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grant and debt inflows
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@jm-rivera
jm-rivera committed Jan 19, 2024
1 parent bfda8b9 commit 58fb8d6
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148 changes: 146 additions & 2 deletions scripts/data/common.py
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Expand Up @@ -3,45 +3,63 @@
import pandas as pd
from bblocks import convert_id

from scripts import config

logging.getLogger("country_converter").setLevel(logging.ERROR)


def multilateral_mapping() -> dict:
return {
"Adaptation Fund": "Adaptation Fund",
"African Dev. Bank": "African Development Bank",
"African Development Bank [AfDB]": "African Development Bank",
"African Development Fund [AfDF]": "African Development Fund",
"African Export-Import Bank": "African Export Import Bank",
"Arab African International Bank": "Arab African International Bank",
"Arab Bank for Economic Dev. in Africa (BADEA)": (
"Arab Bank for Economic Development in Africa"
),
"Asian Forest Cooperation Organisation [AFoCO]": "Asian Forest Cooperation Organisation",
"Arab Bank for Economic Development in Africa [BADEA]": (
"Arab Bank for Economic Development in Africa"
),
"Arab Fund for Tech. Assist. to African Countries": (
"Arab Fund for Technical Assistance to African Countries"
),
"Arab International Bank": "Arab International Bank",
"Arab League": "Arab League",
"Arab Monetary Fund": "Arab Monetary Fund",
"Arab Towns Organization (ATO)": "Arab Towns Organization",
"Arab Fund (AFESD)": "Arab Fund for Economic and Social Development",
"Asian Dev. Bank": "Asian Development Bank",
"Asian Development Bank [AsDB]": "Asian Development Bank",
"Asian Infrastructure Investment Bank": "Asian Infrastructure Investment Bank",
"Bank for International Settlements (BIS)": "Bank for International Settlements",
"Bolivarian Alliance for the Americas (ALBA)": (
"Bolivarian Alliance for the Americas"
),
"Caribbean Community (CARICOM)": "Caribbean Community",
"Caribbean Dev. Bank": "Caribbean Development Bank",
"Caribbean Development Bank [CarDB]": "Caribbean Development Bank",
"Center for Latin American Monetary Studies (CEMLA)": (
"Center for Latin American Monetary Studies"
),
"Central American Bank for Econ. Integ. (CABEI)": (
"Central American Bank for Economic Integration"
),
"Central American Bank for Economic Integration [CABEI]": (
"Central American Bank for Economic Integration"
),
"Central American Bank for Econ. Integration (BCIE)": (
"Central American Bank for Economic Integration"
),
"Central Bank of West African States (BCEAO)": (
"Central Bank of West African States"
),
"CGIAR": "CGIAR",
"Corporacion Andina de Fomento": "Corporacion Andina de Fomento",
"Council of Europe": "Council of Europe",
"Council of Europe Development Bank [CEB]": "Council of Europe",
"Dev. Bank of the Central African States (BDEAC)": (
"Development Bank of the Central African States"
),
Expand All @@ -65,6 +83,7 @@ def multilateral_mapping() -> dict:
"European Coal and Steel Community"
),
"European Development Fund (EDF)": "European Development Fund",
"EU Institutions": "EU Institutions",
"European Economic Community (EEC)": "European Economic Community",
"European Free Trade Association (EFTA)": "European Free Trade Association",
"European Investment Bank": "European Investment Bank",
Expand All @@ -73,11 +92,20 @@ def multilateral_mapping() -> dict:
"European Union": "European Union",
"Fondo Latinoamericano de Reservas (FLAR)": "Fondo Latinoamericano de Reservas",
"Food and Agriculture Organization (FAO)": "Food and Agriculture Organization",
"Food and Agriculture Organisation [FAO]": "Food and Agriculture Organization",
"IFAD": "International Fund for Agricultural Development",
"Foreign Trade Bank of Latin America (BLADEX)": (
"Foreign Trade Bank of Latin America"
),
"Global Environment Facility": "Global Environment Facility",
"Global Environment Facility [GEF]": "Global Environment Facility",
"Global Alliance for Vaccines and Immunization [GAVI]": (
"Global Alliance for Vaccines and Immunization"
),
"Global Fund": "Global Fund to Fight AIDS, Tuberculosis and Malaria",
"International Atomic Energy Agency [IAEA]": "International Atomic Energy Agency",
"Inter-American Dev. Bank": "Inter-American Development Bank",
"Inter-American Development Bank [IDB]": "Inter-American Development Bank",
"International Bank for Economic Cooperation (IBEC)": (
"International Bank for Economic Cooperation"
),
Expand All @@ -88,8 +116,11 @@ def multilateral_mapping() -> dict:
),
"International Investment Bank (IIB)": "International Investment Bank",
"International Labour Organization (ILO)": "International Labour Organization",
"International Labour Organisation [ILO]": "International Labour Organization",
"International Monetary Fund": "International Monetary Fund",
"IMF (Concessional Trust Funds)": "International Monetary Fund",
"Islamic Dev. Bank": "Islamic Development Bank",
"Islamic Development Bank [IsDB]": "Islamic Development Bank",
"Islamic Solidarity Fund for Dev. (ISFD)": "Islamic Solidarity Fund for Development",
"Latin Amer. Conf. of Saving & Credit Coop. (COLAC)": (
"Latin American Conference of Saving and Credit Cooperation"
Expand All @@ -98,58 +129,111 @@ def multilateral_mapping() -> dict:
"Latin American Agribusiness Development Corporation"
),
"Montreal Protocol Fund": "Montreal Protocol Fund",
"Montreal Protocol": "Montreal Protocol Fund",
"Nordic Development Fund": "Nordic Development Fund",
"Nordic Development Fund [NDF]": "Nordic Development Fund",
"Nordic Environment Finance Corporation (NEFCO)": "Nordic Environment Finance Corporation",
"Nordic Investment Bank": "Nordic Investment Bank",
"OPEC Fund for International Dev.": "OPEC Fund for International Development",
"OPEC Fund for International Development [OPEC Fund]": (
"OPEC Fund for International Development"
),
"Org. of Arab Petroleum Exporting Countries (OAPEC)": (
"Organization of Arab Petroleum Exporting Countries"
),
"Plata Basin Financial Dev. Fund": "Plata Basin Financial Development Fund",
"South Asian Development Fund (SADF)": "South Asian Development Fund",
"UN-Children's Fund (UNICEF)": "UNICEF",
"UNICEF": "UNICEF",
"World Health Organisation [WHO]": "WHO",
"UN-Development Fund for Women (UNIFEM)": "UN Development Fund for Women",
"UN Women": "UN Women",
"COVID-19 Response and Recovery Multi-Partner Trust Fund [UN COVID-19 MPTF]": (
"UN COVID-19 Response and Recovery Multi-Partner Trust Fund"
),
"Joint Sustainable Development Goals Fund [Joint SDG Fund]": (
"Joint Sustainable Development Goals Fund"
),
"International Commission on Missing Persons [ICMP]": (
"International Commission on Missing Persons"
),
"WHO-Strategic Preparedness and Response Plan [SPRP]": (
"WHO Strategic Preparedness and Response Plan"
),
"International Centre for Genetic Engineering and Biotechnology [ICGEB]": (
"International Centre for Genetic Engineering and Biotechnology"
),
"World Organisation for Animal Health [WOAH]": "World Organisation for Animal Health",
"UN-Development Programme (UNDP)": "UN Development Programme",
"UNDP": "UN Development Programme",
"UN-Educ., Scientific and Cultural Org. (UNESCO)": "UNESCO",
"UNECE": "UNECE",
"UN-Environment Programme (UNEP)": "UN Environment Programme",
"UNEP": "UN Environment Programme",
"UN-Fund for Drug Abuse Control (UNFDAC)": "UN Fund for Drug Abuse Control",
"UN-Fund for Human Rights": "UN Fund for Human Rights",
"UN-General Assembly (UNGA)": "UN General Assembly",
"UN-High Commissioner for Refugees (UNHCR)": "UN High Commissioner for Refugees",
"UNHCR": "UN High Commissioner for Refugees",
"UNAIDS": "UNAIDS",
"UN-Industrial Development Organization (UNIDO)": (
"UN Industrial Development Organization"
),
"United Nations Industrial Development Organization [UNIDO]": (
"UN Industrial Development Organization"
),
"UN Institute for Disarmament Research [UNIDIR]": (
"UN Institute for Disarmament Research"
),
"UN-INSTRAW": (
"UN International Research and Training Institute for the Advancement of Women"
),
"UN-Office on Drugs and Crime (UNDCP)": "UN Office on Drugs and Crime",
"UN-Population Fund (UNFPA)": "UN Population Fund",
"UNFPA": "UN Population Fund",
"UN Peacebuilding Fund [UNPBF]": "UN Peacebuilding Fund",
"UN-Regular Programme of Technical Assistance": (
"UN Regular Programme of Technical Assistance"
),
"UN-Regular Programme of Technical Coop. (RPTC)": (
"UN Regular Programme of Technical Assistance"
),
"UN-Relief and Works Agency (UNRWA)": "UN Relief and Works Agency",
"UNRWA": "UN Relief and Works Agency",
"UN-UNETPSA": "UN UNETPSA",
"UN-World Food Programme (WFP)": "UN World Food Programme",
"WFP": "UN World Food Programme",
"UN-World Intellectual Property Organization": "UN World Intellectual Property Organization",
"UN-World Meteorological Organization": "UN World Meteorological Organization",
"United Nations Conference on Trade and Development [UNCTAD]": (
"UN Conference on Trade and Development"
),
"North American Development Bank [NADB]": "North American Development Bank",
"WTO - International Trade Centre [ITC]": "International Trade Centre",
"UN Capital Development Fund [UNCDF]": "UN Capital Development Fund",
"OSCE": "Organization for Security and Co-operation in Europe",
"West African Development Bank - BOAD": "West African Development Bank",
"West African Monetary Union (UMOA)": "West African Monetary Union",
"World Bank-IBRD": "WB International Bank for Reconstruction and Development",
"World Bank-IDA": "WB International Development Association",
"International Development Association [IDA]": "WB International Development Association",
"World Bank-MIGA": "WB Multilateral Investment Guarantee Agency",
"World Trade Organization": "World Trade Organization",
"Climate Investment Funds [CIF]": "Climate Investment Funds",
"Global Green Growth Institute [GGGI]": "Global Green Growth Institute",
"Green Climate Fund [GCF]": "Green Climate Fund",
"World Tourism Organisation [UNWTO]": "World Tourism Organization",
"Center of Excellence in Finance [CEF]": "Center of Excellence in Finance",
"Central Emergency Response Fund [CERF]": "Central Emergency Response Fund",
}


def clean_debtors(df: pd.DataFrame, column) -> pd.DataFrame:
df["iso_code"] = convert_id(df[column], from_type="regex", to_type="ISO3")
df["continent"] = convert_id(df[column], from_type="regex", to_type="continent")
df[column] = convert_id(df[column], from_type="regex", to_type="name_short")
df[f"{column}"] = convert_id(df[column], from_type="regex", to_type="name_short")

return df.set_index(["iso_code", column, "continent"]).reset_index()
return df.set_index(["iso_code", f"{column}", "continent"]).reset_index()


def clean_creditors(df: pd.DataFrame, column) -> pd.DataFrame:
Expand Down Expand Up @@ -178,3 +262,63 @@ def clean_creditors(df: pd.DataFrame, column) -> pd.DataFrame:
)

return df


def add_oecd_names(df: pd.DataFrame) -> pd.DataFrame:
from oda_data import set_data_path, read_dac2a

set_data_path(config.Paths.raw_data)

dac2a = read_dac2a(years=range(2010, 2023))

donors = dac2a.filter(["donor_code", "donor"]).drop_duplicates()
recipients = dac2a.filter(["recipient_code", "recipient"]).drop_duplicates()

df = df.merge(donors, on=["donor_code"], how="left")
df = df.merge(recipients, on=["recipient_code"], how="left")

return df


def remove_counterpart_totals(df: pd.DataFrame) -> pd.DataFrame:
return df[~df["counterpart_area"].str.contains(", Total")]


def remove_recipient_totals(df: pd.DataFrame) -> pd.DataFrame:
return df[~df["country"].str.contains(", Total")]


def remove_groupings_and_totals_from_recipients(df: pd.DataFrame) -> pd.DataFrame:
from oda_data import recipient_groupings

groupings = recipient_groupings()["all_developing_countries_regions"]

return df[df["recipient_code"].isin(groupings)]


def remove_non_official_counterparts(df: pd.DataFrame) -> pd.DataFrame:
from oda_data import donor_groupings

official = donor_groupings()["all_official"]

other_official = {
1038: "UN Institute for Disarmament Research",
962: "UN Conference on Trade and Development",
1039: "UN Capital Development Fund",
1045: "North American Development Bank",
1401: "International Trade Centre",
1406: "UN Industrial Development Organization",
910: "Central American Bank for Economic Integration",
1046: "UN Women",
1047: "UN COVID-19 Response and Recovery Multi-Partner Trust Fund",
1048: "Joint Sustainable Development Goals Fund",
1049: "International Commission on Missing Persons",
1050: "WHO Strategic Preparedness and Response Plan",
1054: "World Organisation for Animal Health",
915: "Asian Forest Cooperation Organisation",
1055: "CGIAR",
}

official = official | other_official

return df[df["donor_code"].isin(official)]
47 changes: 34 additions & 13 deletions scripts/data/inflows.py
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Expand Up @@ -2,8 +2,15 @@
import pandas as pd
from bblocks import set_bblocks_data_path, DebtIDS
from scripts import config
from scripts.data.common import clean_debtors, clean_creditors
from oda_data import ODAData, set_data_path, read_dac2a
from scripts.data.common import (
clean_debtors,
clean_creditors,
add_oecd_names,
remove_counterpart_totals,
remove_groupings_and_totals_from_recipients,
remove_non_official_counterparts,
)
from oda_data import ODAData, set_data_path

# set the path for the raw data
set_bblocks_data_path(config.Paths.raw_data)
Expand Down Expand Up @@ -33,12 +40,27 @@ def clean_debt_inflows_output(data: pd.DataFrame) -> pd.DataFrame:


def clean_grants_inflows_output(data: pd.DataFrame) -> pd.DataFrame:
data = data.filter(["year", "donor_name", "recipient_name", "prices", "value"])
# clean debtors
data = clean_debtors(data, "recipient_name")

# clean creditors
data = clean_creditors(data, "donor_name")
data = (
data.pipe(add_oecd_names)
.pipe(remove_non_official_counterparts)
.pipe(remove_groupings_and_totals_from_recipients)
.pipe(clean_debtors, "recipient")
.pipe(clean_creditors, "donor")
.filter(
[
"year",
"iso_code",
"recipient",
"continent",
"donor",
"counterpart_iso_code",
"prices",
"value",
]
)
.rename(columns={"donor": "counterpart_area", "recipient": "country"})
.pipe(remove_counterpart_totals)
)

return data

Expand Down Expand Up @@ -159,19 +181,18 @@ def get_debt_inflows() -> pd.DataFrame:
def get_grants_inflows(constant: bool = False) -> pd.DataFrame:
oda = ODAData(
years=range(config.ANALYSIS_YEARS[0], config.ANALYSIS_YEARS[1] + 1),
include_names=True,
include_names=False,
base_year=config.CONSTANT_BASE_YEAR if constant else None,
prices="constant" if constant else "current",
)

oda.load_indicator("recipient_grants_flow")

data = oda.get_data()
data = oda.get_data().pipe(clean_grants_inflows_output)

return data.pipe(clean_grants_inflows_output)
return data


if __name__ == "__main__":
grants = get_grants_inflows()
# debt_inflows = get_debt_inflows()
...
debt_inflows = get_debt_inflows()

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