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 Appendix_Table_3(df):
df_short_g = pd.DataFrame({
"beliefadjustment_normalized":
df[df["dummynews_goodbad"] == 0][df[df["dummynews_goodbad"] ==
0]['treatgroup'] ==
4]['beliefadjustment_normalized'],
"beliefadjustment_bayes_norm":
df[df["dummynews_goodbad"] == 0][df[df["dummynews_goodbad"] == 0]
['treatgroup'] == 4]
['beliefadjustment_bayes_norm']
})
model_ols = smf.ols(
formula="beliefadjustment_normalized ~ beliefadjustment_bayes_norm",
data=df_short_g)
reg_s_1 = model_ols.fit(cov_type='HC1')
df_short_b = pd.DataFrame({
"beliefadjustment_normalized":
df[df["dummynews_goodbad"] == 1][df[df["dummynews_goodbad"] ==
1]['treatgroup'] ==
4]['beliefadjustment_normalized'],
"beliefadjustment_bayes_norm":
df[df["dummynews_goodbad"] == 1][df[df["dummynews_goodbad"] == 1]
['treatgroup'] == 4]
['beliefadjustment_bayes_norm']
})
model_ols = smf.ols(
formula="beliefadjustment_normalized ~ beliefadjustment_bayes_norm",
data=df_short_b)
reg_s_2 = model_ols.fit(cov_type='HC1')
df["interact_negative_bayes"] = df["beliefadjustment_bayes_norm"] * df[
"dummynews_goodbad"]
model_ols = smf.ols(
formula=
"beliefadjustment_normalized ~ beliefadjustment_bayes_norm + dummynews_goodbad + interact_negative_bayes",
data=df[df['treatgroup'] == 4])
reg_s_3 = model_ols.fit(cov_type='HC1')
Appendix_Table_3 = Stargazer([reg_s_1, reg_s_2, reg_s_3])
Appendix_Table_3.title('Table 8: Belief Adjustment in the Short-Run')
Appendix_Table_3.dependent_variable_name('Belief Adjustment')
Appendix_Table_3.custom_columns([
'Positive Information', 'Negative Information',
'Difference-in-difference'
], [1, 1, 1])
return Appendix_Table_3
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
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 Main_Table_1(df):
df_good = pd.DataFrame({
"beliefadjustment_normalized":
df[df["dummynews_goodbad"] == 0]['beliefadjustment_normalized'],
"dummytreat_direct1month":
df[df["dummynews_goodbad"] == 0]['dummytreat_direct1month'],
"rank":
df[df["dummynews_goodbad"] == 0]['rank'],
"beliefadjustment_bayes_norm":
df[df["dummynews_goodbad"] == 0]['beliefadjustment_bayes_norm']
})
model_ols = smf.ols(
formula="beliefadjustment_normalized ~ dummytreat_direct1month",
data=df_good)
reg_1 = model_ols.fit(cov_type='HC1')
model_ols = smf.ols(
formula=
"beliefadjustment_normalized ~ dummytreat_direct1month + rank + beliefadjustment_bayes_norm",
data=df_good)
reg_2 = model_ols.fit(cov_type='HC1')
df_bad = pd.DataFrame({
"beliefadjustment_normalized":
df[df["dummynews_goodbad"] == 1]['beliefadjustment_normalized'],
"dummytreat_direct1month":
df[df["dummynews_goodbad"] == 1]['dummytreat_direct1month'],
"rank":
df[df["dummynews_goodbad"] == 1]['rank'],
"beliefadjustment_bayes_norm":
df[df["dummynews_goodbad"] == 1]['beliefadjustment_bayes_norm']
})
model_ols = smf.ols(
formula="beliefadjustment_normalized ~ dummytreat_direct1month",
data=df_bad)
reg_3 = model_ols.fit(cov_type='HC1')
model_ols = smf.ols(
formula=
"beliefadjustment_normalized ~ dummytreat_direct1month + rank + beliefadjustment_bayes_norm",
data=df_bad)
reg_4 = model_ols.fit(cov_type='HC1')
#Generating interaction term
df["interact_direct1month"] = df["dummytreat_direct1month"] * df[
"dummynews_goodbad"]
model_ols = smf.ols(
formula=
"beliefadjustment_normalized ~ dummytreat_direct1month + dummynews_goodbad + interact_direct1month",
data=df)
reg_5 = model_ols.fit(cov_type='HC1')
model_ols = smf.ols(
formula=
"beliefadjustment_normalized ~ dummytreat_direct1month + dummynews_goodbad + rank + interact_direct1month + beliefadjustment_bayes_norm",
data=df)
reg_6 = model_ols.fit(cov_type='HC1')
model_ols = smf.ols(
formula=
"beliefadjustment_normalized ~ dummytreat_direct1month + dummynews_goodbad + interact_direct1month + rankdummy1 + rankdummy2 + rankdummy3 + rankdummy4 + rankdummy5 + rankdummy6 + rankdummy7 + rankdummy8 + rankdummy9 + rankdummy1_interact + rankdummy2_interact + rankdummy3_interact + rankdummy4_interact + rankdummy5_interact + rankdummy6_interact + rankdummy7_interact + rankdummy8_interact + rankdummy9_interact",
data=df)
reg_7 = model_ols.fit(cov_type='HC1')
model_ols = smf.ols(
formula=
"beliefadjustment_normalized ~ dummytreat_direct1month + dummynews_goodbad + interact_direct1month + beliefadjustment_bayes_norm + rankdummy1 + rankdummy2 + rankdummy3 + rankdummy4 + rankdummy5 + rankdummy6 + rankdummy7 + rankdummy8 + rankdummy9 + rankdummy1_interact + rankdummy2_interact + rankdummy3_interact + rankdummy4_interact + rankdummy5_interact + rankdummy6_interact + rankdummy7_interact + rankdummy8_interact + rankdummy9_interact",
data=df)
reg_8 = model_ols.fit(cov_type='HC1')
Main_Table_1 = Stargazer(
[reg_1, reg_2, reg_3, reg_4, reg_5, reg_6, reg_7, reg_8])
Main_Table_1.title(
'Table 1 - Belief Adjustment: Direct versus One Month Later')
Main_Table_1.dependent_variable_name('Normalized Belief Adjustment - ')
Main_Table_1.custom_columns([
'Positive Information', 'Negative Information',
'Difference-in-difference',
'Difference-in-difference with rank fixed effects'
], [2, 2, 2, 2])
return Main_Table_1
def Appendix_Table_1(df):
df_good = pd.DataFrame({
"beliefadjustment_normalized":
df[df["dummynews_goodbad_h"] == 0]['beliefadjustment_normalized'],
"dummytreat_direct1month":
df[df["dummynews_goodbad_h"] == 0]['dummytreat_direct1month'],
"rank":
df[df["dummynews_goodbad_h"] == 0]['rank'],
"beliefadjustment_bayes_norm":
df[df["dummynews_goodbad_h"] == 0]['beliefadjustment_bayes_norm']
})
model_ols = smf.ols(
formula="beliefadjustment_normalized ~ dummytreat_direct1month",
data=df_good)
reg_1 = model_ols.fit(cov_type='HC1')
model_ols = smf.ols(
formula=
"beliefadjustment_normalized ~ dummytreat_direct1month + rank + beliefadjustment_bayes_norm",
data=df_good)
reg_2 = model_ols.fit(cov_type='HC1')
df_bad = pd.DataFrame({
"beliefadjustment_normalized":
df[df["dummynews_goodbad_h"] == 1]['beliefadjustment_normalized'],
"dummytreat_direct1month":
df[df["dummynews_goodbad_h"] == 1]['dummytreat_direct1month'],
"rank":
df[df["dummynews_goodbad_h"] == 1]['rank'],
"beliefadjustment_bayes_norm":
df[df["dummynews_goodbad_h"] == 1]['beliefadjustment_bayes_norm']
})
model_ols = smf.ols(
formula="beliefadjustment_normalized ~ dummytreat_direct1month",
data=df_bad)
reg_3 = model_ols.fit(cov_type='HC1')
model_ols = smf.ols(
formula=
"beliefadjustment_normalized ~ dummytreat_direct1month + rank + beliefadjustment_bayes_norm",
data=df_bad)
reg_4 = model_ols.fit(cov_type='HC1')
#Generating interaction term
df["interact_direct1month"] = df["dummytreat_direct1month"] * df[
"dummynews_goodbad"]
model_ols = smf.ols(
formula=
"beliefadjustment_normalized ~ dummytreat_direct1month + dummynews_goodbad_h + interact_direct1month",
data=df)
reg_5 = model_ols.fit(cov_type='HC1')
model_ols = smf.ols(
formula=
"beliefadjustment_normalized ~ dummytreat_direct1month + dummynews_goodbad_h + rank + interact_direct1month + beliefadjustment_bayes_norm",
data=df)
reg_6 = model_ols.fit(cov_type='HC1')
Appendix_Table_1 = Stargazer([reg_1, reg_2, reg_3, reg_4, reg_5, reg_6])
Appendix_Table_1.title(
'Appendix Table 1 - Belief Adjustment: Direct versus One Month Later')
Appendix_Table_1.dependent_variable_name('Normalized Belief Adjustment - ')
Appendix_Table_1.custom_columns([
'Positive Information', 'Negative Information',
'Difference-in-difference'
], [2, 2, 2])
return Appendix_Table_1
model2 = ols("cnt ~ temp_celsius + windspeed_kh", data=wbr).fit()
model2.summary2()
# Independent variable = hum
wbr.hum.hist()
model1_h = ols("cnt ~ hum", data=wbr).fit()
model1_h.summary2()
# Independent variables = temp_celsius + windspeed_kh + hum
model3 = ols("cnt ~ temp_celsius + windspeed_kh + hum", data=wbr).fit()
model3.summary2()
# Compare all models
stargazer = Stargazer([model1_t, model2, model3])
stargazer
stargazer.title("Table 1. A model of bicycle demand in Washington D.C.")
stargazer
#####################
# REGRESSION WITH DUMMIES
# Independent variable = workingday
model1_wd = ols("cnt ~ workingday", data=wbr).fit()
model1_wd.summary2()
# Independent variables = temp_celsius + windspeed_kh + hum + workingday
model4 = ols("cnt ~ temp_celsius + windspeed_kh + hum + workingday",
data=wbr).fit()
model4.summary2()
# The workingday variable is not significative -> but we mantain it just in case