A growing literature relies on natural experiments to establish causal effects in macroeconomics. In diverse applications, natural experiments have been used to verify underlying assumptions of conventional models, quantify specific model parameters, and identify mechanisms that have major effects on macroeconomic quantities but are absent from conventional models. We discuss and compare the use of natural experiments across these different applications and summarize what they have taught us about such diverse subjects as the validity of the Permanent Income Hypothesis, the size of the fiscal multiplier, and about the effects of institutions, social structure, and culture on economic growth. We also outline challenges for future work in each of these fields, give guidance for identifying useful natural experiments, and discuss the strengths and weaknesses of the approach.
|Keywords:||Civic Capital; Fiscal Multiplier; Institutions; Multiple Equilibria; Networks; Permanent Income Hypothesis; Social Structure; Social Ties; Trust|
|JEL:||C1 C9 E21 E62 H31 O11 O14 O43 O50|
The bilateral trade relations between world countries form a complex network, the International Trade Network (ITN), which is involved in an increasing number of worldwide economic processes, including globalization, integration, industrial production, and the propagation of shocks and instabilities. Characterizing the ITN via a simple yet accurate model is an open problem. The classical Gravity Model of trade successfully reproduces the volume of trade between two connected countries using known macroeconomic properties such as GDP and geographic distance. However, it generates a network with an unrealistically homogeneous topology, thus failing to reproduce the highly heterogeneous structure of the real ITN. On the other hand, network models successfully reproduce the complex topology of the ITN, but provide no information about trade volumes. Therefore macroeconomic and network models of trade suffer from complementary limitations but are still largely incompatible. Here, we make an important step forward in reconciling the two approaches, via the introduction of what we denote as the Enhanced Gravity Model (EGM) of trade. The EGM combines the maximum-entropy nature of network models with the established econometric structure of the Gravity Model. Using a single, unified and principled mechanism that is transparent enough to be generalized to other economic networks, the EGM allows trade probabilities and trade volumes to be separately controlled via any combination of dyadic and country-specific macroeconomic variables. We show that the EGM successfully reproduces both the topology and the weights of the ITN, finally reconciling the conflicting approaches. Moreover, it provides a general and simple theoretical explanation for the failure of economic models that do not explicitly focus on network topology: namely, their lack of topological invariance under a change of units.
Ayça Ebru Giritligil
Roberto A. Weber
In many areas of social life, individuals receive information about a particular issue of interest from multiple sources. When these sources are connected through a network, then proper aggregation of this information by an individual involves taking into account the structure of this network. The inability to aggregate properly may lead to various types of distortions. In our experiment, four agents all want to find out the value of a particular parameter unknown to all. Agents receive private signals about the parameter and can communicate their estimates of the parameter repeatedly through a network, the structure of which is known by all players. We present results from experiments with three different networks. We find that the information of agents who have more outgoing links in a network gets more weight in the information aggregation of the other agents than under optimal updating. Our results are consistent with the model of “persuasion bias” of DeMarzo et al. (2003).
|Keywords:||persuasion bias, experiments, bounded rationality|
|JEL:||C92 D03 D83|