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apply_policy.py
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apply_policy.py
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from pandas_helper import *
from res_ind_lib import *
def apply_policy(m_,c_,i_, h_, a_ , policy_name=None, verbose=True):
"""Choses a policy by name, appliesit to m,c,and/or h, and returns new values as well as a policy description"""
#duplicate inputes
m=m_.copy(deep=True)
c=c_.copy(deep=True)
c=c_.copy(deep=True)
i=i_.copy(deep=True)
a=a_.copy() #dictionary, do not attempt to deep copy
if policy_name is None:
desc = "Baseline"
# Transport
elif policy_name=="vtransp":
i.v = i.v.unstack('sector').assign(transport=lambda df:df.transport*0.5).stack()
desc = "Decrease vulnerability\nof transport by 50%"
elif policy_name=="etransp":
i.e = i.e.unstack('sector').assign(transport=lambda df:df.transport*0.5).stack()
desc = "Increase economy resilience to transport disruptions by 50%"
# Pov reduction
elif policy_name=="kp":
c.k = c.k.unstack().assign(poor=lambda df:df.poor*1.1).stack()
desc = "Increase\nincome of the\npoor 10%" # Pov reduction
elif policy_name=="pov_head":
c.n = c.n.unstack().assign(poor=.18, nonpoor=.82).stack()
desc = "Reduce poverty from 20% to 18%"
#Borrow abi
elif policy_name=="borrow_abi":
m.borrow_abi = m.borrow_abi.clip(lower=1)
desc = "Develop contingent finance and reserve funds"
#Scale up abi
elif policy_name=="prepare_scaleup":
m.prepare_scaleup = 1
desc = "Make social protection scalable after a shock"
#Early warnings to 1, except for earthquake
elif policy_name=="shew":
h.shew =1
h.shew=h.shew.unstack("hazard").assign(earthquake=0).stack("hazard").reset_index(). set_index(event_level+[ "income_cat"])
desc = "Universal\naccess to early\nwarnings"
#reconstruction to 2.5 years
elif policy_name=="T_rebuild_K":
m.T_rebuild_K = 2
desc = "Accelerate\nreconstruction\n(by 33%)"
#social_p needs to be at least 50%
elif policy_name=="social_p":
m,c=clip_social_p(m,c,fun=lambda x: x.clip(lower=.33) )
# )
desc = "Increase social\ntransfers to poor\npeople to at\nleast 33%"
elif policy_name=="axfin":
c.axfin=1
desc = "Universal\naccess to\nfinance"
#vpoor = vnonpoor
elif policy_name=="vpvr":
c.v = c.v.unstack().assign(poor=lambda x:x.nonpoor).stack()
desc = "Make poor people's assets as resistant as nonpoor people's assets"
#30% or poor people see their v reduced 30% (not 10pts!!)
elif policy_name=="vp":
n = c.n.unstack().poor
dv = .3 #reduction in v
f =.05 #fractionof nat pop would get the reduction
c.v = c.v.unstack().assign(poor=lambda df:(df.poor*(1-dv*f/n))).stack().clip(lower=0)
desc = "Reduce asset\nvulnerability\n(by 30%) of\npoor people\n(5% of the population)"
#10% or nonpoor people see their v reduced 30%
elif policy_name=="vr":
n = c.n.unstack().nonpoor
dv = .3 #reduction in v
f =.05 #fractionof nat pop would get the reduction
c.v = c.v.unstack().assign(nonpoor=lambda x:(x.nonpoor*(1-dv*f/n))).stack().clip(lower=0)
desc = "Reduce asset\nvulnerability\n(by 30%) of\nnonpoor people\n(5% of the population)"
#10% or poor people see their fA reduced 10%
elif policy_name=="fap":
n = 0.2
dfa = .05 #reduction in fa
h["n"] = h.assign(n=1).n.unstack().assign(poor=0.2,nonpoor=0.8).stack()
fa_event = agg_to_event_level(h,"fa")
h = h.drop("n",axis=1)
h.fa = h.fa.unstack().assign(poor=lambda x:(x.poor-dfa*fa_event/n)).stack().clip(lower=0)
desc = "Reduce exposure\nof the poor by 5%\nof total exposure"
#10% or NONpoor people see their fA reduced 10%
elif policy_name=="far":
n = 0.8
dfa =.05 #fractionof nat pop would get the reduction
h["n"] = h.assign(n=1).n.unstack().assign(poor=0.2,nonpoor=0.8).stack()
fa_event = agg_to_event_level(h,"fa")
h.fa = h.fa.unstack().assign(nonpoor=lambda x:(x.nonpoor-dfa*fa_event/n)).stack().clip(lower=0)
desc = "Reduce\nexposure of the nonpoor by 5%\nof total exposure"
#Exposure equals to nonpoor exposure (mind that exposure in cat_info is OVERWRITTEN by exposure in h)
elif policy_name=="fapfar":
h.fa = h.fa.unstack().assign(poor=lambda x:x.nonpoor).stack()
desc = "Make poor people's exposure the same as the rest of population"
elif policy_name=="prop_nonpoor":
a.update(optionPDS = "prop_nonpoor", optionT="perfect",optionB="unlimited",optionFee="insurance_premium", share_insured=.25)
desc = "Develop market\ninsurance\n(nonpoor people)"
elif policy_name=="prop_nonpoor_lms":
a.update(optionPDS = "prop_nonpoor_lms", optionT="prop_nonpoor_lms",optionB="unlimited",optionFee="insurance_premium", share_insured=.5)
desc = "Develop market insurance (25% of population, only nonpoor)"
elif policy_name=="PDSpackage":
m,c,h,a,desc = apply_policy(m,c,h,a,"borrow_abi")
m,c,h,a,desc = apply_policy(m,c,h,a,"prepare_scaleup")
desc = "Postdisaster\nsupport\npackage"
elif policy_name=="ResiliencePackage":
m,c,h,a,desc = apply_policy(m,c,h,a,"PDSpackage")
m,c,h,a,desc = apply_policy(m,c,h,a,"axfin")
m,c,h,a,desc = apply_policy(m,c,h,a,"T_rebuild_K")
m,c,h,a,desc = apply_policy(m,c,h,a,"social_p")
m,c,h,a,desc = apply_policy(m,c,h,a,"prop_nonpoor_lms")
desc = "Resilience package"
elif policy_name=="ResiliencePlusEW":
m,c,h,a,desc = apply_policy(m,c,h,a,"ResiliencePackage")
m,c,h,a,desc = apply_policy(m,c,h,a,"shew")
desc = "Resilience Package + Early Warning Package"
elif policy_name=="Asset_losses" :
m,c,h,a,desc = apply_policy(m,c,h,a,"fap")
m,c,h,a,desc = apply_policy(m,c,h,a,"far")
m,c,h,a,desc = apply_policy(m,c,h,a,"vp")
m,c,h,a,desc = apply_policy(m,c,h,a,"vr")
m,c,h,a,desc = apply_policy(m,c,h,a,"shew")
desc = "Asset losses package "
elif policy_name=="Asset_losses_no_EW":
m,c,h,a,desc = apply_policy(m,c,h,a,"fap")
m,c,h,a,desc = apply_policy(m,c,h,a,"far")
m,c,h,a,desc = apply_policy(m,c,h,a,"vp")
m,c,h,a,desc = apply_policy(m,c,h,a,"vr")
desc = "Asset losses package (excluding early warnings)"
elif policy_name=="":
pass
if verbose:
print(desc)
return m,c,h, a, desc
def clip_social_p(m,c,fun=lambda x: x.clip(lower=.50) ) :
"""Compute social p from cat_info (c) and macro (m).
Then changes social p to something applying fun
for instance fun=lambda x:.3333 will set social_p at .3333
fun = lambda x: x.clip(lower=.50) gets social p at least at 50%
"""
m = m.copy(deep=True)
c = c.copy(deep=True)
#############
#### preparation
#current conso and share of average transfer
cp = c.c.ix[:,"poor"]
gsp = c.gamma_SP.ix[:,"poor"]
cp.head()
#social_p
cur_soc_sh = gsp* m.gdp_pc_pp *m.tau_tax /cp
cur_soc_sh.head()
#social_p needs to be at least 50%
obj_soc_sh = fun(cur_soc_sh)
# obj_soc_sh.sample(10)
# increase of transfer for poor people
money_needed = (obj_soc_sh*(1-cur_soc_sh)/(1-obj_soc_sh)-cur_soc_sh)*cp
money_needed.head()
#financing this transfer with a tax on the whole population
nu_tx = (m.gdp_pc_pp*m.tau_tax+money_needed*c.n.ix[:,"poor"])/m.gdp_pc_pp
nu_tx.head();
#increasing the share of avg transfer that goes to poor
nu_gsp = (money_needed+cur_soc_sh*cp)/(m.gdp_pc_pp*nu_tx)
#balancing with less for nonpoor
nu_gs = (1-0.2*nu_gsp)/0.8
nu_gs;
#double checking that that the objectife of 50% at least has been met
# new_soc_sh = nu_gsp* m.gdp_pc_pp *nu_tx /(cp+money_needed)
# ((new_soc_sh-obj_soc_sh)**2).sum()
#updating c and m
c.gamma_SP = concat_categories(nu_gsp,nu_gs,index=income_cats);
m.tau_tax = nu_tx
return m,c