How group utility differs from individual utility

Expected utility lies at the core of almost all analyses in Economics that feature uncertainty. While one can always come up with exceptions to the rule, expected utility is by and large accepted as a good characterization of the behavior under uncertainty of individuals. The emphasis is here on individuals, and one can wonder whether the behavior could be different when they act as a group. Group dynamics are complex, and there are plenty of examples where individuals behave differently in a group than alone.

Andrea and Piergiuseppe Morone performed an experiment with students where they tried to elicit responses to risky choices, first individually, then in random groups of two. They confirm that in the first case, expected utility seems to be the best representation of the preferences, but in the second case disappointment aversion (commonly also called loss aversion) seems to be dominating. But this is far from being a proof yet, as only 38 students were part of the experiment. The authors also claim that this shows that preference aggregation drives out expected utility, a statement that can be misinterpreted. Indeed, this does not mean that aggregating individual decisions leads to a rejection of expected utility (say, in a representative agent framework). It only says that in situations where people take joint decisions, expected utility may be dominated.

Geanakoplos on the Leverage Cycle

Yale economist John Geanakoplos discusses his view of the financial crisis. It takes about an hour.

Women and children first? No

The infamous quadruplicate papers of Frey, Savage and Torgler have caused a lot of grief for their multiplicity, yet they yielded a somewhat interesting, yet old result: women and children get priority on maritime disasters, crew are last, and there are some subtle differences among passengers from different nationalities. This result, however, was obtained using a sample of two: the Titanic and the Lusitania. And one also argue that there was some selection bias for the Titanic, as this was a much celebrated inaugural voyage with, let's say, an unusual set of passengers. It could therefore not hurt to increase the sample size.

Mikael Elinder and Oscar Erixson jack up the sample from 2 to 18. And the results are completely reversed. Women are at a distinct disadvantage, crew fare much better than the rest. This is the outcome you would expect from a free-for-all situation where weaker women get pushed aside on the run for the lifeboats. The lesson from this: do not trust a sample of two, even if amplified by four publications.

From the Harvard Baseball Team

Looking at the transition from Malthus to industrialization in Germany using real wages

A standard model with a production function concave in labor will tell you that the marginal productivity of labor, and hence the real wage, decreases as labor increases. This the core relationship in the Malthusian model and has been the reason brought forward why some have observed that England enjoyed relative prosperity after the many deaths due to the Great Plague (and why some think the same will happen to Africa due to the AIDS epidemic). Of course, empirical evidence is somewhat thin for such old times.

Ulrich Pfister, Jana Riedel and Martin Uebele add an new data point to this by construction measured of real wages in Germany for the years 1500 to 1850, which they compare to population size. And they confirm the above. The Thirty Year War, which lead to significant population loss, was a period of significantly higher welfare for the survivors than before. This kind of relationship weakened over time though, probably reflecting that new factors became important in production. And it appears this change happened before the typical date we set for the Industrial Revolution in Germany.

Seasonality in house prices

There is a marked seasonal cycle in many housing markets. Sale volumes and house prices are significantly higher in the Summer and lower in the Winter. Evidently there should be some arbitrage, by selling high and buying low and renting in between for those who are genuinely moving or simply holding on to real estate for speculators. Possibly, the transaction costs are too high for this to happen. Or maybe the market for houses is not fluid enough for price not to cycle in a predictable way.

Cemil Selcuk picks up on this second idea and builds a search model where the supply is smaller in the Winter in the sense that the probability of finding an appropriate house is lower. As a result, there are fewer successful matches in the Winter, and they happen with a lower price because of the discount cost of waiting for better opportunities in the Summer and because the matches in the Winter are of lower quality. This is a rather trivial theoretical result, and it would be nice to know whether it approaches quantitatively the seasonal differences that are observed.

On Economic Forecasting