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Business Dynamics Python Library

From the CORGIS Dataset Project

By Austin Cory Bart, Joung Min Choi and Bo Guan
Version 3.0.0, created 9/1/2021
Tags: government, united states, us, usa, business, businesses, firms, establishments, jobs, census

Overview

The Business Dynamics Statistics (BDS) includes measures of establishment openings and closings, firm startups, job creation and destruction by firm size, age, and industrial sector, and several other statistics on business dynamics. The U.S. economy is comprised of over 6 million establishments with paid employees. The population of these businesses is constantly churning -- some businesses grow, others decline and yet others close. New businesses are constantly replenishing this pool. The BDS series provide annual statistics on gross job gains and losses for the entire economy and by industrial sector, state, and MSA. These data track changes in employment at the establishment level, and thus provide a picture of the dynamics underlying aggregate net employment growth.

There is a longstanding interest in the contribution of small businesses to job and productivity growth in the U.S. Some recent research suggests that it is business age rather than size that is the critical factor. The BDS permits exploring the respective contributions of both firm age and size.

BDS is based on data going back through 1976. This allows business dynamics to be tracked, measured and analyzed for young firms in their first critical years as well as for more mature firms including those that are in the process of reinventing themselves in an ever changing economic environment.

If you need help understanding the terms used, check out these definitions.

https://www.census.gov/data/tables/time-series/econ/bds/bds-tables.html

Explore Structure

Each row represents Reports of business dynamics statistics in a state on a given year.

Index Type Example Value
0 dict { }
1 dict (same structure)
2 dict (same structure)
... ... ...
Key Type Example Value Description
"State" str "Alabama" The state that this report was made for (full name, not the two letter abbreviation).
"Year" int 1978 The year that this report was made for.
"Data" dict { }
Key Type Example Value Description
"DHS Denominator" int 972627 The Davis-Haltiwanger-Schuh (DHS) denominator is the two-period trailing moving average of employment, intended to prevent transitory shocks from distorting net growth. In other words, this value roughly represents the employment for the area, but is resistant to sudden, spiking growth.
"Number of Firms" int 54597 The number of firms in this state during this year.
"Calculated" dict { }
"Establishments" dict { }
"Firm Exits" dict { }
"Job Creation" dict { }
"Job Destruction" dict { }
Key Type Example Value Description
"Net Job Creation" int 74178 The sum of the Job Creation Rate minus the Job Destruction Rate.
"Net Job Creation Rate" float 7.627 The sum of the Job Creation Rate and the Job Destruction Rate, minus the Net Job Creation Rate.
"Reallocation Rate" float 29.183 The sum of the Job Creation Rate and the Job Destruction Rate, minus the absolute Net Job Creation Rate.
Key Type Example Value Description
"Entered" int 10457 The number of establishments that entered during this time. Entering occurs when an establishment did not exist in the previous year.
"Entered Rate" float 16.375 The number of establishments that entered during this time divided by the number of establishments. Entering occurs when an establishment did not exist in the previous year.
"Exited" int 7749 The number of establishments that exited during this time. Exiting occurs when an establishment has positive employment in the previous year and zero this year.
"Exited Rate" float 12.135 The number of establishments that exited during this time divided by the number of establishments. Exiting occurs when an establishment has positive employment in the previous year and zero this year.
"Physical Locations" int 65213 The number of establishments in this region during this time.
Key Type Example Value Description
"Count" int 5248 The number of firms that exited this year.
"Establishment Exit" int 5329 The number of establishments exited because of firm deaths.
"Employments" int 28257 The number of employments destroyed as a result of firm deaths.
Key Type Example Value Description
"Births" int 76167 The number of jobs that were created because of firm births in the past year.
"Continuers" int 139930 The number of jobs at continuing establishments that were created in the last yaer.
"Count" int 216097 The number of jobs that were created in the last year.
"Rate" float 22.218 The number of jobs that were created in the last year divided by the DHS denominator. The result is the rate at which jobs have been created.
"Rate/Births" float 7.831 The number of jobs that were created because a new firm born in the past year, divided by the DHS denominator. The result is the rate at which jobs have been created because of firm births.
Key Type Example Value Description
"Continuers" int 81829 The number of jobs at continuing establishments that were destroyed in the last year.
"Count" int 141919 The number of jobs that were destroyed in the last year.
"Deaths" int 60090 The number of jobs that were destroyed because of firm deaths that were destroyed in the last year.
"Rate" float 14.591 The number of jobs that were destroyed in the last year divided by the DHS denominator. The result is the rate at which jobs have been destroyed.
"Rate/Deaths" float 6.178 The number of jobs that were destroyed because of firm deaths that were destroyed in the last year divided by the DHS denominator. The result is the rate at which jobs have been destroyed because of firm death.

Downloads

Download all of the following files.

  1. business_dynamics.py
  2. business_dynamics.data

Usage

import business_dynamics
record = business_dynamics.get_record()

Documentation

get_record()
Returns a list of dictionaries representing record.