¿What is Significant Asymmetric Information?

1. Somewhere near second and third year of economic studies, one is told about Asymmetric Information. The concept looks very clear, which stands for the fact that economic agents don't hold the same amount of information before any transaction. Thus, their incentives are not properly sustained on a complete information scheme and the negotiation (i.e. bargaining) will be distorted. 

2. Basic microeconomic models assume that agents have complete information, specially over price and its determinants, for instance, the quality of the goods, the depreciation period and so on. This assumption inhibits the possibility to discuss if the markets are efficient while assigning prices. Nevertheless, one should know that assumption is false, at least from a qualitative approach(1)

3. In fact, information is not a dichotomy (complete information/no information at all), it's actually a quantitative question, a variable not a parameter, i.e. poor information, medium information, lots of information, etc. This variable could be fitted in this range INFi < 0 : 1> which should be a continuous variable (but we could used it as a discrete one for academic purposes). As one can observe, both lower and upper limit are false, contrary to basic economic assumptions. Although, a quantitative approach may be able to simply the range, and classify data into "no information" and "complete information" for academic or policy purposes.

4. However, the information analysis shouldn't be only unilateral. A bilateral approach provides a comparative analysis of the information between agents, which was branded as "asymmetric information".

5. Furthermore, the evaluation of asymmetries on information is referred that Agent A may have more information than Agent B, thus, he (or she) is better prepared to negotiation a specific transaction. Therefore, INFA is greater or lower than INFB.

6. This difference (which is the indicator of the asymmetry) may also be described as DIF = \INFA - INFB\. One already knows that there should be a difference but, as the quantitative analysis suggests, one must verify if that difference is significant or not.

7. If the difference is non-significant, there isn't asymmetric information from a quantitative approach. The opposite results on relevant information asymmetries.

8. One can see that the final result is going to look the same, the bilateral dichotomy "non-asymmetric information"/asymmetric information", but the process used to arrive to that conclusion is less arbitrary nor assumed, which turns in favor of a more rigorous economic analysis. 

9. Of course, one can find such asymmetries everywhere, but a general characteristic per se cannot explain the issues risen on price determination. In fact, a generality, meaning a common characteristics in two or more groups of observations, lowers the chance that this variable is able to explain any difference on price determination between a control group and an intervention group. 

10. For example, if Market X has Agent A and Agent B with asymmetric information, and Market Y  (Agent C and Agent D) has the same problem, one shouldn't use these qualitative asymmetries per se to find any difference between market X and Market Y.

11. However, if one applies the quantitative analysis, it will be possible to find an actual difference on the asymmetries from an statistical approach. Therefore, it is possible that, in this case, Market X has non-significant asymmetries between A and B, but Market Y does have significant asymmetries. Such difference will be helpful to explain any observable difference on price determination between those markets.

12. In sum, one should disregard asymmetric information as an regular assumption or parameter, and proceed to analyze it accordingly, as a variable with a quantitative analysis plus statistical approaches. The results of that analysis might be helpful to have a rigorous conclusion on the behavior of price determination rather than an automatic affirmation that every distortion is caused by the asymmetries  without checking if it is really significant or not. 


(1) In fact, a quantitative approach may be used to sustain the qualitative approach. In other words, giving the fact that information is not a dichotomy, a quantitative analysis, including statistical approaches, may support such a dichotomy for policy or conclusive purposes. 


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