Markets play a vital role in allocating scarce sources in modern economies. However, recent booms and busts claim that markets may not always fulfill this role well. A current example may be the US housing boom of 2002-08, which preceded the truly amazing Economic Crisis. Even though the economy is at a boom, and housing and stock values were growing, overall productivity growth slowed, as proven in figure 1.
A line chart showing Figure 1: Total factor productivity development in the U . s . States declined throughout the housing boom from 2002 to 2008.
Figure 1: Total factor productivity development in the U . s . States declined throughout the housing boom from 2002 to 2008.
A possible explanation is the fact that investors expect any investment to become lucrative during booms and, therefore, dwindle diligent in selecting where you can invest. Eventually, this less informed investing worsens the allocation of sources throughout the economy, ultimately decreasing overall productivity. Although suggestive, this narrative is loose and can’t be fully evaluated with no theory of knowledge acquisition and it is macroeconomic effects. My paper’s contribution would be to provide this type of model by which information acquisition in markets varies using the condition from the economy.
Model and Results
I model an engaged economy populated by businesses that differ in how productive they’re. Households work and save by purchasing firms’ stock, but they don’t understand how productive each firm is. To beat this issue, households can buy details about firm productivity, where greater quality details are more costly. If households have better information, they are able to better identify more lucrative firms and invest more inside them. Consequently, better informed households result in more effective markets, meaning more productive firms receive more capital. The central element of the model may be the household’s decision about how many details to get, which depends upon the condition from the economy.
The primary outcome is that whether booms strengthen or weaken the motivation to get high-quality information depends upon which factor drives the boom. Booms driven by fundamental factors-for instance, increases in overall productivity-encourage information acquisition, enhance the allocation of capital, while increasing overall productivity far above the first improvement . During this type of boom, households acquire better information as their stakes are greater as firms tend to be more productive. Therefore, if large increases in asset costs are justified by technologies, policy makers don’t have to be concerned as markets become much more efficient.
This picture is reversed if booms are impelled by non-fundamental factors-for instance, excessive optimism. Such non-fundamental booms discourage information acquisition, worsen the allocation of capital, and reduce overall productivity. Households acquire worse information during non-fundamental booms because expectations about prices being excessive or lacking typically reduce the effectiveness of high-quality information. Within the extreme situation, when households expect all shares to become overpriced, firm-specific information becomes entirely useless, because the household knows to not buy in almost any situation. This result follows evidence in the US housing boom, where productivity declined as asset prices increased.
Policy makers ought to be concerned if non-fundamental factors drive booms. However, just how can they separate fundamental and non-fundamental booms? The model provides a solution to this important question by predicting an optimistic correlation between dispersion in asset returns and knowledge acquisition.
If households don’t acquire information, no new information reaches the marketplace and asset prices generally relocate exactly the same direction. In comparison, if households acquire high-quality information, a sizable volume of information reaches the marketplace and asset prices move based on this firm-specific information. Therefore, if asset prices increase overall, but firms are undistinguishable, the boom will probably be driven by optimism. In comparison, if asset prices increase and you will find still winners and losers, information acquisition still occurs, and fundamentals likely fuel the boom.
Policy makers may use this understanding to “lean from the wind” when confronted with excessive optimism or pessimism. A good example of such policies is big-scale asset purchases, which central banks used intensively in most civilized world. Forever of the use, asset purchases happen to be suspected of distorting asset prices and worsening capital allocation (e.g., DNB 2017). The model supplies a laboratory to evaluate this critique.
The model confirms that asset purchases can inflate asset prices, discouraging information acquisition and worsening capital allocation. However, asset purchases may also possess the opposite effect if they’re accustomed to decrease mispricing in markets, for example during depressions. Within this situation, asset purchases make asset prices less distorted, encourage information acquisition, and improve capital allocation. Therefore, to make use of asset purchases properly, central banks have to know which pressure is driving the boom or bust.
A line chart showing Figure 2: Viewed with the lens of my model, the united states us dot-com boom prior to 2001 was likely driven by productivity, whereas the housing boom between 2002 and 2008 was driven by optimism.
Figure 2: Viewed with the lens of my model, the united states us dot-com boom prior to 2001 was likely driven by productivity, whereas the housing boom between 2002 and 2008 was driven by optimism.
There’s ample empirical evidence that booms can worsen the allocation of capital (Gopinath et al. 2017 Doerr 2018) and labor (Borio et al. 2015) and reduce overall productivity (Gorton and Ordonez 2020). Furthermore, recent empirical evidence by Davilá and Parlatore (2020) shows that cost informativeness indeed decreased throughout the US housing boom (figure 2). In comparison, cost informativeness and productivity elevated throughout the us dot-com boom prior to 2001. Viewed with the lens of my model, this pattern shows that the us dot-com boom from the 1990s was driven by productivity, whereas the united states housing boom between 2002 and 2008 was driven by optimism.
Ilja Kantorovitch (@IKantorovitch) is really a PhD candidate at Universitat Pompeu Fabra. You’ll find much more of Ilja’s research at https://world wide web.kantorovitch.eu/.
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Dávila, Eduardo and Parlatore, Cecilia, “Identifying Cost Informativeness,” National Bureau of monetary Research (2020).
DNB, “2016 Annual report,” De Nederlandsche Bank (2017).
Doerr, Sebastian, “Collateral, Reallocation, and Aggregate Productivity: Evidence in the U.S. Housing Boom,” SSRN Electronic Journal (2018), pp. 1-66.
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