M/Ph*SjdZddlmZddlZddlmZddlmZGddZ Gdd Z dS) aV Author: Austin Adams This class implements Oaxaca-Blinder Decomposition. It returns a OaxacaResults Class: OaxacaBlinder: Two-Fold (two_fold) Three-Fold (three_fold) OaxacaResults: Table Summary (summary) Oaxaca-Blinder is a statistical method that is used to explain the differences between two mean values. The idea is to show from two mean values what can be explained by the data and what cannot by using OLS regression frameworks. "The original use by Oaxaca's was to explain the wage differential between two different groups of workers, but the method has since been applied to numerous other topics." (Wikipedia) The model is designed to accept two endogenous response variables and two exogenous explanitory variables. They are then fit using the specific type of decomposition that you want. The method was famously used in Card and Krueger's paper "School Quality and Black-White Relative Earnings: A Direct Assessment" (1992) General reference for Oaxaca-Blinder: B. Jann "The Blinder-Oaxaca decomposition for linear regression models," The Stata Journal, 2008. Econometrics references for regression models: E. M. Kitagawa "Components of a Difference Between Two Rates" Journal of the American Statistical Association, 1955. A. S. Blinder "Wage Discrimination: Reduced Form and Structural Estimates," The Journal of Human Resources, 1973. )dedentN)OLS) add_constantcDeZdZdZ d dZddZdd Z dd ZdS) OaxacaBlinderae Class to perform Oaxaca-Blinder Decomposition. Parameters ---------- endog : array_like The endogenous variable or the dependent variable that you are trying to explain. exog : array_like The exogenous variable(s) or the independent variable(s) that you are using to explain the endogenous variable. bifurcate : {int, str} The column of the exogenous variable(s) on which to split. This would generally be the group that you wish to explain the two means for. Int of the column for a NumPy array or int/string for the name of the column in Pandas. hasconst : bool, optional Indicates whether the two exogenous variables include a user-supplied constant. If True, a constant is assumed. If False, a constant is added at the start. If nothing is supplied, then True is assumed. swap : bool, optional Imitates the STATA Oaxaca command by allowing users to choose to swap groups. Unlike STATA, this is assumed to be True instead of False cov_type : str, optional See regression.linear_model.RegressionResults for a description of the available covariance estimators cov_kwds : dict, optional See linear_model.RegressionResults.get_robustcov_results for a description required keywords for alternative covariance estimators Notes ----- Please check if your data includes at constant. This will still run, but will return incorrect values if set incorrectly. You can access the models by using their code as an attribute, e.g., _t_model for the total model, _f_model for the first model, _s_model for the second model. Examples -------- >>> import numpy as np >>> import statsmodels.api as sm >>> data = sm.datasets.ccards.load() '3' is the column of which we want to explain or which indicates the two groups. In this case, it is if you rent. >>> model = sm.OaxacaBlinder(df.endog, df.exog, 3, hasconst = False) >>> model.two_fold().summary() Oaxaca-Blinder Two-fold Effects Unexplained Effect: 27.94091 Explained Effect: 130.80954 Gap: 158.75044 >>> model.three_fold().summary() Oaxaca-Blinder Three-fold Effects Endowments Effect: 321.74824 Coefficient Effect: 75.45371 Interaction Effect: -238.45151 Gap: 158.75044 T nonrobustNcPtt|ddkrB|j|}t j|t j|}}d|_||_||_ ||_ t j ||d|_ ||_ ||_|dd|f}t j||f}t j|} ||_|t j|dd|f| dk} |t j|dd|f| dk} |t j|dddf| dk} |t j|dddf| dk} t j | |d} t j | |d} | dddf} | dddf} |dddf|_t)| t)| c|_|_| | z |_|rX|jdkrM| | } } | | } } | | z |_| d| dc| d<| d<| |_|durXt5| d} t5| d} t5|j d|_ t5|j d|_ t j| d|_t j| d|_t;| | |||_t;| | |||_ dS) NpandasaxisrFprependcov_typecov_kwds)!strtypefindcolumnsget_locnparray two_fold_type bifurcaterrdeleteneumarkexoghasconst column_stackuniquebi_colwhereendoglenlen_flen_smeangapbir exog_f_mean exog_s_meanrfit_f_model_s_model)selfr%rrr swaprrr#r+exog_fexog_sendog_fendog_ss X/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/stats/oaxaca.py__init__zOaxacaBlinder.__init__ws] tDzz??   ) )R / / ,,Y77I(5//28D>>4E!"    yyq999    aaal#00 Yv   bhtAAAyL1RU:;;<bhtAAAyL1RU:;;<qqq!t1!5667qqq!t1!5667691555691555!!!Q$-!!!Q$-111a4[ !$Ws7|| DJ<<>>GLLNN2  (DHqLL&WG#VFF||~~ 6DHa5"Q%LBqE2a5 u  !&%888F!&%888F$TY>>>DI' eDDDDL7622276222GV,,001   GV,,001   Gz?c~ |j|j}|j|j}|j|jd|jz g}|j}|j}g}g}g} g} g} t d|D]F} t j|j|j f} |j }t| }t j d||}| |} ||}t j||d}|t j|dd|f|dk}|t j|dd|f|dk}| t j| dddf|dk}| t j| dddf|dk}t j||d}t j||d}|dddf}|dddf}| dddf} |j}|jdurDt%|d}t%|d}t%|d}t%|d}t'|||j|j}t'|||j|j}t j|d}t j|d}|d kru||z |jz}||j|jz z}||z |j|jz z}||||| ||d krZt|}t|}|d kr"|||zz |jz|||zz |jzz} n|d krd |j|jzz} n|dkr"|d|jz|d|jzz} n|dkr7t'| ||j|j}!|!j} nIt'| ||j|j}!t j|!j|} ||j| z z|| |jz zz}"||z | z}#| |"| |#Ht5||zt5|d|z z}%}$|d krt jt j||%|$t jt j||%|$t jt j| |%|$gS|d kr\t jt j| |%|$t jt j| |%|$gSdS)z{ A helper function to calculate the variance/std. Used to keep the decomposition functions cleaner Nr r)highsizer Frrcottonreimers?self_submittednuemark) submitted_nsubmitted_confsubmitted_weightr+rrangerr!r#r%rr&randomrandintrr$rr rrr.rrr)paramsappendintstdsort)&r1 decomp_typenconfrHr+rendow_eff_list coef_eff_list int_eff_list exp_eff_listunexp_eff_list_r%ramountsamplesrr3r4r5r6rr/r0r,r- endow_effcoef_effint_effr'r(t_params_t_model unexplained explainedr=lows& r7variancezOaxacaBlinder.variances   ' A   *&D  ,%D))  WN    q!U /U /AOT[$*$=>>E9DZZFi''V'DDG'NE=Diia888G"(49 #5A#>??@F"(49 #5A#>??@FBHU111a4[BqE%9::;GBHU111a4[BqE%9::;GYvyq999FYvyq999FaaadmGaaadmG!!!Q$KE .M}%%%fe<<<%fe<<<#D%888&w>>>7F++//0H7F++//0H'&q111K'&q111Ka(;6(/I &(/HO*KL&4Oho5%%i000$$X...##G,,,,!!F F  H,, % 7(/ I/(/A HH#i//"ho&GHHH"&666(+ho=*1-?@H #i//"5'2266!% 7  H (HH #5$//33!% 4  H "y)DDH*ho.HI8ho#=> );6(B %%k222##I...DMM3qAH~#6#6c !  rw~..s4x899rw}--c$h788rw|,,SX677  A  rw~..s4x899rw|,,SX677  r9Fc||_||_d|_d}|j|jz |jjz|_|j|jj|jjz z|_ |j|jz |jj|jjz z|_ |dur| d}t|j|j |j |j fd|S)a Calculates the three-fold Oaxaca Blinder Decompositions Parameters ---------- std: boolean, optional If true, bootstrapped standard errors will be calculated. n: int, optional A amount of iterations to calculate the bootstrapped standard errors. This defaults to 5000. conf: float, optional This is the confidence required for the standard error calculation. Defaults to .99, but could be anything less than or equal to one. One is heavy discouraged, due to the extreme outliers inflating the variance. Returns ------- OaxacaResults A results container for the three-fold decomposition. NTr?std_val)rFrGrHr,r-r0rLr\r/r]r^rd OaxacaResultsr*)r1rOrRrSrgs r7 three_foldzOaxacaBlinder.three_fold1s," $  t/ / M !( M 4=#7 7  (4+;; M 4=#7 7   $;;mmA&&G ^T]DL$( C     r9pooledc||_||_d}||_||_|dkrP|j|j|jzz |jjz|j|j|jzz |jjzz|_ n5|dkr#d|jj|jjzz|_ n |dkrI|td|d|z g}|d|jjz|d|jjzz|_ n|d krPt|j |j |j|j |_|jj|_ ngt|j |j|j|j |_t'j|jj|j|_ |j|jj|j z z|j|j |jjz zz|_|j|jz |j z|_|d ur|d }t7|j|j|jfd | S)a Calculates the two-fold or pooled Oaxaca Blinder Decompositions Methods ------- std: boolean, optional If true, bootstrapped standard errors will be calculated. two_fold_type: string, optional This method allows for the specific calculation of the non-discriminatory model. There are four different types available at this time. pooled, cotton, reimers, self_submitted. Pooled is assumed and if a non-viable parameter is given, pooled will be ran. pooled - This type assumes that the pooled model's parameters (a normal regression) is the non-discriminatory model. This includes the indicator variable. This is generally the best idea. If you have economic justification for using others, then use others. nuemark - This is similar to the pooled type, but the regression is not done including the indicator variable. cotton - This type uses the adjusted in Cotton (1988), which accounts for the undervaluation of one group causing the overevalution of another. It uses the sample size weights for a linear combination of the two model parameters reimers - This type uses a linear combination of the two models with both parameters being 50% of the non-discriminatory model. self_submitted - This allows the user to submit their own weights. Please be sure to put the weight of the larger mean group only. This should be submitted in the submitted_weights variable. submitted_weight: int/float, required only for self_submitted, This is the submitted weight for the larger mean. If the weight for the larger mean is p, then the weight for the other mean is 1-p. Only submit the first value. n: int, optional A amount of iterations to calculate the bootstrapped standard errors. This defaults to 5000. conf: float, optional This is the confidence required for the standard error calculation. Defaults to .99, but could be anything less than or equal to one. One is heavy discouraged, due to the extreme outliers inflating the variance. Returns ------- OaxacaResults A results container for the two-fold decomposition. NrArBrCrDzPlease submit weightsr rrErTr@rf)rFrGrrHr'r(r/rLr0r_ ValueErrorrr%rr.rrr`rrrrr,r-rarbrdrhr*)r1rOrrHrRrSrgs r7two_foldzOaxacaBlinder.two_fold^sFB"* 0 H $ $ dj4:569MMtzDJ67$-:NNPDMMi ' '4=#7$-:N#NODMM . . .' !8999 0!6F2FG  #dm&::"1% (<<= MM i ' ' DL99==>DM!M0DMM  DI66::;DMIdm&:DNKKDM   4t} D E  1E!E FH*T-==N $;;mmA&&G  t~tx 8!W    r9)TTrN)r:r;)FNN)FrjNNN)__name__ __module__ __qualname____doc__r8rdrirmr9r7rr7s==H ? ? ? ? Bwwwwr+ + + + ^    n n n n n n r9rc eZdZdZddZdZdS)rha, This class summarizes the fit of the OaxacaBlinder model. Use .summary() to get a table of the fitted values or use .params to receive a list of the values use .std to receive a list of the standard errors If a two-fold model was fitted, this will return unexplained effect, explained effect, and the mean gap. The list will always be of the following order and type. If standard error was asked for, then standard error calculations will also be included for each variable after each calculated effect. unexplained : float This is the effect that cannot be explained by the data at hand. This does not mean it cannot be explained with more. explained : float This is the effect that can be explained using the data. gap : float This is the gap in the mean differences of the two groups. If a three-fold model was fitted, this will return characteristic effect, coefficient effect interaction effect, and the mean gap. The list will be of the following order and type. If standard error was asked for, then standard error calculations will also be included for each variable after each calculated effect. endowment effect : float This is the effect due to the group differences in predictors coefficient effect : float This is the effect due to differences of the coefficients of the two groups interaction effect : float This is the effect due to differences in both effects existing at the same time between the two groups. gap : float This is the gap in the mean differences of the two groups. Attributes ---------- params A list of all values for the fitted models. std A list of standard error calculations. Nc0||_||_||_dSN)rLrO model_type)r1resultsrvrgs r7r8zOaxacaResults.__init__s $r9cx|jdkr|jJttd|jddd|jddd|jddnjttd |jd|jd|jd|jd|jd|jd kr|jZttd |jddd |jddd |jddd|jd ddSttd |jddd|jddd |jddd|jddd |jddd|jddd|jd ddSdS)zG Print a summary table with the Oaxaca-Blinder effects r@NzT Oaxaca-Blinder Two-fold Effects Unexplained Effect: rz.5fz# Explained Effect: r z Gap: a Oaxaca-Blinder Two-fold Effects Unexplained Effect: {:.5f} Unexplained Standard Error: {:.5f} Explained Effect: {:.5f} Explained Standard Error: {:.5f} Gap: {:.5f}r?zT Oaxaca-Blinder Three-fold Effects Endowment Effect: z% Coefficient Effect: z% Interaction Effect: z+ Endowment Standard Error: z- Coefficient Standard Error: z- Interaction Standard Error: )rvrOprintrrLformat)r1s r7summaryzOaxacaResults.summarys ?a  x-%)[^---$(;q>---k!n --- &v KN HQK KN HQK KN   " ?a  x-#';q>---&*[^---&*[^ --- k!n ---     -#';q>---,08A;---&*[^ --- .2Xa[ --- &*[^ ---.2Xa[---k!n---        r9ru)rnrorprqr8r{rrr9r7rhrhsB//b%%%% :::::r9rh) rqtextwraprnumpyr#statsmodels.regression.linear_modelrstatsmodels.tools.toolsrrrhrrr9r7rs**V333333000000U U U U U U U U p qqqqqqqqqqr9