Indicator 2.c.1 Indicator of food price anomalies

Indicator 2.c.1: Indicator of food price anomalies

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

Food and Agriculture Organization of the United Nations (FAO)

Definition:

The indicator of food price anomalies (IFPA) identifies market prices that are abnormally high. The IFPA relies on a weighted compound growth rate that accounts for both within year and across year price growth. The indicator directly evaluates growth in prices over a particular month over many years, taking into account seasonality in agricultural markets and inflation, allowing to answer the question of whether or not a change in price is abnormal for any particular period.

Concepts:

The indicator of price anomalies (IFPA) relies on two compound growth rates (CGR’s), a quarterly compound growth rate (CQGR) and an annual compound growth rate (CAGR). A CGR is a geometric mean^[1] 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 smoothes the effect of volatility of price changes. The CGR is the growth in any random variable from time period $t_A$ to $t_B$, raised to the power of one over the length of the period of time being considered.

${CXGR}_t={\left(\frac{P_{t_B}}{P_{t_A}}\right)}^{\frac{1}{t_B-t_A}}-1$ (1)

where:

${CXGR}_t$ is the quarterly or annual compound growth rate in month t

$P_{t_A}$is the price at the beginning of the period

$P_{t_B}$ is the price at the end of the period,

$t_B-t_A$ is the time in months between periods $A$ and $B$.

Index and Percent.

FAO relies on official domestic price data that it compiles in the Food Price Monitoring and Analysis (FPMA) tool to calculate and monitor the indicator. Five cereal products will be monitored: maize & maize products, wheat & wheat flour, rice, sorghum and millet. While diets across the world have become more diversified with increasing incomes, cereals still account for 45 percent of a person’s daily caloric intake, making this commodity group the most important in terms of its contribution to caloric intake, particularly for low-income populations (FAOSTAT, 2017). For the purpose of a more comprehensive coverage at the global level, FAO also calculates IFPA on countries’ officially reported food price indices as reported in FAOSTAT, which facilitates cross country comparisons as it uses a national level food basket covering all the most important commodities consumed. While the basket differs from country to country, this approach is more reflective of national and global trends as countries have predefined the commodities that have the most impact on local consumers. This approach also facilitates the implementation of the indicator as countries will not be asked to create a new index or modify existing methodologies.

For the Food CPI, the FAOSTAT monthly CPI & Food CPI database was based on the ILO CPI data until December 2014. In 2014, IMF-ILO-FAO agreed to transfer global CPI data compilation from ILO to IMF. Upon agreement, CPIs for all items and its subcomponents originates from the International Monetary Fund (IMF), and the UN Statistics Division (UNSD) for countries not covered by the IMF. However, due to a limited time coverage from IMF and UNSD for a number of countries, the Organisation for Economic Co-operation and Development (OECD), the European statistics (EUROSTAT), the Latin America and the Caribbean statistics (CEPALSTAT), Central Bank of Western African States (BCEAO), Eastern Caribbean Central Bank (ECCB) and national statistical office website data are used for missing historical data from IMF and UNSD food CPI. The FAO CPI dataset for all items (or general CPI) and the Food CPI, consists of a complete and consistent set of time series from January 2000 onwards. It further contains regional and global food CPIs compiled by FAO using population weights to aggregate across countries.

The sources of the price information are numerous and are listed for each price series in the FPMA tool at https://fpma.apps.fao.org/giews/food-prices/tool/public/#/home.

For the Food Price Indices, the source is FAOSTAT http://www.fao.org/faostat/en/#data/CP.

It is appropriate to caution the reader that the indicator is just a guide to understanding market dynamics. As such, one cannot rely on it as the sole element to determine whether a food price in a particular market at a given time is abnormally high or low due to the direct effects of local policies. Results must be weighed with other available information on market fundamentals, macroeconomic context and external shocks. The main challenge in implementing the indicator is data availability and data quality. The calculation of the indicator requires an uninterrupted monthly price series (i.e. if more than 3 consecutive months of data are missing the series may be dropped) of at least 5 years, which include the year being analysed and the 4 preceding years to generate averages and standard deviations. Finally, the indicator is calculated on real price terms to net out the effects of inflation and compare prices in constant money terms over time. However, if food items’ contribution to CPI is high, it induces downward bias in food real price – i.e., it underestimates the extent of the price increase (nominal prices or a non-food CPI could be used).

Mathematically the IFPA for a particular year $y$ in month $t\mathrm{\ }$ is calculated as the weighted sum of the quarterly indicator of food price anomalies ${(QIFPA}_{yt}^{}$), and the annual indicator of food price anomalies$\mathrm{\ }{(AIFPA}_{yt}^{})$.

$\left(\frac{{CXGR}_{yt}-{\overline{W\_CXGR}}_t}{{\widehat{\sigma }}_{{W\_CXGR}_t}}\right)={XIFPA}_{yt}^{}$ (2)

Where:

${CXGR}_{yt}$ is either the quarterly or annual compound growth rate in month t for year y

${\overline{W\_CXGR}}_t$ is the weighted average of either the quarterly or annual compound growth rate for month t across years y

${\widehat{\sigma }}_{{W\_CXGR}_t}$ is the weighted standard deviation of either the quarterly or annual compound growth rate for month t over years y,

${XIFPA}_{yt}^{}$ is either the quarterly or annual indicator of a price anomaly in month t for year y.

Then IFPA is defined as:

${IFPA}_{yt}=\gamma {QIFPA}_{yt}^{}+\left(1-\gamma \right){AIFPA}_{yt}^{}$ (3)

Where:

${IFPA}_{yt}$ is the indicator of food price anomalies in year $y\mathrm{\ }$and month $t$

${QIFPA}_{yt}^{}$ is the quarterly indicator of food price anomalies in year $y\mathrm{\ }$and month $t$

${AIFPA}_{yt}^{}$ is the annual indicator of food price anomalies in year $y\mathrm{\ }$and month $t$

$\gamma$ is a weight with a value of 0.4.

The weight $\gamma$ establishes the relative importance of quarterly (${QIFPA}_{yt}^{}$) anomalies to the year-on-year price variations (${AIFPA}_{yt}^{})$. The weight $\gamma$ is set to 0.4, giving a weight of 0.6--$\left(1-\gamma \right)$-- to abnormal price growth from year-to-year. This is done to better capture the price level relative to its seasonal trends, which is measured to the price level a year earlier. SDG indicator 2.c.1 is then calculated as the arithmetic mean over $t$ months of the ${IFPA}_{yt}$ as follows:

${IFPA}_y=\frac{1}{t}\sum _{i=1}^t{IFPA}_{yt}$ (4)

Where:

${IFPA}_y$ is the annual indicator of food price anomalies in year $y$

${IFPA}_{yt}$ is the indicator of food price anomalies in year $y\mathrm{\ }$and month $t$

$t$ is the number of months in a year

2024-07-29

Food and Agriculture Organization of the United Nations (FAO)

Not applicable

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