get_worker_panel

get_worker_panel(N_workers=500, N_firms=50, N_years=11, seed=42)

Generate a worker-firm panel dataset with two-way fixed effects.

Inspired by Abowd, Kramarz & Margolis (1999). Workers switch firms with ~20 % probability each year. Both worker and firm fixed effects contribute to wages.

Parameters

Name	Type	Description	Default
N_workers	int	Number of workers.	`500`
N_firms	int	Number of firms.	`50`
N_years	int	Number of years in the panel (starting from 2000).	`11`
seed	int	Random seed for reproducibility.	`42`

Returns

Name	Type	Description
	pd.DataFrame	Columns: worker_id, firm_id, year, female, experience, tenure, log_wage, worker_fe, firm_fe.

Examples

import pyfixest as pf

data = pf.get_worker_panel()

# two-way worker and firm fixed effects, as in AKM
fit = pf.feols("log_wage ~ experience + tenure | worker_id + firm_id", data)
fit.summary()

###

Estimation:  OLS
Dep. var.: log_wage, Fixed effects: worker_id + firm_id
sample: None = all
Inference:  iid
Observations:  5500

| Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5% |   97.5% |
|:--------------|-----------:|-------------:|----------:|-----------:|-------:|--------:|
| experience    |      0.021 |        0.001 |    22.547 |      0.000 |  0.019 |   0.023 |
| tenure        |      0.011 |        0.002 |     6.830 |      0.000 |  0.008 |   0.014 |
---
RMSE: 0.192 R2: 0.908 R2 Within: 0.159

The true effects are returned as the columns worker_fe and firm_fe, so estimates can be compared against them.