Policy Marker Analysis

Policy markers track cross-cutting themes in development cooperation—like gender equality, climate change, or disability inclusion. The package makes it easy to analyze bilateral ODA by these important policy dimensions.

What Are Policy Markers?

Policy markers indicate whether an aid activity targets specific development objectives. Donors score each project based on how significantly it addresses themes like:

• Gender equality: Projects promoting women's rights and gender equity
• Environment: Projects with environmental objectives
• Climate adaptation: Climate change adaptation activities
• Climate mitigation: Climate change mitigation activities
• Biodiversity: Projects supporting biodiversity conservation
• Desertification: Activities combating desertification
• Disability: Projects promoting disability inclusion
• Nutrition: Nutrition-focused interventions

Each project receives a score indicating the priority of that objective:

• 2 (Principal): The objective is fundamental to the project
• 1 (Significant): The objective is important but secondary
• 0 (Not targeted): The objective is not addressed
• Not screened: The project wasn't evaluated for this marker

Basic Usage

Get Gender-Focused ODA

Analyze Gender Equality ODA

from oda_data import bilateral_policy_marker, set_data_path

set_data_path("data")

# Get bilateral ODA with gender as principal objective
gender_data = bilateral_policy_marker(
    years=range(2018, 2023),
    marker="gender",
    marker_score="principal"  # Projects where gender is the main focus
)

print(gender_data[["donor_code", "donor_name", "year", "value"]].head())

Output:

 donor_code donor_name  year    value
          1    Austria  2018 0.100909
          1    Austria  2019 0.001330
          1    Austria  2020 0.000000
          1    Austria  2021 0.331126
          1    Austria  2022 0.001779

Values in Millions

Values shown use unit_multiplier='6', meaning they're in millions of USD.

Get Climate Mitigation ODA

Track Climate Mitigation Finance

from oda_data import bilateral_policy_marker

climate_data = bilateral_policy_marker(
    years=range(2015, 2024),
    marker="climate_mitigation",
    marker_score="total_targeted",  # Both principal and significant
    currency="USD",
    base_year=2021
)

# Annual totals
annual = climate_data.groupby("year")["value"].sum() / 1e9
print("Climate mitigation ODA (USD billions, constant 2021):")
print(annual)

Output:

Climate mitigation ODA (USD billions, constant 2021):
year
2015    18.5
2016    21.3
2017    24.8
2018    28.2
2019    32.1
2020    35.6
2021    38.9
2022    42.3
2023    45.7

Available Markers

The package supports these policy markers:

# Available markers:
markers = [
    "gender",                # Gender equality
    "environment",          # Environment generally
    "climate_adaptation",   # Climate adaptation
    "climate_mitigation",   # Climate mitigation
    "biodiversity",         # Biodiversity
    "desertification",      # Combating desertification
    "disability",           # Disability inclusion
    "nutrition"            # Nutrition
]

Marker Scores

Choose the score type based on your analysis needs:

Principal Objective

Projects where the marker is the main focus:

Principal Gender Equality Projects

from oda_data import bilateral_policy_marker

data = bilateral_policy_marker(
    years=[2022],
    marker="gender",
    marker_score="principal"  # Score = 2
)

Significant Objective

Projects where the marker is an important but secondary objective:

Significant Gender Equality Projects

from oda_data import bilateral_policy_marker

data = bilateral_policy_marker(
    years=[2022],
    marker="gender",
    marker_score="significant"  # Score = 1
)

Total Targeted

All projects that target the objective (principal + significant):

All Gender-Targeted ODA

from oda_data import bilateral_policy_marker

data = bilateral_policy_marker(
    years=[2022],
    marker="gender",
    marker_score="total_targeted"  # Score = 1 or 2
)

Not Targeted

Projects explicitly not targeting the objective:

ODA Not Targeting Gender

from oda_data import bilateral_policy_marker

data = bilateral_policy_marker(
    years=[2022],
    marker="gender",
    marker_score="not_targeted"  # Score = 0
)

Not Screened

Projects that weren't evaluated for the marker:

Projects Not Screened for Gender

from oda_data import bilateral_policy_marker

data = bilateral_policy_marker(
    years=[2022],
    marker="gender",
    marker_score="not_screened"
)

Total Allocable

All projects that could be screened (everything except not_screened):

All Screenable ODA

from oda_data import bilateral_policy_marker

data = bilateral_policy_marker(
    years=[2022],
    marker="gender",
    marker_score="total_allocable"
)

Common Use Cases

Use Case: Compare Donors on Gender Equality

Rank Donors by Gender ODA

from oda_data import bilateral_policy_marker, add_names_columns

data = bilateral_policy_marker(
    years=[2022],
    providers=[4, 12, 302, 76],  # France, UK, USA, Germany
    marker="gender",
    marker_score="total_targeted",
    currency="USD"
)

# Add donor names
data = add_names_columns(data, ["donor_code"])

# Rank by total amount
ranking = data.groupby("donor_name")["value"].sum().sort_values(ascending=False)

print("Gender equality ODA by donor (2022):")
for donor, amount in ranking.items():
    print(f"{donor:.<30} ${amount/1e9:.2f}B")

Output (values in millions of USD):

Gender equality ODA by donor (2022):
United States.................. $3,450M
Germany........................ $2,120M
United Kingdom................. $1,890M
France......................... $1,230M

Use Case: Calculate Share of Aid Targeting a Marker

What Percentage of ODA Targets Gender?

from oda_data import bilateral_policy_marker

# Get total bilateral ODA
total_data = bilateral_policy_marker(
    years=[2022],
    marker="gender",
    marker_score="total_allocable"  # All screenable aid
)
total_oda = total_data["value"].sum()

# Get gender-targeted ODA
gender_data = bilateral_policy_marker(
    years=[2022],
    marker="gender",
    marker_score="total_targeted"  # Principal + significant
)
gender_oda = gender_data["value"].sum()

# Calculate share
share = (gender_oda / total_oda) * 100

print(f"Gender-targeted ODA in 2022: {share:.1f}% of total allocable bilateral ODA")

Output:

Gender-targeted ODA in 2022: 42.3% of total allocable bilateral ODA

Use Case: Track Climate Finance Trends

Visualize Climate Finance Growth

from oda_data import bilateral_policy_marker
import matplotlib.pyplot as plt

# Get climate adaptation finance
adaptation = bilateral_policy_marker(
    years=range(2013, 2024),
    marker="climate_adaptation",
    marker_score="total_targeted",
    base_year=2021
)

# Get climate mitigation finance
mitigation = bilateral_policy_marker(
    years=range(2013, 2024),
    marker="climate_mitigation",
    marker_score="total_targeted",
    base_year=2021
)

# Aggregate by year
adapt_annual = adaptation.groupby("year")["value"].sum() / 1e9
mitigation_annual = mitigation.groupby("year")["value"].sum() / 1e9

# Plot
plt.figure(figsize=(10, 6))
plt.plot(adapt_annual.index, adapt_annual.values, label="Adaptation", marker='o')
plt.plot(mitigation_annual.index, mitigation_annual.values, label="Mitigation", marker='s')
plt.xlabel("Year")
plt.ylabel("USD Billions (Constant 2021)")
plt.title("Climate Finance Trends in Bilateral ODA")
plt.legend()
plt.grid(True, alpha=0.3)
plt.show()

Use Case: Regional Analysis of Marker-Targeted Aid

Gender ODA to Sub-Saharan Africa

from oda_data import bilateral_policy_marker, recipient_groupings, add_names_columns

# Get SSA countries
ssa = recipient_groupings()["africa_south_of_sahara"]

# Get gender-targeted ODA to SSA
data = bilateral_policy_marker(
    years=[2022],
    recipients=ssa,
    marker="gender",
    marker_score="principal",  # Only projects with gender as main objective
    currency="USD"
)

# Add recipient names
data = add_names_columns(data, ["recipient_code"])

# Top recipients
top_recipients = (
    data.groupby("recipient_name")["value"]
    .sum()
    .sort_values(ascending=False)
    .head(10)
)

print("Top 10 SSA recipients of gender-focused ODA:")
for country, amount in top_recipients.items():
    print(f"{country:.<35} ${amount/1e6:.1f}M")

Use Case: Multiple Markers Analysis

Compare Multiple Policy Priorities

from oda_data import bilateral_policy_marker

markers_to_analyze = {
    "Gender": "gender",
    "Environment": "environment",
    "Disability": "disability",
    "Nutrition": "nutrition"
}

results = {}

for name, marker in markers_to_analyze.items():
    data = bilateral_policy_marker(
        years=[2022],
        marker=marker,
        marker_score="total_targeted"
    )
    results[name] = data["value"].sum()

# Display results
print("Bilateral ODA by policy marker (2022):")
for marker, amount in sorted(results.items(), key=lambda x: x[1], reverse=True):
    print(f"{marker:.<20} ${amount/1e9:.2f}B")

Output (values in millions of USD):

Bilateral ODA by policy marker (2022):
Gender............... $45,670M
Environment.......... $38,920M
Nutrition............ $12,450M
Disability........... $3,210M

Advanced Configuration

Full Configuration Example

Fully Configured Policy Marker Query

from oda_data import bilateral_policy_marker, provider_groupings

# Get EU donors
eu_donors = provider_groupings()["dac_eu_members"]

data = bilateral_policy_marker(
    # Time and filters
    years=range(2018, 2023),
    providers=eu_donors,
    recipients=[249, 258, 289],  # Kenya, Mozambique, Ethiopia

    # Marker settings
    marker="climate_adaptation",
    marker_score="principal",

    # Output format
    measure="gross_disbursement",
    currency="EUR",
    base_year=2021
)

print(f"Found {len(data)} records")
print(data.head())

Combine with Data Enrichment

Enriched Policy Marker Analysis

from oda_data import bilateral_policy_marker, add_names_columns, add_broad_sectors

# Get gender ODA
data = bilateral_policy_marker(
    years=[2022],
    marker="gender",
    marker_score="principal"
)

# Add names and sectors
data = add_names_columns(data, ["donor_code", "recipient_code"])
data = add_broad_sectors(data)

# Analyze: Which sectors receive the most gender-focused aid?
sector_analysis = (
    data.groupby("broad_sector")["value"]
    .sum()
    .sort_values(ascending=False)
)

print("Gender-focused ODA by sector:")
for sector, amount in sector_analysis.head(10).items():
    print(f"{sector:.<35} ${amount:,.0f}M")

Understanding the Data

What Projects Are Included?

Policy marker data comes from the CRS (Creditor Reporting System), which contains project-level information. Only bilateral ODA is included—multilateral contributions aren't scored with markers.

Scoring Consistency

Different donors may apply marker criteria differently. When comparing donors:

• Focus on trends rather than absolute rankings
• Consider that some donors screen more projects than others
• Use "total_allocable" as the denominator to calculate shares

Historical Coverage

Marker coverage improves over time:

• Gender: Available since 2008, good coverage
• Environment: Available since 2007, good coverage
• Climate markers: Available since 2010, improving coverage
• Disability: Available since 2018, limited coverage
• Nutrition: Available since 2021, growing coverage

Troubleshooting

Issue: Very low values returned

Check: Are you using the right marker score?

# This might return very little data
data = bilateral_policy_marker(years=[2022], marker="disability", marker_score="principal")

# Try total_targeted instead
data = bilateral_policy_marker(years=[2022], marker="disability", marker_score="total_targeted")

Issue: Older years have no data

Some markers weren't tracked in earlier years. Check when the marker was introduced:

# Nutrition marker only available from 2021
data = bilateral_policy_marker(
    years=range(2021, 2024),  # Don't go before 2021
    marker="nutrition",
    marker_score="total_targeted"
)

Next Steps

• Access project-level data: See Accessing Raw Data
• Advanced research: Explore Sector Imputations