Ekonomska teorija običajno predpostavlja, da je gospodarstvo uravnoteženo iz vidika pomena podjetij in da nobeno podjetje ni bolj pomembno od ostalih iz vidika celotnega gospodarstva. Toda podatki kažejo, da so vsa gospodarstva močno koncentrirana in da velja pravilo 20 : 80 – 20% podjetij ustvari 80% outputa in izvoza in zaposluje 80% vseh zaposlenih. Če k temu dodate še network učinke, torej, da je neko podjetje nadpovprečno pomemben kupec vmesnih proizvodov, potem lahko ugotovite, da centralni limitni teorem in predpostavka glede normalne porazdelitve šokov odpovesta. Velik šok v poslovanju nekega velikega podjetja ima lahko izjemno velike učinke navzdol po vsej dobaviteljski verigi in lahko izzove tudi velik makroekonomski šok. Po domače rečeno, če kihne Mercator ali Gorenje, ima lahko to pomembne makroekonomske posledice.

To so zasnove raziskovanja v zadnjih letih na področju t.i. “granularne ekonomije”. Spodaj je izsek iz zadnje (teoretične) raziskave, ki so jo naredili Daron Acemoglu, Asuman Ozdaglar & Alireza Tahbaz-Salehi. In to je tisto, kar me trenutno najbolj rajca in mi ne da spati. Izziv je te učinke pokazati v realnem gospodarstvu.

Most empirical studies in macroeconomics approximate the deviations of aggregate economic variables from their trends with a normal distribution. Besides its relative success in capturing salient features of the behavior of aggregate variables in the US and other OECD countries, this approach has a natural justification: since most macro variables (such as GDP) are obtained from combining more disaggregated ones, it is reasonable to expect that a central limit theorem-type result should imply that such macro variables are normally distributed. As an implicit corollary to this observation, most of the literature treats the standard deviations of aggregate variables as sufficient statistics for measuring aggregate economic fluctuations.

This picture, however, changes dramatically once large deviations are also taken into account. Panels (c) and (d) of Figure 1 show the same quantile-quantile plots for the entire US postwar sample. It is easy to notice that both graphs exhibit sizable and systematic deviations from the normal line at both ends. This observation highlights that even though the normal distribution does a fairly good job in approximating the nature of fluctuations during most of the sample, it severely underestimates the most consequential fact about business cycle fluctuations, namely, the frequency of large economic contractions.

Motivated by these patterns, this paper makes two related points. First, it argues that even though GDP is obtained from aggregating microeconomic variables, there is no guarantee that it should be normally distributed — even in the absence of aggregate shocks. Rather, input-output linkages within the economy can reshape the distribution of aggregate variables. Thus, in general, the standard deviation of aggregate variables may not be a sufficiently informative statistic for the nature of aggregate fluctuations.

Second, we argue that the failure of normality manifests itself in the form of significantly higher “tail risks”. In particular, we show that the propagation of microeconomic shocks through input-output linkages can lead to more frequent and larger economic downturns than what is predicted by the normal distribution. We also demonstrate that the emergence of such macroeconomic tail risks can coexist with approximately normally distributed fluctuations away from the tails, consistent with the pattern of US GDP fluctuations documented in Figure 1.

Though there are many different modeling approaches that can generate significant tail risks (e.g., models with financial crises or multiple equilibria), in this paper, we focus on the role of input-output linkages across disaggregated subsectors along the lines of Long and Plosser (1983) and Acemoglu et al. (2012). This choice is motivated by two considerations. First, a model consisting of many sectors subject to idiosyncratic shocks is a natural starting point for generating aggregate fluctuations that are reasonably well-approximated by a normal distribution. Second, as shown in Acemoglu et al. (2012), certain types of input-output linkages can generate significant aggregate volatility (and comovement across sectors) from idiosyncratic microeconomic shocks. They are thus also a potential candidate for generating large deviations or tail risks.

In order to develop the link between the nature of macroeconomic tail risks, microeconomic shocks, and the input-output structure of the economy, we first study the class of balanced economies as a benchmark. These are economies in which all sectors play roughly symmetric roles as input suppliers to others. For this class of economies, we show that, regardless of the distribution of microeconomic shocks, not only GDP fluctuations are normally distributed, but also large economic downturn are exponentially unlikely. In other words, absent any other amplification mechanisms or aggregate shocks—and in contrast to the patterns observed in Figure 1—the likelihood of large contractions in such economies is infinitesimal. Furthermore, we show that the standard deviation of log GDP can serve as a sufficient statistic for the likelihood of large downturns.

Our subsequent analyses, however, establish that the above mentioned results for balanced economies do not hold for general economies. More specifically, the implications of our theoretical results can be summarized as follows:

First, the propagation of microeconomic shocks through input-output linkages in an unbalanced economy — where some sectors play a much more important role than others as inputs suppliers to the rest of the economy — can lead to the emergence of significant macroeconomic tail risks. Furthermore, we show that the frequency of large GDP contractions is highly sensitive to the nature of microeconomic shocks. In particular, micro shocks with slightly thicker tails can lead to a significant increase in the likelihood of large economic downturns.

Second, depending on the distribution of microeconomic shocks (and in contrast to the case of balanced economies), the economy may exhibit significant macroeconomic tail risks even though aggregate fluctuations away from the tails can be well-approximated by a normal distribution, an outcome consistent with the pattern of US postwar GDP fluctuations documented in Figure 1. This result is a consequence of the fact that the propagation of shocks over input-output linkages can concentrate the risks at the tails of the distribution.

Third, our results imply that the standard deviation of aggregate variables (such as log GDP) may no longer serve as a sufficient statistic for the likelihood of large economic downturns: two economies subject to micro shocks drawn from different distributions may experience large recessions with significantly different frequencies, even when their GDP fluctuations have identical standard deviations. The juxtaposition of our last two results underscores the importance of studying the determinants of large recessions, since such macroeconomic tail risks may vary significantly even across economies that exhibit otherwise identical behavior for moderate deviations.

Fourth, to highlight the key role of input-output linkages in creating macroeconomic tail risks, we showthat an economy with unbalanced inputs-output linkages subject to thin-tailed (e.g., exponentially distributed) microeconomic shocks exhibits deep recessions as frequently as a balanced economy subject to heavy-tailed (e.g., Pareto distributed) shocks. In this sense, our results provide a novel solution to what Bernanke, Gertler, and Gilchrist (1996) refer to as the “small shocks, large cycles puzzle” by arguing that the interaction between the underlying input-output structure of the economy and the shape of the distribution of microeconomic shocks is of first-order importance in determining the nature of aggregate fluctuations.

Finally, we demonstrate that the forces identified above not only lead to the emergence of macroeconomic tail risks, but can also generate “tail comovements,” defined as simultaneous large falls in the output of many sectors. This type of comovement is akin to the effects of aggregate shocks.

Vir: Daron Acemoglu, Asuman Ozdaglar & Alireza Tahbaz-Salehi