of a Coin Toss on Major Life Decisions and Subsequent Happiness (No. w22487). National Bureau of Economic Research. Available here.

Yes, I want my fund manager to be poor

How difficult is it to find a fund manager to whom you would entrust your money; to assess his quality before it is too late? Apparently, one way is to look at his family background as it has been recently shown to be a decent signal of future performance. In particular, Chuprinin and Sosyura (2016) found that fund managers from poor families outperform those coming from a wealthier background.

To explain this phenomena, the authors argued that wealthy family background makes it easier to move up into a managerial position as the applicants face less barriers. In contrast, for an applicant from a poor family it is heavy going. As a result, to succeed as a manager with a poor family background, one needs to possess top skills. In other words, the selection process causes that while the rich applicants do not need to meet the highest criteria, the poor ones do.

The authors also claimed that it would necessarily mean that there is more dispersion among the fund managers from rich families as not all of them satisfy the highest criteria. On the contrary, performance of the successful applicants from less rich families are likely to be more similar. As a matter of fact, they found higher volatility in the results among the fund managers with wealthier background and thus provided evidence in favor of their arguments. The picture shows the distribution of income of the general male population and that of the managers’ fathers.

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Reference: Chuprinin, O., & Sosyura, D. (2016, May). Family Descent as a Signal of Managerial Quality: Evidence from Mutual Funds. In University of Miami, School of Business Administration, 6th Miami Behavioral Finance Conference. Available here.

Do we need universities to grow?

The presence of universities in a particular region is argued to have a positive effect on GDP per capita. To test this claim empirically, Valero and Reenen (2016) made use of a huge database of almost 15000 universities in 1500 regions. Their empirical exercise suggests that there is a positive and significant impact of the existence of a university in a region on the region’s GDP per capita and this conclusion seems to be robust to different specifications. They found that a doubled number of universities is associated with a 4% increase of GDP per capita.

Additionally, the authors identified four main channels through which the positive effect is likely to influence the economy. In particular, unsurprisingly, more universities increase human capital which is believed to increase productivity and thus the GDP per capita as well. Apart from human capital, universities and their environment strengthen innovation activity, measured by the number of registered patents. Further, it is widely known that institutions represent a key determinant of economic growth. Specifically, some of the institutions such as democracy and political culture are claimed to be necessary for growth, especially in developed countries. Finally, there is a direct effect of higher economic activity from the existence of the university in a particular region (construction of the buildings etc.) and higher demand from professors and students. The empirical results indicate that growth is driven by both human capital and innovation, though the effects of these are small in magnitude. The impact of democracy and institutions also seems to be positive although rather in the long term. To sum up, the authors conclude that the presence of universities impacts growth also in other ways than simply via the increase in demand caused by higher economic activity.

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Reference: Valero, A., & Van Reenen, J. (2016). The economic impact of universities: Evidence from across the globe (No. w22501). National Bureau of Economic Research. Available here.

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.