Inverse Optimal Tax Class Object

Inverse Optimal Tax Class Object#

inverse_optimal_tax.py Class

iot.inverse_optimal_tax#

class iot.inverse_optimal_tax.IOT(data, income_measure='e00200', weight_var='s006', eti=0.25, dist_type='log_normal', kde_bw=None, mtr_smoother='kreg', mtr_smooth_param=1000, kreg_bw=[120000])[source]#

Computes social welfare weights across the income distribution given data, tax policy parameters, and behavioral parameters.

Parameters:

  • data (Pandas DataFrame) – micro data representing tax payers. Must include the following columns: income_measure, weight_var, mtr

  • income_measure (str) – name of income measure from data to use

  • weight_var (str) – name of weight measure from data to use

  • eti (scalar or dict) – compensated elasticity of taxable income w.r.t. the marginal tax rate. If a dict, must have keys knot_points and eti_values with equal-length lists.

  • dist_type (None or str) – type of distribution to use if parametric; if None, then non-parametric bin weights

  • kde_bw (scalar or None) – bandwidth for KDE estimation

  • mtr_smoother (None or str) – method used to smooth the mtr function; if None, then use bin average mtrs

  • mtr_smooth_param (scalar) – parameter for mtr_smoother

  • kreg_bw (array_like) – bandwidth for kernel regression

compute_income_dist(data, income_measure, weight_var, dist_type, kde_bw=None)[source]#

Compute the distribution of income (parametrically or not) from the raw data.

This method computes the probability density function and its derivative.

Parameters:

  • data (Pandas DataFrame) – micro data representing tax payers. Must include the following columns: income_measure, weight_var, mtr

  • income_measure (str) – name of income measure from data to use

  • weight_var (str) – name of weight measure from data to use

  • dist_type (None or str) – type of distribution to use if parametric, if None, then non-parametric bin weights

  • kde_bw (array_like) – bandwidth for kernel regression

Returns:

  • z (array_like): income grid points

  • F (array_like): cumulative distribution function at each z

  • f (array_like): density at each z

  • f_prime (array_like): slope of the density function at each z

Return type:

tuple

compute_mtr_dist(data, weight_var, income_measure, mtr_smoother, mtr_smooth_param, kreg_bw)[source]#

Compute marginal tax rates over the income distribution and their derivative.

Parameters:

  • data (Pandas DataFrame) – micro data representing tax payers. Must include the following columns: income_measure, weight_var, mtr

  • weight_var (str) – name of weight measure from data to use

  • income_measure (str) – name of income measure from data to use

  • mtr_smoother (None or str) – method used to smooth the mtr function; if None, then use bin average mtrs

  • mtr_smooth_param (scalar) – parameter for mtr_smoother

  • kreg_bw (array_like) – bandwidth for kernel regression

Returns:

  • mtr (array_like): mean marginal tax rate for each income bin

  • mtr_prime (array_like): rate of change in marginal tax rates

    for each income bin

Return type:

tuple

df()[source]#

Return all vector attributes in a DataFrame format.

Returns:

DataFrame with all inputs/outputs

for each income bin

Return type:

df (Pandas DataFrame)

sw_weights()[source]#

Return the social welfare weights for a given tax policy.

See Jacobs, Jongen, and Zoutman (2017) and Lockwood and Weinzierl (2016) for details.

\[g_{z} = 1 + \theta_z \varepsilon^{c}\frac{T'(z)}{1-T'(z)} + \varepsilon^{c}\frac{zT''(z)}{(1-T'(z))^{2}}\]
Returns:

  • g_z (array_like): social welfare weights via analytical

    formula

  • g_z_numerical (array_like): social welfare weights via

    the Lockwood and Weinzierl numerical formula

Return type:

tuple

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