id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-343688-g6bevqxq	Ahundjanov, B. B.	Power Law in COVID-19 Cases in China	2020-07-27		.txt	text/plain	5604	385	65	We show that the upper tail of COVID-19 cases in Chinese cities is well described by a power law distribution, with exponent less than one, and that a random proportionate growth model predicated by Gibrat's law is a plausible explanation for the emergence of the observed power law behavior. 4 The robust fit of power law to cross-sectional distribution of COVID-19 cases in Chinese cities potentially provides macro-level evidence for random proportionate growth posited by Gibrat's law. In summary, our estimation results and diagnostic tests provide strong evidence that the COVID-19 cases in Chinese cities can be well characterized by the power law (Pareto) distribution. 1101 In light of the discussion in Section 4.1, the confirmation of Gibrat's law for COVID-19 cases in Chinese cities provides a plausible explanation for the emergence of power law behavior shown for the data. We presented empirical evidence for a power law distribution for the upper tail of the number of COVID-19 cases in Chinese cities.	./cache/cord-343688-g6bevqxq.txt	./txt/cord-343688-g6bevqxq.txt