Sustainable Development Goals - 17 Goals to Transform our World

Indicator of food price anomalies

This table provides metadata for the actual indicator available from US statistics closest to the corresponding global SDG indicator. Please note that even when the global SDG indicator is fully available from US statistics, this table should be consulted for information on national methodology and other US-specific metadata information.

Actual indicator available
Actual indicator available - description
Date of national source publication
Method of computation Computation of the indicator requires the availability of a series of monthly prices and involves three steps. Step 1. Calculating the quarterly and annual compound growth rates. A CGR is the growth rate in a time series over a certain amount of time. It is computed as [see report]. A quarterly CGR (CQGR) is calculated by considering periods of three months between ____ and __0, while an annual CGR (CAGR) is calculated by considering a period of 12 months. The importance to consider both CQGR and CAGR derives from the need to take into account the presence of marked seasonal variability in many agricultural prices, with prices growing more or less steadily over the year from their minimum, occurring at harvest period. Step 2. Calculating the weighted average and standard deviation of both CQGR and CAGR. The historic distributions of CGRs are characterized by the mean and the standard deviation of past CGR values. A different distribution of CGRs is computed per each calendar month. Time weights are used to make sure that the more recent past has a higher weight in the calculation of the mean and standard deviation of the distribution of CGRs, so that more recent price dynamics are not overshadowed by past extreme events which could prevent the detection of significant market shocks on prices. Step 4. Computing the indicator of price anomalies. First, the difference between the monthly CGR and the historic average CGR is computed for each month and then normalized with respect to the historic standard deviation. Based on the results, a price anomaly is recorded in each month for which the normalized difference is equal or greater than one. Then, the frequency of price anomalies is computed for both the quarterly and the annual CGRs and the final indicator of price anomalies for month t (________ ) is computed as a weighted average of the frequency of price anomalies in the quarterly CGR and the frequency of price anomalies based on the annual CGR. For further details, see Baquedano 2014 (2015?)
Scheduled update by national source
U.S. method of computation
Comments and limitations
Date metadata updated
Disaggregation geography
Unit of measurement
Disaggregation categories
International and national references
Time period
Scheduled update by SDG team

This table provides information on metadata for SDG indicators as defined by the UN Statistical Commission. Complete global metadata is provided by the UN Statistics Division.

Indicator name Indicator of food price anomalies
Target name 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.
Global indicator description The proposed indicator of food price anomalies measures the number of "Price Anomalies" that occur on a given food commodity price series over a given period of time.ConceptsThe volatility of a given food commodity price series is measured through the quarterly and annual Compound Growth Rates (CGR), of the monthly price level. The mean and standard deviation of the observed historic CGR values define what is considered to be "normal"volatility for the particular price series being considered. A "Price Anomaly" is then defined as the recording, in a given month, of a CGR that is greater than the historic mean CGR for that month by one standard deviation or more.
UN designated tier 2
UN custodial agency FAO
Link to UN metadata Link opens in a new window
Agency Staff Name
Agency Survey Dataset
Link to data source