Trade and the size distribution of firms: Evidence from the German Empire

What effect did trade have on the size distribution of firms during the first wave of globalization? Three historical datasets from the German Empire between 1875 and 1907 were collected and harmonized to answer this question. This paper combines industry census and bilateral railway trade data from the same industry and region along with industry-level tariff data. The evidence shows that increases in aggregate trade caused the share of firms to shift from smaller to larger firms. Exogenous decreases in tariffs caused an increase in the share of the largest firms. The regional distributive effects of trade on inequality between firms that are discussed in the contemporaneous literature were already present during the first wave globalization.

The Size Distribution of Firms and Industrial Water Pollution: A Quantitative Analysis of China

We argue that misallocation across firms amplifies industrial water pollution by distorting the firm size distribution in China. Firm-level data indicate that larger firms are more likely to use clean technology but face higher distortions. In a heterogeneous firms model with an endogenous choice of pollution treatment technologies, we show that distortions that increase with firm-level TFP lower the adoption of clean technology, amplify aggregate pollution intensity, and lower aggregate output. Quantitatively, eliminating these correlated distortions would increase output by 30 percent and decrease pollution by 20 percent. Meanwhile, environmental regulations have sizable impact on pollution but limited effects on aggregate output. (JEL O13, O14, P28, P31, Q52, Q53, Q58)

Issues in the quantitative analysis of the SIPRI arms industry database

Although the Stockholm International Peace Research Institute’s data on the 100 largest arms (and military services) producing firms is very widely used for various purposes, there is relatively little quantitative statistical analysis of it. This article discusses some of the issues involved in the econometric analysis of the data. This is complicated by the difficulty of modeling the processes of mergers, acquisitions, and divestments which drives entry and exit from the list. Various models are estimated to examine (a) the relationship between arms sales and military expenditure, (b) the evolution of concentration and the size distribution of firms, (c) the cross-section relationship between size and growth of firms, (d) the times-series properties of the arms sales of individual firms, and (e) of arms sales by country of ownership.

Classification of cryptocurrency coins and tokens by the dynamics of their market capitalizations

We empirically verify that the market capitalizations of coins and tokens in the cryptocurrency universe follow power-law distributions with significantly different values for the tail exponent falling between 0.5 and 0.7 for coins, and between 1.0 and 1.3 for tokens. We provide a rationale for this, based on a simple proportional growth with birth and death model previously employed to describe the size distribution of firms, cities, webpages, etc. We empirically validate the model and its main predictions, in terms of proportional growth (Gibrat's Law) of the coins and tokens. Estimating the main parameters of the model, the theoretical predictions for the power-law exponents of coin and token distributions are in remarkable agreement with the empirical estimations, given the simplicity of the model. Our results clearly characterize coins as being ‘entrenched incumbents’ and tokens as an ‘explosive immature ecosystem’, largely due to massive and exuberant Initial Coin Offering activity in the token space. The theory predicts that the exponent for tokens should converge to 1 in the future, reflecting a more reasonable rate of new entrants associated with genuine technological innovations.

Inference in the Real World

In this chapter I provide a mix of detailed cross-disciplinary examples to illustrate the method in real-world settings. The examples in this chapter illustrate modeling and inference in a relatively simple set of problems. After exploring single-parameter applications under very limited information, I consider multi-parameter problems, beginning with the inference of a two-parameter size distribution of firms. This demonstrates a main characteristic of social science problems where the available information is most often insufficient to provide a very exact inference. Then a simple ecological example is formulated. It provides an interesting theoretical application of analyzing complex ecological networks based on very limited macro-level information. The chapter concludes with a simple formulation of efficient network and information aggregation. A few shorter examples are provided as well.