economic module integration

Author: wptharindu

inputs/en/demo_national_input.xlsx

@@ -349,63 +349,55 @@ def get_time_trends(self):
 
     @translate
     def get_economic_cost(self):
-        econo_inputs        = utils.read_sheet(self._spreadsheet, _("Economic inputs"), cols=[0], dropna=False)
-        life_exp            = econo_inputs.loc[_("Life expectancy at birth")].values[0]
-        gdp_growth          = econo_inputs.loc[_("GDP growth rate")].values[0]
-        gdp_pc              = econo_inputs.loc[_("Life expectancy at birth")].values[0]
-        per_earn_realised   = econo_inputs.loc[_("Percentage of lifetime earning actually realized")].values[0]
-        age_end_prod        = econo_inputs.loc[_("Age for end of productivity")].values[0]
-        age_sta_prod        = econo_inputs.loc[_("Age start productivity")].values[0]
-        fem_lb_part         = econo_inputs.loc[_("Female labor force participation")].values[0]
-        prop_pw_benef       = econo_inputs.loc[_("Proportion of pregnancy to apply benefits to")].values[0]
-        inc_iq_bf           = econo_inputs.loc[_("Increase in IQ due to early breastfeeding")].values[0]
-        inc_ear_iq          = econo_inputs.loc[_("Increase in earnings due to increase in one IQ pt")].values[0]
-        lab_share_gdp       = econo_inputs.loc[_("Labor share of GDP")].values[0]
-        low_earn_stunted    = econo_inputs.loc[_("Percentage of lower lifetime earning of a stunted individual")].values[0]
-        produc_gain_anemia  = econo_inputs.loc[_("Gains in productivity from averted childhood anemia")].values[0]
-        produc_gain_lbw     = econo_inputs.loc[_("Productivity gains due to averted low birth weight")].values[0]
-        wage_share_manual   = econo_inputs.loc[_("Wage Share in GDP for manual occupations")].values[0]
-        produc_light_lab    = econo_inputs.loc[_("Gains productivity in light labour")].values[0]
-        blue_col_share      = econo_inputs.loc[_("Blue-Collar Share employment in total employment")].values[0]
-        heavy_manu_lab      = econo_inputs.loc[_("Wage Share of Heavy manual labor")].values[0]
-        produc_heavy_lab    = econo_inputs.loc[_("Gains in productivity in heavy labor")].values[0][0]
+        econo_inputs            = utils.read_sheet(self._spreadsheet, _("Economic inputs"), cols=[0], dropna=False)
+        gdp_growth              = econo_inputs.loc[_("GDP growth rate")].values[0]
+        gdp_pc                  = econo_inputs.loc[_("GDP per capita")].values[0]
+        discount_rate           = econo_inputs.loc[_("Rate of dicounting")].values[0]
+        per_earn_realised       = econo_inputs.loc[_("Percentage of lifetime earning actually realized")].values[0]
+        age_end_prod            = econo_inputs.loc[_("Age for end of productivity")].values[0]
+        age_sta_prod            = econo_inputs.loc[_("Age start productivity")].values[0]
+        fem_lb_part             = econo_inputs.loc[_("Female labor force participation")].values[0]
+        prop_pw_benef           = econo_inputs.loc[_("Proportion of pregnancy to apply benefits to (maternal anemia)")].values[0]
+        inc_iq_bf               = econo_inputs.loc[_("Increase in IQ points due to early breastfeeding")].values[0]
+        inc_ear_iq              = econo_inputs.loc[_("Increase in earnings due to increase in one IQ pt")].values[0]
+        lab_share_gdp           = econo_inputs.loc[_("Labor share of GDP")].values[0][0]
+        low_earn_stunted        = econo_inputs.loc[_("Percentage of lower lifetime earning of a stunted individual")].values[0]
+        produc_loss_anemia      = econo_inputs.loc[_("Loss in productivity from due to childhood anemia")].values[0]
+        produc_loss_lbw         = econo_inputs.loc[_("Loss in productivity due to low birth weight")].values[0]
+        produc_loss_mat_ane     = econo_inputs.loc[_("Loss in productivity due to maternal anemia")].values[0]
+       
+        econo_inputs            = utils.read_sheet(self._spreadsheet, _("Economic inputs"), cols=[0], skiprows=18)
 
-        econo_inputs        = utils.read_sheet(self._spreadsheet, _("Economic inputs"), cols=[0], skiprows=22)
+        num_people              = econo_inputs.loc[_("Number of people left alive (starting with 100,000 births)")].values.tolist()
 
-        num_people          = econo_inputs.loc[_("Number of people")].values.tolist()
-        
-        self.econ_inputs = {"Life expectancy at birth": life_exp,
-                               "GDP growth rate": gdp_growth,
-                               "GDP per capita": gdp_pc ,
+        self.econ_inputs = {"GDP growth rate": gdp_growth,
+                               "GDP per capita": gdp_pc,
+                               "Discount rate": discount_rate,
                                "Percent of lifetime earning actually realized": per_earn_realised,
                                "Productivity year end": age_end_prod,
                                "Productivity year start": age_sta_prod,
                                "Female labor force participation": fem_lb_part,
-                               "Proportion of pregnancy to apply benefits to": prop_pw_benef,
+                               "Proportion of pregnancy to apply benefits to (maternal anemia)": prop_pw_benef,
                                "Percentage of pregnant women 15-19 years": self.pw_agedist[0],
                                "Percentage of pregnant women 20-29 years": self.pw_agedist[1],
                                "Percentage of pregnant women 30-39 years": self.pw_agedist[2],
                                "Percentage of pregnant women 40-49 years": self.pw_agedist[3],
-                               "Increase in IQ due to EBF": inc_iq_bf,
+                               "Increase in IQ points due to early breastfeeding": inc_iq_bf,
                                "Rate of increase in earnings from IQ": inc_ear_iq,
                                "Labor share of GDP": lab_share_gdp,
                                "Percent lower lifetime earning of a stunted": low_earn_stunted,
-                               "Productivity gain from averting child anemia": produc_gain_anemia,
-                               "Productivity gain from averting LBW": produc_gain_lbw,
-                               "Wage Share in GDP for manual occupations": wage_share_manual,
-                               "Gains productivity in light labour": produc_light_lab,
-                               "Blue-Collar Share employment in total employment": blue_col_share,
-                               "Wage Share of Heavy manual labor": heavy_manu_lab,
-                               "Gains in productivity in heavy labor": produc_heavy_lab,
-                               "Number of people per 100,000": num_people}
+                               "Productivity loss due to child anemia": produc_loss_anemia,
+                               "Productivity loss due to LBW": produc_loss_lbw,
+                               "Loss in productivity due to maternal anemia": produc_loss_mat_ane,
+                               "Number of people left alive (starting with 100,000 births)": num_people}
 
     def get_lifetable(self):
         all_ages = [a for a in range(5,66)]
         age_cats = ["5-9 years", "10-14 years", "15-19 years", "20-24 years", "25-29 years",
                     "30-34 years", "35-39 years", "40-44 years", "45-49 years", "50-54 years",
                     "55-59 years", "60-64 years","65-69 years"]
 
-        number_list = self.econ_inputs["Number of people per 100,000"]
+        number_list = self.econ_inputs["Number of people left alive (starting with 100,000 births)"]
         people_number = {age_cat: 0 for age_cat in age_cats}
         for c, cat in enumerate(age_cats):
             people_number[cat] = number_list[c]

nutrition/data.py

@@ -30,13 +30,9 @@ def __init__(self, dataset: Dataset, t=None, adjust_cov=False, timeTrends=False,
         self.enforce_constraints_year = enforce_constraints_year
 
         # For economic loss
-        self.cost_wasting = self.demo_data.cost_wasting
-        self.cost_stunting = self.demo_data.cost_stunting
-        self.cost_child_death = self.demo_data.cost_child_death
-        self.cost_pw_death = self.demo_data.cost_pw_death
-        self.cost_child_anaemic = self.demo_data.cost_child_anaemic
-        self.cost_pw_anaemic = self.demo_data.cost_pw_anaemic
-
+        self.econ_inputs = self.demo_data.econ_inputs # this comes as a dictionary of several economic inputs reqired for economic cost/benefit calculations
+        self.life_table = self.demo_data.life_table
+        
     @property
     def locale(self):
         return self.ss.locale
@@ -145,18 +141,93 @@ def _track_rates(self):
         self.child_mortrate[self.year] = 1000 * np.sum(self.child_deaths) / np.sum(self.annual_births)
         self.pw_mortrate[self.year] = 1000 * np.sum(self.pw_deaths) / np.sum(self.annual_births)
 
+    # def _track_economic_loss(self):
+    #     """To calculate the economic cost of children become stunting or wasting for the country.
+    #     Simply, the cost of stunting and cost of wasting have been input over the databook."""
+    #     oldest = self.children.age_groups[-1]
+    #     rate = oldest.ageingRate
+    #     self.stunting_cost[self.year] += oldest.num_stunted() * rate * self.cost_wasting
+    #     self.wasting_cost[self.year] += sum(oldest.num_wasted(cat) for cat in self.ss.wasted_list) * rate * self.cost_stunting
+    #     self.child_death_cost[self.year] = np.sum(self.child_deaths) * self.cost_child_death
+    #     self.pw_death_cost[self.year] = np.sum(self.pw_deaths) * self.cost_pw_death
+    #     self.child_anaemic_cost[self.year] = oldest.num_anaemic() * rate * self.cost_child_anaemic
+    #     self.pw_anaemic_cost[self.year] = self.pw.num_anaemic() * self.cost_pw_anaemic
+    
     def _track_economic_loss(self):
-        """To calculate the economic cost of children become stunting or wasting for the country.
-        Simply, the cost of stunting and cost of wasting have been input over the databook."""
-        oldest = self.children.age_groups[-1]
-        rate = oldest.ageingRate
-        self.stunting_cost[self.year] += oldest.num_stunted() * rate * self.cost_wasting
-        self.wasting_cost[self.year] += sum(oldest.num_wasted(cat) for cat in self.ss.wasted_list) * rate * self.cost_stunting
-        self.child_death_cost[self.year] = np.sum(self.child_deaths) * self.cost_child_death
-        self.pw_death_cost[self.year] = np.sum(self.pw_deaths) * self.cost_pw_death
-        self.child_anaemic_cost[self.year] = oldest.num_anaemic() * rate * self.cost_child_anaemic
-        self.pw_anaemic_cost[self.year] = self.pw.num_anaemic() * self.cost_pw_anaemic
-
+        bene_age_start = self.econ_inputs[_("Productivity year start")]
+        bene_age_end   = self.econ_inputs[_("Productivity year end")]
+        start_year = self.demo_data.t[0]
+        
+        self.child_death_cost[self.year]     = 0
+        self.stunting_cases_cost[self.year]  = 0
+        self.mat_death_cost[self.year]       = 0
+        self.ch_anemia_cost[self.year]       = 0
+        self.bf_benefit[self.year]           = 0
+        self.lbw_cost[self.year]             = 0
+        
+        for a, age in enumerate(range(bene_age_start, bene_age_end + 1)):
+            probability_5yr  = self.life_table[(self.life_table[_("Age")] == age)][_("Proportion left since age 5")].item()
+            
+            
+            """Stunting cases"""
+                                       
+            this_stun_cases_cost = self.stunted[self.year] * probability_5yr * self.econ_inputs[_("Percent of lifetime earning actually realized")]\
+                                         * self.econ_inputs[_("Percent lower lifetime earning of a stunted")] * self.econ_inputs[_("GDP per capita")] * ((1 + self.econ_inputs[_("GDP growth rate")]) ** (self.year + age - 1))\
+                                             * ((1 + self.econ_inputs[_("Discount rate")]) ** -(self.year + age - 1))
+            
+            """Child Anemia cases"""
+            child_anemia = self.child_anaemprev[self.year] * self.child_less_5years[self.year] / 5
+            this_ch_anem_cases_cost = child_anemia * probability_5yr * self.econ_inputs[_("Percent of lifetime earning actually realized")]\
+                                         * self.econ_inputs[_("Productivity loss due to child anemia")] * self.econ_inputs[_("Labor share of GDP")]\
+                                             * self.econ_inputs[_("GDP per capita")] * ((1 + self.econ_inputs[_("GDP growth rate")]) ** (self.year + age - 1))\
+                                             * ((1 + self.econ_inputs[_("Discount rate")]) ** -(self.year + age - 1))
+              
+            """ Child deaths"""                            
+            this_ch_death_cost = self.child_deaths[self.year] * probability_5yr * self.econ_inputs[_("Percent of lifetime earning actually realized")]\
+                                          * self.econ_inputs[_("GDP per capita")] * ((1 + self.econ_inputs[_("GDP growth rate")]) ** (self.year + age - 1))\
+                                             * ((1 + self.econ_inputs[_("Discount rate")]) ** -(self.year + age - 1))
+            
+            """Additional EBF"""
+            num_bf = self.child_1_6months[self.year] * self.child_bfprev[self.year] * 2
+            this_bf_cognitive_benefit = num_bf * probability_5yr * self.econ_inputs[_("Percent of lifetime earning actually realized")]\
+                                          * self.econ_inputs[_("Increase in IQ points due to early breastfeeding")] * self.econ_inputs[_("Rate of increase in earnings from IQ")] * self.econ_inputs[_("Labor share of GDP")]\
+                                              * self.econ_inputs[_("GDP per capita")] * ((1 + self.econ_inputs[_("GDP growth rate")]) ** (self.year + age))\
+                                                  * ((1 + self.econ_inputs[_("Discount rate")]) ** -(self.year + age - 1))
+               
+            """Low Birth Weight children averted"""
+            this_lbw_cost =  self.child_sga[self.year] * probability_5yr * self.econ_inputs[_("Percent of lifetime earning actually realized")]\
+                                            * self.econ_inputs[_("Labor share of GDP")] * self.econ_inputs[_("Productivity loss due to LBW")] * self.econ_inputs[_("GDP per capita")]\
+                                                * ((1 + self.econ_inputs[_("GDP growth rate")]) ** (self.year + age)) * ((1 + self.econ_inputs[_("Discount rate")]) ** -(self.year + age - 1))
+            
+            """Maternal deaths averted"""
+            probability_15yr = self.life_table[(self.life_table["Age"] == age)][_("Proportion left since age 15")].item()
+            probability_25yr = self.life_table[(self.life_table["Age"] == age)][_("Proportion left since age 25")].item()
+            probability_35yr = self.life_table[(self.life_table["Age"] == age)][_("Proportion left since age 35")].item()
+            probability_45yr = self.life_table[(self.life_table["Age"] == age)][_("Proportion left since age 45")].item()
+            
+                            
+            this_maternal_death_cost = self.pw_deaths[self.year] * (probability_15yr * self.econ_inputs[_("Percentage of pregnant women 15-19 years")] + probability_25yr * self.econ_inputs[_("Percentage of pregnant women 20-29 years")]\
+                                            + probability_35yr * self.econ_inputs[_("Percentage of pregnant women 30-39 years")] + probability_45yr * self.econ_inputs[_("Percentage of pregnant women 40-49 years")]) * self.econ_inputs[_("Percent of lifetime earning actually realized")]\
+                                                * self.econ_inputs[_("Female labor force participation")] * self.econ_inputs[_("GDP per capita")] * ((1 + self.econ_inputs[_("GDP growth rate")]) ** (self.year + a - 1))\
+                                                  * ((1 + self.econ_inputs[_("Discount rate")]) ** -(self.year + a - 1))
+            
+            
+            """"Totalling the future lifetime benefits for the averted/additional cases for this year [t]"""
+            
+            self.child_death_cost[self.year]       = self.child_death_cost[self.year] + this_ch_death_cost
+            self.stunting_cases_cost[self.year]    = self.stunting_cases_cost[self.year] + this_stun_cases_cost
+            self.ch_anemia_cost[self.year]         = self.ch_anemia_cost[self.year] + this_ch_anem_cases_cost
+            self.bf_benefit[self.year]             = self.bf_benefit[self.year] + this_bf_cognitive_benefit
+            self.lbw_cost[self.year]               = self.lbw_cost[self.year] + this_lbw_cost
+            self.mat_death_cost[self.year]         = self.mat_death_cost[self.year] + this_maternal_death_cost
+        
+        pw_ane = self.pw_anaemic[self.year] / 0.75
+        self.mat_anemia_cases_cost[self.year]  = pw_ane * (self.econ_inputs[_("Loss in productivity due to maternal anemia")] * self.econ_inputs[_("Proportion of pregnancy to apply benefits to (maternal anemia)")] * self.econ_inputs[_("Female labor force participation")]\
+                                                                * self.econ_inputs[_("GDP per capita")]) * ((1 + self.econ_inputs[_("GDP growth rate")]) ** (self.year - 1)) * ((1 + self.econ_inputs[_("Discount rate")]) ** -(self.year - 1))
+        
+        self.total_econ_costs[self.year] =  self.child_death_cost[self.year] + self.stunting_cases_cost[self.year] + self.ch_anemia_cost[self.year] +  self.lbw_cost[self.year] + self.mat_death_cost[self.year] + self.mat_anemia_cases_cost[self.year]
+    
+    
     def _track_total_pop(self):
         self.pop_sizes[self.year] = dict()
         for pop in self.pops:

nutrition/model.py

@@ -127,12 +127,14 @@ def default_trackers(prev=None, rate=None):
         "child_6_23months",
         "child_less_5years",
         "num_pw",
-        "stunting_cost",
-        "wasting_cost",
         "child_death_cost",
-        "pw_death_cost",
-        "child_anaemic_cost",
-        "pw_anaemic_cost",
+        "stunting_cases_cost",
+        "ch_anemia_cost",
+        "bf_benefit",
+        "lbw_cost",
+        "mat_death_cost",
+        "mat_anemia_cases_cost",
+        "total_econ_costs",
         "pw_mortrate",
         "total_popn",
         "pop_rate",
@@ -212,12 +214,14 @@ def pretty_labels(direction=False, locale=None):
             pgettext("plotting", "Number of children 6-23 months"),
             pgettext("plotting", "Number of children <5 years"),
             pgettext("plotting", "Number of pregnant women"),
-            pgettext("plotting", "Economic cost of stunting"),
-            pgettext("plotting", "Economic cost of wasting"),
-            pgettext("plotting", "Economic cost of child death"),
-            pgettext("plotting", "Economic cost of pregnant woman death"),
-            pgettext("plotting", "Economic cost of ID anaemic child"),
-            pgettext("plotting", "Economic cost of ID anaemic pregnant woman"),
+            pgettext("plotting", "Economic cost of child deaths (discounted)"),
+            pgettext("plotting", "Economic cost of stunting (discounted)"),
+            pgettext("plotting", "Economic cost of child anemia (discounted)"),
+            pgettext("plotting", "Economic benefits of EBF (discounted)"),
+            pgettext("plotting", "Economic cost of low birth weight chidren (discounted)"),
+            pgettext("plotting", "Economic cost of maternal deaths (discounted)"),
+            pgettext("plotting", "Economic cost of maternal anemia (discounted)"),
+            pgettext("plotting", "Total economic cost (discounted)"),
             pgettext("plotting", "Pregnant women mortality rate"),
             pgettext("plotting", "Total population"),
             pgettext("plotting", "Population growth rate"),
@@ -255,7 +259,7 @@ def relabel(old, direction=False, lower=False, locale=None):
 
 
 def get_sign(obj):
-    max_obj = ["thrive", "child_notanaemic", "child_healthy", "child_notwasted"]
+    max_obj = ["thrive", "child_notanaemic", "child_healthy", "child_notwasted", "bf_benefit"]
     if obj in max_obj:
         return -1
     else: