Zipf's law is one of the few quantitative reproducible regularities
found in e- nomics. It states that, for most countries, the size
distributions of cities and of rms (with additional examples found in
many other scienti c elds) are power laws with a speci c exponent: the
number of cities and rms with a size greater thanS is inversely
proportional toS. Most explanations start with Gibrat's law of
proportional growth but need to incorporate additional constraints and
ingredients introducing deviations from it. Here, we present a general
theoretical derivation of Zipf's law, providing a synthesis and
extension of previous approaches. First, we show that combining Gibrat's
law at all rm levels with random processes of rm's births and deaths
yield Zipf's law under a "balance" condition between a rm's growth and
death rate. We nd that Gibrat's law of proportionate growth does not
need to be strictly satis ed. As long as the volatility of rms' sizes
increase asy- totically proportionally to the size of the rm and that
the instantaneous growth rate increases not faster than the volatility,
the distribution of rm sizes follows Zipf's law. This suggests that the
occurrence of very large rms in the distri- tion of rm sizes described
by Zipf's law is more a consequence of random growth than systematic
returns: in particular, for large rms, volatility must dominate over the
instantaneous growth rate.