Ewens and Marx (2017) use a series of state-level law changes in the U.S. from 1995--2016 to study the impact of founder replacement on startup outcomes. The data and code below allow others to incorporate the law changes into their research.

The csv file contains four variables: year,state, cncChange and note_LawChange defined as:

• year: the year of the law change. The best year to assume is treated years are those after this year.
• state: two-letter abbreviation of state.
• cncChange: -1 or 1 which represents the incremental change in the strength of the law change. -1 indicates a weakening, 1 indicates a strengthening, relative to the previous year.
• note_LawChange: string containing notes about the change and whether it was used in the original study.

The do file can be run in any program as do createCNCvariables.do after you update the two global variables in the file. The simple file creates the variable cncChange and note_LawChange for state-years with non-compete law changes. See above for variable definitions.

Alternatively, you can take the csv file and merge it into your data:

* your data is temp.dta
* Load up the csv and save as local tempfile
insheet using "stateYear.csv", comma clear
tempfile state_years
save `state_years'
* Load up your main data
use temp, clear
* Merge on the CNC law changes
merge m:1 state year using `state_years', keep(1 3) nogen

The R file can be loaded with source and will add two new variables to your data cncChange and note_LawChange.

Alternatively, you can take the csv file and merge it into your data:

# Load the csv file
state_years <- read.csv("stateYear.csv", stringsAsFactors=FALSE)
# your data is currentData 
# Merge onto the current data.  Result is newData with new columns.
newData<- merge(state_years, currentData, by.x=c("state", "year"), by.y=c("state_yourData", "year_yourData"), all.y = TRUE)

Some sample code to import the csv file and merge onto your existing data:

import numpy as np
import pandas as pd

# Assume that you have currentData as your data with 'state' and 'year'
stateYear = read_csv("stateYear.csv")
newData = pd.merge(currentData,
                 stateYear,
                 on=['state','year'])

@article{ewensMarx2017founder,
  title={Founder replacement and startup performance},
  author={Ewens, Michael and Marx, Matt},
  journal={The Review of Financial Studies},
  volume={31},
  number={4},
  pages={1532--1565},
  year={2017},
  publisher={Oxford University Press}
}

Ewens, Michael, and Matt Marx. "Founder replacement and startup performance." The Review of Financial Studies 31.4 (2017): 1532-1565.