How to estimate markups and production functions? 

Here you can find code to estimate production functions and firm-level markups from firm-level income statements.  This code comes in two version, STATA and Python.

Stata:  can be downloaded from Maarten De Ridder website:  here

Python:  can be downloaded here. This code  reproduce the estimation of markup and the underlying production function coefficients (and discussed below) from:  

The Rise of Market Power and the Macroeconomic Implications, De Loecker, Eeckhout, and Unger,  The Quarterly Journal of Economics, Volume 135, Issue 2, May 2020, Pages 561–644, https://doi.org/10.1093/qje/qjz041

The Python Code and How to Use it

Code structure

The zip file contains:

• a readme file

• ProdFun_VPublic.py: a Python code that contains the production function estimation class.

• Clean_Data.do: stata script which cleans the Compustat data.

• Run_Compustat_VPublic.py: a Python code that estimates the production function and markup on Compustat data.

• DEFL_Y.dta: a stata dataset that contains some deflators.

Note: you will need to bring your Compustat data

How does it work?

First, run the Clean_data.do in your Stata, after changing some path. This will compute the cleaned dataset COMPUSTAT_clean.csv.

Second Run_Compustat_HarmonicMean_VPublic.py with your python. This will perform the estimation and save the firm-level markups in Compustat_Markups_Python_V1.dta and the production function coefficients in the Stata datasets  Compustat_Betas_Python_V1.dta and Compustat_BetasRoll_Python_V1.dta. The class ProdFun_VPublic.py allows for many different options that are explained directly in the script.

Our code produces the figure above that plots sales-weighted simple mean markup and the sales-weighted harmonic mean markup:

• The times series for the average markups below can be downloaded here. 

•  The estimates from the original paper  can be downloaded here from Jan De Loecker's website.