Food Security, Nutrition & Poverty
Food Security
The IPC Acute Food Insecurity (IPC AFI) classification
provides strategically relevant information to decision makers that focuses on
short-term objectives to prevent, mitigate or decrease severe food insecurity.
The IPC website and API also includes access to the
Cadre Harmonisé (CH) data,
which targets countries in the Sahel and West Africa.
Although the CH is a distinct food security classification system,
it aligns with IPC standards in terms of assessment processes and outcomes.
Summary
Parameters Returned
The table below describes the parameters returned from this endpoint.
For available query parameters, please see the
API sandbox.
- We have p-coded the source data by taking the admin 1 and 2 names,
and applying the algorithm from
hdx-python-country,
which uses phonetic name matching and manual overrides
- Where admin 1 names could not be p-coded, the provided p-codes from the
source data are at national level
- Where admin 2 names could not be p-coded, the provided p-codes from the
source data are at admin 1 level if possible or national level if not
Usage Notes
- The data are available at national, admin 1 and admin 2 levels
- The admin name from the provider is supplied along with p-codes and
corresponding standardised admin names where available
- The total population (
ipc_phase="all") is not necessarily equal to the sum of
the populations in phases 1-5. The differences are usually small (due to
rounding errors), or because there is no IPC phase data
- Due to the above, the sum of the IPC fractions from phases 1-5 may not be
exactly equal to 1
- Food security statistics from countries in West Africa and the Sahel
come from the Cadre Harmonisé
- Not all geographical sub-divisions in the IPC correspond to known
administrative levels. In particular, several countries contain point-like
regions that correspond to urban centres, or population groups such as IDPs
and refugees. There are also country-specific disclaimers that we
present in the table below:
| ISO3 |
Country Name |
Notes |
| BDI |
Burundi |
Admin 2 regions do not correspond with the COD boundaries and were not p-coded |
| COD |
Democratic Republic of the Congo |
Kinshasa is sub-divided and not p-codable at the admin 2 level |
| ETH |
Ethiopia |
Some admin 2 regions are sub-divided into clusters and not p-codable |
| MRT |
Mauritania |
Some admin 2 regions have been grouped together, and thus are not individually p-codable |
| NER |
Niger |
Some regions are divided into accessible and non-accessible areas, and the term "ville" is used to denote urban areas |
| NGA |
Nigeria |
There are several admin 2 regions which share a name, and some of them have a (1) appended to the name, which we do not p-code |
| SDN |
Sudan |
Many admin 2 regions have been grouped together, and thus are not individually p-codable |
| SOM |
Somalia |
Admin 2 regions in Somalia are sub-divided, thus we to not attempt to assign p-codes |
| ZAF |
South Africa |
Admin 2 regions are a mix of admin levels, thus we do not attempt to assign p-codes at admin 2 |
Food Prices & Market Monitor
The World Food Programme Price Database covers foods such as maize, rice,
beans, fish, and sugar for 98 countries and some 3000 markets. It is updated
weekly but primarily contains data with a monthly update frequency. For a
detailed methodology, see WFP's
Market Analysis Guidelines.
Summary
Parameters Returned
The table below describes the parameters returned from this endpoint.
For available query parameters, please see the
API sandbox.
- The reference period is computed by converting date from the “date” column,
originally presented as 15th day of a particular month, into a range spanning
the entire month
- The source data is not p-coded, however we have used the admin 1 and 2 names
to p-code most markets. See WFP Market
for more details.
Poverty Rate
The global Oxford Multidimensional Poverty Index
(MPI) measures multidimensional poverty in over 100 developing countries,
using internationally comparable datasets. The MPI assesses poverty through
three main dimensions: health, education, and living standards, each of which
is represented by specific indicators. For each country, MPI trends over time
are supplied if available. Relevant OPHI methodological notes are
58,
59 and
60.
Summary
Parameters Returned
The table below describes the parameters returned from this endpoint.
For available query parameters, please see the
API sandbox.
| Parameter |
Description |
Source |
resource_hdx_id |
Unique resource UUID on HDX |
Resource |
mpi |
The multidimensional poverty index, derived as a product of the headcount_ratio and intensity_of_deprivation. Note that this metric is presented as a fraction, while the others are percentages. |
|
headcount_ratio |
The percentage of people deprived in 33% or more indicators |
|
intensity_of_deprivation |
The average proportion of indicators in which people are deprived, given as a percentage |
|
vulnerable_to_poverty |
The percentage of people deprived in 20-33% of indicators |
|
in_severe_poverty |
The percentage of people deprived in 50% or more indicators |
|
reference_period_start |
The start date for which the data are applicable |
|
reference_period_end |
The end date for which the data are applicable |
|
location_code |
Location p-code, based on the ISO-3 (ISO 3166 alpha-3) standard |
Location |
location_name |
Location name, based on the "short name" from the UN M49 Standard |
Location |
admin1_code |
Admin 1 p-code from Common Operational Datasets |
Admin 1 |
admin1_name |
Admin 1 name from Common Operational Datasets or original data source |
Admin 1 |
admin_level |
Admin level |
Admin Level |
- For rows in the original data with two timepoints, we take each timepoint as
single entry into HDX HAPI
- The reference period is constructed using the full range of the year or year
range presented in the “year” column, pertaining to the timepoint in
question, of the original data
Usage Notes
- The data are available at the national and admin 1 level
- The admin name from the provider is supplied along with p-codes and
corresponding standardised admin names where available
- We use p-codes from the source data which was p-coded by taking the admin 1
names, and applying the algorithm from
hdx-python-country
- Where admin 1 names could not be p-coded, the provided p-codes from the
source data are at national level
- Trends are estimated using indicators in the global MPI that are harmonised
across the time periods and are used where data are available for a country
- For any country where trends are unavailable in the source, the latest data
(which is not harmonised across time) are used instead