def calc_probability_LD_migration_zvoleff(person, time):
"""
Calculates the probability of local-distant migration for an agent, using
the results of Alex Zvoleff's empirical analysis of the CVFS data,
following the results of the analysis conducted by Massey et al. (2010).
"""
#########################################################################
# Intercept
inner = rcParams['migration.ld.zv.coef.intercept']
if person.is_in_school():
inner += rcParams['migration.ld.zv.coef.in_school']
inner += person.get_years_schooling() * rcParams['migration.ld.zv.coef.years_schooling']
#######################################################################
# Household level variables
household = person.get_parent_agent()
inner += rcParams['migration.ld.zv.coef.own_farmland'] * household._own_land
#######################################################################
# Neighborhood level variables
neighborhood = household.get_parent_agent()
inner += rcParams['migration.ld.zv.coef.log_market_min_ft'] * np.log(neighborhood.NFOs['market_min_ft'] + 1)
#########################################################################
# Other controls
if person.get_sex() == "female":
inner += rcParams['migration.ld.zv.coef.female']
ethnicity = person.get_ethnicity()
assert ethnicity!=None, "Ethnicity must be defined"
if ethnicity == "HighHindu":
# This was the reference level
pass
elif ethnicity == "HillTibeto":
inner += rcParams['migration.ld.zv.coef.ethnicHillTibeto']
elif ethnicity == "LowHindu":
inner += rcParams['migration.ld.zv.coef.ethnicLowHindu']
elif ethnicity == "Newar":
inner += rcParams['migration.ld.zv.coef.ethnicNewar']
elif ethnicity == "TeraiTibeto":
inner += rcParams['migration.ld.zv.coef.ethnicTeraiTibeto']
age = person.get_age_years()
if (age >= 15) & (age <= 24):
inner += rcParams['migration.ld.zv.coef.age15-24']
elif (age > 24) & (age <= 34):
inner += rcParams['migration.ld.zv.coef.age24-34']
elif (age > 34) & (age <= 44):
inner += rcParams['migration.ld.zv.coef.age34-44']
elif (age > 44) & (age <= 55):
inner += rcParams['migration.ld.zv.coef.age45-55']
elif (age > 55):
# Reference class
pass
prob = 1./(1 + np.exp(-inner))
if rcParams['log_stats_probabilities']:
logger.debug("Person %s local-distant migration probability %.6f (age: %s)"%(person.get_ID(), prob, person.get_age_years()))
return prob
def calc_probability_marriage_zvoleff(person, time):
"""
Calculates the probability of marriage for an agent, using the results of
Alex Zvoleff's empirical analysis of the CVFS data, following the results
of the analysis conducted by Yabiku (2006).
"""
inner = rcParams['marrtime.zv.coef.(Intercept)']
ethnicity = person.get_ethnicity()
assert ethnicity!=None, "Ethnicity must be defined"
if ethnicity == "HighHindu":
# This was the reference level
pass
elif ethnicity == "HillTibeto":
inner += rcParams['marrtime.zv.coef.ethnicHillTibeto']
elif ethnicity == "LowHindu":
inner += rcParams['marrtime.zv.coef.ethnicLowHindu']
elif ethnicity == "Newar":
inner += rcParams['marrtime.zv.coef.ethnicNewar']
elif ethnicity == "TeraiTibeto":
inner += rcParams['marrtime.zv.coef.ethnicTeraiTibeto']
# Gender
if person.get_sex() == "female":
inner += rcParams['marrtime.zv.coef.genderfemale']
age = person.get_age_years()
inner += rcParams['marrtime.zv.coef.age'] * age
inner += rcParams['marrtime.zv.coef.I(age^2)'] * (age ** 2)
# Neighborhood characteristics
neighborhood = person.get_parent_agent().get_parent_agent()
log_percent_agveg = np.log((neighborhood._land_agveg / neighborhood._land_total)*100 + 1)
inner += rcParams['marrtime.zv.coef.interp_logpercagveg'] * log_percent_agveg
inner += rcParams['marrtime.zv.coef.SCHLFT'] * neighborhood.NFOs['school_min_ft']
inner += rcParams['marrtime.zv.coef.HLTHFT'] * neighborhood.NFOs['health_min_ft']
inner += rcParams['marrtime.zv.coef.BUSFT'] * neighborhood.NFOs['bus_min_ft']
inner += rcParams['marrtime.zv.coef.MARFT'] * neighborhood.NFOs['market_min_ft']
inner += rcParams['marrtime.zv.coef.EMPFT'] * neighborhood.NFOs['employer_min_ft']
# Schooling
inner += rcParams['marrtime.zv.coef.schooling_yrs'] * person.get_years_schooling()
if person.is_in_school():
inner += rcParams['marrtime.zv.coef.in_school']
# Account for monthly differences in marriage rates - some days (and
# months) are more auspicious for marriage than others.
month_num = int(np.mod(np.round(time*12, 0), 12) + 1)
if month_num == 1:
# This was the reference level
pass
elif month_num == 2:
inner += rcParams['marrtime.zv.coef.month2']
elif month_num == 3:
inner += rcParams['marrtime.zv.coef.month3']
elif month_num == 4:
inner += rcParams['marrtime.zv.coef.month4']
elif month_num == 5:
inner += rcParams['marrtime.zv.coef.month5']
elif month_num == 6:
inner += rcParams['marrtime.zv.coef.month6']
elif month_num == 7:
inner += rcParams['marrtime.zv.coef.month7']
elif month_num == 8:
inner += rcParams['marrtime.zv.coef.month8']
elif month_num == 9:
inner += rcParams['marrtime.zv.coef.month9']
elif month_num == 10:
inner += rcParams['marrtime.zv.coef.month10']
elif month_num == 11:
inner += rcParams['marrtime.zv.coef.month11']
elif month_num == 12:
inner += rcParams['marrtime.zv.coef.month12']
prob = 1./(1 + np.exp(-inner))
if rcParams['log_stats_probabilities']:
logger.debug("Person %s marriage probability %.6f (age: %s)"%(person.get_ID(), prob, person.get_age_years()))
return prob