Stereotypes and Asymmetric Information Markets

One of the most criticized forms of preconception is indeed the stereotypes. Stereotypes are dangerous and often misleading,  which makes us wonder why do they exist? Where did they come from?
When you first start studying Economics, the very first model you learn is a simple 2 dimensional graph with price and quantity containing curves representing the market forces of supply and demand. Those market forces drive society to an equilibrium, where in a perfectly competitive market, without market failure, it will be the socially desirable outcome (I.e. the outcome that maximizes social welfare).
But, if there exists any market failure, then the market forces by themselves can no longer drive economy to a socially desirable outcome. One of these market failures is asymmetric information. (To be more precise, is a type of incomplete markets, but I won’t get into this matter).
Asymmetric Information happens everytime one of the parts in the transaction has more information regarding the object in trade then the other part (buying a used car, hiring insurance, etc.). Because not everyone has the same access to information, transaction will make benefits to be “skewed” to one side and resources to be allocated inefficiently. 
So if the problem is lack of information (more specifically the lack of access to such information), people will try hard to minimize this information gap by looking for “signs” that would give you a clue to the real value of things, or the true state or nature that is now hidden. And this is how stereotypes are born.
I am not saying they are right, or that they are any better than the uncertainty themselves (after all, if you are looking for signs due to lack of access to information,  then there is no reason to believe that the signs aren’t just as wrong).
But then there are the statisticians and actuaries,  who basically spend their professional lives seeking for the mathematical validity of stereotypes (or in a broader sense the validity of explanatory variables), and trying to provide the market with better information to drive us to a better equilibrium. That is the reason why car insurance would have different prices for different age groups, and gender, etc.
There is a still a key issue with this: even though statistics is a great tool to mitigate uncertainty,  it doesn’t necessarily diminishes information asymmetry,  once companies have a lot more access to statisticians than consumers.
I hope this was somewhat informative to you, and if it wasn’t entertaining, I hope it at least motivated you to seek understanding a little bit more of stats.

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