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STOCKS-TO-USE RATIOS AS INDICATORS OF VULNERABILITY TO SPIKES IN

GLOBAL CEREAL MARKETS

Brian D. Wright

Department of Agricultural and Resource Economics College of Natural Resources

University of California, Berkeley

2nd Session of the AMIS Global Food Market Information Group

ROME, FAO HEADQUARTERS Wednesday, 3 October 2012

based on work presented in a paper of the same title:

STOCKS-TO-USE RATIOS AS INDICATORS OF VULNERABILITY TO SPIKES IN GLOBAL CEREAL

MARKETS

September, 2012

AMIS Paper IG-12/4

by

Eugenio Bobenrieth, Brian Wright, and Di Zeng

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Post- “Inside Job” I perceive a need for disclosure:

Recent or current grant support:

• AMIS initiative of G20

• Energy Biosciences Initiative (UC Berkeley, UIUC, LBL, BP, funded by BP) – researches cellulosic biofuels

• USDA

• NIH

• NSF

• USPTO

• Giannini Foundation

Disclosure (contd.) • Current consulting relationships:

– World Bank

– FAO

• No recent positions in commodity markets

• No investments in agricultural input or service providers, or significant commodity market or energy market participants.

• In past year, I was a consultant/expert witness engaged by an entity that produces and exports agricultural products. I was recently an expert witness in a case involving generic drug entry in pharmaceuticals

• I recently made a presentation at a major agricultural bank

• Last month I was compensated by a leading investment firm for a presentation at their head office

• I am happy to identify any of the firms involved in the above activities, should any audience member request that I do so.

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This presentation:

Focus: Needs of policy makers, not traders Time frame: • Crop year • Pre-biofuels market behavior*

*See Wright, B.D. Applied Economic Perspectives and Policy (2011) volume 33, number 1, pp. 32–58. doi:10.1093/aepp/ppq033 for effects of biofuels onmarkets

This presentation:

Questions: Are stocks data relevant to grain market shortages? Are grains stocks-to-use ratios useful additions to price and production information?

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Maize Real Price Deflator: Manufactures Unit Value (MUV)

Maize Real Price and Trend Deflator: Manufactures Unit Value (MUV)

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Co-movement: Real prices of wheat, rice, maize

and calories (natural logarithm scale)

Correlations: Real De-trended Price

Wheat Maize Rice Calories

Wheat 1.0000

Maize 0.7875 1.0000

Rice 0.5803 0.6280 1.0000

Calories 0.8318 0.8598 0.9133 1.0000

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• Assumption:

Price Spikes coincide with real shortages

First Question: Does price summarize all market

information?

• Is price a sufficient statistic of market conditions and outlook?

• If markets are “perfect,” yes

– Then we can all go home (with unemployment benefits?)

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Do prices predict spikes? Transition matrix for calorie price:

To (percentiles)

0-20 20-40 40-60 60-80 80-100

From

(percentiles)

0-20 0.500 0.250 0.000 0.250 0.000

20-40 0.300 0.300 0.200 0.200 0.000

40-60 0.111 0.222 0.333 0.222 0.111

60-80 0.000 0.300 0.100 0.200 0.400

80-100 0.111 0.000 0.333 0.222 0.333

The challenge for predicting spikes: Global data are problematic

• Price is at best an average of diverse prices consumers actually pay for grain consumed

• Production data vary in accuracy

• Stocks data are estimates, guesses and worse…

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Second Question If we had perfect production forecasts

one year ahead, would they explain what current prices do not reveal?

• Let’s take a look:

Do realized prices reflect current production? Was largest major grains production shortfall during 1970s

spikes?

Source: Calculated using Data from PSD Online, USDA.

Note. World grains = wheat + maize + milled rice. All quantities converted into Calories assuming, for wheat 3338Kcal per Kg,

for maize 3650 Kcal per Kg, and for milled rice 3656 Kcal per Kg).

2010/2011 is projection.

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Does Increasing Production Volatility explain spikes?Where is 1972/3? Shortfalls Increasing?

Source: Calculated using Data from PSD Online, USDA.

Third Question Can realized stocks add anything

realized production data cannot tell us?

• Let’s take a look:

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Maize Stocks/Use Ratios - with and without China

Maize Stocks/use ratio vs. de-trended real price

Inverse relation clear (cf. production)

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Rice: Stocks/Use Ratios - with and without China

Rice Stocks/use ratio vs. de-trended real price

Inverse relation less clear

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Calories: Stocks/use vs. de-trended real price

A clear inverse relation

Calorie stocks/use ratio has highest correlation with detrended real price of each major grain

Wheat

detrended real

price

Maize

detrended real

price

Rice

detrended real

price

Calories

detrended real

price

Wheat stock/use ratio

-0.4018 -0.4413 -0.3438 -0.4344

Maize stock/use ratio

-0.3971 -0.5034 -0.4356 -0.5156

Rice stock/use ratio

-0.2286 -0.2048 -0.1731 -0.2136

Calories stock/use ratio

-0.4996 -0.5723 -0.4729 -0.5792

Years: 1961-2007 Stocks/use ex-China

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Calorie stocks/use ratio has highest correlation with detrended real price of each major grain

Wheat

detrended real

price

Maize

detrended real

price

Rice

detrended real

price

Calories

detrended real

price

Wheat stock/use ratio

-0.4018 -0.4413 -0.3438 -0.4344

Maize stock/use ratio

-0.3971 -0.5034 -0.4356 -0.5156

Rice stock/use ratio

-0.2286 -0.2048 -0.1731 -0.2136

Calories stock/use ratio

-0.4996 -0.5723 -0.4729 -0.5792

Years: 1961-2007 Stocks/use ex-China

Can carryover stocks-use ratio help predict shortages?

Transition matrix for calorie SUR.

To (percentiles)

0-20 20-40 40-60 60-80 80-100

From

(percentiles)

0-20 0.625 0.125 0.125 0.125 0.000

20-40 0.200 0.300 0.300 0.200 0.000

40-60 0.222 0.333 0.111 0.333 0.000

60-80 0.000 0.300 0.200 0.100 0.400

80-100 0.000 0.000 0.222 0.333 0.444

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To understand relation of stocks to price spikes better, we need a little storage theory:

• Storers smooth out troughs in price and low-value consumption after high harvests by “buying low to sell high”

• Storers smooth expected shortages if cash is available:

– invest in stocks, raise current price, reduce expected shortage

Role of storage arbitrage

[ ( 1)]( ) cost of storage 0

1

[ ( 1)]( ) cost of storage 0

1

E P tP t if stocks

r

E P tP t if stocks

r

Key relations: Buy when low, sell when high

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Econ 101: Consumption demand curve

Econ 101: Consumption demand curve

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Add demand for stocks (non-negative)

Demand for consumption plus stocks

Note: Not like a constant elasticity consumer demand

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Equivalent shocks

Demand for consumption

Market demand, inclusive of stocks

Quantity

Price

Different impact

on prices

With stocks

Without stocks

When stocks are low, price

becomes very sensitive to

disturbances in supply

Why is price much more sensitive to shocks when stocks are minimal?

Theory: Effects of storage.

• Storers smooth out peaks after unexpected shocks, but only until their stocks run out

• When stocks run out, price spikes are required, to force consumers to respond one-for-one to shocks

SPIKES OCCUR ONLY IF STOCKS ARE MINIMAL

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We estimated a model of each grain market using only price data

• Avoids using “bad” stocks data

• Follows estimation tradition in this area

– but used Max Likelihood approach

– Need detrended background model for consistency

– Messy details…

We got results like this, (for wheat)

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We got results like this, (for wheat)

• Then we reconstructed stocks and consumption at each price

We got results like this (for wheat)

e.g. RED/GREEN = Stocks/Use at Price=0.9

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Wheat SUR and reconstruction from model: - recalibrated for mean (to include essential working

stocks) and range

Evidence of relationship: De-trended price vs. SUR and SUR

reconstruction for Wheat

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Choice of “thresholds” is up to AMIS - one illustrative example relates to an

inflexion point:

Inflexion point 1.05

Rice SUR and reconstruction from model: - a less good fit

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Rice: De-trended price vs. SUR and SUR reconstruction

Problems!

Calorie SUR and reconstruction from model: - fits much better

muc=

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Evidence of useful relationship: De-trended price vs. SUR for Calories

SUR sometimes a better warning indicator than price (e.g. 1972)

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Maize

Back to nominal values: Stockout prices and critical prices

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Conclusions

• Stocks data are useful additions to prices and production forecasts

• Econometric model helps explain market behavior as response to fluctuations in consumption demand and production as moderated by stockholding, without resorting to wild conjectures

• Grain calorie substitution is crucial to evaluating information on production, prices and stocks

Thanks to my colleagues:

• Carlo Cafiero, FAO

• Juan Bobenrieth, Universidad del Bio-Bio, Chile

• Yang Xie, ARE, University of California, Berkeley

• Josef Schmidhuber, FAO

• Phil Abbott, Purdue University

• Olivier Mahul, World Bank

• Will Martin, World Bank

• Wallace Tyner

• Marc Sadler, World Bank

• Stefan Tangerman

• Phil Verleger

• Jeff Williams, UC Davis

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Thanks to my coauthors:

• Professor Eugenio Bobenrieth

– Catholic University, Santiago, Chile

• Di Zeng – Doctoral Candidate, ARE, University of California,

Berkeley

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CALORIE PRICE TRANSMISSION MATRIX

To

80-100 60-80 40-60 20-40 0-20

From

80-100 0.500 0.250 0.000 0.250 0.000

60-80 0.300 0.300 0.200 0.200 0.000

40-60 0.111 0.222 0.333 0.222 0.111

20-40 0.000 0.300 0.100 0.200 0.400

0-20 0.111 0.000 0.333 0.222 0.333

Table 4. Correlation coefficient matrix between detrended real price and stock/use ratio

(ex-China), 1961-2007

Wheat

detrended real

price

Maize

detrended real

price

Rice

detrended real

price

Calories

detrended real

price

Wheat

stock/use ratio -0.4018 -0.4413 -0.3438 -0.4344

Maize

stock/use ratio -0.3971 -0.5034 -0.4356 -0.5156

Rice

stock/use ratio -0.2286 -0.2048 -0.1731 -0.2136

Calories

stock/use ratio -0.4996 -0.5723 -0.4729 -0.5792

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Correlations: Detrended real price and stock-to-use ratio,

Wheat

detrended real

price

Maize

detrended real

price

Rice

detrended real

price

Calories

detrended real

price

Wheat stock/use ratio

-0.4018 -0.4413 -0.3438 -0.4344

Maize stock/use ratio

-0.3971 -0.5034 -0.4356 -0.5156

Rice stock/use ratio

-0.2286 -0.2048 -0.1731 -0.2136

Calories stock/use ratio

-0.4996 -0.5723 -0.4729 -0.5792

Years: 1961-2007 Stocks/use ex-China

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Maize Real price and production:

deviations from trend

Rice Real price and production: Log deviations from trend

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Wheat Stocks/use ratio vs. de-trended real price