def get_table3(df):
### regressions:
rslt = smf.ols(formula="stdgrade ~ treat + pol1+ pol1t",
data=df,
weights=df["kwgt"]).fit(
cov_type='cluster',
cov_kwds={'groups': df["studentid"]})
rslt1 = rslt
formula2 = "stdgrade ~ treat + treatmentvol + treatmentfor + volcourse + forcourse + pol1 + pol1t + pol1vol + pol1tvol + pol1for + pol1tfor"
rslt = smf.ols(formula=formula2, data=df, weights=df["kwgt"]).fit(
cov_type='cluster', cov_kwds={'groups': df["studentid"]})
rslt2 = rslt
### Table stargazer:
stargazer = Stargazer([rslt1, rslt2])
stargazer.custom_columns(["column 1", "column 4"], [1, 1])
stargazer.title("Table 3 - Effects on standardized grades")
stargazer.show_model_numbers(False)
stargazer.significant_digits(2)
stargazer.covariate_order(["treat", "treatmentvol", "treatmentfor"])
stargazer.rename_covariates({
"treat":
"1st-year GPA is below 7",
"treatmentvol":
"Attendance is voluntary x treatment",
"treatmentfor":
"Absence is penalized x treatment"
})
stargazer.show_degrees_of_freedom(False)
stargazer.add_line('Fixed Effects', ['No', 'No'])
return stargazer
def get_table_4and7(dependent_var, data):
'''
argument:dependent variable, dataset
return:either table4 or table7 depending on the input dataset
'''
model_1 = sm_api.OLS(data[dependent_var],
sm_api.add_constant(data["state"])).fit()
model_2 = sm_api.OLS(
data[dependent_var],
sm_api.add_constant(data[["state", "bk", "kfc", "roys",
"co_owned"]])).fit()
model_3 = sm_api.OLS(data[dependent_var],
sm_api.add_constant(data["GAP"])).fit()
model_4 = sm_api.OLS(
data[dependent_var],
sm_api.add_constant(data[["GAP", "bk", "kfc", "roys",
"co_owned"]])).fit()
model_5 = sm_api.OLS(
data[dependent_var],
sm_api.add_constant(data[[
"GAP", "bk", "kfc", "roys", "co_owned", "southj", "centralj",
"pa1", "pa2"
]])).fit()
Table = Stargazer([model_1, model_2, model_3, model_4, model_5])
Table.rename_covariates({
'state': 'New Jersey dummy',
'GAP': 'Initial wage GAP'
})
Table.add_line('Controls for chain and ownership',
['No', 'Yes', 'No', 'Yes', 'Yes'])
Table.add_line('Controls for region', ['No', 'No', 'No', 'No', 'Yes'])
F2 = model_2.f_test(
'(state = 0), (bk = 0), (kfc = 0), (roys =0),(co_owned= 0),(const=0)'
).pvalue.round(3)
F4 = model_4.f_test(
'(GAP = 0), (bk = 0), (kfc = 0), (roys =0),(co_owned= 0),(const=0)'
).pvalue.round(3)
F5 = model_5.f_test(
'(GAP = 0), (bk = 0), (kfc = 0), (roys =0),(co_owned= 0),(const=0), (southj=0),(centralj=0),(pa1=0),(pa2=0)'
).pvalue.round(3)
if dependent_var == "change_in_FTE":
Table.add_line('Probability value for controls',
['-', F2, '-', F4, F5])
Table.title("Models for " + dependent_var)
Table.covariate_order(['state', 'GAP'])
print("The mean and standard deviation of the dependent variable are",
data[dependent_var].mean(), "and", data[dependent_var].std(),
",respectively.")
return Table
reg_df = strategy_ret_df.copy()
reg_df = pd.merge(reg_df, ff_df, left_index=True, right_index=True)
strategy_name_list = list(strategy_ret_df.columns)
results_list = []
for name in strategy_name_list:
# to have the same name for all variables
reg_df_tmp = reg_df.rename({name: "ret"}, axis=1)
results_list.append(
smf.ols(formula="ret ~ MKT + SMB + HML + CMA + RMW",
data=reg_df_tmp * 12).fit())
# Outputting short regression results:
stargazer = Stargazer([results_list[0], results_list[3], results_list[6]])
stargazer.custom_columns(['D 30', 'prob 20', 'prob 40'], [1, 1, 1])
stargazer.covariate_order(['Intercept', 'MKT', 'SMB', 'HML', 'RMW', 'CMA'])
stargazer.show_degrees_of_freedom(False)
f = open(
"/Users/rsigalov/Dropbox/2019_Revision/Writing/Predictive Regressions/tables/disaster_sort_reg_on_ff.tex",
"w")
f.write(stargazer.render_latex())
f.close()
# Doing extended regression table where I do regressions of strategy return on
# (1) just the market, (2) FF 3 factors and (3) FF 5 factors.
results_list = []
for name in ["D_30", "p_20_30"]:
# to have the same name for all variables
reg_df_tmp = reg_df.rename({name: "ret"}, axis=1)
results_list.append(
smf.ols(formula="ret ~ MKT", data=reg_df_tmp * 12).fit())
def df_table12(df, name):
df_table12 = df[[
f'{name}_C2', f'{name}_instrument_C2_thresh', f'{name}_I', 'trust',
'democ', 'lnpopulation', 'lnArea', 'lnGDP_pc', 'protestants',
'muslims', 'catholics', 'latitude', 'LOEnglish', 'LOGerman',
'LOSocialist', 'LOScandin', 'mtnall'
]].dropna(axis=0)
df_demo = df_table12[df_table12.democ > 1]
dep1 = df_table12['trust']
dep2 = df_demo['trust']
exo1 = sm.add_constant(df_table12[f'{name}_C2'])
exo2 = sm.add_constant(df_table12[[
f'{name}_C2', f'{name}_I', 'lnpopulation', 'lnArea', 'lnGDP_pc',
'protestants', 'muslims', 'catholics', 'latitude', 'LOEnglish',
'LOGerman', 'LOSocialist', 'LOScandin', 'democ', 'mtnall'
]])
exo3 = sm.add_constant(df_demo[[
f'{name}_C2', f'{name}_I', 'lnpopulation', 'lnArea', 'lnGDP_pc',
'protestants', 'muslims', 'catholics', 'latitude', 'LOEnglish',
'LOGerman', 'LOSocialist', 'LOScandin', 'democ', 'mtnall'
]])
ins1 = sm.add_constant(df_table12[f'{name}_instrument_C2_thresh'])
ins2 = sm.add_constant(df_table12[[
f'{name}_instrument_C2_thresh', f'{name}_I', 'lnpopulation', 'lnArea',
'lnGDP_pc', 'protestants', 'muslims', 'catholics', 'latitude',
'LOEnglish', 'LOGerman', 'LOSocialist', 'LOScandin', 'democ', 'mtnall'
]])
ins3 = sm.add_constant(df_demo[[
f'{name}_instrument_C2_thresh', f'{name}_I', 'lnpopulation', 'lnArea',
'lnGDP_pc', 'protestants', 'muslims', 'catholics', 'latitude',
'LOEnglish', 'LOGerman', 'LOSocialist', 'LOScandin', 'democ', 'mtnall'
]])
reg1 = sm.OLS(dep1, exo1).fit(cov_type='HC1')
reg2 = sm.OLS(dep1, exo2).fit(cov_type='HC1')
reg3 = sm.OLS(dep2, exo3).fit(cov_type='HC1')
reg4 = IV2SLS(dep1, exo1, ins1).fit()
reg5 = IV2SLS(dep1, exo2, ins2).fit()
reg6 = IV2SLS(dep2, exo3, ins3).fit()
stargazer = Stargazer([reg1, reg2, reg3, reg4, reg5, reg6])
stargazer.covariate_order([f'{name}_C2', f'{name}_I'])
stargazer.rename_covariates({
f'{name}_C2':
'Segregation $\hat{S}$ ('
f'{name}'
')',
f'{name}_I':
'Fractionalization $F$ ('
f'{name}'
')'
})
stargazer.custom_columns(['OLS', 'OLS', 'OLS', '2SLS', '2SLS', '2SLS'],
[1, 1, 1, 1, 1, 1])
stargazer.add_line('Controls', ['No', 'Yes', 'Yes', 'No', 'Yes', 'Yes'])
stargazer.add_line('Sample',
['Full', 'Full', 'Democ', 'Full', 'Full', 'Democ'])
if name == 'ethnicity':
stargazer.title('Panel A. Ethnicity')
return stargazer
else:
stargazer.title('Panel B. Language')
return stargazer
def table10_11(df, name, democ):
full_x = [
f'{name}_I', f'{name}_C2', 'lnpopulation', 'lnGDP_pc', 'protestants',
'muslims', 'catholics', 'latitude', 'LOEnglish', 'LOGerman',
'LOSocialist', 'LOScandin', 'democ', 'mtnall'
]
ins = [
f'{name}_I', f'{name}_instrument_C2_thresh', 'lnpopulation',
'lnGDP_pc', 'protestants', 'muslims', 'catholics', 'latitude',
'LOEnglish', 'LOGerman', 'LOSocialist', 'LOScandin', 'democ', 'mtnall'
]
df_10_11_1 = df[[
f'{name}_C2', f'{name}_I', f'{name}_instrument_C2_thresh',
'lnpopulation', 'lnGDP_pc', 'protestants', 'muslims', 'catholics',
'latitude', 'LOEnglish', 'LOGerman', 'LOSocialist', 'LOScandin',
'democ', 'mtnall', 'icrg_qog'
]].dropna(axis=0)
df_10_11_2 = df[[
f'{name}_C2', f'{name}_I', f'{name}_instrument_C2_thresh',
'lnpopulation', 'lnGDP_pc', 'protestants', 'muslims', 'catholics',
'latitude', 'LOEnglish', 'LOGerman', 'LOSocialist', 'LOScandin',
'democ', 'mtnall', 'ef_regul', 'ef_corruption', 'ef_property_rights'
]].dropna(axis=0)
df_10_11_3 = df[[
f'{name}_C2', f'{name}_I', f'{name}_instrument_C2_thresh',
'lnpopulation', 'lnGDP_pc', 'protestants', 'muslims', 'catholics',
'latitude', 'LOEnglish', 'LOGerman', 'LOSocialist', 'LOScandin',
'democ', 'mtnall', 'taxevas'
]].dropna(axis=0)
if democ == 'democracy':
df_10_11_1 = df_10_11_1[df_10_11_1.democ >= 1]
df_10_11_2 = df_10_11_2[df_10_11_2.democ >= 1]
df_10_11_3 = df_10_11_3[df_10_11_3.democ >= 1]
x1 = sm.add_constant(df_10_11_1[full_x])
x2 = sm.add_constant(df_10_11_2[full_x])
x3 = sm.add_constant(df_10_11_3[full_x])
ins1 = sm.add_constant(df_10_11_1[ins])
ins2 = sm.add_constant(df_10_11_2[ins])
ins3 = sm.add_constant(df_10_11_3[ins])
else:
x1 = sm.add_constant(df_10_11_1[[f'{name}_I', f'{name}_C2']])
x2 = sm.add_constant(df_10_11_2[[f'{name}_I', f'{name}_C2']])
x3 = sm.add_constant(df_10_11_3[[f'{name}_I', f'{name}_C2']])
ins1 = sm.add_constant(
df_10_11_1[[f'{name}_I', f'{name}_instrument_C2_thresh']])
ins2 = sm.add_constant(
df_10_11_2[[f'{name}_I', f'{name}_instrument_C2_thresh']])
ins3 = sm.add_constant(
df_10_11_3[[f'{name}_I', f'{name}_instrument_C2_thresh']])
y1 = df_10_11_1['icrg_qog']
y2 = df_10_11_2['ef_corruption']
y3 = df_10_11_2['ef_property_rights']
y4 = df_10_11_2['ef_regul']
y5 = df_10_11_3['taxevas']
est1 = sm.OLS(y1, x1).fit(cov_type='HC1')
est2 = IV2SLS(y1, x1, ins1).fit()
est3 = sm.OLS(y2, x2).fit(cov_type='HC1')
est4 = IV2SLS(y2, x2, ins2).fit()
est5 = sm.OLS(y3, x2).fit(cov_type='HC1')
est6 = IV2SLS(y3, x2, ins2).fit()
est7 = sm.OLS(y4, x2).fit(cov_type='HC1')
est8 = IV2SLS(y4, x2, ins2).fit()
est9 = sm.OLS(y5, x3).fit(cov_type='HC1')
est10 = IV2SLS(y5, x3, ins3).fit()
stargazer = Stargazer(
[est1, est2, est3, est4, est5, est6, est7, est8, est9, est10])
stargazer.custom_columns([
'ICRG quality of gov', 'EF Corruption', 'EF Property rights',
'EF Regulation', 'Tax eva'
], [2, 2, 2, 2, 2])
stargazer.show_model_numbers(False)
stargazer.covariate_order([f'{name}_C2', f'{name}_I'])
stargazer.rename_covariates({
f'{name}_C2':
'Segregation $\hat{S}$ ('
f'{name}'
')',
f'{name}_I':
'Fractionalization $F$ ('
f'{name}'
')'
})
stargazer.add_line('Method', [
'OLS', '2SLS', 'OLS', '2SLS', 'OLS', '2SLS', 'OLS', '2SLS', 'OLS',
'2SLS'
])
if democ == 'democracy':
stargazer.title('Panel B. Democracies sample, all controls')
return stargazer
else:
stargazer.title('Panel A. Full sample, no additional controls')
return stargazer
def table6(df, alternative=True):
df_6E = df[[
'ethnicity_C2', 'ethnicity_I', 'ethnicity_C',
'ethnicity_instrument_C_thresh', 'ethnicity_instrument_C2_thresh',
'lnpopulation', 'lnGDP_pc', 'protestants', 'muslims', 'catholics',
'latitude', 'LOEnglish', 'LOGerman', 'LOSocialist', 'LOScandin',
'democ', 'mtnall', 'RulLaw', 'country'
]].dropna(axis=0)
df_6L = df[[
'language_C2', 'language_I', 'language_C',
'language_instrument_C_thresh', 'language_instrument_C2_thresh',
'lnpopulation', 'lnGDP_pc', 'protestants', 'muslims', 'catholics',
'latitude', 'LOEnglish', 'LOGerman', 'LOSocialist', 'LOScandin',
'democ', 'mtnall', 'RulLaw', 'country'
]].dropna(axis=0)
df_6R = df[[
'religion_C2', 'religion_I', 'religion_C',
'religion_instrument_C_thresh', 'religion_instrument_C2_thresh',
'lnpopulation', 'lnGDP_pc', 'protestants', 'muslims', 'catholics',
'latitude', 'LOEnglish', 'LOGerman', 'LOSocialist', 'LOScandin',
'democ', 'mtnall', 'RulLaw', 'country'
]].dropna(axis=0)
df_6E_demo = df_6E[df_6E.democ >= 1]
df_6L_demo = df_6L[df_6L.democ >= 1]
df_6R_demo = df_6R[df_6R.democ >= 1]
x1 = sm.add_constant(df_6E[[
'ethnicity_instrument_C2_thresh', 'ethnicity_I', 'lnpopulation',
'lnGDP_pc', 'protestants', 'muslims', 'catholics', 'latitude',
'LOEnglish', 'LOGerman', 'LOSocialist', 'LOScandin', 'democ', 'mtnall'
]])
x2 = sm.add_constant(df_6L[[
'language_instrument_C2_thresh', 'language_I', 'lnpopulation',
'lnGDP_pc', 'protestants', 'muslims', 'catholics', 'latitude',
'LOEnglish', 'LOGerman', 'LOSocialist', 'LOScandin', 'democ', 'mtnall'
]])
x3 = sm.add_constant(df_6R[[
'religion_instrument_C2_thresh', 'religion_I', 'lnpopulation',
'lnGDP_pc', 'protestants', 'muslims', 'catholics', 'latitude',
'LOEnglish', 'LOGerman', 'LOSocialist', 'democ', 'mtnall'
]])
x4 = sm.add_constant(df_6E_demo[[
'ethnicity_instrument_C2_thresh', 'ethnicity_I', 'lnpopulation',
'lnGDP_pc', 'protestants', 'muslims', 'catholics', 'latitude',
'LOEnglish', 'LOGerman', 'LOSocialist', 'LOScandin', 'democ', 'mtnall'
]])
x5 = sm.add_constant(df_6L_demo[[
'language_instrument_C2_thresh', 'language_I', 'lnpopulation',
'lnGDP_pc', 'protestants', 'muslims', 'catholics', 'latitude',
'LOEnglish', 'LOGerman', 'LOSocialist', 'LOScandin', 'democ', 'mtnall'
]])
x6 = sm.add_constant(df_6R_demo[[
'religion_instrument_C2_thresh', 'religion_I', 'lnpopulation',
'lnGDP_pc', 'protestants', 'muslims', 'catholics', 'latitude',
'LOEnglish', 'LOGerman', 'LOSocialist', 'democ', 'mtnall'
]])
y1 = df_6E['ethnicity_C2']
y2 = df_6L['language_C2']
y3 = df_6R['religion_C2']
y4 = df_6E_demo['ethnicity_C2']
y5 = df_6L_demo['language_C2']
y6 = df_6R_demo['religion_C2']
est1 = sm.OLS(y1, x1).fit(cov_type='HC1')
est2 = sm.OLS(y2, x2).fit(cov_type='HC1')
est3 = sm.OLS(y3, x3).fit(cov_type='HC1')
est4 = sm.OLS(y4, x4).fit(cov_type='HC1')
est5 = sm.OLS(y5, x5).fit(cov_type='HC1')
est6 = sm.OLS(y6, x6).fit(cov_type='HC1')
x1a = sm.add_constant(df_6E[[
'ethnicity_instrument_C_thresh', 'ethnicity_I', 'lnpopulation',
'lnGDP_pc', 'protestants', 'muslims', 'catholics', 'latitude',
'LOEnglish', 'LOGerman', 'LOSocialist', 'LOScandin', 'democ', 'mtnall'
]])
x2a = sm.add_constant(df_6L[[
'language_instrument_C_thresh', 'language_I', 'lnpopulation',
'lnGDP_pc', 'protestants', 'muslims', 'catholics', 'latitude',
'LOEnglish', 'LOGerman', 'LOSocialist', 'LOScandin', 'democ', 'mtnall'
]])
x3a = sm.add_constant(df_6R[[
'religion_instrument_C_thresh', 'religion_I', 'lnpopulation',
'lnGDP_pc', 'protestants', 'muslims', 'catholics', 'latitude',
'LOEnglish', 'LOGerman', 'LOSocialist', 'democ', 'mtnall'
]])
x4a = sm.add_constant(df_6E_demo[[
'ethnicity_instrument_C_thresh', 'ethnicity_I', 'lnpopulation',
'lnGDP_pc', 'protestants', 'muslims', 'catholics', 'latitude',
'LOEnglish', 'LOGerman', 'LOSocialist', 'LOScandin', 'democ', 'mtnall'
]])
x5a = sm.add_constant(df_6L_demo[[
'language_instrument_C_thresh', 'language_I', 'lnpopulation',
'lnGDP_pc', 'protestants', 'muslims', 'catholics', 'latitude',
'LOEnglish', 'LOGerman', 'LOSocialist', 'LOScandin', 'democ', 'mtnall'
]])
x6a = sm.add_constant(df_6R_demo[[
'religion_instrument_C_thresh', 'religion_I', 'lnpopulation',
'lnGDP_pc', 'protestants', 'muslims', 'catholics', 'latitude',
'LOEnglish', 'LOGerman', 'LOSocialist', 'democ', 'mtnall'
]])
y1a = df_6E['ethnicity_C']
y2a = df_6L['language_C']
y3a = df_6R['religion_C']
y4a = df_6E_demo['ethnicity_C']
y5a = df_6L_demo['language_C']
y6a = df_6R_demo['religion_C']
est1a = sm.OLS(y1a, x1a).fit(cov_type='HC1')
est2a = sm.OLS(y2a, x2a).fit(cov_type='HC1')
est3a = sm.OLS(y3a, x3a).fit(cov_type='HC1')
est4a = sm.OLS(y4a, x4a).fit(cov_type='HC1')
est5a = sm.OLS(y5a, x5a).fit(cov_type='HC1')
est6a = sm.OLS(y6a, x6a).fit(cov_type='HC1')
df_6Lb = df_6L.set_index('country')
df_6Lb_demo = df_6L_demo.set_index('country')
x2b = sm.add_constant(df_6Lb[[
'language_instrument_C_thresh', 'language_I', 'lnpopulation',
'lnGDP_pc', 'protestants', 'muslims', 'catholics', 'latitude',
'LOEnglish', 'LOGerman', 'LOSocialist', 'LOScandin', 'democ', 'mtnall'
]].drop(index='usa'))
x5b = sm.add_constant(df_6Lb_demo[[
'language_instrument_C_thresh', 'language_I', 'lnpopulation',
'lnGDP_pc', 'protestants', 'muslims', 'catholics', 'latitude',
'LOEnglish', 'LOGerman', 'LOSocialist', 'LOScandin', 'democ', 'mtnall'
]].drop(index='usa'))
y2b = df_6Lb['language_C'].drop(index='usa')
y5b = df_6Lb_demo['language_C'].drop(index='usa')
est2b = sm.OLS(y2b, x2b).fit(cov_type='HC1')
est5b = sm.OLS(y5b, x5b).fit(cov_type='HC1')
stargazer = Stargazer([est1, est2, est3, est4, est5, est6])
stargazer_a = Stargazer([est1a, est2a, est3a, est4a, est5a, est6a])
stargazer_b = Stargazer([est2b, est5b])
stargazer.covariate_order([
'ethnicity_instrument_C2_thresh', 'ethnicity_I',
'language_instrument_C2_thresh', 'language_I',
'religion_instrument_C2_thresh', 'religion_I'
])
stargazer.rename_covariates({
'ethnicity_instrument_C2_thresh': 'Instrument E',
'ethnicity_I': '$F$ (ethnicity)',
'language_instrument_C2_thresh': 'Instrument L',
'language_I': '$F$ (language)',
'religion_instrument_C2_thresh': 'Instrument R',
'religion_I': '$F$ (religion)'
})
stargazer.custom_columns([
'E$\hat{S}$', 'L$\hat{S}$', 'R$\hat{S}$', 'E$\hat{S}$', 'L$\hat{S}$',
'R$\hat{S}$'
], [1, 1, 1, 1, 1, 1])
stargazer.show_model_numbers(False)
stargazer.add_line(
'Sample',
['Full', 'Full', 'Full', 'Democracy', 'Democracy', 'Democracy'])
stargazer.title('Panel A. Segregation index $\hat{S}$')
stargazer_a.covariate_order([
'ethnicity_instrument_C_thresh', 'ethnicity_I',
'language_instrument_C_thresh', 'language_I',
'religion_instrument_C_thresh', 'religion_I'
])
stargazer_a.rename_covariates({
'ethnicity_instrument_C_thresh': 'Instrument E',
'ethnicity_I': '$F$ (ethnicity)',
'language_instrument_C_thresh': 'Instrument L',
'language_I': '$F$ (language)',
'religion_instrument_C_thresh': 'Instrument R',
'religion_I': '$F$ (religion)'
})
stargazer_a.custom_columns([
'E$\\tilde{S}$', 'L$\\tilde{S}$', 'R$\\tilde{S}$', 'E$\\tilde{S}$',
'L$\\tilde{S}$', 'R$\\tilde{S}$'
], [1, 1, 1, 1, 1, 1])
stargazer_a.show_model_numbers(False)
stargazer_a.add_line(
'Sample',
['Full', 'Full', 'Full', 'Democracy', 'Democracy', 'Democracy'])
stargazer_a.title('Panel B. Segregation index $\\tilde{S}$')
stargazer_b.covariate_order(['language_instrument_C_thresh', 'language_I'])
stargazer_b.rename_covariates({
'language_instrument_C_thresh': 'Instrument L',
'language_I': '$F$ (language)'
})
stargazer_b.custom_columns(['L$\\tilde{S}$', 'L$\\tilde{S}$'], [1, 1])
stargazer_b.show_model_numbers(False)
stargazer_b.add_line('Sample', ['Full', 'Democracy'])
stargazer_b.title(
'Panel C. Segregation index $\\tilde{S}$ for language with sample excluding the US'
)
return [stargazer, stargazer_a, stargazer_b]
def table3_7(df, regression_type):
df_3_7E = df[[
'ethnicity_C2', 'ethnicity_instrument_C2_thresh', 'ethnicity_I',
'lnpopulation', 'lnGDP_pc', 'protestants', 'muslims', 'catholics',
'latitude', 'LOEnglish', 'LOGerman', 'LOSocialist', 'lnArea',
'LOScandin', 'democ', 'mtnall', 'RulLaw'
]].dropna(axis=0)
df_3_7L = df[[
'language_C2', 'language_instrument_C2_thresh', 'language_I',
'lnpopulation', 'lnGDP_pc', 'protestants', 'muslims', 'catholics',
'latitude', 'LOEnglish', 'LOGerman', 'LOSocialist', 'lnArea',
'LOScandin', 'democ', 'mtnall', 'RulLaw'
]].dropna(axis=0)
df_3_7R = df[[
'religion_C2', 'religion_instrument_C2_thresh', 'religion_I',
'lnpopulation', 'lnGDP_pc', 'protestants', 'muslims', 'catholics',
'latitude', 'LOEnglish', 'LOGerman', 'LOSocialist', 'lnArea',
'LOScandin', 'democ', 'mtnall', 'RulLaw'
]].dropna(axis=0)
exo = sm.add_constant(df_3_7E[[
'ethnicity_C2', 'ethnicity_I', 'lnpopulation', 'lnGDP_pc',
'protestants', 'muslims', 'catholics', 'latitude', 'LOEnglish',
'LOGerman', 'LOSocialist', 'LOScandin', 'lnArea', 'democ', 'mtnall'
]])
exo2 = sm.add_constant(df_3_7E[['ethnicity_C2', 'ethnicity_I']])
exo3 = sm.add_constant(df_3_7L[[
'language_C2', 'language_I', 'lnpopulation', 'lnGDP_pc', 'protestants',
'lnArea', 'muslims', 'catholics', 'latitude', 'LOEnglish', 'LOGerman',
'LOSocialist', 'LOScandin', 'democ', 'mtnall'
]])
exo4 = sm.add_constant(df_3_7L[['language_C2', 'language_I']])
exo5 = sm.add_constant(df_3_7R[[
'religion_C2', 'religion_I', 'lnpopulation', 'lnGDP_pc', 'protestants',
'muslims', 'catholics', 'latitude', 'LOEnglish', 'LOGerman',
'LOSocialist', 'lnArea', 'democ', 'mtnall'
]])
exo6 = sm.add_constant(df_3_7R[['religion_C2', 'religion_I']])
if regression_type == 'IV2SLS':
reg = IV2SLS(
df_3_7E['RulLaw'], exo,
sm.add_constant(df_3_7E[[
'ethnicity_instrument_C2_thresh', 'ethnicity_I',
'lnpopulation', 'lnGDP_pc', 'protestants', 'muslims',
'catholics', 'latitude', 'LOEnglish', 'LOGerman',
'LOSocialist', 'LOScandin', 'democ', 'mtnall', 'lnArea'
]])).fit()
reg2 = IV2SLS(
df_3_7E['RulLaw'], exo2,
sm.add_constant(
df_3_7E[['ethnicity_instrument_C2_thresh',
'ethnicity_I']])).fit()
reg3 = IV2SLS(
df_3_7L['RulLaw'], exo3,
sm.add_constant(df_3_7L[[
'language_instrument_C2_thresh', 'language_I', 'lnpopulation',
'lnGDP_pc', 'protestants', 'muslims', 'catholics', 'latitude',
'LOEnglish', 'LOGerman', 'LOSocialist', 'LOScandin', 'democ',
'mtnall', 'lnArea'
]])).fit()
reg4 = IV2SLS(
df_3_7L['RulLaw'], exo4,
sm.add_constant(
df_3_7L[['language_instrument_C2_thresh',
'language_I']])).fit()
reg5 = IV2SLS(
df_3_7R['RulLaw'], exo5,
sm.add_constant(df_3_7R[[
'religion_instrument_C2_thresh', 'religion_I', 'lnpopulation',
'lnGDP_pc', 'protestants', 'muslims', 'catholics', 'latitude',
'LOEnglish', 'LOGerman', 'LOSocialist', 'democ', 'mtnall',
'lnArea'
]])).fit()
reg6 = IV2SLS(
df_3_7R['RulLaw'], exo6,
sm.add_constant(
df_3_7R[['religion_instrument_C2_thresh',
'religion_I']])).fit()
elif regression_type == 'OLS':
reg2 = sm.OLS(df_3_7E['RulLaw'], exo2).fit(cov_type='HC1')
reg = sm.OLS(df_3_7E['RulLaw'], exo).fit(cov_type='HC1')
reg4 = sm.OLS(df_3_7L['RulLaw'], exo4).fit(cov_type='HC1')
reg3 = sm.OLS(df_3_7L['RulLaw'], exo3).fit(cov_type='HC1')
reg6 = sm.OLS(df_3_7R['RulLaw'], exo6).fit(cov_type='HC1')
reg5 = sm.OLS(df_3_7R['RulLaw'], exo5).fit(cov_type='HC1')
stargazer = Stargazer([reg2, reg, reg4, reg3, reg6, reg5])
stargazer.covariate_order([
'ethnicity_C2', 'ethnicity_I', 'language_C2', 'language_I',
'religion_C2', 'religion_I', 'lnpopulation', 'lnGDP_pc', 'lnArea',
'protestants', 'muslims', 'catholics', 'latitude', 'LOEnglish',
'LOGerman', 'LOSocialist', 'LOScandin', 'democ', 'mtnall', 'const'
])
stargazer.rename_covariates({
'ethnicity_C2': 'Segregation $\hat{S}$ (ethnicity)',
'ethnicity_I': 'Fractionalization $F$ (ethnicity)',
'language_C2': 'Segregation $\hat{S}$ (language)',
'language_I': 'Fractionalization $F$ (language)',
'religion_C2': 'Segregation $\hat{S}$ (religion)',
'religion_I': 'Fractionalization $F$ (religion)',
'lnpopulation': 'ln (population)',
'lnGDP_pc': 'ln (GDP per capita)',
'lnArea': 'ln (average size of region)',
'protestants': 'Pretestants share',
'muslims': 'Muslmis Share',
'catholics': 'Catholics share',
'latitude': 'Latitude',
'LOEnglish': 'English legal origin',
'LOGerman': 'German legal origin',
'LOSocialist': 'Socialist legal origin',
'LOScandin': 'Scandinavian legal origin',
'democ': 'Democratic tradition',
'mtnall': 'Mountains',
'const': 'Constant'
})
return HTML(stargazer.render_html())
model1 = sm.OLS(y, x).fit()
model2 = sm.OLS(y, x_withfemale).fit()
model1.summary()
model2.summary()
# =============================================================================
# STARGAZER MODEL OUTPUTS
# =============================================================================
from stargazer.stargazer import Stargazer
stargazer = Stargazer([model1, model2])
stargazer.custom_columns(['Base Model', 'Spesified Model'], [1, 1])
stargazer.significant_digits(2)
stargazer.covariate_order([
'const', 'propwomen', 'oppospower', 'gdpcap', 'sepowerdist', 'youthunemp'
])
stargazer.rename_covariates({
'const': 'Constant',
'oppospower': 'Opposition Power',
'gdpcap': 'GDP($)',
'sepowerdist': 'Class Political Power',
'youthunemp': 'Unemployed Youth %',
'propwomen': 'Female Property Rights'
})
stargazer.cov_spacing = 3
print(stargazer.render_latex())
def ols_regression_formatted(data,
specifications,
as_latex=False,
covariates_names=None,
covariates_order=None):
"""
Creates formatted tables for different dependent variables and specifications
Input:
data (df): Dataframe containing all necessary variables for OLS regression
specifications (dictionary): dependent variables as keys and list of specifications
as values
as_latex (bool): specify whether Output as table or Latex code
covariate_names (dict): dictionary with covariate names as in "data" as keys and new
covariate names as values
Output:
list_of_tables (list of stargazer tables): list of formatted tables
"""
# Create dictionary which connects dependent variables with formatted tables
dict_regression_tables = {}
# Generate regressions
for depvar in specifications.keys():
regression_list = []
specification_list = specifications[depvar]
list_all_covariates = []
for specification in specification_list:
estimation_equation = depvar + " ~ " + specification
regression = smf.ols(data=data, formula=estimation_equation).fit()
regression_list.append(regression)
# Create set of all variables for this dependent variable
list_all_covariates = list(
set(list_all_covariates +
regression.params.index.values.tolist()))
# Format table with stargazer
formatted_table = Stargazer(regression_list)
# No dimension of freedoms and blank dependent variable
formatted_table.show_degrees_of_freedom(False)
formatted_table.dependent_variable_name("")
# Optional: Change order of covariates
if covariates_order is not None:
covariates_order_depvar = list(
OrderedSet(covariates_order).intersection(list_all_covariates))
list_remaining_covariates = list(
OrderedSet(list_all_covariates).difference(
OrderedSet(covariates_order_depvar)))
covariates_sorted = list(
OrderedSet(covariates_order_depvar).union(
list_remaining_covariates))
covariates_sorted.remove("Intercept")
covariates_sorted = covariates_sorted + ["Intercept"]
formatted_table.covariate_order(covariates_sorted)
# Optional: Change name of covariates
if covariates_names is not None:
formatted_table.rename_covariates(covariates_names)
# Add table or latex code to dictionary
if as_latex is True:
dict_regression_tables[depvar] = formatted_table.render_latex()
# Delete tabular environment around it
dict_regression_tables[depvar] = dict_regression_tables[
depvar].replace("\\begin{table}[!htbp] \\centering\n", "")
dict_regression_tables[depvar] = dict_regression_tables[
depvar].replace("\\end{table}", "")
else:
dict_regression_tables[depvar] = formatted_table
return dict_regression_tables
