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.

https://www.census.gov/data/developers/data-sets/business-dynamics.html

Explore Structure




Index Type Example Value
0 dict { }
... ... ...
Key Type Example Value Comment
"Count" int 191522 The number of jobs that were created in the last year.
"Continuers" int 101653 The number of jobs at continuing establishments that were created in the last yaer.
"Rate" float 20.5 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 9.6 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.
"Births" int 89869 The number of jobs that were created because of firm births in the past year.
Key Type Example Value Comment
"Continuers" int 85855 The number of jobs at continuing establishments that were destroyed in the last year.
"Count" int 144746 The number of jobs that were destroyed in the last year.
"Rate" float 15.5 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 6.3 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.
"Deaths" int 58891 The number of jobs that were destroyed because of firm deaths that were destroyed in the last year.
Key Type Example Value Comment
"State" str "Alabama" The state that this report was made for (full name, not the two letter abbreviation).
"Data" dict { }
"Year" int 1977 The year that this report was made for.
Key Type Example Value Comment
"Count" int 5623 The number of firms that exited this year.
"Establishment Exit" int 5641 The number of establishments exited because of firm deaths.
"Job Destruction" int 36602 The number of jobs destroyed as a result of firm deaths.
Key Type Example Value Comment
"Job Destruction" dict { }
"Number of Firms" int 52371 The number of firms in this state during this year.
"Job Creation" dict { }
"Calculated" dict { }
"Firm Exits" dict { }
"Establishments" dict { }
"DHS Denominator" int 933909 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.
Key Type Example Value Comment
"Exited Rate" float 13.1 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.
"Entered Rate" float 17.2 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.
"Entered" int 10634 The number of establishments that entered during this time. Entering occurs when an establishment did not exist in the previous year.
"Exited" int 8057 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.
"Physical Locations" int 62852 The number of establishments in this region during this time.
Key Type Example Value Comment
"Reallocation Rate" float 31.0 The sum of the Job Creation Rate and the Job Destruction Rate, minus the absolute Net Job Creation Rate.
"Net Job Creation" int 46776 The sum of the Job Creation Rate minus the Job Destruction Rate.
"Net Job Creation Rate" float 5.0 The sum of the Job Creation Rate and the Job Destruction Rate, minus the Net Job Creation Rate.

Downloads

Download all of the following files.

Usage

This library has 1 function you can use.
import business_dynamics
list_of_record = business_dynamics.get_businesses()
Additionally, some of the functions can return a sample of the Big Data using an extra argument. If you use this sampled Big Data, it may be much faster. When you are sure your code is correct, you can remove the argument to use the full dataset.
import business_dynamics
# These may be slow!
list_of_record = business_dynamics.get_businesses(test=True)

Documentation

 business_dynamics.get_businesses(test=False)

Returns financial data about all the states.