<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Firm Dynamics | Alejandro Rojas Bernal</title><link>https://www.alejandrorojasbernal.com/tag/firm-dynamics/</link><atom:link href="https://www.alejandrorojasbernal.com/tag/firm-dynamics/index.xml" rel="self" type="application/rss+xml"/><description>Firm Dynamics</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><lastBuildDate>Mon, 14 Sep 2026 12:25:00 +0000</lastBuildDate><image><url>https://www.alejandrorojasbernal.com/media/icon_hu291e38167b71616d362811ae46ba5ec5_13233712_512x512_fill_lanczos_center_3.png</url><title>Firm Dynamics</title><link>https://www.alejandrorojasbernal.com/tag/firm-dynamics/</link></image><item><title>Icarus and Daedalus: Non-Gaussian Micro Shocks and Aggregate Productivity Risk</title><link>https://www.alejandrorojasbernal.com/working-papers/icarus-and-daedalus-non-gaussian-micro-shocks-and-aggregate-productivity-risk/</link><pubDate>Mon, 14 Sep 2026 12:25:00 +0000</pubDate><guid>https://www.alejandrorojasbernal.com/working-papers/icarus-and-daedalus-non-gaussian-micro-shocks-and-aggregate-productivity-risk/</guid><description>&lt;p>How does productivity evolve at the microeconomic level, and how does that evolution shape aggregate risk? Using restricted-use plant-product data from India&amp;rsquo;s Annual Survey of Industries (2010-11 to 2022-23), we construct annual physical-output Solow-residual growth accounting for markups, inventories, and heterogeneous input costs. Productivity growth is sharply peaked and fat-tailed, with nonlinear mean reversion that varies with producer size. Larger producers recover more strongly after adverse shocks and retain more of their favorable gains-the Daedalian level effect-yet their shocks extend farther into both tails conditional on entry. Smaller producers exhibit sharper reversals after large favorable shocks-Icarian fallout. We capture these patterns with a three-regime Markov normal mixture conditioned on productivity history and size. Our law of motion yields the distribution of aggregate technology growth. Relative to a size-dependent Gaussian distribution, our regime model has lower aggregate dispersion but substantially thicker standardized tails and raises the probability of an aggregate technology contraction from 0.05% to 0.31%. Removing size dependence while retaining three regimes instead lowers expected growth and raises contraction probability to 2.47%. These separately estimated alternatives show that nonnormality and size dependence have distinct implications for aggregate risk. Granularity is distributional: aggregation weights determine whose shocks matter; productivity dynamics determine the risks they carry.&lt;/p></description></item></channel></rss>