get_ivf_data

get_ivf_data(N=2000, seed=1234)

Synthetic data for the motherhood penalty IV application (IVF instrument).

DGP

Unobserved confounder: career_ambition ~ N(0, 1)

First stage (num_children on ivf_success): num_children = 1.2 - 0.4career_ambition + 0.8ivf_success + N(0, 0.5) → ivf_success is relevant (first-stage coefficient ≈ 0.8, F >> 10)

Outcome (structural equation): earnings = 10 + 0.6career_ambition + TRUE_EFFECTnum_children + N(0, 1) TRUE_EFFECT = -0.15

OVB formula for naive OLS (earnings ~ num_children): bias ≈ gamma_ambition * Cov(num_children, ambition) / Var(num_children) ≈ 0.6 * (-0.4) / 0.57 ≈ -0.42 beta_OLS ≈ -0.15 + (-0.42) ≈ -0.57 (overstates the penalty) beta_IV ≈ -0.15 (recovers the true effect)

Parameters

Name	Type	Description	Default
N	int	Number of observations. Default is 2000.	`2000`
seed	int	Random seed. Default is 1234.	`1234`

Returns

Name	Type	Description
	pandas.DataFrame	Columns: `earnings`, `num_children`, `ivf_success`.

Examples

import pyfixest as pf

data = pf.get_ivf_data()

# OLS is biased by unobserved career ambition, IV recovers the true -0.15
pf.etable(
    [
        pf.feols("earnings ~ num_children", data),
        pf.feols("earnings ~ 1 | num_children ~ ivf_success", data),
    ]
)

	earnings
	(1)	(2)
coef
num_children	-0.631*** (0.034)	-0.222*** (0.066)
Intercept	10.764*** (0.059)	10.123*** (0.107)
stats
Observations	2,000	2,000
R²	0.145	-
Significance levels: * p < 0.05, p < 0.01, * p < 0.001. Format of coefficient cell: Coefficient (Std. Error)