http://www.fao.org/economic/ess/ess-events/afcas/afcas25/en/

1. African Commission on Agriculture Statistics / Commission africaine des statistiques agricoles,
Entebbe, Uganda, 13 - 17 Nov 2017
AGENDA ITEM 3.3:
INDICATOR OF FOOD PRICE ANOMALIES
Felix Baquedano
Economist/ FAO/ Trades and Markets Division

2. SDG GOAL, TARGET & INDICATOR
SDG Target 2.c
Adopt measures to ensure the proper functioning of
food commodity markets and their derivatives and
facilitate timely access to market information,
including on food reserves, in order to help limit
extreme food price volatility.
SDG Indicator 2.c.1: Indicator of Food Price Anomalies
(IFPA)
The IFPA is an indirect indicator of Target 2.c, as it is a
measure of food price volatility, detecting abnormal
growth of prices in food markets.
SDG 2: End hunger, achieve food security and improved nutrition and
promote sustainable agriculture

3. AFCAS25, Entebbe, Uganda 13-17 Nov 2017
• The connection between food and national security was brought into
sharp focus during the food price crisis of 2007/2008.
• In a globalised world, keeping an eye on food commodity prices and a
careful watch for price hikes has never been more important.
• In many countries, market prices are sometimes the only source of
information available to assess the severity of a local shock to either
access or availability of food.
SDG GOAL, TARGET & INDICATOR CON’T

4. INDICATOR FORMULA
The indicator of food price anomalies is composed of two sub-indicators:
𝐼𝐹𝑃𝐴 𝑡 = 𝛼
𝐶𝑄𝐺𝑅 𝑦𝑡 − 𝐶𝑄𝐺𝑅𝑡
𝜎 𝐶𝑄𝐺𝑅 𝑡
+ 1 − 𝛼
𝐶𝐴𝐺𝑅 𝑦𝑡 − 𝐶𝐴𝐺𝑅𝑡
𝜎 𝐶𝐴𝐺𝑅 𝑡
Where 𝛼 is equal to 0.40
𝐶𝑄𝐺𝑅 𝑦𝑡 and 𝐶𝐴𝐺𝑅 𝑦𝑡 are the quarterly and annual compound
growth rates in year y and month t respectively
𝐶𝑄𝐺𝑅𝑡 and 𝐶𝐴𝐺𝑅𝑡 are weighted means of the quarterly and annual compound growth
rates in month t
𝜎 𝐶𝑄𝐺𝑅 𝑡
and 𝜎 𝐶𝐴𝐺𝑅 𝑡
are weighted standard devaitions of the quarterly and annual compound
growth rates in month t

5. INDICATOR METHODOLOGY
• The indicator monitors key commodities for food security at a national level and at a
regional/global level the overall price level of food. To accomplish this the indicator is
calculated for a set of commodities (Maize, Rice, Wheat, Millet/Sorghum) and on the
food price sub-index of the consumer price index as reported by the IMF and National
sources.
• Data sources:
• The data is obtained from national market Information systems, IMF and national
statistics agencies
• For the commodity prices please visit FAOs Food Price Monitoring and Analysis
(FPMA) Tool http://www.fao.org/giews/food-prices/tool/public/#/home
• For the Food Indeces (NOT TO BE CONFUSED WITH FAOs FOOD PRICE INDEX) visit
http://www.fao.org/faostat/en/#data/CP

6. INDICATOR METHODOLOGY, CON’T
The basis for the IFPA is a weighted sum of two compound growth rates (CGR). A CGR is a
geometric mean that assumes that a random variable grows at a steady rate,
compounded over a specific period of time
Because it assumes a steady rate of growth the CGR smooth’s the effect of volatility of
periodic price movements. This is advantages especially when dealing with highly volatile
price series.
This indicator is commonly used in the financial sector as a way to rank by annual rates of
growth of stocks or portfolio valuations. In this world high growth implies high returns.

7. AFCAS25, Entebbe, Uganda 13-17 Nov 2017
INDICATOR METHODOLOGY, CON’T
January February March April May
50 35 65 52 75
-30% 86% -20% 44%
Average 20%
50 55 61 68 75
11% 11% 11% 11%
Compound 11%
Illustrating an compound growth rate

8. AFCAS25, Entebbe, Uganda 13-17 Nov 2017
INDICATOR METHODOLOGY, CON’T
The CGR is the growth in any random variable from the
beginning of the period 𝑡0 to the end of the period 𝑡 𝑛 , raised
to the power of one over the length of the period of time being
considered, as highlighted in the equation below:
𝐶𝑋𝐺𝑅𝑡 =
𝑃𝑡 𝑛
𝑃𝑡0
1
𝑛−1
− 1
Where:
• t is period
• 𝑃𝑡 𝑛
is price at the end of the period
• 𝑃𝑡0
is price at the beginning of the period
• 𝑛 −1: number of months in the period
Calculating the CGR

9. AFCAS25, Entebbe, Uganda 13-17 Nov 2017
INDICATOR METHODOLOGY, CON’T
We define three levels for the indicator
0.5 ≤ 𝐼𝐹𝑃𝐴 𝑡 < 1 𝑃𝑟𝑖𝑐𝑒 𝑊𝑎𝑡𝑐ℎ
𝐼𝐹𝑃𝐴 𝑡 ≥ 1 𝑃𝑟𝑖𝑐𝑒 𝐴𝑙𝑒𝑟𝑡
𝑜. 𝑤. 𝑁𝑜𝑟𝑚𝑎𝑙 (𝑋𝑃𝑡
𝑁
)

10. AFCAS25, Entebbe, Uganda 13-17 Nov 2017
Empirical RESULTS
Oct-15 Mean-W SD-W Indicator
CQGR -3.64 -4.20 3.06 0.18
CAGR 0.73 0.20 2.09 0.25
IPA 0.23
Sample Results: El Salvador White Maize October 2015

11. AFCAS25, Entebbe, Uganda 13-17 Nov 2017
Empirical RESULTS, CON’T
Time
Period
Quarterly
Indicator
Annual
Indicator
Indicator
of Food
Price
Anomalies
2009 -0.66 -3.48 -2.35
2010 1.26 -0.23 0.36
2011 0.97 3.52 2.50
2012 -0.73 -1.77 -1.35
2013 -0.09 -0.36 -0.25
2014 1.16 0.70 0.88
2015 -0.11 0.92 0.51
2016 -0.54 -0.41 -0.46
Sample Results: El Salvador white maize annual results

12. AFCAS25, Entebbe, Uganda 13-17 Nov 2017
In 2016, twenty one countries experienced high or
moderately high domestic prices relative to their historical
levels for one or more staple cereal food commodities.
Maize was the commodity that recorded the highest
number of country and markets with price anomalies.
Thirteen of the twenty one countries were in Sub-Saharan
Africa, where the main causes of high prices were
domestic output declines, currency depreciation and, in
some countries, insecurity. Localized increases in fuel
prices provided further support. In Latin America and the
Caribbean, reduced harvests and currency weakness,
coupled with trade reforms aimed at boosting exports,
were key drivers of the observed higher price levels.
Empirical RESULTS, CON’T

13. AFCAS25, Entebbe, Uganda 13-17 Nov 2017
Empirical RESULTS, CON’T
High and moderately high cereal prices in 2016 based on the Indicator of Food Price Anomalies
(IFPA) 1
Source: FAO-GIEWS
1 The IFPA is a weighted average of the standardized differences from the monthly means of rolling quarterly and
annual compound growth rates. The inclusion in the list is based on GIEWS analysis of the IFPA results.
2 Twelve month average of monthly IFPA. High defined as IFPA>=1; Moderately high defined as 0.4=<IFPA=<0.99.
Reference area name
Time Period
(Year)
Disaggregation classification Observation value
Nigeria 2016Maize (white) 15.63
Swaziland 2016Maize meal 2.90
Argentina 2016Maize (yellow) 2.00
Brazil 2016Maize (yellow) 1.80
Mozambique 2016Maize (white) 1.80
Burundi 2016Maize 1.64
Bolivia 2016Maize (yellow) 1.29
Rwanda 2016Maize 0.98
Uganda 2016Maize 0.90
Peru 2016Maize (yellow) 0.84
Malawi 2016Maize 0.73
Haiti 2016Maize meal (local) 0.56
Nigeria 2016Rice (milled, local) 7.49
Angola 2016Rice (milled) 5.64
Mozambique 2016Rice 2.53
Swaziland 2016Rice 1.66
Brazil 2016Rice (paddy) 1.09
Bangladesh 2016Rice (Medium) 0.52
Morocco 2016Wheat (Soft) 2.31
Zimbabwe 2016Wheat (flour) 1.19
Bolivia 2016Wheat (flour, imported) 0.71
India 2016Wheat (flour) 1.17
Namibia 2016Wheat (flour) 0.55
Nigeria 2016Sorghum (white) 8.42
South Sudan 2016Sorghum (Feterita) 6.24
Ethiopia 2016Sorghum (white) 1.05
Niger 2016Sorghum (local) 0.62
Nigeria 2016Millet 2.19
Niger 2016Millet (local) 0.62
Namibia 2016Millet 1.98

14. INDICATOR POLICY USE AND INTERPRETATION
• Feeding into FAO’s Global Information
and Early Warning System (GIEWS) and
its activities of Food Price Monitoring
and Analysis (FPMA) at country level,
the indicator of food price anomalies
offers governments regular price
information on a basket of goods.
• Results are disseminated and analysed
through the FPMA website and bulletin
on a monthly basis with the aim of
providing early warning to countries
where there is a potential impact on
economic access to key food products
as a result of abnormally high food
prices. It helps countries ensure
appropriate measures can be taken to
soften the blow when consumer
markets fluctuate.

15. INDICATOR LIMITATIONS
• The indicator of food price anomalies is just a rough guide of market dynamics.
As such, one cannot rely on it as the sole element to consider when giving a
food security alert or characterizing prices as abnormally high. Instead its’
results must be weighed with other available information on market
fundamentals and possible short term policy shocks that can explain these price
movements.
• This is especially important when evaluating whether or not the observed
shocks
• in prices will persist or are transitory.
• Moreover, the indicator does not attempt to directly assign causality to the
implementation of any given policy or market strategy, nor can it do so.
• From January 2018, FAO will begin monitoring at a global level anomalies in the food
component of the consumer price index
• This will facilitate cross country and regional comparisons and monitoring as it will be
based on a nationally defined food basket

16. IMPLEMENTATION CHALLENGES
The main challenge in implementing the indicator is data availability and data quality.
The indicator is very sensitive to both issues and in particular data gaps. A time series of
at least 60 monthly data points are required to calculate the indicator.
In terms of data availability countries have made significant investments in collecting and
disseminating commodity price data at a national level. However, accessing this
information at a global level still at times remains challenging.
Some of these challenges can me remedied through targeted capacity development.
Which FAO has already taken into consideration and which is discussed in the next slide.

17. FAO – CAPACITY DEVELOPMENT/TECHNICAL ASSISTANCE
FAO calculates the indicator of food price anomalies using country level data, but no
country calculates the indicator on its own. However, from 2018 FAO will facilitate the
ability for any country to calculate the indicator
FAO is now developing a module in the FPMA Tool, which would allow countries to
calculate the indicator automatically. Furthermore in 2018 FAO will launch it’s e-learning
course on the indicator in four languages (English, Spanish, French, and Russian)
Further implementation of the FPMA Tool at the country level will enable reporting on
the indicator. Currently the FPMA Tool is being deployed in 13 countries and in the
Central America region FAO and other partners are helping integrate regional commodity
prices for 6 countries.

18. FOR MORE INFORMATION, PLEASE CONTACT:
INDICATOR 2.C.1 –FELIX.BAQUEDANO@FAO.ORG
18