Abstract

Why do institutions grow? Despite nearly a century of scientific effort, there remains little consensus on this topic. This paper offers a new approach that focuses on energy consumption. A systematic relation exists between institution size and energy consumption per capita: as energy consumption increases, institutions become larger. I hypothesize that this relation results from the interplay between technological complexity and human biological limitations. I also show how a simple stochastic model can be used to link energy consumption with firm dynamics.

Highlights

  • Throughout the last century, there has been a recurrent desire to connect human social evolution to changes in energy consumption [1,2,3,4]

  • The motivation is simple: the laws of thermodynamics dictate that any system that exists far from equilibrium must be supported by a flow of energy [5]

  • It has proved difficult to move from grand pronouncements based on the laws of thermodynamics to a quantitative understanding of the relation between energy use and social evolution [6]

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Summary

Introduction

Throughout the last century, there has been a recurrent desire to connect human social evolution to changes in energy consumption [1,2,3,4]. This paper is concerned with one particular aspect of social change: the growth in size of the institutions that control human labor. Institution size refers to the amount of human labor (i.e employment) controlled by an organization. Under this definition/metric of institution size, I demonstrate that a pervasive, positive correlation exists between institution size and energy use per capita. Using data on firm age and firm size to constrain a stochastic model, I demonstrate that firm dynamics are likely related to rates of energy consumption, and I offer a prediction of what this relation should look like.

Theories of institutional size
Energy and institution size
The ‘how’ question
A stochastic model
Estimating variations in firm dynamics
The ‘why’ question
Social coordination and human biology
Hierarchy and institution size
Causality
Conclusions
A Sources and Methodology
B Assessing Size Bias within Firm Databases
Compustat
Database Distributions
Database Size Distribution
USA Macro Data
C The Firm Size Distribution as a Variable Power Law
D Testing Gibrat’s Law Using the Compustat Database
Fitted Scale Parameter by Percentile
E Instability of the Gibrat Model
F Properties of Stochastic Models
20 Model US Data
G Bias and Error in the GDP Labor Time Method
H A Hierarchical Model of the Firm
An Agrarian Model of Institution Size
Findings
Predicted Energy Use

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