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Datasets Overview

ODA Reader provides access to six datasets covering official development assistance (ODA), other official flows (OOF), and development finance. Each dataset serves different analytical needs.

Quick Reference

Dataset What It Contains Use When
DAC1 Aggregate flows by donor Analyzing overall ODA trends, donor performance
DAC2a Bilateral flows by donor-recipient Recipient-level analysis
CRS Project-level microdata Sector analysis, project details, activity-level data
CPA Country Programmable Aid The share of aid donors programme at country level
Multisystem Multilateral system usage Analyzing multilateral channels and contributions
AidData Chinese development finance Chinese aid flows

DAC1: Aggregate Flows

What it contains: Total ODA and OOF by donor, aggregated across all recipients and sectors. This is the highest-level view of development assistance.

Key dimensions:

  • Donor (bilateral donors and multilateral organizations)
  • Measure type (ODA, OOF, grants, loans, etc.)
  • Flow type (commitments, disbursements, grant equivalents)
  • Price base (current or constant prices)
  • Unit measure (USD millions, national currency, etc.)

Use when:

  • You need donor-level totals
  • Analyzing overall ODA trends over time
  • Comparing donor performance
  • Working with high-level aggregates

Example:

from oda_reader import download_dac1

# Get all DAC1 data for 2020-2022
data = download_dac1(start_year=2020, end_year=2022)

# Filter for ODA disbursements in constant prices
oda_constant = download_dac1(
    start_year=2020,
    end_year=2022,
    filters={
        "measure": "1010",  # Net ODA
        "flow_type": "1140",  # Disbursements
        "price_base": "Q"  # Constant prices
    }
)

Read more about filtering →

Bulk download: The full dataset is available as a single file via bulk_download_dac1(). See Bulk Downloads for details.

DAC2a: Bilateral Flows by Recipient

What it contains: Bilateral ODA and OOF flows broken down by both donor and recipient country. Shows who gives to whom.

Key dimensions:

  • Donor (bilateral donors)
  • Recipient (receiving countries and regions)
  • Measure type (bilateral ODA, imputed multilateral, etc.)
  • Price base (current or constant)

Use when:

  • Analyzing flows to specific recipient countries
  • Understanding bilateral relationships
  • Studying geographic distribution of aid
  • Comparing different donors to the same recipient

Example:

from oda_reader import download_dac2a

# Get flows to Sub-Saharan Africa from all donors
africa_flows = download_dac2a(
    start_year=2020,
    end_year=2022,
    filters={"recipient": "289"}  # Sub-Saharan Africa (regional code)
)

# Get flows from Germany to East African countries
germany_eastafrica = download_dac2a(
    start_year=2022,
    end_year=2022,
    filters={
        "donor": "DEU",
        "recipient": ["KEN", "TZA", "UGA", "RWA"]
    }
)

CRS: Creditor Reporting System (Project-Level Microdata)

What it contains: Individual project and activity-level data with detailed information about each development assistance activity. This is the most granular dataset.

Key dimensions:

  • Donor
  • Recipient
  • Sector (purpose codes at various levels of detail)
  • Channel (implementing organization type)
  • Modality (grant, loan, equity, etc.)
  • Flow type
  • Microdata flag (True for project-level, False for semi-aggregates)

Use when:

  • You need project-level details (descriptions, amounts, sectors)
  • Analyzing sector-specific flows
  • Understanding implementation channels
  • Detailed activity-level analysis

Important: CRS defaults to microdata (project-level). For semi-aggregates matching the online Data Explorer view, set microdata: False in filters.

Example (microdata):

from oda_reader import download_crs

# Get all health sector projects from Canada
health_projects = download_crs(
    start_year=2022,
    end_year=2022,
    filters={
        "donor": "CAN",
        "sector": "120"  # Health sector (3-digit code)
    }
)

# Each row is a project with description, amount, dates, etc.

Example (semi-aggregates):

# Get semi-aggregated CRS data (matches online Data Explorer)
semi_agg = download_crs(
    start_year=2022,
    end_year=2022,
    filters={
        "donor": "USA",
        "recipient": "NGA",
        "microdata": False,
        "channel": "_T",  # Total across all channels
        "modality": "_T"  # Total across all modalities
    }
)

Performance note: The CRS API is slow for large queries. Consider using bulk downloads for full dataset access.

CPA: Country Programmable Aid

What it contains: The share of bilateral ODA that donors programme for individual partner countries. CPA strips out flows a partner country has no say over — debt relief, humanitarian aid, in-donor refugee and student costs, administrative costs, and other non-programmable items. The OECD publishes it as a separate dataflow (DSD_CPA@DF_CRS_CPA) derived from the CRS, so it shares the CRS schema, dimensions, and filter set.

Key dimensions: Same as the CRS — donor, recipient, sector, channel, modality, flow type, and the microdata flag.

Use when:

  • You want the country-programmable slice of aid rather than total bilateral ODA
  • Comparing how much of each donor's aid is programmable at country level
  • Tracking programmable aid to specific recipients or sectors over time

Important: Like the CRS, download_cpa defaults to microdata (microdata=True, i.e. MD_DIM=DD), returning project-level records. There is no grant-equivalent dataflow for CPA, so as_grant_equivalent is not available.

Example:

from oda_reader import download_cpa

# Get all CPA records for 2022
cpa = download_cpa(start_year=2022, end_year=2022)

# Country-programmable aid from the United States to Nigeria
us_nga = download_cpa(
    start_year=2022,
    end_year=2022,
    filters={"donor": "USA", "recipient": "NGA"}
)

The available filters match the CRS and can be listed with get_available_filters("cpa").

Multisystem: Members' Use of the Multilateral System

What it contains: Data on how DAC members use the multilateral aid system, including core contributions to multilateral organizations and earmarked funding.

Key dimensions:

  • Donor
  • Recipient (multilateral organizations)
  • Channel (specific multilateral organizations)
  • Flow type (commitments, disbursements)
  • Measure type

Use when:

  • Analyzing multilateral contributions
  • Understanding core vs. earmarked funding
  • Studying specific multilateral channels (World Bank, UN agencies, etc.)

Example:

from oda_reader import download_multisystem

# Get all multilateral contributions from France
france_multilateral = download_multisystem(
    start_year=2020,
    end_year=2022,
    filters={"donor": "FRA"}
)

# Get contributions to World Bank IDA
ida_contributions = download_multisystem(
    start_year=2020,
    end_year=2022,
    filters={"channel": "44002"}  # IDA
)

Performance note: Like CRS, Multisystem API can be slow. Bulk download is available for the full dataset.

AidData: Chinese Development Finance

What it contains: Project-level data on Chinese development finance activities, compiled by AidData. Covers official finance from China that may not be reported to the OECD.

Key dimensions:

  • Commitment year
  • Recipient country
  • Sector
  • Project descriptions
  • Flow amounts and types

Use when:

  • Analyzing Chinese development finance
  • Comparing DAC donors with China

Example:

from oda_reader import download_aiddata

# Get all AidData records for 2015-2020
chinese_aid = download_aiddata(start_year=2015, end_year=2020)

# AidData is downloaded as bulk file, filtered by year after download

Note: AidData comes from Excel files from the Aid Data website, not the OECD API. It uses a different schema than DAC datasets.

Discovering Available Filters

Each dataset has different dimensions you can filter by. Use get_available_filters() to see what's available:

from oda_reader import get_available_filters

# See available filters for each dataset
dac1_filters = get_available_filters("dac1")
dac2a_filters = get_available_filters("dac2a")
crs_filters = get_available_filters("crs")
cpa_filters = get_available_filters("cpa")
multisystem_filters = get_available_filters("multisystem")

Learn more about filtering →

Next Steps