REVISIÓN BIBLIOGRÁFICA
3.3 INYECCIÓN INTRACITOPLASMÁTICA DE ESPERMATOZOIDES (ICSI)
The effects of information are particularly difficult to study in the field. Indeed, field evidence suffers from the simultaneity of different information types (e.g. public versus private) and from the problem of filtering out which information really affected decisions. In laboratory experiments, the experimenter can control subjects’ information and, thereby, establish causal effects.
Morris and Shin (2002) have shown that public signals may reduce welfare if different agents’ actions are strategic complements. The reason is that equilibrium weights on public signals are higher than the Bayesian weights, because agents have an incentive to coordinate their actions and public signals are more informative about other agents’ beliefs than private signals. Some experiments (e.g. Cornand and Heinemann 2014a; and Shapiro et al. 2014) highlight the multiplier effects of public information, by measuring the relative weights that subjects put on public versus private signals when deciding about their actions in a generalized beauty-contest game à la Morris and Shin (2002). The main result of these experiments is that
subjects attribute more weight to public than to private signals, but the weight on public signals is, in general, smaller than predicted by the equilibrium and too small to generate welfare detrimental overreactions.28
Baeriswyl and Cornand (2016) qualify the previous results in terms of overreaction:
They find that the weights that subjects attribute to the more precise signal is smaller than the Bayesian weight. If private signals are more precise than public signals, subjects’ behavior gets closer to the equilibrium prediction with its potential negative welfare implications.
While the assumption that economic agents treat information rationally is made very often in macroeconomics, this experiment recalls that subjects do not necessarily follow Bayesian updating and instead may be biased. There are many examples showing that subjects may not deal with information rationally: anchoring effects of initial information, overweighting of memorable and salient information and confirmatory bias to mention but a few.29
For central banks, the question arises, whether and how they should release information that has the potential of damaging welfare. In a theoretical analysis, extending Morris and Shin (2002), Cornand and Heinemann (2008) have shown that central banks should never completely withhold information, even if it is imprecise and a publication would reduce welfare. Instead, the (theoretically) optimal communication strategy is a limited degree of publicity, in which the central bank discloses information only to a sub-group of agents. The lower degree of publicity contains overreactions and leads to welfare improvements compared to entirely withholding imprecise signals. Baeriswyl and Cornand (2014) show that a limited degree of transparency (i.e. disclosing public information to all subjects as private signals with an added idiosyncratic noise) can achieve the same expected welfare as a limited degree of publicity. They test this prediction in an experiment and show that both information structures effectively reduce the multiplier effects of public information. For reasons of accountability and fairness, they argue that central banks should favor limited transparency when they want to avoid overreactions. However, in the light of the results by Baeriswyl and Cornand (2016), the issue only arises, if a central bank publishes information that is less precise than private information. It is remarkable that negative welfare effects of imprecise public information can be avoided if the precision is deliberately reduced by adding idiosyncratic noise.
28 For further details, see Cornand and Heinemann (2014b, 2015).
29 See Holden (2012, p.5) for more examples and details.
6. CONCLUSION
The experimental literature in macroeconomics and large group dynamics is growing rapidly.
Macro is catching up with other fields in exploiting experimental tools. It is now rather common to test the microfoundations of macro-models and it is now also possible to put a DSGE model in the laboratory. The main advantages are: bench-testing of alternative policies, capturing strategic uncertainty and biases from standard preferences or RE, and accounting for heterogeneity in agents’ expectations and behavior without getting lost in the wilderness where
“everything goes.”
The experimental method in macroeconomics should be seen as complementary to field empirics. Both empirical approaches can help us to improve macroeconomic theory and get more realistic impulse response functions to unexpected shocks and changes in policy rules.
While experimental macroeconomics deserves more and more attention and has been useful in many respects already, it still faces some challenges.
First, replicating experiments in different frames or settings allows testing the robustness of experimental results and improves their external validity. Thus, we need studies beyond the mere replication of experiments in identical frames. Associated with this, we need studies that target the external validity directly, for example by providing systematic comparisons of results from laboratory experiments, surveys, and field empirics.
Second, there does not yet exist a generally accepted method for experiments in macroeconomics. We need more interaction between experimental macroeconomists, practitioners from central banks, and theorists to agree on acceptable tests for theoretical predictions or policy measures. This interaction should also foster the development of models that account for experimental results. There are some models that are based on experimental outcomes in the field of labor economics, but much less has been done in the field of monetary or fiscal policy. Key issues are to account for observed heterogeneity and for strategic uncertainty. Heuristic switching models and level-k models may be useful in this respect.
Incorporating the findings of experimental economics in macro-modelling are challenging tasks that deserve further attention.
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