STRENGTHENING AGRICULTURAL MARKET INFORMATION
SYSTEMS (AMIS) GLOBALLY AND IN SELECTED COUNTRIES
(BANGLADESH/INDIA/NIGERIA) USING INNOVATIVE METHODS
AND DIGITAL METHODOLOGY
MTF/GLO/359/BGM
IMPROVED METHODOLOGY FOR ESTIMATION OF FOOD STOCKS
PHILIP ABBOTT
NOVEMBER 2013
FOOD AND AGRICULTURAL ORGANIZATION OF THE UNITED NATIONS
Consulting report to the Food and Agriculture Organization of the United Nations. Philip Abbott
is Professor of Agricultural Economics, Purdue University, West Lafayette, IN, 47907 USA. The
author appreciates helpful input from Carola Fabi, Elisabetta Carfagna, Romeo Recide, Adelabu
Ayotunde, David Babaloa, Denis Drechsler, Abdolreza Abbassian, Nancy Chin, Steve Wiggins,
Barbara Rater and numerous additional colleagues at FAO, USDA and elsewhere. The views
expressed as well as any errors or omissions remain the responsibility of the author.
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IMPROVED METHODOLOGY FOR ESTIMATION OF FOOD STOCKS
Summary
One recommendation that came following the food crisis of 2007-08 was that agricultural market
information systems needed improvement. Stocks positions were prominent in these
recommendations as they inform international and domestic prices, and because they are an
indicator of food availability, hence one measure of food security. Existing methods to estimate
stocks exhibit numerous problems, creating considerable uncertainty in available data.
While stocks are emphasized in market analysis, literature on how to estimate stocks is quite
limited, and documentation of stocks estimation methodologies is brief and incomplete. Stocks
estimates are often derived as a residual from food balance or supply utilization accounts, not
survey data. Conflicting candidate residual variables and weak empirical foundations mean this
method is likely to lead to poor estimates. Methods for resolving conflicts and the empirical
foundations of this method are no better documented than are stocks surveys. Discussions with
those involved in implementing stocks surveys nevertheless reveal a consistent set of best
practices in the few cases where surveys are now conducted.
This report is an initial effort aimed at improving stocks estimation methodology in developing
countries. Since best practices are mostly found in developed countries, but stocks data is not
collected in many developed countries, methodology for those cases is also considered, as is the
evolution of recommended methods as a country develops. This reports objective are therefore to
examine current and best practices in estimating stocks positions and to develop from that review
an annotated outline for a set of Guidelines for our recommended stocks estimation
methodology. Since our overall recommendation is that surveying and data collection on stocks
is strongly preferred, the Guidelines consider issues in implementation of such surveys, in both
developed and developing country supply chains.
The categories of issues included in the Guidelines include:
Introduction - Why measure stocks
Overall recommendations
What to measure
When and how often to measure
Identifying relevant agents for surveys
Collection methods and logistics
Sample strategy and design
Questionnaires
Documentation
Results reporting
Issues in each of these categories are discussed and recommendations are made based on the
review of best practices.
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Our recommendations emphasize the desirability of estimating stocks utilizing both commercial
and on-farm surveys, conducted separately on an annual or seasonal (quarterly) basis. This
information is needed to complement existing public stocks information that is often the only
information currently available. The goal is to capture carry-out stocks from one crop year
(season) to the next. It is useful to measure public, commercial and on-farm stocks, and to report
stocks positions in each of these categories. Surveys with limited scope, rather than
comprehensive rural household surveys, are required to insure a focus on accurate stocks data
collection.
Since the most serious constraint to implementing this recommendation is the cost of
implementing the stocks surveys, occasionally our detailed recommendations consider fallback
positions for developing countries. Second-best strategies to improve estimation of stocks as a
residual from food balance accounting are considered, recognizing the problems that approach
brings.
While there may be a number of ways to marginally improve agricultural market information,
including stocks information, that are better than the status quo, any such strategy is almost
certainly going to require additional data collection and additional budgetary expenses. In the
best practice cases, stock data collection is seen as the cost-effective approach.
The three countries targeted in the FAO project funded by the Bill and Melinda Gates
Foundation (BMGF) on improving agricultural market information –Nigeria, Bangladesh and
India – exhibit a range of methods to estimate stocks currently. The Philippines is considered in
another FAO project, and the Philippines appears to be successfully implementing both
commercial and on-farm surveys, and in fact has been doing so since 1980. In Nigeria there is
recognition of the need to collect survey data on stocks, but funding constrains survey
implementation. We identified there issues with the stocks questions that were part of
comprehensive rural surveys, and were uncertain as to future for implementation of the rural
household and commercial stocks surveys. In Bangladesh and India only official stocks data –
stocks held by public agencies or parastatals firms – are available. Hence the “target countries”
examined here include the Philippines in addition to Nigeria, Bangladesh and India, since the
Philippines serves as an example of good developing country practices.
Overall national stocks positions remain in most cases estimated imperfectly as a residual in food
balance equilibrium, or incompletely as public stocks. We believe each of these countries would
experience better market performance and more easily achieve food security goals if improved
stocks estimation methodology, incorporating data from farm and commercial surveys focused
on stocks, were adopted.
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Introduction
In 2007 and 2008 international agricultural commodity prices spiked to record nominal levels,
and the period was called a “food crisis” by those who saw this contributing to food insecurity
worldwide (FAO, 2008). At the time of the crisis, many causes for the commodity price spikes
were suggested, but prominent analysts argued the events were too complex to sort among
potential causal factors (e.g. Trostle, 2008). Controversy persists on both the causes and
consequences of high agricultural prices in 2007 and 2008 (Heady and Fan, 2010; Gilbert, 2011).
The role of stocks held worldwide is among competing explanations. Wright (2009) asserted that
stocks-to-use ratios were informative as usual in explaining market prices using revised data
after the fact. Abbott, Hurt and Tyner (2011), however, noted that commercial interests at the
time of the crisis viewed that the traditional stock-to-use relationships broke down during this
crisis, and they provided data from USDA’s WASDE reports (WAOB, 2011) showing that
expected stocks appeared to be much larger during the crisis than would be suggested by the high
market prices observed then.
One reason behind the inability to sort among causes of the food crisis, and the persistent
controversy, is that the quality of market data is questionable. A key recommendation to the
G8/G20 examination of food security following this event by collaboration among many
interested international organizations (FAO et al, 2011) was that agricultural market information
needed to be improved. The role of poor grain and oilseed stocks data was highlighted both in
that report and by G8/G20 recommendations. The U.N.’s High Level Task Force on the Global
Food Security Crisis also emphasized the need for improved agricultural market information
systems, and emphasized the need for better stocks data (UNHLTF, 2009).
Recognizing this need for more transparency and coordination in international food markets,
Ministers of Agriculture of the G20 decided to launch the Agricultural Market Information
System (AMIS) in June 2011, which is hosted by the Food and Agriculture Organization of the
United Nations (FAO) under the responsibility of the FAO’s Statistical Division (AMIS, 2013).
AMIS is a collaborative food information initiative to strengthen synergies and improve data
reliability in global commodity markets, initially focusing on wheat, maize, rice and soybeans.
Participants in AMIS include G20 countries plus Spain and seven additional major exporting and
importing countries that have been invited to participate based on their leading role in
international grain and oilseed markets. AMIS is supported by a joint Secretariat located in the
Food and Agriculture Organization of the United Nations (FAO), comprising nine international
organizations with the capacity to collect, analyze and disseminate information on the food
market situation and on policies that affect it. This initiative has also led to the “CountrySTAT”
system, comparable to its “FAOSTAT” worldwide agricultural database, to assist developing
countries in integrating and making publicly available agricultural data. AMIS has received
funding from the Bill and Melinda Gates Foundation (BMFG) to examine and improve
marketing information systems specifically in Bangladesh, India, and Nigeria. One component of
the BMFG funded project is to improve stocks estimation in those cases, developing a
methodology that would be broadly applicable in developing countries.
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This report presents an initial effort at improving stocks estimation methodology in the countries
targeted by the BMFG funded project. We also closely examine the Philippines, involved in
another AMIS project, as a case where best practices for a developing country may be
considered, since it is implementing stocks surveys.1 Two principal tasks of this effort are
addressed. First, literature on stocks estimation methodologies in the targeted countries and
elsewhere (worldwide) has been reviewed in order to identify current “best practices” in
estimating stocks data. Second, based on lessons from that review an outline of a “Guidelines for
stocks estimation methodology” document has been created. In this report recommendations for
each component of those Guidelines as well as analysis of issues leading to those
recommendations are provided. An annotated bibliography of literature review has also been
created (see Appendix).
Recommendations resulted from both literature review by the author and consultations with
experts. The author participated in several video conferences with FAO colleagues, where
preliminary findings and possible recommendations were discussed. FAO staff contacted
representatives in the four target countries and provided the information they were able to obtain.
A representative of the Philippines Bureau of Agricultural Statistics (Romeo Recide) participated
in one of the video conferences, sharing experience there. A video conference was also held with
representatives from the agriculture and statistics ministries in Nigeria (Adelabu Ayotunde and
David Babaloa). The author also contacted colleagues at USDA and universities, and searched
websites for statistical agencies in many countries to identify current practice in estimating
stocks data. The discussion below is the author’s synthesis of findings from these numerous
sources.
Several problems were encountered in executing this project, reflecting the poor state of stocks
estimation methodology currently. Most countries now estimate stocks as a residual in a food
balance or supply-utilization equilibrium. That methodology, discussed further below, utilizes
incomplete information on several components in the balance, resulting in potentially high errors
in the stocks change residual. At best it estimates changes in stocks from one year to the next,
and not the level of stocks, which can be important in assessing the influence of stocks positions
on market outcomes. Undocumented “rules of thumb” and “expert judgment” are used to allocate
residual unknowns across several variables, including food use, feed use and stocks. In only a
few instances are data collected via surveys or interviews to establish stocks data or the other
unknown variables.
In major exporting countries, where agricultural market information is viewed as critical by
commercial interests, stocks surveys are conducted. Information on stocks surveys was found for
the United States, Canada, and Brazil, but in each of those cases only very limited documentation
was publicly available.2 For example, in most of these cases documentation of stocks surveys
available on the web is less than one page long, and we needed to contact responsible individuals
We will subsequently include the Philippines among “targeted countries” since it is our best example of
how to conduct stocks surveys in a developing country.
2
Stocks surveys previously conducted by Australia were also found, but that effort stopped in late 2012.
That case will also be examined here.
1
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in administering agencies to obtain copies of questionnaires. Manuals for survey administration
were not available, and we encountered reluctance on the part of those agencies in providing any
such manuals. The only developing country in which we found evidence of stocks surveys being
successfully implemented was the Philippines. Their effort is documented on their
“CountrySTAT Philippines” website to a more complete extent than was found in other cases.
While numerous academic researchers and staff at international organizations write about the
importance of information on stocks (e.g. Wiggins and Keats, 2009; Lilliston and Ranallo, 2012;
Galtier, 2013; Abbott, 2010), literature on the process of collecting stocks data is extremely
limited. When we contacted colleagues who work on the implications of stocks positions, we
found that several did not know how stocks data were collected or estimated in their own
country, probably because in many of those cases there were no stocks surveys. Analysts were
quite aware, however, that stocks data in many cases are of poor quality, and that available data
may reflect only official stocks held by public agencies or are the result of an imprecise
estimation process. Hence, they supported both better stocks management policies and
improvement in market information systems that include better stocks estimation methodology.
While the literature is limited, it is possible to identify from current practice what might
constitute “best practices” methodology, largely based on the few countries that now implement
stocks surveys. That was also informed by discussions with individuals involved in collecting
and using stocks data at FAO, USDA, the Philippines and elsewhere. The principal
recommendation that follows is that more data collection is required for better information to
emerge. For those that value such information, collection of stocks data has proven to be easier
to implement than collection of use data on an annual basis. If a food balance residual approach
is to continue, however, various component estimates need much firmer empirical foundation.
Literature review and the recommendations that follow are reported in two parts below. First,
background information is provided describing the current state of stocks data estimation for the
four targeted developing countries, in the FAO food balance methodology, and in countries
where surveys were found. That section begins with definitions of stocks that relate to when and
why they are collected, as well as analysis on why stocks are held, informing what data is
desirable, hence collection methodology. An outline for Guidelines on Stocks Estimation
methodology then is presented, followed by examination of issues and recommendations for
each component of that outline, drawing from the literature reviewed and from experience where
surveys are conducted. A summary of problems in stocks measurement when surveys are not
conducted, and what fallback strategies might be pursed when a country decides it cannot afford
to implement surveys precedes the conclusions. An annotated bibliography is provided in the
Appendix. Links to questionnaires obtained are available in an Endnote library used to generate
that bibliography and store documents.
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Background
Background information on current practices in estimating stocks data begins with issues in
defining stocks and various types of stocks. Implications for estimation methodology of the
various reasons why stocks are held are then explored. Then current data estimation practice is
examined.
Stocks definitions
Stocks may be defined as the quantities of a commodity held in storage by any of the various
agents along a supply chain from farmer to consumer at an instant in time. Definitions of
categories of stocks reflect which agents hold stocks, why the stocks are held, and what purpose
the stocks ultimately serve. Practical definitions for stocks must also define the commodity for
which data is to be collected, differentiating forms in which it may be found (e.g. paddy rice
versus milled rice).
One key aspect of stocks is when they are held. While stocks may be measured at any point in
time during a crop year, the stocks held at the end of one crop year and carried into the next may
well be the most informative for market outcomes and price formation. These stocks influence
the linkage of prices across crop years according to the theory of storage (Wright, 2001). They
are referred to as annual carry-out stocks, equal to annual carry-in stocks of the next crop year as
reported in annual food balance or supply-utilization accounts. When these stocks are large,
prices are linked across years according to the cost of storage, while domestic prices over time
may disconnect when a poor harvest is anticipated and stocks are low. The focus of methodology
presented here is on determining these annual carry-out stocks.
Within year stocks may also be of interest to food security concerns. For example, Stephens and
Barrett (2011), among others, have argued that very poor subsistence farmers may be forced to
sell grain shortly after harvest at low prices and then must buy grain before the next harvest at
much higher prices. Stocks within year and the cost of storage influence the seasonality of prices,
hence this behavior and its implications for food security policy. Market behavior may also be
influenced by short term stocks data that reveals how crops are being rationed and uses adjusted
as a crop year proceeds. Data to examine these issues is even less likely to be available, and
typically a focused survey by researchers on specific issues must be done.
In the case of rice, there are often as many as three crops harvested per year. In such cases the
extent of carry-out from one crop season and into the next may also be of interest. Agent may
respond both by changing storage and consumption across seasons, but also by changing
seasonal production to the extent feasible.
Our recommendation will emphasize the need for annual carry-out stocks, recognizing the
importance of seasonality and multiple crops in determining short run food security outcomes.
Hence, at a minimum stocks data should be collected annually, but if resources permit, data
should be collected quarterly or according to crop seasons when there are multiple crops per
year. In each case the crop year is defined as beginning (the first day of the first month) when
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harvest commences, the current practice. This is when stocks will be at the lowest level during a
crop year.
Another key distinction often drawn is between working stocks and reserves. Sometimes the
alternative to working stocks is referred to as buffer stocks, or strategic reserves. Working
stocks, or pipeline stocks, are the quantities of a commodity held by a food processor, livestock
feeder or other agent in the normal course of its activity to insure continuous operation. It is
likened to transportation of crude oil or natural gas, where an amount of those commodities
remains in the pipeline as long as it is operating and the commodity is flowing to its final
destination. Hence, it has been observed that stocks never go to zero, even when a bumper crop
is anticipated following a short crop, because these working or pipeline stocks exist and must be
accounted for. Data on stocks in these instances, when a good year follows a bad year, informs
how large these working stocks are.
Storage in excess of working stocks have been referred to as reserves (or buffer stocks), or stocks
held in order to influence market outcomes or maintain food supplies across crop years. Specific
categories of reserves are identified when the reserve satisfies a specific purpose. For example,
emergency reserves are typically stocks held by or for a public agency to meet food aid or public
distribution requirements in the future (Rashid and Lemma, 2011). The stocks held by the Food
Corporation of India, for example, insure that supplies are available for India’s public
distribution programs. The “farmer-owned reserve” in the U.S. illustrates a very different case, in
which stocks were held based on incentives from agricultural policy that is intended to influence
market price outcomes. 3
Another distinction is between public (or official) and private stocks. Here we shall presume that
definition applies on the basis of who actually owns the commodity in storage. If a public entity
stores grain, that may be referred to as public or official reserves, as in the cases of the Food
Corporation of India or the Commodity Credit Corporation in the U.S. Private stocks refer to
stocks held by farmers, traders or users of a commodity.
These definitions may be ambiguous when applied to actual stocks held by an agent, especially
for private agents where the motivation to hold stocks is not established by legislation or policy.
The U.S. farmer owned reserve represented a case where public policy motivates stockholding,
but a private entity actually held and legally owned the stocks. In the case of the India Food
Corporation, capacity constraints on the corporation may mean that private firms are contracted
to hold some of the reserves. Moreover, stocks may be accumulated to influence market prices,
as dictated by agricultural policy and its outcomes, even if they are held along with emergency
reserves. Even the distinction between working stocks and reserves may be ambiguous, in that a
private agent may hold stocks both as pipeline or working stocks, and to insure against changing
market conditions, without a clear distinction as to how much of stocks held at a point in time are
actually the minimum working stock required.
When surveying to collect data on stocks, the current practice is to ask agents how much of a
commodity is in storage, and not to ask its purpose. Whether stocks are public or private is
3
This policy measured was repealed in the 1996 U.S. farm bill.
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identified by ownership status, and not by the purpose of stocks. Only when stocks are held to
satisfy a public mandate may some of these definitional distinctions be clarified. Hence, a
complete estimation methodology will require collecting data from all agents, public and private,
along the supply chain, and should report holdings of sets of agents allowing further analysis. It
will not specifically identify stocks according to these subcategory definitions.
While the methodology recommended here will emphasize annual carry-out stocks, it will be
useful to consider further the purposes for which stock are used in order to understand where and
why they will be held under specific circumstances, informing data collection methodology.
Purposes of stocks
For annual crops like maize, harvest occurs over a few months at the beginning of the crop year,
while consumption is spread over the entire year. Stocks allow consumption smoothing over the
year and may spill over to the next year based on prices and expected production. For crops that
exhibit multiple seasons over a year, such as rice, different seasons result in different quantities
and qualities of the rice. Stocks again help to smooth consumption and hedge against future
shortfalls. Several specific purposes for stocks can then be identified after recognizing the
seasonality of agricultural production, including:
Local/household food security
Regional trade and domestic seasonal pricing
International trade
Supplies for public distribution and food aid
Working inventories for livestock producers and food processors
Subsistence farmers store grain to insure their families’ consumption over the year and into the
next year. Whether a farmer remains a subsistence farmer or sells some of his crop in the market
(generates a marketed surplus) depends on the size of his crop and the price he faces. Whether a
farmer remains at subsistence or becomes a commercial farmer changes as countries develop and
as market circumstances change, creating difficulty in identifying who is in each category.
Commercial farmers may store for their own consumption, but may also speculate on market
prices since prices are typically lowest at harvest and rise afterwards. Hence, one motivation for
farmers is to insure its household’s food security. Another is to maximize profit from sales in the
market.
Households in developing countries may also stockpile food, even if they are not producers. The
Philippines stocks survey includes non-farm households to capture this behavior, while in the
emerging and developed countries this is not considered important. In any case, households will
store to varying degrees to insure household food security, and that extent may depend on
income, on how well the storage and distribution market is believed to operate, and on whether
the agent produces the commodity.
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Traders may speculate on seasonal or annual price variations, and may offer storage services to
farmers. The role of commercial storage increases substantially as economies develop and the
marketing chain improves. The behaviors of commercial farmers, traders, and commercial
storage operators determines the evolution of market prices. Stocks positions of these entities
relative to use may be one of the best indicators determining expected future prices. These agents
determine seasonal price patterns and links between prices across crop years.
Traders offer transportation and distribution services in addition to stockholding and facilitation
of commercial transactions. Commodities are considered as stocks when in transit, whether
owned by a farmer, trader, end user or transportation agent.
International trade may also require stockholding since availability for imports or demand for
exports may not coincide with consumption requirements or harvest dates. Moreover, it may take
time for imports to arrive once a shortfall is expected, and this timing can influence market
outcomes. Storage for trade and for the domestic market may be indistinguishable or overlap.
Trade possibilities and domestic market conditions, including production and use, will determine
the evolution of market prices, and stocks will play a key role in that evolution.
G8/G20 recommendations for better market information were motivated by the problems in
explaining international agricultural commodity price spikes and the food security issues they
raised (FAO et. al., 2011). Subsequent research has found that some developing countries
successfully disconnected their domestic markets from world markets (at a cost); some countries
were poorly integrated with world markets so domestic conditions continued to dominate; and
some countries were especially vulnerable to high world prices (Abbott and Borot de Battisti,
2011; Heady and Fan, 2011). Hence, international trade concerns may have brought attention to
the poor state of stocks data, but food security concerns in developing countries are also
dependent on domestic market conditions where stocks interventions affect both domestic price
and availability.
Governments intervene in markets both to stabilize market prices and to insure food security of
vulnerable groups. Stocks have been an integral part of stabilization strategies in the past, and
polices have often resulted in accumulations of stocks across crop years. As noted earlier, to
facilitate operation of safety nets, and specifically food aid or public subsidized distributions,
governments may acquire emergency reserves. NGOs who operate safety net programs may also
hold emergency reserves. Moreover, governments may hold stocks to simultaneously manage
domestic markets and to insure supplies for safety nets.
Finally, private intermediaries may hold stocks in the course of their normal operations. Food
and feed processors and livestock producers may run pipelines and may hold stocks to insure
availability for continuous operation, as well as to obtain inputs at the lowest cost. Given
seasonal pricing patterns, it may be advantageous to purchase early in the crop year and store.
Recognition of the various roles played by stocks helps in identifying which agents should be
surveyed in order to collect comprehensive stocks data. Relevant agents include public agencies;
parastatals and quasi-public marketing boards; subsistence farmers; commercial farmers; traders
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– large and small; transportation agents; commercial storage facilities; livestock producers; feed
compounders; food processors; and households.
In addition, issues as to whether certain agents may be ignored while adequately capturing
national or regional stocks levels arise. In some cases subsistence farmers might not be surveyed,
on the grounds that their stocks do not influence market outcomes, but that requires correctly
identifying whether a farmer will always be subsistence, never generating a marketed surplus.
This approach also ignores food security concerns of those subsistence farmers. In some
instances commercial farmers are not surveyed on the argument that they do not hold stocks –
that assertion should be empirically verified. In most instances greatest attention is paid to public
entities and to very large traders, on the argument that the bulk of stocks are in their hands. It is
highly advisable that information from a census of agriculture be complemented by similar
information from a careful review of actors along the supply chain to determine how important
different agents may be in a specific country case.
The uses for which stocks data are collected also argue for differing levels of inclusion in stocks
surveys. If stocks data inform market participants and policymakers interested in stabilizing
interventions, data on commercial farmers, end users and traders may suffice. Addressing food
security concerns means collection of subsistence farmer data and possibly even household data.
While sampling from all relevant agents is advisable, collecting data is necessary to inform these
uses.
Existing stocks estimation methods
Stocks data currently available may be generated using one of three approaches. Stocks data may
include only public records of government agencies or parastatals marketing boards. While that
information is valuable, it is incomplete. Stocks data for countries in such cases would include
no information on farmers, traders, or end users. The more common practice is to estimate stocks
from food balance or supply utilization equilibrium – hereafter referred to as the residual
approach. Problems with that approach are discussed below. The third alternative is to survey
relevant agents and report the outcome of that survey as well as extrapolation from the samples
collected to national estimates of stocks positions. We consider this last approach to constitute
“best practice”, based on the problems with the residual approach.
The starting point for the residual approach is food balance, or supply-utilization equilibrium:
Carry-in stocks + production + imports = supply = demand
= food use + feed use + industrial use, waste, losses and seed use + exports + carry-out stocks
This accounting identity must hold over a crop year, since all sources of supply and all types of
demands are included in the accounting (in principle). Production, imports and exports are
typically observed, so are reasonably well known, although data quality can vary. They are
nevertheless based on better data than stocks or any use category. If uses were known, change in
stocks (carry-int – carry-outt) could be calculated (as a residual) from this relationship,
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recognizing that carry-in from one year equals carry-out from the prior year. Nevertheless, this
only yields change in stocks, and a level must be established in some year based on outside
information. Typically stocks are only a fraction of supply and use, so small errors in supply or
use estimation will result in large relative errors in stocks. A potential problem, which has arisen
in the past, is that an error in setting an initial stocks level can make apparent stocks go negative,
a physical impossibility. In its PS&D dataset, USDA reports stocks levels for many countries,
incorporating a judgment based estimate of an initial stocks level typically from national
statistics. The FAO in its FAOSTAT database now only reports changes in stocks, which cannot
be used as a measure of carry-out stocks.4
In estimating stocks using this residual approach, given the state of currently available use data, a
serious identification problem arises. One can only estimate one variable from this one equation,
and that variable could be total use, a use component, or change in stocks. But one can identify
only one of those, and several variables are normally unknown. Rules of thumb and/or “expert
judgment” are applied to resolve this problem, effectively adding (imperfect) information. But
that information is never well documented and is dependent on the quality of the judgment
applied. Not surprisingly, differing reputable sources apply different judgments and so report
differing “data”.
Household surveys are often used to estimate human consumption. But they are conducted at
best every third year and generally less often in most developing countries. Industrial use could
be obtained from firm surveys, but that is the norm only in developed countries. Feed use data is
almost never directly collected – this is now the residual in USDA’s domestic supply-utilization
balances.5 Fixed percentages are often applied for waste, losses and seed use, and may be based
on very old data or on no data at all. Hence, the less well known terms besides stocks in the food
balance equation are not based on sound, empirically based methodology. Country cases
discussed below will provide more detail on this problem and its practical resolution currently.
Stocks data is likely to be more easily collected than is use data. Stocks are observed at a point in
time. Uses are flows, so their estimation from a survey would require recall by the interviewee
over an extended period, or from more frequent surveying. In the case of human consumption,
for example, interviewees are typically asked to remember consumption over a relatively brief,
very recent period. If market conditions or expectations change during a crop year, use may also
vary, and that change would not be captured in quite infrequent surveys. Use data would also
necessitate more frequent conversion from some processed form to grain equivalent. The risk of
double counting is also greater. For example, if both livestock producers and feed compounders
were surveyed, both might report the same grain ultimately used by the livestock producers.
Transactions data would be needed to be collected to avoid such double counting. As a
consequence, we will see that in the cases where surveys are used (U.S., Canada, Brazil and the
4
The FAO GIEWS database and reports publish regularly stock estimates/forecasts, using very much a
similar approach followed by USDA/PS&D
5
USDA collects stocks data for national supply and use balance estimates, but has to rely on data
collection methods used elsewhere for their PS&D dataset.
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Philippines), stocks are surveyed frequently and use is the residual computed from supplyutilization accounts.
Estimating stocks levels over time using the residual approach can reveal large errors due to its
application. The best example of this is the problem of estimating Chinese stocks around 2001,
which was and still is done using this approach. This problem first surfaced in the later 1990s
when FAO discovered that its presumed stocks estimates for China could turn negative given the
faster expansion in domestic utilization than domestic production (GIEWS, 2001; Committee on
Food Security, 2001). Both USDA and FAO dramatically increased stocks levels as new
information revealed serious anomalies – in the USDA case this revision amounted to 164
million metric tons, a 250 percent increase (Hsu and Gale, 2001). Hence, it is possible when
estimating stocks levels using this approach to have apparent stock levels go negative, as we will
also see later for estimates in Bangladesh (Jabbar, 2009). Reporting only changes in stocks
avoids this problem, but results in incomplete information relevant to market behavior.
Another example of the problems from using a residual approach to data estimation is the case of
Afghanistan around the time of the recent food crisis (in 2008). In Afghanistan, even trade levels
are not well known. The only data that are accurate to any degree are production levels by
province. Trade is estimated by the government as a residual, based on production data and a
crude consumption estimate. These numbers are the official data reported to FAO. USDA data
for this period are quite different, relying on differing assumption but still without consideration
of stocks positions. But there has been substantial production variation, and consultation with
traders reveals that trade levels have not varied to fully offset production changes – there must
have been stocks accumulation and drawn down, but no information on its extent is available
(Halimi, 2011). Poor availability of other components of the supply-utilization balance mean
stocks cannot be accurately estimated in this case.
Based on the relative ease of collecting stocks data versus other essentially unknown
information, our recommendation is to survey farmer and commercial entities, separately, and
not rely on the residual approach for stocks estimation. If anomalies are seen in food balance or
supply-use balance based on results from a stocks survey, they should be a clue to suggest
revisiting the methodology for stocks surveys, and not the basis for ad hoc revision of supplyutilization balance and stocks information.
Evidence from our review of current stocks estimation practices in a variety of settings will show
how these problems are currently addressed.
14 |
What is done in targeted countries: Philippines, Nigeria, India, Bangladesh
The four case study developing countries targeted here reveal several different approaches to
stocks estimation, and so several different resolutions to the problems discussed above. Each
case is discussed below.
Philippines
The Philippines has been collecting stocks data monthly for over thirty years (since 1980) for
rice and maize. Their approach is documented on the “CountrySTAT Philippines” web pages
(Bureau of Agricultural Statistics, 2013c). Interviews are conducted of a panel of households that
includes both farm and non-farm households. There is also a separate survey of commercial
entities (traders and end users) that hold stocks. Questionnaires utilized for each set of agents
were obtained from the Bureau of Agricultural Statistics (2013q). They are relatively simple
questionnaires that focus entirely on stocks positions – they are not integrated with other farm,
household or firm surveys. The household interviews are conducted over the first four days of
each month, and shortly thereafter a monthly report is posted on the BAS website. The report
identifies household, commercial and official (National Food Authority - NFA) stocks
separately, and arrives at a national stocks estimate.
Discussions with a Philippines representative indicated they believe monthly surveys may be
more frequent than is necessary. That information has been available to commercial interests for
a long time, however, and once such information is provided, taking it away brings political
resistance. The representative suggested that quarterly, seasonal, or even annual surveys might
well suffice. The cost of doing interview based surveys monthly is not insignificant.
The Philippines case also highlighted the need to carefully define commodities and their state of
processing. The household surveys ask about both paddy and milled rice. Corn grits in addition
to corn grains stocks data are collected. Rice data are reported as milled, and a conversion is used
to go from holdings of paddy to its milled equivalent. Corn data is also converted to grain
equivalent. The units of measure must also be specified. In their questionnaire, local units of
measure are used and then are converted to metric tons for reporting. A system for keeping track
of and converting familiar units of measure to standard measures is necessary. In another case, it
was apparent that confusion can arise on quantities in interviews if procedures related to what
units are used are not made clear.
The sampling strategy for household surveys starts with provinces where rice or maize is grown.
In each province barangays (villages or neighborhoods in urban areas) are randomly chosen, and
households within those barangays are sampled. In 2013 this survey covered 1079 barangays and
15,122 households. It is a panel, in that the same household is surveyed each month when
possible. The Census of Agriculture was used to establish the sampling frame and population
sizes. National stocks estimates are generated from sample outcomes and information on
population sizes.
15 |
The household survey is conducted by the Bureau of Agricultural statistics. The commercial
survey is conducted by the NFA, who also reports their own holdings. The commercial survey
focus is on commercial warehouses, not all end users. Documentation is more complete for the
household surveys than for the commercial stocks survey. This survey is conducted by a
regulatory agency rather than the statistics bureau in hopes that this will elicit greater
cooperation, so a higher response rate. The Philippines representative indicated that establishing
lists for the commercial survey was relatively straightforward, but getting cooperative responses
to surveys was harder. He also stated that responses tended to be biased downward for large
stockholders, in part because stocks in transit would be missed.
Quality control for surveys involves reviews of the survey instrument, careful training of
interviewers, and spot checks by supervisors on data collected.
There is ongoing debate in the Philippines on this approach to stocks surveys. For example, one
issue is whether better information would be obtained if retail outlets rather than urban
households were surveyed. As noted above, the frequency of this survey is also being examined.
The current approach is also better suited to food use than to feed use of maize.
Nigeria
Information was provided from Nigeria indicating that they had been attempting to conduct
surveys of stocks both for farmers and for commercial entities (traders and warehouse operators).
We discussed these surveys with representatives of the National Statistics Bureau (David
Babaloa) and Federal Ministry of Agriculture and Rural Development (Adelabu Ayotunde).
Results from those surveys could not be verified on the website of the responsible national
statistics agency (National Statistics Bureau - NBS). We learned that funding limitations mean
the implementation of these surveys has been suspended.
We were provided a set of power point slides describing a commercial stocks survey manual
(NFA Nigeria, 2013). Our discussions with Nigerian representatives indicated this survey was
run only once, in 2010, due to the funding restrictions. According to that document, the Nigerian
commercial survey gathers data from retailers, wholesalers and warehouses. It focuses on
distribution rather than end users. Data is to be collected at the national, regional and provincial
level. A stratified sampling approach is followed. To the extent possible, all large entities, as
defined by the quantity of stocks held, are surveyed, and a sample is taken of smaller entities.
National estimates are extrapolated from the samples. Lists of commercial entities are created
and maintained by the NFA (National Food Agency – a division of FMARD) to establish both
population and sample sizes.
We also obtained a brief document describing surveys of farm households to collect stocks data
there. It indicates that the National Statistics Bureau surveys only farm households in
collaboration with the Federal Ministry of Agriculture and Rural Development (FMARD).
According to our interview, this survey was conducted from 2002 to 2010, when funding
restrictions again prevented continuation of data collection. Samples are generated from data of
16 |
the National Population Commission. A separate attempt is made to list corporate farms.
Corporate farms in Nigeria are identified as those farms that keep accounting and sales records
and so behave as a business. This is not the same distinction as subsistence versus commercial
farms, as there would be farms generating a marketed surplus who do not keep records. Results
are delineated according to the corporate versus household farm distinction, and it is believed
data are much better for corporate farms. The questionnaire provided was a comprehensive rural
household survey, covering many topics beside stocks information. Separate questionnaires were
used for “crop” versus “livestock” households. There were not grain stocks questions on the
“livestock” household survey provided. On the household survey, there is a section on “record of
what you set aside from own production” and questions related to annual production, own
consumption and sales. No dates are indicated for when the survey was taken, or what date the
observed stocks relate to. Only the year in which the interview was conducted is retained with
the sample record. More detailed information was provided on stocking capacity than on actual
stocks held. It is not possible to accurately extrapolate carry-out stocks from information now on
these questionnaires.
The NBS and FMARD representatives indicated that Nigeria does not attempt to construct food
balance accounts. Hence, it is not currently estimating stocks positions. It also appears that the
funding restrictions now seriously limit data collection even on agricultural production.
We also found questions on the Nigerian national household survey conducted periodically
(about every five years) and available through the World Bank (2013) website on Living
Standards Measurement Studies (LSMS) that related to stocks positions. Once again, we did not
find reports that utilized information from those questions on stocks.
The future state of implementation of these surveys is uncertain. In his review for IFPRI,
Akinyele (2009) stated that “There is a dearth of national surveys providing datasets for the
analysis of food and nutrition security in rural Nigeria. Though there have been a number of
individual and institutional efforts and attempts at generating databases on food and nutrition
security for Nigeria, these efforts are hampered by inadequate funds to implement large-scale
surveys.” During and after the 2007-08 food crisis Nigeria has pursued numerous efforts to
improve food security, including construction of storage capacity (Olomola, 2012). Inclusion of
stocks questions in surveys likely reflects sensitivity to concerns with inadequate stocks data.
The funding issues noted by Akinyele were evident in our discussions with Nigerian officials.
There were serious problems noted with how their approach, inclusion of stocks questions in
comprehensive surveys, would inform stocks positions, however.
Bangladesh
Representatives from Bangladesh indicated to us that they now collect no stocks information,
and do not attempt to estimate stocks in a food balance framework. Information on public stocks
of food grains can be found on the website of the Food Planning and Monitoring Unit (FPMU)
(2013), however. The FAS/USDA GAIN report for Bangladesh (FAS, 2013b) also indicates the
extent of public stocks and their relation to the public distribution system, and provides an
estimate of private stocks for wheat but not rice without stating a source of information.
17 |
Jabbar (2009) reviewed stocks information in Bangladesh in a study for FAO similar to this one,
but focused entirely on Bangladesh. He stated that prior to trade liberalization and to the
elimination of the parastatal marketing board in 1992, stocks data was collected from traders by
the Ministry of Agriculture. He also indicated that the current Ministry of Food and Disaster
Management maintains internal stocks estimates based on a residual approach. He argues,
however, that the approach is circular in that it utilizes data on consumption also generated by a
residual approach. Bangladesh does not makes these stocks estimates public information.
In his study, Jabbar was highly critical of the internal stocks estimates generated for Bangladesh
using the residual approach. In addition to the circularity or identification problem, he examined
various use components including waste and seed use. He argued that dated information and
rules of thumb that had become inaccurate as conditions changed, making stocks estimates
highly inaccurate. He noted that consumption estimates did not take into account seasonality,
price effects or year to year variations in availability. His critiques would apply to residual based
stocks estimates wherever the various components of use are estimated using an inaccurate,
dated empirical foundation.
Jabbar (2009) also examined several attempts at a one-time estimation of stocks in Bangladesh,
intended to show that subsistence farmers can hold significant levels of on-farm stocks. Similar
critiques of accounting practice, identification, and basing estimates on weak prior information
led him to question the accuracy of those estimates, which generally follow some sort of residual
approach applied at a household level, combined with a household survey to establish use levels.
He also takes issue with some of the better known information, such as production, noting the
effect that errors in these data can lead to large relative errors in stocks estimates. An early study
by Chowdhury (1992) argued that 62% of rice stocks were held on farm and only 17% by
traders. These studies generally were focused on estimating marketed surplus rather than stocks.
India
India provided FAO no information on stocks estimation methodology. India runs an extensive
public food distribution system managed by the Food Corporation of India. Data on monthly
stocks positions of that parastatal agency are readily available on its website (Food Corporation
of India, 2013). Its stocks are largely the result of national procurement and public distribution
policies (Hoda and Gulati, 2013; Department of Food & Public Distribution in India, 2013). The
government also prescribes a strategic reserve in addition to “buffer stocks” to influence market
outcomes, held by the Food Corporation.
As stocks have recently accumulated beyond the storage capacity of India’s Food Corporation,
the government has launched a program to contract with private warehouses for storage (Oryza,
2013). Since that storage is to augment capacity of the Food Corporation, stocks held there likely
will be included with the reports of official stocks.
USDA’s Foreign Agricultural Service (FAS, 2013i) publishes an Indian Grain and Feed Annual
as part of its series of GAIN reports. According to that report “Estimates of privately-held wheat
18 |
stocks are not available, but are expected to be minimal in parts due to risks stemming from antihoarding provisions of the Essential Commodities Act. The PS&D table does not include
privately-held stocks” (FAS, 2013, p.11). Hence, Indian legislation limits private storage, and
reports do not estimate private storage there. Nevertheless, it is likely that on-farm storage of
grain, particularly for subsistence farmers, could be considerable, as in the case of Bangladesh,
but current information on its extent is not available. A relatively old study (Shukla, 1988)
estimated that 70% of farm produce was stored by farmers for their own consumption.
FAO Food Balance Methodology
The Food and Agriculture Organization of the UN (FAO) publishes data on food balances for
most commodities and 245 countries (or territories) worldwide in its FAOSTAT database. Its
methodology is described extensively in FAO, Food Balance Sheets: A Handbook (FAO, 2001).
In the past FAOSTAT reported stocks levels, but now these food balance sheets only report
changes in stocks. Many stocks variation entries are zero or absent. The FAO also notes that
even when stocks levels were reported, stocks data on a calendar year basis do not correspond
with annual carry-out stocks.
Data in FAOSTAT come from reports of member country governments. Often those national
data only report production, consumption and trade. According to the FAO Handbook (FAO,
2001), “Moreover, information on stock changes and losses/waste are often nearly non-existent
or, at best, only fractional in its coverage, e.g. commercial stocks of some commodities may
occasionally be available from official sources or marketing authorities.” They also state that
“The estimate of food available for human consumption is usually derived as a residual. Since
the estimate of food available for human consumption is derived as a residual, its reliability
would depend on the availability and accuracy of the other components on which it is based.”
Thus, there is a serious identification problem faced by FAO staff in estimating stocks change
data from a residual approach. As is the case for other data sources, the FAO must apply expert
judgment to reconcile conflicts and arrive at final, published data.
We asked FAOSTAT staff if they had evidence of stocks data being estimated based on surveys
of farmers and/or traders rather than being treated as a residual in supply-utilization accounts.
They did not identify any cases beyond those we had earlier found and will describe here where
stocks surveys are conducted. They indicated that their reports only present change in stocks, so
national governments may not be motivated to provide them with stocks data.
Traditionally, FAOSTAT data has been reported on a calendar year basis, not for crop years. One
of the reasons given for moving to changes in stocks is that those estimates must also correct for
errors introduced by reporting data that crosses two crop years. For example, consumption will
reflect availability both from the calendar year reported and another calendar year. While these
data may be directly compared across countries, since they are at a common date, their use in
informing market outcomes or availability is limited by this timing issue.
19 |
FAO analysts in its Trade and Markets Division (separate from the Statistics Division that
maintains FAOSTAT) maintain and report data that includes estimates of stocks levels, and data
on a crop year basis for individual countries. The FAO Statistical Programme of Work (FAO,
2013s) indicates these data are largely meant for internal purposes. Nevertheless, those data do
appear in the bi-annual GIEWS “Food Outlook” reports (e.g. GIEWS, 2001) as well as “Crop
Prospects and Food Situation” reports (GIEWS, 2013) which are produced four times a year.
Since 2012, AMIS statistical database also allows users to extract FAO/GIEWS supply and
demand balances for wheat, rice, maize and soybeans for the world and the individual AMIS
countries (AMIS, 2013). Those data are generated mostly using expert judgments by FAO
commodity analysts and country officers who use variety of sources, including information
obtained from their frequent crop assessment visits to countries.
USDA – NASS, FAS and PS&D
The U.S. Department of Agriculture (USDA) reports stocks data not only for the U.S. but also
worldwide in its PS&D database. The PS&D database is similar to FAOSTAT food balances in
that it reports supply and utilization (food balance) accounts for many countries and
commodities. The PS&D database is more limited in its country and commodity coverage than is
FAOSTAT, but it covers an extensive list of importing and exporting countries and includes all
the grains and oilseeds of interest here. PS&D data is reported on a crop year basis for the crop
year relevant to each country. Hence, data are not for stocks at a single point in time, limiting
international comparability. FAS explicitly note this comparability issue on its website, but
worldwide stocks estimates are nevertheless assembled from this data.
The PS&D dataset is maintained by the Foreign Agricultural Service (FAS) of USDA. FAS
employs agricultural attaches who work in embassies around the world, and who report data for
use in PS&D to Washington DC. Domestic agricultural stocks data is collected by the National
Agricultural Statistical Service (NASS), who runs both on-farm and commercial surveys to
collect U.S. grain and oilseed stocks. The World Agricultural Outlook Board under the Office of
the USDA Chief Economist combines this information into data on past and projections of future
stocks positions in its WASDE reports (WAOB, 2013). The Economic Research Service (ERS)
compiles quarterly supply and utilization accounts in its feed grain database (ERS, 2013).
Interestingly, representatives from FAS and ERS sit on the WAOB board issuing WASDE
reports, but NASS is not represented there. NASS stocks reports are issued independently and
prior to WASDE reports.
A comparison of the methodologies used by NASS for U.S. data versus FAS for PS&D data
highlights the difficulties in getting worldwide stocks data. NASS uses surveys to get national
stocks information for the U.S. (only), whereas FAS in most instances must rely on a residual
approach to assemble PS&D data since only a few other countries survey for stocks data.
NASS
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The NASS stocks reports are a model for best practice in a developed country. NASS conducts
separate surveys of farmers and commercial storage agents quarterly. The farmer survey is
limited in scope, and may ask questions about area planted, yield or production in addition to
stocks. A comprehensive rural household survey is done separately from the stocks survey.
Short, simple questionnaires ask about these stock holdings as well as capacity. The off-farm
survey looks at grains and oilseeds stored “in any commercial facility off the farm.” (NASS,
2013s). This includes surveying commercial entities in states that may not be important
producers of a commodity. Stocks data are reported for on-farm and off-farm stocks by state and
nationally, although some state data is withheld to protect the privacy of a few large operators.
The on-farm survey is conducted for a sample of 66,000 farmers. The off-farm survey attempts a
complete enumeration of commercial storage and surveys 8,800 entities. The response rate for
commercial storage is about 90%. Population size estimates are used to turn sample information
into state and national stocks estimates. The Census of Agriculture is used to develop the onfarm samples, and lists of commercial entities are continually updated by NASS staff. NASS
indicated initial creation of the lists was enabled by extension office and regulatory agency
contacts, but they need to stay in contact with private sector interests to keep up to date on an
evolving supply chain. A food balance approach is used to insure that reliable stocks estimates
are generated by checking survey estimates against market aggregate information. Brief
documentation is included at the end of each stocks report. NASS provided us with copies of
questionnaires used for both on-farm and off-farm surveys (NASS, 2013q). They were not
available from the NASS website.
NASS uses a hierarchical system of data collection. Ideally, data might be entered online or in
response to an email message. NASS is attempting to implement this approach, but reports that
response rates remain very low. Surface mail surveys are sent to farmers and commercial
entities, and replies may be by mail or FAX. If responses are not received, telephone interviews
are pursued. As a last resort, interviewers may go to a farm or firm to collect data. Occasionally,
interviewers go to farms or firms to check data reliability. This process attempts to use the most
cost effective information collection method first, and then relies on more costly methods to
insure a high response rate and accurate information.
FAS – PS&D
FAS publishes Grain: World Markets and Trade (FAS, 2013g) using the information that serves
as the basis for PS&D data. According to FAS, “Information is gathered from official statistics
of foreign governments and other foreign source materials, reports of U.S. agricultural attachés
and Foreign Service officers, office research, and related information.” Hence, multiple sources
of information are consulted and judgment is applied to reconcile differences. The USDA has
greater latitude than FAO in revising data from foreign governments as its data reflects USDA
judgment, not a compilation of official foreign government data.6
6
The FAO/GIEWS data are collected, or derived, more along the lines done by the USDA and hence
don’t always reflect official numbers.
21 |
The data collected by FAS originate from attaches in embassies worldwide. “USDA'S Global
Agriculture Information Network (GAIN) provides timely information on the agricultural
economy, products and issues in foreign countries since 1995 that are likely to have an impact on
United States agricultural production and trade. U.S. Foreign Service officers working at posts
overseas collect and submit information on the agricultural situation in more than 130 countries
to USDA's Foreign Agricultural Service (FAS), which maintains the GAIN reports.” (FAS,
2013g). Data submitted by attaches is reviewed in Washington DC before being incorporated
into final PS&D data.
Recent GAIN reports from each of the four project target countries as well as for other countries
were consulted to see how stocks were addressed in those reports. Above the Indian GAIN report
was cited, and it indicated that stocks data estimates for India include only official stocks as
reported by the Food Corporation (FAS, 2013i). Similar statements were found for Bangladesh
(FAS, 2013b). The treatment of stocks varied in the other GAIN reports, although none indicated
that stocks data was based on farm or commercial surveys. Sometimes there was a discussion of
stocks positions, and sometimes that was informed by consultations with private sector interests
in the country in question. More often, however, there would be a table of proposed data for
inclusion in PS&D that included stocks, but with no discussion of where those stocks data came
from. Tracing reports backwards suggested that stocks were often the residual after estimates of
use were made. It is apparent that PS&D data is limited by the same poor state of primary
information as are other stocks estimates. Whether it is better than other estimates depends on the
quality of the judgment applied to revise stocks and use data by attaches. FAS did not respond to
several inquiries on whether there were guidelines provided to attaches to help make those
judgments.
Other cases
Primary data of the sort that is of interest here is generally collected and reported by a national
statistical service rather than by a ministry of agriculture. The agricultural statistics services in
the U.S. and the Philippines are exceptions to this norm, but are cases where a high value is
placed on stocks information. A web search of national and agricultural statistical services yield
a few more cases where stocks surveys are conducted. Stocks surveys were found for Canada,
Australia, and Brazil.
Stocks surveys conducted by Statistics Canada are similar to those done by NASS/USDA. One
survey collects information “on commercial elevator stocks of corn and soybeans and the
industrial use of corn.” (Statistics Canada, 2013c) Hence, both grain elevators and end users are
surveyed. The commercial survey is conducted three times per year by mail, and response is
“mandatory.”7 Online and telephone interview responses are an optional alternative. A stratified
sample of elevators is collected, and the entire small number of end users is contacted. Results
are checked against and contribute to national supply and utilization accounts. On farm data
7
While Statistics Canada uses this language, it is not clear what mandatory means, as it encounters nonrespondents and does not specify what legal actions (if any) might be taken in those cases.
22 |
collection is part of a set of farm surveys that also estimate area, yield and production. Stocks are
part of those surveys in December, March and July/August – hence providing both crop year and
calendar year information. Farm surveys are conducted by “mandatory” computer assisted phone
interviews. Samples are drawn from lists based on the Census of Agriculture. Brief
documentation is provided on the Statistics Canada (2013f,c) website.
The Australian Bureau of Statistics conducted separate monthly surveys of grain handlers and
wheat uses until late 2012. Their surveys have covered on farm stocks including stocks held by
livestock feeders as well as stocks held by other end users. Hence, unlike USDA, Australia
attempted to measure by survey feed use. Their surveys included questions on both stocks and
use. After 2009 about 1,000 “users” have been surveyed, including wheat growers, dairy
operators and animal feedlot operators. The grain handlers survey covered all major bulk grain
storage operators. Once again, a census of large traders and storage operators was attempted.
Reports differentiate storage by bulk handlers, growers and end users. “The collection does not
attempt to measure the total amount of grain held in storage facilities in Australia. Regional or
small storage operators are not represented in these statistics.” (ABS, 2013). It appears that these
surveys have been discontinued as of November, 2012. Documentation is still available at the
Australian Bureau of Statistics (2013) website.
Brazil conducts twice yearly surveys of commercial stock holders, “having as collection units the
establishments dedicated to storage and dry storage services or those which store farm products
or their derivatives.” (IBGE, 2013s). Reports separately indicate public, private and cooperative
stocks in conventional and bulk warehouses as well as silos. Metadata for these surveys is only
available in Portuguese. While Brazil conducts on-farm surveys to estimate area, yield and
production, it does not appear that those surveys include questions on stocks (see IBGE, 2013f).
Colleagues familiar with Brazil argue that the recently established, large commercial farms in
Brazil do not hold stocks, so this methodology may suffice. It would serve commercial interest
better than food security concerns, as stocks of subsistence farmers would not be captured.
Food balance metadata were reviewed for the other countries in addition to the Philippines who
participate in the “CountrySTAT” projects, whether funded by BMFG or by other partners, and
who have already launched websites. This included 22 African countries, Bhutan and Haiti
(FAO, CountrySTAT, 2013c). None showed any evidence of stocks surveys being conducted,
and in fact most did not report food balance accounts.
Searches were done on websites of national statistical services of a number of other countries,
including Thailand, Indonesia, Malaysia, Vietnam Argentina, Pakistan, Vietnam and Mexico. In
none of these cases was stocks data found to be available, and there was no indication that stocks
surveys were being conducted. Even in the European Union, it appears stocks are determined by
a residual approach and not from surveys. For example, we found only three monthly stocks
survey reports from the United Kingdom, one in 2008 and the other two in 2009, but none before
or afterwards. Evidently, the food crisis and subsequent G8/G20 recommendations brought a
short-lived response in this case. Searches were limited by language constraints, as some
national websites reported critical information only in the local language.
23 |
What constitutes best practices?
Current practices by NASS/USDA and Statistics Canada provide good models to represent best
practices for stocks estimation methodology in developed countries. They both collect stocks
data, using quite similar methodologies. Their practices also constitute a goal to ultimately be
achieved by statistics agencies in developing economies. The case of the Philippines for rice and
maize constitutes best practices observed in a developing country, and will serve as our model
for recommendations that may differ between developed and developing economies.
What distinguishes these best practices cases is that surveys of both commercial agents and onfarm (as well as household behavior) are used to estimate stocks positions. While the Philippines
now relies on personal interviews, the developed countries utilize a hierarchical strategy to
collect data. More cost effective electronic data collection is attempted first, with fallbacks to
mail surveys, then telephone interviews, then personal interviews are used. This insures adequate
response rates, so more accurate data. This method of data collection is probably the biggest
difference between what developed versus developing countries would need to implement.
Subsistence farming is a bigger factor in developing countries, but on-farm stocks continue to be
important even as the supply chain develops and there is more commercial storage available as
an option.
Utilizing a residual approach to estimating stocks – the most common method – is likely to
generate errors. Small errors in availability or use become large relative errors in stocks
estimates. Approximations are based on rules of thumb that have weak empirical foundations yet
may vary over time. Countries in which agricultural outcomes are important, especially to
commercial interests, now survey to collect stocks data. Other variables serve as the residual
from food balance, as stocks are easier to measure annually than use categories. Our
recommendations are based on practices in these cases. Fallback strategies are discussed for
developing countries when the costs of surveying for stocks is likely to be prohibitive. But the
most likely outcome is weaker stocks data, as other costly surveys are likely needed to fill
information gaps in the other elements of food balance sheets.
Annotated Guidelines – Outline
An outline for a document setting Guidelines for estimation of stocks data using a methodology
based on farm and commercial surveys should include the following components:
Introduction/Background – Why measure stocks
Overall recommendations:
Surveys are preferred to estimating stocks as a residual in the food balance equilibrium. Both onfarm and commercial surveys should be conducted, separately.
24 |
What to measure
When and how often to measure
Identifying relevant agents for surveys
Collection methods and logistics
Sample strategy and design
Questionnaires
Documentation
Results reporting
The introduction to this document would indicate its objectives, and why those objectives are
important. Some background on existing stocks estimation methodology along the lines of an
abbreviated version of the background section of this document, and on the approach
recommended in the Guidelines, might be included. The next section presents and justifies the
overall recommendation, which strongly prefers collection of survey data the over the residual
approach to stocks estimation. Each subsequent section identifies and examines issues relevant to
how a survey should be conducted, and what documentation and results reporting should be
included.
25 |
ANNOTATED GUIDELINES – ISSUES & RECOMMENDATIONS
This section examines issues related to each component of the above outline and offers a
recommendation based on our evaluation of best practices in current stocks estimation
methodology. In each section, issues are listed with commentary based on observed best
practices. Then our recommendations are indicated. Where appropriate, fallback
recommendations for developing countries will also be presented. The recommendations
represent this author’s evaluation and synthesis of current best practices.
Introduction/ Background – Why measure stocks
Issues
The introduction to Guidelines for stocks estimation methodology should motivate why stocks data
should be collected, based on data needs of both policymakers and market participants.
Stocks data inform both market performance and food security outcomes. Policymakers who might
intervene in markets and who implement food security policy would benefit from better market
information
Purposes of stocks identified above included: local/household food security, regional trade and
domestic seasonal pricing, international trade, supplies for public distribution and food aid, and
working inventories for livestock producers and food processors
Several definitions of types of stocks exist.
The introduction should clarify relevant definitions. Annual carry-out stocks are the focus here because
they are the most relevant information determining market performance, and are likely the most relevant
information to policymakers for both market interventions and for food security measures.
Recommendations
The introduction should indicate the rationale behind stocks measurement – why this information is
important. It should link stocks measurement to market performance and to the policy environment. It
should show how stocks data might be used to formulate food security policy as well as market
interventions, and what data needs for policymakers might be. Any lessons from this review would also
inform what is collected and how it is best collected.
Annual stocks carry-out is the most relevant measure both for market performance and for food security
policy.
An abbreviated version of the introduction to this document might serve to motivate why better stocks
data is needed. It now includes sections on stocks definitions and on purposes of stocks.
26 |
Overall recommendations
Issues
Should stocks data be estimated from survey data, or can it be based on a calculation as a
residual in food balance or supply-utilization equilibrium?
There are several candidate variables that might serve as the residual term from food balance
accounts – stocks, human consumption or feed use. Consumption and feed use are likely to vary
with availability, and current methods typically don’t capture this. Household surveys to measure
use are done infrequently, and have in the past at times been inconsistent with aggregate supplyuse balance. Rules of thumb to allocate a shortfall across several candidate residual terms are
now ad hoc and not made public information. Errors in some “minor terms” like waste, losses or
seed use are approximated a constant fractions that can be dated, and are not updated as
technology improves or market conditions change. These ratios may change over time.
Adjustments to reconcile the residual identification problem are sometimes informed by informal
interviews of commercial agents, which probably improves estimates but may not be consistently
applied. Small errors in production or use would lead to large relative errors in stocks.
While production and trade data are more accurately known than use, there can be errors in those
data, as well. Collecting stocks data is a check on that availability data. Stocks data is easier to
collect than use data. Those countries that use stocks surveys treat some use category as the
residual (e.g. feed use in the U.S.).
If stocks data is not collected, the residual method only reveals change in stocks. An initial
stocks level must be estimated at some point, and errors in that estimate can lead to errors in
subsequent levels estimate, even to the point that levels estimates go negative. The level,
indicating working stocks when they are low, can be useful information for both market
performance and food security. Reconciling stocks survey results with supply-utilization
accounts is done by those who survey to check all data. Rather than making ad hoc changes to
stocks estimates, discrepancies should lead to investigation of whether survey or aggregation
procedures need fixing.
Should stocks data surveys collect data from commercial traders and storage operators? Farmers?
Or just from public and parastatals agencies?
Reporting only public (official) stocks misses a large fraction of stocks held by private agents.
On farm stocks can be large, though they may be held in part by subsistence farmers who do not
generate a marketed surplus. The exporting countries who have better developed supply chains
and commercial storage infrastructure still find substantial on farm stocks, so they survey
farmers. It may be less costly to survey commercial interests, so countries may fall back to only
surveying them, and not farmers, but the stocks held on farm will then be missed. It is useful to
do less frequent, more comprehensive surveying to insure relevant agents are included in
surveys.
27 |
Separate questionnaires are required for commercial storage versus on farm stocks. Commercial
stocks surveys are highly specialized – questionnaires are simple, asking only about stocks and
storage capacity.
Farm surveys of stocks are often integrated with surveys to measure production, area, and yield.
Combining stocks surveys with more comprehensive rural household surveys is likely to
deemphasize stocks data collection. Stocks questions may not get asked, and comprehensive
surveys now doing this lose track of when stocks are held. Stocks surveys in best practice cases
are done quickly around precise dates related to the crop year – as harvest begins - to capture
annual carry-out stocks.
Recommendations
Surveys are preferred to estimating stocks as a residual in the food balance equilibrium. Both onfarm and commercial surveys should be conducted, separately.
On farm stocks surveys may be integrated to a limited extent with production and area surveys.
They should not be part of a comprehensive rural household survey. Simple stocks surveys
would not differ for large versus small farms, while more comprehensive surveys could be quite
different.
Reporting of stocks positions by government agencies or parastatals is important. Stocks reports
would usefully include how much is held as public stocks, by commercial interests, and on farm.
Inconsistencies revealed in stocks estimates relative to food balance should trigger improvements
in surveying procedures rather than ad hoc data adjustments.
Fallback: If stocks surveys of private agents (farmers and commercial storage) are not conducted,
the empirical foundations of the residual estimation strategy need to be substantially
strengthened. That would likely require consumption and use surveys as well as more frequent
rural surveys which provide data for assumptions on consumption, waste, loses, seed use and
other items approximated in the food balance accounts.
Stocks level estimates are needed to gauge market impacts. Stocks variation is useful but
incomplete. Surveys are needed to arrive at stocks levels.
What to measure
Issues
What definition of stocks should survey questions be based on?
Stocks surveys capture annual or seasonal carry-out stocks. Ambiguities mean it may be difficult
or impossible to identify which subcategory of stocks (e.g. working stocks or buffer stocks) a
particular stock of grain is held for, so questions do not now make such distinctions. Categories
such as emergency reserves or buffer stocks are more likely to apply to public stocks, where a
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legislative mandate can be identified. It will not be possible to differentiate working stocks from
reserves in questionnaires of private agents.
Stocks should be measured for which commodities? In what form – as grain, milled or processed
products?
The focus of this project, and the concern expressed in G8/G20 recommendations, was to
improve information for grains and oilseeds. While stocks positions are collected for many
commodities in countries that survey stocks, for both grain market performance and food
security, a first effort should focus on grain and oilseed stocks. This project has limited oilseeds
to soybeans, given their importance in world markets. One might consider adding other oilseeds
important to food security in specific situations.
Rice is typically reported in milled form, but data will need to be collected for both paddy and
milled rice with appropriate conversion factors applied. For wheat, flour stocks may be collected
from miller or end user surveys, but in the grain market the dominant measure is wheat as grain.
Processed products for coarse grains are not included in on-farm or commercial stocks surveys.
As for flour, major traders may report stocks for soybean meal and oil, but the major stocks
influencing prices are beans, not the outcomes of crushing. At least as a first pass at improving
stocks estimates, data on grains and soybeans should be collected.
Should stocks be measured for non-tradable commodities?
While the focus of the G8/G20 effort may have been on issues related to international trade, it is
common for some grains to be non-tradable, and yet contribute significantly to food security
outcomes. For example, coarse grains such as sorghum, millet, beans, pulses and even maize in
some cases are non-tradables in many Sub-Saharan African countries, yet are significant dietary
staples. Moreover, tradable and non-tradable cereals are substitutes in consumption in those
cases. Substitution into non-tradables was an important coping strategy in these countries during
the food crisis.
Recommendations
Stocks should be measured as the quantity of a commodity in storage at the specific point in the
crop year when stocks are at their lowest level, coinciding with the first day of the month when
harvest begins. The goal is to capture annual carry-out (end-of season stocks), hence also carryin stocks separately for public commercial and on-farm stockholders. Other definitions only
apply to public stocks.
At a minimum, paddy and milled rice as well as wheat and coarse grain in grain-equivalent
should be collected. Definitions for questionnaires need to delineate clear commodity definitions.
Conversion factors are needed to convert to a standard form and standard units of measure.
Survey should collect data on all major cereals and soybeans, whether tradable or not.
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Limiting surveys to grains and soybeans is a fallback strategy. Surveys of stocks, where they are
now conducted, capture some processed products like flour, corn grits, meal for feed, and oil.
When and how often to measure
Issues
When during a year should a stocks survey be conducted? Should it be conducted more often
than once per year?
The goal of surveying is to arrive at annual carry-out or equivalent carry-in stocks estimates, the
variable that best informs market outcomes. Monthly or quarterly data helps to inform market
behavior over the short term. In countries where stocks data is now seen to matter, stocks are
collected less frequently than monthly, but more often than annually. Multiple crops during the
year suggest more frequent surveying is required to capture seasonal behaviors.
It is important to properly define crop years, and so identify dates corresponding to when harvest
begins. Hence, these definitions allow stocks to be identified as annual or seasonal carry-out.
Should the survey capture calendar year or crop year data?
Crop year data better reflects both market and food security outcomes. Calendar year data
reflects multiple crop years and more than one production cycle. Calendar year data neither
informs market prices nor is a clear indicator of food security. Key components of food balance
(notably production) will be best measured on a crop year basis, although some components (e.g.
trade) may only be known on an annual basis. Trade data in developed countries is generally
known more frequently. Use data will depend on whether any use surveying is done, and over
what time frame.
How quickly does the survey need to be conducted?
Surveys need to be done quite near the relevant date – this implies a short, focused questionnaire.
Stocks surveys are now collected over a very short period (4 to 6 days) on and after the target
date of the survey. Comprehensive surveys tend to stretch over long periods of time, so relating
measured stocks to specific dates becomes problematic.
Recommendations
Surveys should be conducted at the beginning of each crop year. Where there are multiple crops
during a calendar year, this means stocks should be surveyed more than once per year. In
countries where this is done, more frequent surveying is typical, to obtain annual and fiscal year
measures as well as to provide timely short term information on market performance. Quarterly
surveys are now most often the best practice case.
Stocks surveys need to be done over a few days after the target date of the survey. This implies
use of a short, focused questionnaire.
30 |
Fallback: At a minimum, stocks surveys should be conducted once per year. More frequent
surveying is desirable, but monthly surveys are probably more frequent than is necessary.
Identifying relevant agents for surveys
Issues
Who should be surveyed/ interviewed? How are lists of survey subjects created and maintained?
Should the sampling approach be a list frame or an area frame?
All agents along the supply chain who might hold stocks should be in the population from which
samples are drawn.
Surveys need to follow a sampling procedure that insures a nationally representative sample.
Lists rather than area frame sampling is utilized for targeted stocks surveys.
Public records may offer a starting point for lists, but an agency need to be responsible for list
creation and maintenance, recognizing that the relevant populations will evolve over time,
especially as a sector develops.
How are populations and samples of survey subjects established to insure nationally
representative samples?
A census of agriculture or existing rural household surveys may prove useful in establishing lists
of farmers to be surveyed as well as determining the population sizes that the samples are to
represent. It may also aid in informing a stratification strategy.
A separate “census” of traders, commercial storage operators, and end users (food processors,
livestock feeders, etc.) is needed to determine who stores and how much, hence establishing
populations for sampling.
Lists evolve as a supply chain develops. Moreover, there is entry and exit of firms and new
agents may emerge as important.
Regulatory filings may be a starting point for list creation and maintenance, since trade is often
regulated. Trade associations may also provide information, as well as firm surveys.
Recommendations
A list frame approach better fits stocks estimation than an area frame approach. Lists will need to
be maintained for both commercial and on-farm surveys.
List for the commercial survey needs to include all agents along a supply chain who may hold
stocks – traders, commercial storage operators, and end users (food processors, livestock feeders,
etc.). All potential sources should be tapped by an agency responsible for maintaining lists, as list
membership will vary over time and as sector (marketing infrastructure) develops.
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Lists for on-farm surveys may come from a census of agriculture or from ongoing rural
household surveying.
Collection methods and logistics
Issues
What is the preferred method to collect information – online (web/email), mail (postal service),
telephone, or interview?
Online methods are most cost effective, followed by mail, then telephone, then interviewing.
Online response rates have been quite low, even in developed countries. Response rates improve
as data collection becomes more costly. In developed countries agencies pursue the least costly
method first, but then use increasingly costly approaches to insure adequate response rates.
Occasionally, they also conduct interviews to verify data and check accuracy.
In developing countries, infrastructure for online, telephone and even mail may be inadequate, so
that interviewing is very likely to be required. Online methods may “leapfrog” mail in
developing countries as technology improves faster than postal service.
Experience with rural surveys should guide methods and logistics for stocks surveys.
Statistical agencies have considerable experience with survey implementation. Procedures need
to be established for supervision, monitoring and quality control. Visual inspections may be
necessary to insure data accuracy, and that correct units of measurement are applied.
The implementing authority is sometimes a regulatory agency. The hope is that farms or firms
will feel compelled to respond to the survey. Surveys are done in collaboration with the statistics
agency in this case. In some cases survey participations was “mandated”, but missing
observations occurred in those cases as well.
Should there be separate commercial and rural household/farm surveys?
A rural household/farm survey may be used to collect limited, timely data on the upcoming
harvest – area yield or production. It might also ask questions about post-harvest losses or other
issues where cost efficiency can be achieved without taking focus away from stocks data
collection. Commercial surveys should and now generally do focus on stocks only.
Recommendations
A sequential data collection method starting with online collection and moving to personal
interviews is preferred, but in developing countries a greater extent of interviewing will be
needed. Follow-up using more direct methods is needed to address missing responses. Response
rates and infrastructure may improve as a sector evolves or country develops. Commercial stocks
data collection is likely to be easier, and methods there may evolve faster.
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On farm surveys should be conducted by the entity experienced with surveying – typically a
statistical agency. Commercial stocks may be done under the authority of a regulatory agency,
but should be done in collaboration with an entity experienced in surveying and should focus on
stocks.
Sample strategy and design
Issues
How does the country insure a nationally representative sample? What stratification strategies
are used to accomplish this? What regional information is collected?
Agencies first identify a regional disaggregation, typically at the province or state level. Surveys
may differ across regions, for example because crops grown differ by region. But storage is often
in regions where crops are not grown.
A list frame approach better fits stocks estimation than area frame. Lists are developed in regions
to establish population size and from which samples are chosen. A more comprehensive survey
of commercial storage may be needed to complement existing rural surveys in order to develop
lists and establish population sizes.
Stratified sampling procedures are utilized. Agencies in all cases where commercial surveys are
conducted now attempt to collect information from all the large commercial storage entities –
complete enumeration. Samples representative of large farms are bigger than for small farms.
Stratification strategies now utilize information on: political entities (states, countries); regions
by type of production; farm/business size (production, acreage, sales, animal numbers, and
capitalization); and other factors. Computer models impute data for missing observations and for
populations in an automated fashion.
More detailed recommendations on sampling strategies are being developed in the framework of
the Global Strategy to Improve Agricultural and Rural Statistics (FAO, 2013g).
Should non-farm households be surveyed? Should farms that do not generate a marketed surplus
be excluded?
Subsistence farmers may develop to become commercial farmers, and their food security when
they do not generate a marketed surplus is dependent on stocks held. This information is more
relevant to food security than to market performance.
Retail stocks may be a better indicator of food supplies than household stocks in urban areas.
Recommendations
Complete enumeration of large commercial storage entities and large farms is recommended, and
is current “best practice”. Stratified samples of smaller farmers and traders are more cost
effective. Lists based on a complete enumeration are needed to establish procedures to
extrapolate from samples to the population. Samples need to be sufficiently large to insure
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nationally representative results. The sampling strategy should be based on stocks positions
rather than driven by needs of an integrated survey. Samples taken over time are now a panel,
with replacement when a sample is “lost”. Firm entry and exit is expected and is addressed in
establishing and maintaining lists.
Models need to be developed to estimate data when there are missing observations, including
where a census of traders is sought. Models also use stratification design to estimate totals.
Households should only be surveyed if there is evidence they hold significant stocks. Subsistence
farmers may generate marketed surplus under better conditions, and their food security is more
dependent on stocks, so they should be surveyed.
Questionnaires
Issues
Should questionnaires be comprehensive or target stocks positions?
Simple questionnaires focused on stocks positions are now utilized in “best practice cases.” This
is not to be a household survey, so one should limit other questions (like own consumption,
household food balance).
Research papers and focused studies have taken a food balance approach to questions to check
on various components of the balance, including stocks.
What should be included on the questionnaire? In what units of measure?
Stocks are now surveyed in local units of measure, with conversions to standard units by
interviewers. Goods to be surveyed include at least the focus commodities – grains and oilseeds.
Questions need to include differentiated products – white versus yellow corn, grits, paddy and
milled rice, flour – with conversion factors to convert to grain equivalent. Sometimes other
goods, and questions on “stratification” variables (e.g. household demographic or income
information, firm size) might be useful.
Ownership status is a concern, as stocks may be owned by one entity (e.g. a farmer) but held by
another (e.g. commercial storage facility). Stocks may have just been sold, and stocks may be “in
transit.” Questions may be included to clarify ownership status, and questions need to be clear on
what is held versus what is owned.
In developed countries questions ask about storage capacity. This concept may be less relevant
for subsistence farmers, households, and where storage methods are less well developed. Even in
developed countries there is “temporary” capacity used when harvests are large.
Will farmers or traders reveal stocks information accurately? Are there implications for
questionnaire design based on “sensitivity” issues?
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Countries that do not conduct surveys often state that stocks positions are sensitive information
agents may be reluctant to reveal. In those countries where surveys are now conducted, this has
not proven to be a serious problem.
Pretesting questionnaires may reveal and address sensitivity issues, and is needed in any case to
validate interview procedures. It would be useful to review existing strategies used in rural
household survey methodology to deal with respondents’ reluctance to answer questions.
Recommendations
Simple questionnaires should be developed focusing on stocks on hand at the time interview is
conducted (or report is provided via phone, web or mail). If there are public stocks incentives to
private stockholders, or ownership issues, the survey should ask questions to reveal status. One
should use local units and pretest, with a strategy to insure units errors do not show up in
calculations. Capacity is less relevant for on farm stocks in poor countries.
Documentation
Issues
Even in the “best practices” cases public, thorough documentation of methods to estimate stocks
data were not publicly available.
Documentation of the methodology to survey for stocks data available at the end of reports or
online typically were only one page long. In no case did we obtain questionnaires online. We did
manage to get questionnaires for several countries (see appendix) but only received one
“manual”, which was also brief and incomplete.
The metadata section of CountrySTAT provided fuller documentation than was found in most
over cases. Hence, documentation of the Philippines household and farm surveys was better than
in most other cases. We still needed to obtain questionnaires from Philippine authorities.
Documentation of the adjustments to reconcile conflicts in estimating stocks versus use in the
residual approach, and the “rules of thumb” used to approximate terms like seed use, losses or
waste, were also never made publicly available. This compounds the uncertainty of the residual
approach to stocks estimation. While the FAO Handbook (2001) acknowledged many of the
problems with this process, practical guidelines to resolve those problems were not presented.
We did not find published guidelines in other cases, such as USDA’s PS&D database, either.
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Recommendations
Publicly available and complete documentation, including sampling strategy, survey coverage,
questionnaires, timing, and adjustments/procedures to move from sample results to population
predictions is needed. Especially if the residual approach rather than surveys continue to be
adopted, better documentation, even than is found in the “best practices” cases, should be
provided. AMIS is attempting to require this in the metadata sections of its CountrySTAT
websites, and can be helpful in insuring better documentation.
Results reporting
Issues
How soon after surveys and in what manner should reports from stocks surveys be published?
What detail should be included?
Reports of the existing surveys are now published quickly (within a couple of weeks of
completion of the survey), providing markets and policymakers with timely data. The reports
should separately reveal public stocks, commercial stocks and on-farm (or household) stocks.
Existing reports generally provide this detail on a regional basis (state or province).
Surveys and reports are generally done by a statistical agency, sometimes within an agriculture
ministry. Analysis and projections are done by a separate agency. For example, NASS/USDA
publishes stocks reports while WAOB, FAS, and ERS assembled past and projected supplyutilization accounts.
Researchers have had access to survey data, subject to certain restrictions, in the past. This stocks
survey data would be a valuable resource for research on food security and market research.
Several countries state that they check survey results against other components of food balance
or supply-utilization accounts. Procedures to make adjustments if discrepancies were detected
were not made clear.
Recommendations
For stocks estimates to be useful, reports must be issued quickly after surveys (less than one
month). They should include detail (public, commercial, on-farm; regional data; etc).
It would be useful to make underlying survey data publicly available, probably on a slower
schedule. That may require restrictions to insure interviewee/respondent privacy.
The entity that estimates (projects) future stocks, and reports food balances, should remain
different from the one reporting existing stocks estimates that result from surveys. Discrepancies
between survey estimates and food balance should be resolved by improving sampling strategy,
and errors in other components, not ad hoc adjustments that persist as data.
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If Stocks Surveys are Not Implemented
The premise behind our recommendation is that better agricultural market information requires
that countries collect more information. There are not methodological tricks to elicit better
information if existing data collection practices continue. There are not “best practice”
methodologies that extract better information from non-existent data. The few countries that
survey stocks, and on which our best practice recommendations are based, are countries where
grain and oilseed markets are important, so information is valued and expense is incurred to
collect additional information. There are admittedly relatively few countries now incurring such
expenses. Most countries collect production and trade data annually and human consumption
data infrequently (every three to five years, and only short term observations at that). It may well
be the case that few developing countries, and even few developed countries, will be willing to
increase their budgets for better agricultural market information. Therefore, we consider here
some of the fallback positions and their consequences.
We have already identified some fallback positions in our recommendations concerning
implementation of stocks surveys. Best practices countries collect data quarterly or more
frequently. Data would improve significantly over the status quo in most cases if data were
collected annually rather than not at all. Best practices countries collect information on a wide
range of commodities. Our recommendations are to start with the AMIS commodities – rice,
wheat, maize and soybeans. Millet and sorghum as well as other oilseeds, beans, pulses and
cassava may be important to food security as they are important dietary staples, particularly in
Africa. The AMIS focus prioritizes market performance information over food security
information.
Further fallback strategies in implementing stocks surveys might also be considered. It may be
the case that adequate market information (certainly better than the status quo) would be
obtained if only commercial stocks surveys, and not on-farm surveys, were conducted. Once
again this puts priority on market performance over food security, but commercial stocks may
proxy well for overall stocks. Research is needed to determining whether this is the case in
countries where both commercial and on-farm stocks are now collected.8 A complicating factor
is that most cases where there are now stocks surveys are developed countries. In developing
countries marketing chains are evolving and commercial storage is partially replacing on-farm
storage. Developed country markets are not a good model on which to estimate proxy
relationships that may be changing over time. Without any data on on-farm stocks, formulae to
estimate overall stocks will not have a solid empirical foundation. If this approach were
followed, alternative stratified sampling strategies might also be examined. In the best practice
cases, countries sample more heavily large commercial storage. Smaller samples (with larger
sampling error) might suffice if sufficient data was collected mostly from the larger market
participants. In practice, this may be a way to characterize what USDA and FAO experts do
8
It is less likely to be the case that publicly held stocks proxy well for overall stocks, because they are
most often the consequence of policy measures and public distribution schemes and may not be
representative of behaviors by private agents who either are seeking to insure their own food security or
who are hedging/speculating based on expected market price fluctuations.
37 |
when they construct food balance sheets without data collection, but informally collect of
information from key market participants.
The alternative to data collection is estimation of stocks as the residual in food balance
accounting. The fundamental problem behind stocks estimation via a residual approach is the
identification problem discussed earlier. It was recognized that one equation cannot be used to
solve for several unknowns. Under status quo data collection, typically both stocks and various
use categories are unknowns. Experts must now use rules of thumb or informal (undocumented)
models to allocate supply changes across stocks and use changes. (It may be the case that
estimated models, such as demand systems, that are found using food balance or supply
utilization accounting as data are really just rediscovering the “model” used by the expert to
generate the “data”.)
In the spirit of our premise that more information is needed, it may be the case that data
collection could focus on use (human consumption, feed use, seed use, industrial use, and
waste/losses) rather than on stocks. It should be noted immediately that the best practice
countries now choose to estimate stocks rather than use. The several reasons noted above for
why this is that case are based on the notion that it is easier to observe stocks rather than flows
accurately. Nevertheless, a country might choose to increase its frequency of rural (and urban)
household consumption surveys and may use firm surveys to attempt to estimate feed and
industrial use. “Minor” categories such as seed use, waste and losses would also benefit from
inclusion of questions on comprehensive (infrequent) agricultural surveys to provide a firmer
empirical foundation for rules of thumb now used. The goal with this strategy would be to
improve information (via data collection) on the other unknowns of the food balance residual so
that this approach would yield a better estimate of stocks. Budgetary allocation decisions on
overall agricultural market information would need to weigh the pros and cons of collecting
stocks data versus use data. The best practice cases now come out on the side of collecting stocks
data.
In the absence of underlying country level market information, the FAO in its estimation of food
balances for FAOSTAT and GIEWS and the USDA in its estimation of stocks (outside the U.S.)
as part of its PS&D supply-utilization accounts, both rely on the residual approach and “expert
judgment.” They indicate a number of ways to improve information, and analysts often have
considerable experience in countries for which they must estimate data, but they are still
seriously hampered in coming up with accurate stocks estimates when there is not actual data on
the market in question. Neither documents the procedures they use to make stocks estimates (or
use estimates) in the absence of data. While countries might generate better national information
if they employed analysts with expertise comparable that that found in FAO and USDA, data
accuracy will remain limited by the underlying information behind that data. What is included
(e.g. public versus private stocks) and information accuracy, is likely to vary considerably across
countries, as can be seen from USDA’s GAIN reports.
While there may be a number of ways to marginally improve agricultural market information,
including stocks information, that is better than the status quo, any such strategy is almost
38 |
certainly going to require additional data collection and additional budgetary expenses. In the
best practice cases, stock data collection is seen as the cost-effective approach.
Conclusions
The objectives of this report were to assess current “best practices” in estimating grain and
oilseed stocks data and to develop from those practices an annotated outline of a document that
would provide Guidelines to a recommended stocks estimation methodology. While the most
frequently found method was to compute stocks as a residual in a supply-utilization accounting
equilibrium or food balance, numerous problems with that approach were identified. Moreover,
in cases where stocks data are now highly valued – several large exporting countries, surveys are
used to estimate stocks positions. Both on-farm surveys and surveys of commercial agents who
store grains or oilseeds are conducted, separately. In a few cases, only commercial surveys were
conducted, and in one case non-farm household stocks were also surveyed.
Our recommendations emphasize the desirability of estimating stocks utilizing both commercial
and on-farm surveys, conducted on an annual or seasonal (quarterly) basis. The goal is to capture
carry-out stocks from one crop year (season) to the next. It is useful to measure public,
commercial and on-farm stocks, and to report stocks positions in each category. Some of the
functional classifications of stocks (e.g. working versus reserve stocks) would be ambiguous to
stockholders, so simple questions should suffice that do not attempt to clarify such distinctions.
Surveys with limited scope, at most also seeking area, yield and production information or
losses, and to enable stratification, rather than comprehensive rural household surveys, are
required to insure a focus on accurate stocks data collection.
Since the most serious constraint to implementing this recommendation is the cost of
implementing the stocks surveys, occasionally our detailed recommendations consider fallback
positions for developing countries. For example, in best practice cases data is generally collected
on a quarterly basis or more frequently, but as a starting point annual surveys would represent
significant improvement in most developing countries. Also, the commodity focus here is on
grains and soybeans, while in observed best practice cases wider commodity coverage was
generally found. While we subsequently considered further fallback strategies, those approaches
still presumed that better market information requires more data collection.
The three countries targeted in the FAO project funded by the Bill and Melinda Gates
Foundation (BMGF) on improving agricultural market information –Nigeria, Bangladesh and
India – exhibit a range of methods to estimate stocks currently. The Philippines is considered in
another FAO project, and the Philippines appears to be successfully implementing both
commercial and on-farm surveys, and in fact has been doing so since 1980. In Nigeria there is
recognition of the need to collect survey data on stocks, but funding constrains survey
implementation. We identified there issues with the stocks questions that were part of
comprehensive rural surveys, and were uncertain as to future for implementation of the rural
household and commercial stocks surveys. In Bangladesh and India only official stocks data –
stocks held by public agencies or parastatals firms – are available. Hence the “target countries”
39 |
examined here included the Philippines in addition to Nigeria, Bangladesh and India, since the
Philippines serves as an example of good country practices.
Overall stocks positions remain in most cases estimated imperfectly as a residual in food balance
equilibrium, or incompletely as public stocks. We believe each of these countries would
experience better market performance and more easily achieve food security goals if improved
stocks estimation methodology, incorporating data from farm and commercial surveys focused
on stocks, were adopted.
40 |
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Questionnaires. Manila: Department of Agriculture, Government of the Philippines.
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Department of Agriculture, Government of the Philippines.
BUREAU OF AGRICULTURAL STATISTICS 2013s. Palay and Corn Stock Survey (PCSS1)
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41 |
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FAS/USDA 2013w. Grain: World Markets and Trade. Washington DC: USDA Foreign
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42 |
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Oilseeds in Storage. Washington DC: National Agricultural Statistics Service (NASS),
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Nigeria: National Bureau of Statistics, Government of Nigeria.
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43 |
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WIGGINS, S. & KEATS, S. 2009. Grain stocks and price spikes. London: Overseas
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44 |
Appendix: Annotated Bibliography
Abbott, P. (2010). Stabilization Policies in Developing Countries after the 2007-08 Food Crisis.
OECD Working Paper TAD/CA/APM/WP(2010)44. Paris, OECD.
Conventional best practice advice for risk management strategies tends to focus on longrun agricultural development, trade liberalization, the provision of safety nets and private market
solutions to risk. However, if world price spikes like those observed in 2008 are an infrequent
but real event, policy recommendations need to take into account the greater prevalence of
market failures in many developing countries and associated underdevelopment of marketing
institutions. While policy should rely on liberal trade in most years, a short-run stocks policy
may be a viable option, due to delays in import arrival, imperfect information on the harvest, and
inter-seasonal price dynamics. Moreover, trade policy adjustments are likely to be perceived as
necessary when infrequent world price spikes reoccur. The challenge to implementing such
policies lies in ensuring consistent, predictable and transparent governance so that interventions
make outcomes better, not worse.
Abbott, P. and A. Borot de Battisti (2011). "Recent Global Food Price Shocks: Causes,
Consequences and Lessons for African Governments and Donors." Journal of African
Economies 20(suppl 1): i12-i62.
Dramatic increases in international agricultural commodity prices began in 2006 and
peaked in July 2008. An equally remarkable and rapid decline of those prices then ensued,
accompanied by extreme volatility in those prices. Not all agricultural commodities increased to
the same extent—grains and oilseed prices increased the most, with rice among the most
expensive at the peak and rising as much as crude oil, while prices of some African exports
(cocoa, coffee and cotton) increased to a much smaller extent than the grains. High commodity
prices quickly raised farmgate prices in developed countries. In developing countries, poor
market integration and border barriers may have limited pass-through of these prices to the
farmgate, but there was more rapid food price and general inflation than occurred in many
developed countries. Countries were impacted to differing extents, and food riots occurred in the
most affected cases. It has been noted that underlying fundamentals of food price inflation differ
by the extent of development, as poor countries have smaller distribution costs but higher budget
shares in basic staples. Import dependence, tradable versus non-tradable status of grains and
whether there were home goods substitutes influenced the extent of price transmission. Many
developing countries reacted by altering trade and domestic agricultural policies and attempted to
stabilize domestic markets. Importing countries reduced tariffs and taxes in many cases, and food
subsidies were increased in some cases. Export taxes were enacted to protect domestic users, and
bans of exports were applied in some extreme cases—explaining the especially large increase in
rice prices. While impacts on domestic prices vary across country cases, disentangling the role of
policy response from market integration would require further work. Policy responses were
complicated by disagreement at the time that prices were rising as to whether the increases
would be permanent or short lived, which in turn depends on the root causes of the increases—
over which there has been considerable debate. Consensus has emerged on some factors, while
controversy over macroeconomic relationships persists. They were also complicated by dynamic
adjustments of related prices, which were not instantaneous. For example, fertilizer prices did not
fall until several months after grain prices fell. Policy responses of national governments in
Africa and elsewhere in the developing world contrast sharply with initiatives recommended by
45 |
the international community. International organizations, development banks and donors
emphasize emergency relief and longer term agricultural development, whereas national
governments heavily utilized market interventions through trade and domestic policy. In this
paper, the roles of border policy, domestic agricultural policy, market integration and retail food
margins will be considered. Special emphasis will be placed on what actual policy adjustments
were taken and how well they worked. Given the presumed underlying causes of high and then
low food prices, and the uncertainty of future global commodity prices, policy options now
available to developing countries will be explored. These will include short-run safety nets,
market interventions and long-run incentives to agricultural development. In this part, special
emphasis will be put on what happened in Africa and how it should respond in the future. Some
of the key issues in the current debate on expanding African agricultural sectors, including price
stabilization and fertilizer subsidization, will be explored. In the process, we will evaluate how
far various methodologies have taken us in providing an understanding of the consequences of
these recent events, and providing a sound basis for policy recommendations.
Abbott, P. C., C. Hurt, and W. Tyner (2011). What’s Driving Food Prices in 2011? Oak Brook,
IL, Farm Foundation.
We have identified five key issues that are important elements of the agricultural
commodity price story: • Two big, persistent demand shocks. Biofuels demands, particularly for
corn, and Chinese soybean imports have increased in recent years and remain at high levels.
Both the size and persistence of these shocks affect market outcomes.
• Greater market inelasticity. More inelastic agricultural markets mean prices are both
higher in response to demand shocks and are now more volatile. Combined effects of different
components leading to greater inelasticity have a bigger impact than each component would
separately.
• Weather and stocks. Poor harvests due to weather are more important in 2011 than in
2008. Price increases are now more consistent with low stocks-to-use ratios. • Chinese policy.
Chinese trade and stocks policies, which vary across commodities, are critical factors
conditioning the impact of income growth and dietary transition on world market outcomes. It is
necessary to understand Chinese self-sufficiency to interpret world supply-utilization and stocks
data. Being nearly self-sufficient in grains, the Chinese are largely disconnected from those
requirements.
• Macroeconomic factors. While changes are not so dramatic in 2011, the dollar
exchange rate remains we
macroeconomic factors—including worldwide economic growth—that influence the expected
high level of agricultural commodity prices even if there are not production shortfalls.
Akinyele, I. O. (2009). Ensuring Food and Nutrition Security in Rural Nigeria: An Assessment
of the Challenges, Information Needs, and Analytical Capacity. Abuja, Nigeria, IFPRI-Abuja
The main objective of this knowledge review was to collect and summarize available
secondary literature on food and nutrition security in rural Nigeria. There is a dearth of national
surveys providing datasets for the analysis of food and nutrition security in rural Nigeria. Though
there have been a number of individual and institutional efforts and attempts at generating
46 |
databases on food and nutrition security for Nigeria, these efforts are hampered by inadequate
funds to implement large-scale surveys.
AMIS (2013). "Stocks and utilization." Agricultural Market Information System. from
http://www.amis-outlook.org/amis-monitoring/indicators/stocks/en/
The Agricultural Market Information System (AMIS) is a G20 initiative to enhance food
market transparency and encourage coordination of policy action in response to market
uncertainty. The initial focus of AMIS is on four crops that are particularly important in
international food markets, namely wheat, maize, rice and soybeans. Low stock levels have had
a large impact on price volatility during recent food price surges. Stocks can provide an effective
temporary buffer against an unexpected supply or demand shock, so estimating and monitoring
their size at global level, especially as regards expected utilization, helps determine market risk.
The stock level of major exporters is a particularly important indicator of available supply in
global markets
Australian Bureau of Statistics (2013). "Wheat Use and Stocks, Australia." from
http://www.abs.gov.au/AUSSTATS/[email protected]/Lookup/7307.0Explanatory%20Notes1May%202
010?OpenDocument#.
This publication presents estimates from various Australian Bureau of Statistics (ABS)
wheat surveys and from administrative data relating to wheat exports. The Australian Bureau of
Agricultural and Resource Economics (ABARE) uses the data in this publication to prepare a
monthly report on the Australian wheat industry. Estimates in this publication are based on
information obtained from the following ABS surveys:
 Grain (Wheat) Handlers Stocks Survey (GHSS) - Monthly
 Wheat Use Survey (WUSC) - Annual Coverage
 Wheat Use Survey (WUS) - Monthly
 Wheat Export Sales Survey (WESS) - Monthly
Barrett, C. B. (2002). Food Security and Food Assistance Programs. Handbook of Agricultural
Economics, Vol. 2 B. L. Gardner and G. C. Rausser. Amsterdam, Elsevier.
Widespread hunger and malnutrition persist today despite considerable growth in per
capita food availability. This has prompted an evolving conceptualization of food security and of
mechanisms to attain and maintain food security. This chapter discusses both food security and
food assistance programs designed to respond to threats to food security.
Bureau of Agricultural Statistics (2013c). "CountrySTAT: Philippines." from
http://CountrySTAT.bas.gov.ph/?cont=1.
The CountrySTAT is a web-based system that integrates national food and agricultural
statistical information to ensure harmonization of national data and metadata collections for
analysis and policy making. In recognition of the existing and potential uses of the
CountrySTAT, the Philippine Statistical System (PSS) through the Philippine Statistical
Association (PSA) has taken on the challenge of establishing the CountrySTAT Philippines. This
has been made possible through a Letter of Agreement between the Food and Agriculture
Organization of the United Nations (FAO) and the PSA by which funds were provided by FAO
in order to implement the project “Strengthening the National Statistical Systems of Selected
47 |
Countries in the Asian and Pacific Region”. One major component of the Project is the
installation of the CountrySTAT which calls for the development of the metadata system. The
preparation and publication of metadata is another major component of the Project.
Bureau of Agricultural Statistics (2013cq). Commercial Stocks Survey Questionnaire. Manila,
Department of Agriculture, Government of the Philippines.
Commercial Stocks Survey Questionnaire in the Philippines.
Bureau of Agricultural Statistics (2013m). "Palay and Corn Stock Survey (PCSS)." Metadata for
National Agricultural Statistics of the Philippines. from http://CountrySTAT.bas.gov.ph/?cont=2.
The BAS (then BAEcon) in coordination with the National Food Authority (NFA) has
come up with the survey in monitoring the levels of rice and corn stocks in the country
(household stocks for BAS; commercial and NFA stocks for NFA). The survey aims to generate
estimates of the current stock of rice, palay, corn and corn grits in farm and non-farm
households.
Bureau of Agricultural Statistics (2013q). Palay and Corn Stock Survey (PCSS1) Questionnaires.
Manila, Department of Agriculture, Government of the Philippines.
The BAS (then BAEcon) in coordination with the National Food Authority (NFA) has
come up with the survey in monitoring the levels of rice and corn stocks in the country
(household stocks for BAS; commercial and NFA stocks for NFA). The survey aims to generate
estimates of the current stock of rice, palay, corn and corn grits in farm and non-farm
households.
Bureau of Agricultural Statistics (2013r). Rice and Corn Stocks Inventory. Manila, Department
of Agriculture, Government of the Philippines.
Stocks report issued by BAE in the Philippines.
Bureau of Agricultural Statistics (2013s). Palay and Corn Stock Survey (PCSS1) Design. Manila,
Department of Agriculture, Government of the Philippines.
The BAS (then BAEcon) in coordination with the National Food Authority (NFA) has
come up with the survey in monitoring the levels of rice and corn stocks in the country
(household stocks for BAS; commercial and NFA stocks for NFA). The survey aims to generate
estimates of the current stock of rice, palay, corn and corn grits in farm and non-farm
households.
Chowdhury, N. (1992). Rice markets in Bangladesh: a study in structure, conduct and
performance. Dhaka, USAID report.
Estimates of marketed surplus and on-farm stockholding in Banlagadesh.
Committee on World Food Security (2001). Assessment of the World Food Security Situation.
Rome, FAO.
The document begins with a review of the current situation with respect to the food and
nutrition status in the developing world. In the final part two special issues are discussed. First
the reason for discarding the ratio of end-of-season cereal stocks to global utilization is
48 |
explained. Finally a proposal for the balance of the core indicators to be used in future
assessment documents is recommended for adoption by the Committee.
Department of Food & Public Distribution in India (2013). "Ministry of Consumer Affairs, Food
& Public Distribution." from http://dfpd.nic.in/?q=node/1
The primary Policy objective of the Department of Food & Public Distribution is to
ensure food security for the country through timely and efficient procurement and distribution of
foodgrains. This involves procurement of various foodgrains, building up and maintenance of
food stocks, their storage, movement and delivery to the distributing agencies and monitoring of
production, stock and price levels of foodgrains. The focus is on incentivizing farmers through
fair value of their produce by way of Minimum Support Price mechanism, distribution of
foodgrains to Below Poverty Line (BPL) families and covering poor households at the risk of
hunger under Antyodaya Anna Yojna (AAY), establishing grain banks in food scarce areas and
involvement of Panchayati Raj Institutions in Public Distribution System (PDS).
ERS (2013). Feed grain database, USDA.
This database contains statistics on four feed grains (corn, grain sorghum, barley, and
oats), foreign coarse grains (feed grains plus rye, millet, and mixed grains), hay, and related
items. This includes data published in the monthly Feed Outlook and previously annual Feed
Yearbook. Data are monthly, quarterly, and/or annual depending upon the data series.
FAO (2001). Food Balance Sheets, A Handbook. Rome, FAO.
The purpose of this Handbook is to provide member countries and interested institutions
with the basic methodology regarding the preparation of food balance sheets. It is also intended
for use in training activities of nationals from developing countries in the construction of food
balance sheets. After a brief historical background, the document discusses data sources,
concepts and definitions regarding various elements of the food balance equation. It also gives
numerical illustrations on how to prepare commodity balances. Furthermore, examples regarding
applications and use of food balance sheet data in the analysis of national food situations, levels
and trends are given.
FAO (2008). The State of Food Insecurity in the World: 2008. Rome, FAO.
The State of Food Insecurity in the World 2008 represents FAO’s ninth progress report
on world hunger since the 1996 World Food Summit (WFS). In previous editions, FAO has
expressed deep concern over the lack of progress in reducing the number of hungry people in the
world, which has remained persistently high. This year’s report focuses on high food prices,
which are having a serious impact on the poorest populations in the world, drastically reducing
their already low purchasing power. High food prices have increased levels of food deprivation,
while placing tremendous pressure on achieving internationally agreed goals on hunger by 2015.
This report also examines how high food prices present an opportunity to relaunch smallholder
agriculture in the developing world.
FAO (2013c). "CountrySTAT." from http://www.fao.org/economic/ess/CountrySTAT/en/.
49 |
CountrySTAT is a web-based information technology system for food and agriculture
statistics at the national and subnational levels. In practice, it acts as a one stop center which
centralizes and integrates the data coming from various sources and allows to harmonize it
according to international standards while ensuring data quality and reliability.
FAO (2013f). FAOSTAT database. Rome, FAO.
The Statistics Division of the FAO has launched a new version of the FAOSTAT, which
is part of the organization's mission to improve data collection and dissemination for
development and the fight against global hunger and malnutrition. The new platform continues to
offer free and easy access to data for 245 countries and 35 regional areas from 1961 through the
most recent year available. Enhanced features include browsing and analysis of data, an
advanced interactive data download, and enhanced data exchange through web services.
FAO (2013g). "Global strategy to improve agricultural and rural statistics." from
http://www.fao.org/economic/globalstrategy/en/.
The initiative to develop the global strategy came as a response to the declining quantity and
quality of agricultural statistics. The global strategy will also address the emerging data
requirements posed by the Millennium Development Goals (MDGs), mainly on biofuels, global
warming, the environment and food security.
FAO (2013s). FAO Statistical Programme of Work 2012/13. Rome, FAO.
This is the second compilation of a consolidated FAO Statistical Programme of Work. It
gives an overview of all ongoing main statistical activities as well as those planned for the
biennium 2012/13 by the Statistics Division and all other FAO Divisions active in the field of
statistics. The Programme provides detailed description of all the individual statistical activities
classified into the following five categories of activities:
FAO, IFAD, et al. (2011). Price Volatility in Food and Agricultural Markets: Policy Responses.
Rome and Paris, FAO and OECD.
Under the Food Security pillar of the Seoul Multi-year Action Plan on Development, the
G20 “request that FAO, IFAD, IMF, OECD, UNCTAD, WFP, the World Bank and the WTO
work with key stakeholders to develop options for G20 consideration on how to better mitigate
and manage the risks associated with the price volatility of food and other agriculture
commodities, without distorting market behaviour, ultimately to protect the most vulnerable”.
This report has been prepared by FAO, IFAD, IMF, OECD, UNCTAD,WFP, the World Bank,
the WTO, IFPRI and the UN HLTF.
The approach taken in this report reflects the view of the collaborating international
organizations that price volatility and its effects on food security is a complex issue with many
dimensions, agricultural and non-agricultural, short and long-term, with highly differentiated
impacts on consumers and producers in developed and developing countries. The report begins
with a discussion of volatility and of the ways in which volatility affects countries, businesses,
consumers and farmers. Lessons learned from recent experiences are briefly reviewed as well as
the factors determining likely levels of volatility in future. This report offers suggestions for a
systematic and internationally coordinated response building on the lessons learned as a result of
the 2007-2008crisis.
50 |
FAS/USDA (2011). PS&D Online database. Washington DC, USDA
This database contains current and historical official USDA data on production, supply
and distribution of agricultural commodities for the United States and key producing and
consuming countries
FAS/USDA (2013b). Bangladesh Grain and Feed Annual. GAIN (Global Agricultural
Information Network) Report. Washington DC, USDA Foreign Agricultural Service.
Stocks: In MY 2011/12, the GOB distributed 1.4 million tons of rice through the Public
Distribution System (compared 1.6 million tons in MY 2010/11). In MY 2012/13, public
distribution is estimated to reach 1.7 million tons (1 million tons for free distribution to the most
food insecure, and 0.7 million tons for sale at subsidized prices). As a result, ending stocks are
estimated to fall to 881,000 tons in MY2012/13, down from 1.34 million tons in MY 2011/12.
As of December 31, 2012, GOB rice stocks were estimated at 1.2 million tons, nearly level with
the previous year. The GOB has set a 1.3 million ton target for domestic rice procurement in MY
2012/13 (compared to an actual procurement of one million tons in MY 2011/12).
In MY
2012/13, public sector beginning wheat stocks are estimated at 310,000 tons, unchanged from the
level in MY 2011/12. As of February 2013, GOB wheat stocks declined to 211,000 tons due to
the decrease in imports. Private sector wheat stocks, which were estimated at 1.77 million tons at
the beginning of the MY 2011/12, fell to 536,000 tons at the beginning of MY 2012/13. During
the July-February period of MY 2012/13, the GOB distributed 465,000 tons of wheat under
PFDS, up by 37 percent from the distribution during the same period in the previous year.
Assuming that the GOB meets its procurement targets, wheat stocks after PFDS distribution are
expected to reach 355,000 tons by the end of MY 2012/13.
FAS/USDA (2013g). "Global Agricultural Information Network (GAIN)." GAIN reports. from
http://gain.fas.usda.gov/Pages/Default.aspx.
USDA'S Global Agriculture Information Network (GAIN) provides timely information
on the agricultural economy, products and issues in foreign countries since 1995 that are likely to
have an impact on United States agricultural production and trade. U.S. Foreign Service officers
working at posts overseas collect and submit information on the agricultural situation in more
than 130 countries to USDA's Foreign Agricultural Service (FAS), which maintains the GAIN
reports. Production, Supply, and Distribution (PSD) data in GAIN reports are NOT official
USDA data, but represent estimates made by FAS Attachés. Official USDA PSD data are
determined after analyzing all overseas reports and drawing on additional sources, including
more than 1,500 documents received from private and public sources around the world, global
weather information, and satellite imagery analysis. After this analysis, official USDA data are
released in USDA's World Agricultural Supply and Demand Estimates monthly report and in
FAS' World Production, Market, and Trade reports.
FAS/USDA (2013i). India Grain and Feed Annual. GAIN (Global Agricultural Information
network) Report. Washington DC, USDA Foreign Agricultural Service.
Stocks: Despite the government’s efforts to offload wheat in the domestic and export
markets, government-held wheat stocks on February 1, 2013, were was estimated at 30.8 million
tons compared to 23.4 million tons at the same time last year. Estimates of privately-held wheat
51 |
stocks are not available, but are expected to be minimal in parts due to risks stemming from antihoarding provisions of the Essential Commodities Act. The PS&D table does not include
privately-held stocks. Source: Food Corporation of India, GOI Assuming continued strong
exports and domestic off take in February/March, MY 2012/13 ending stocks are estimated at
23.8 million tons compared to 20.0 million tons for MY 2011/12 ending stocks. MY 2013/14
ending stocks are forecast lower at 20.8 million tons on expected higher off-take under the PDS
as the higher total cost of government wheat will limit open market sales. However, these stocks
are nearly three times the government’s desired stocks of 7 million tons (4.0 million tons buffer
and 3.0 million tons of strategic reserve).
FAS/USDA (2013n). Nigeria Grain and Feed Annual. GAIN (Global Agricultural Information
Network) Report. Washington DC, USDA Foreign Agricultural Service.
Stocks: Most flour mills in Nigeria are located at sea ports, where space for storage
facilities is limited. Millers only have capacity to keep stocks that can sustain milling operations
for one month, a maximum of 250,000 tons. Industry sources estimate actual stock holdings are
at an average of 200,000 tons. Most flour mills in Nigeria are located at sea ports, where space
for storage facilities is limited. Millers only have capacity to keep stocks that can sustain milling
operations for one month, a maximum of 250,000 tons. Industry sources estimate actual stock
holdings are at an average of 200,000 tons.
FAS/USDA (2013w). Grain: World Markets and Trade. Washington DC, USDA Foreign
Agricultural Service.
Information is gathered from official statistics of foreign governments and other foreign
source materials, reports of U.S. agricultural attachés and Foreign Service officers, office
research, and related information.
FMARD/NBS Nigeria (2013f). Methods of Data collection and Analysis for estimating stocks of
wheat, maize, rice and soybean in Nigeria. Abuja, Nigeria, the National Bureau of Statistics
(NBS) and Federal Ministry of Agriculture and Rural Development (FMARD). .
Agricultural data in Nigeria are usually collected and analysed by the National Bureau of
Statistics (NBS) working in close collaboration with the Federal Ministry of Agriculture and
Rural Development (FMARD). Methods used is same for all category of produce including
estimates stocks of wheat, maize, rice and soybean.
FMARD/NBS Nigeria (2013q). Questionnaires for Nigeria. Abuja, Nigeria, National Bureau of
Statistics (NBS) and Federal Ministry of Agriculture and Rural Development (FMARD). .
Agricultural data in Nigeria are usually collected and analysed by the National Bureau of
Statistics (NBS) working in close collaboration with the Federal Ministry of Agriculture and
Rural Development (FMARD). Methods used is same for all category of produce including
estimates stocks of wheat, maize, rice and soybean.
Food Corporation of India (2013). "Stocks." from http://fciweb.nic.in/stocks.
Data on stocks held by the Food Corporation of India
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Food Planning and Monitoring Unit (FPMU) (2013). Bangladesh Food Situation Report, July
2013. Bangladesh Food Situation Reports. Dhaka, Bangladesh, Ministry of Food and Disaster
Management, Food Planning and Monitoring Unit (FPMU).
Public Stock of Foodgrain: The last fiscal year’s ending public stock of foodgrain on 30th
June, 2012 was 1.25 mmt and the ending stock on 30th June 2013 was 1.02 mmt. The public
imports of foodgrain FY 2012-13 was significantly lower than the quantity of public imports
arrived in FY 2011-12. The decreasing closing stock would be attributed to Lower public
imports but almost equal quantity of public distribution in FY 2012-13 caused lower level of
ending stock (1.02 mmt) on 30 June 2013. Also decline of public stock in last two quarters (JanJun/13) was also due to higher PFDS distribution during this period.
G8-G20 France (2011). Official Website of the French Presidency of the G20 and G8
Galtier, F. (2013). The need to correct WTO rules on public stocks. Paris, CIRAD.
The question of public stockholding for food security will be at the center of the next
WTO negotiations in Bali in December 2013. Broadly speaking, two approaches (which are not
mutually exclusive) have been proposed. The first one is to add flexibility for individual
countries that are at risk of exceeding their Amber Box limits: under specific threshold
conditions, countries would be allowed to build food reserves even if it implies that their
domestic support for agriculture exceeds their Aggregate Measurement of Support (AMS) bound
level. The second approach is more ambitious. It aims to modify the rules used to calculate the
contribution of public stocks to AMS. The present note aims to provide a first step toward a
consensus by focusing on a technical issue: showing that current WTO rules strongly
overestimate the real subsidies to agriculture provided by public stocks and correcting the rules
accordingly.
GIEWS (2013). Crop Prospects and Food Situation. Rome, FAO.
Outlook for global cereals supply, with special attention to food security in developing
countries.
GIEWS (2001). Food Outlook. Rome, Global information and early warning system on food and
agriculture, FAO.
The estimates of cereal stocks in China (Mainland) have been revised substantially
upward for all years beginning in 1980, leading to significantly higher figures for global stocks
than were reported previously. However, the revisions, although large in absolute terms, only
represent statistical adjustments in the historical supply and consumption series in China and,
therefore, have negligible or no impact on the market fundamentals (see box on page 18).
Gilbert, C. L. (2010). "How to understand high food prices." Journal of Agricultural Economics
61(2): 398-425.
Agricultural price booms are better explained by common factors than by marketspecific factors such as supply shocks. A capital asset pricing model-type model shows why one
should expect this and Granger causality analysis establishes the role of demand growth,
monetary expansion and exchange rate movements in explaining price movements over the
period since 1971. The demand for grains and oilseeds as biofuel feedstocks has been cited as the
main cause of the price rise, but there is little direct evidence for this contention. Instead, index
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based investment in agricultural futures markets is seen as the major channel through which
macroeconomic and monetary factors generated the 2007–2008 food price rises.
Halimi, G. H. (2011). Can Afghanistan Achieve Self-Sufficiency in Wheat? Limitations Due to
Market Integration. Department of Agricultural Economics. West Lafayette, IN, Purdue
University. M.S.
Following drought and the dramatic increase in the wheat and flour prices in Afghanistan
during 2007-2008, the country experienced a bumper harvest in wheat production in 2009.
According to MAIL (2009), Afghanistan produced 97% of its wheat requirement in that year,
and the country imported to meet a deficit of only 190,000 metric tons. But USDA data show
that Afghanistan imported 2.5 million metric tons of wheat and flour in that year. One potential
factor that can explain the big difference in import volume reported by MAIL versus USDA is
the different views on wheat and flour market structure. MAIL assumes that markets are well
integrated between rural and urban areas and the overall national deficit is made up by imports.
USDA data suggest that rural areas are isolated from urban centers. Domestic production is
consumed or stocked in rural regions, and most of urban populations are supplied by imported
wheat and flour.
Headey, D. and S. Fan (2010). Reflections on the Global Food Crisis. Washington, DC, IFPRI.
Despite this complexity, the assessment presented here suggests that some explanations
still hold up much better than others. This set of interconnected factors includes rising energy
prices, the depreciation of the U.S. dollar, low interest rates, and investment portfolio
adjustments in favor of commodities. All these factors are related to a range of underlying global
macroeconomic phenomena that affected both food and nonfood commodities. As for
agriculture, specifically, energy prices are a significant supply cost in cereal production, but
rising energy revenues also fueled increased cereal demand from energy-exporting nations.
However, a major effect of rising energy prices was the consequent surge in demand for biofuels.
Demand for biofuels had a stronger effect on maize than on other biofuel crops (such as
oilseeds), although knock-on effects for other food items may have been substantial (especially
for soybeans). Interestingly, we also find that the surge in U.S. maize production for biofuels was
of an order-of-magnitude equivalent to the primary explanation of the 1972–74 crisis—the surge
in U.S. wheat exports to the Soviet bloc.
Hoda, A. and A. Gulati (2013). India’s Agricultural Trade Policy and Sustainable Development.
Geneva, ICTSD Programme on Agricultural Trade and Sustainable Development.
This paper examines India’s agricultural trade policy mainly from the perspectives of
public policy objectives, especially providing food security to the poor within the overall goal of
inclusive and sustainable development, but also against the benchmark of the WTO rules and
India’s commitments therein. The analysis covers in detail domestic support measures and
market access issues in agriculture, and the way forward in terms of policies that can promote
efficiency (least trade and production distorting) while simultaneously ensuring food security
and the sustainability of agricultural production.
Hsu, H.-H. and F. Gale (2001). USDA Revision of China Grain Stock Estimates. China:
Agriculture in Transition.
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On May 10, 2001, USDA released a new set of ending stock estimates for China wheat,
corn, and rice that more accurately reflected world market conditions. Total grain stocks were
raised 164 million tons, a net increase of 250 percent from April 2001’s estimates. For years, the
earlier estimates performed well as an indicator of tightness in Chinese and world markets for
grain. However, new information from China’s agricultural census, various government
statements, and inconsistencies between stock estimates and China’s trading patterns made it
apparent that a revision of China grain stock estimates was needed.
Instituto Brasileiro de Geografia e Estatística (IBGE) (2013f). "Systematic Survey of
Agricultural Production." from
http://www.ibge.gov.br/english/estatistica/indicadores/agropecuaria/lspa/default.shtm.
Provides monthly information about the forecast and monitoring of agricultural harvests,
with estimates for production, average yield and planted and harvested areas, having the
municipal districts as collection units. This farm survey does not include data on stocks.
Instituto Brasileiro de Geografia e Estatística (IBGE) (2013s). "Survey of Stocks." from
http://www.ibge.gov.br/english/estatistica/indicadores/agropecuaria/estoque/default.shtm.
This survey provides conjuncture data on the volume and spatial distribution of stocks of
the main farm products and on the units where they are stored, having as collection units the
establishments dedicated to storage and dry storage services or those which store farm products
or their derivatives. The survey was started in 1958 and interrupted in the period 1966 to 1971.
International Monetary Fund (IMF) (2013). "International Commodity Prices." from
http://www.imf.org/external/np/res/commod/index.aspx.
Market Prices for Non-Fuel and Fuel Commodities, 2003-2013 Annual, quarterly, and
monthly. 8 price indices and 49 actual price series.
Jabbar, M. (2009). Estimation of Private Stock of Rice in Bangladesh: In Search of a Practicable
Methodology. Dhaka, Bangladesh, The National Food Policy Capacity Strengthening Program
(NFPCSP), implemented by the Food and Agriculture Organization of the United Nations (FAO)
and the Food Policy Monitoring Unit (FPMU), Ministry of Food and Disaster Management,
Government of the People’s Republic of Bangladesh.
This paper has the following objectives in relation to estimation of marketed surplus and
private stock of rice, the principal food grain in the country:
•
To review the available evidence on marketed and marketable surplus of rice and
the trend and pattern of marketing and private stocks of rice giving attention to methodologies
employed in the generation of those data, and identification of methodological gaps, if any, that
might require further research.
•
Based on the outcome of the review, propose a practical methodological
framework for monitoring private stocks of rice at regular intervals and at minimum cost without
compromising on the quality of the results.
Lilliston, B. and A. Ranallo, Eds. (2012). Grain Reserves and the Food Price Crisis: Selected
Writings from 2008–2012. Minneapolis, Institute for Agriculture and Trade Policy.
This collection provides an overview of recent writing on reserves, to point to work in
progress, and to encourage a more open and rigorous debate about how reserves. There is a
55 |
growing sense that the global agricultural marketplace has changed in recent years, and that these
changes have led to dramatic increases in price volatility. Food security strategies, at the local,
regional, national and international levels. IATP has sought input from a variety of sources to
round out the information, and we thank the many contributors to this reader, and to others that
gave their time and knowledge to putting the reader together.
Malaysia Department of Statistics (2013). "Metadata - Publication Level." from
http://www.statistics.gov.my/portal/index.php?option=com_content&view=article&id=252&lan
g=en
Links to publications documenting statistics collected in Malaysia.
NASS/USDA (2013c). "Surveys: Off-Farm Grain Stocks." from
http://www.nass.usda.gov/Surveys/Guide_to_NASS_Surveys/Off-Farm_Grain_Stocks
The Off-Farm Grain Stocks surveys provide detailed estimates of grains, oilseeds, and
pulse crops stored in any commercial facility off the farm. Off-farm stocks surveys are
conducted in every State for barley, canola, corn, flaxseed, mustard seed, oats, rapeseed,
safflower, sorghum, soybeans, sunflowers, and wheat. Grain stocks frequently move to areas
other than where produced, thus requiring coverage by all States to fully account for all off-farm
stocks. Minnesota, North Dakota, and South Dakota are the only States estimating rye stocks.
Austrian winter peas, chickpeas (garbanzos), dry edible peas, and lentils are estimated in
California, Idaho, Minnesota, Montana, Nebraska, North Dakota, Oregon, South Dakota, and
Washington. The target population is all commercial grain storage operations, including grain
and oilseed processing plants, terminals, and any other facilities that store grains, oilseeds, and
pulse crops (excluding peanuts and rice) that would not be classified as a farm. Separate rice
stocks surveys are conducted in Arkansas, California, Louisiana, Mississippi, Missouri, and
Texas. Peanut stocks are estimated for the U.S. only.
NASS/USDA (2013q). Agricultural Survey Interviewer's Manual, Chapter 10 - Grains and
Oilseeds in Storage. Washington DC, National Agricultural Statistics Service (NASS),
Agricultural Statistics Board, United States Department of Agriculture (USDA).
NASS collects information from two major sources to publish grain stocks estimates.
Estimates of grains and oilseeds stored on-farms, combined with data for off-farm stocks,
provide a total quantity of grains and oilseeds in storage. At the designated point in time (for
example, June 1), these estimates represent the total stocks of grains and oilseeds in the U.S.
Data for the on-farm storage of grain and oilseeds comes directly from the Agricultural Surveys
Program and its sampling of farms nationwide. Instructions for completing this portion of the Ag
Survey are on the following pages.
NFA Nigeria (2010). Revised Manual of Operations, Commercial Stocks Survey. Lagos,
Nigeria, National Bureau of Statistics, Government of Nigeria.
This survey estimates commercial rice and corn stocks inventory at national, regional and
provincial levels. Powerpoint document describing survey methodology.
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NFPCSP (2013). "National Food Policy Capacity Strengthening Programme ". from
http://www.nfpcsp.org/agridrupal/.
The National Food Policy Capacity Strengthening Programme helps build Bangladesh’s
institutional and human capacities to design, implement, and monitor food security policies. As
part of these efforts, it has assisted the Government in the creation of a comprehensive food
security policy and planning framework. It provides training and advice for strengthening food
security governance. It advises on the composition and mandate of key food security institutions
and provides training to Government staff. Apart from offering direct advice to the Government,
the Programme also promotes food security research by national institutions and better access to
food-security related information. It promotes knowledge- and dialogue-based debates and
decision making, involving various government and non-governmental actors.
Olomola, A. S. (2012). The Political Economy of Food price Policy in Nigeria. WIDER Working
paper. Helsinki, Finland., UNU WIDER.
This study sought to (i) identify the types and time horizon of the policy measures
adopted to address the 2008 food crisis in Nigeria, (ii) examine the agricultural commodity price
trend and determine the effects of the crisis in the country, (iii) analyse the political economy
context of the policy responses in terms of determining the role of key actors and the factors
circumscribing the adopted policies (iv) analyse the dynamics of decision making among the
various actors in the policy process and (v) assess the socio-economic consequences of the policy
responses. Data for the study were obtained from National Bureau of Statistics, FAO office,
Federal Ministry of Agriculture and Rural Development, National Planning Commission, Central
Bank of Nigeria and National Food Reserve Agency and through interaction with key
stakeholders. The interaction involved holding a workshop in Abuja to which key stakeholders
were invited. The forum provided an opportunity for engagement and exchange of information
on each of the objectives of the study and explanations of the roles of various actors.
Oryza (2013). India Hopes Private Warehouses Will Reduce Food Grain Storage Problem - See
more at: http://oryza.com/print/13243#sthash.2DLg32EW.dpuf. Oryza.
The Indian government hopes that private businesses will help reduce the mismatch
between the country’s food grain storage capacity and growing production.
Rashid, S. and S. Lemma (2011). Strategic grain reserves in Ethiopia Washington DC, IFPRI.
Holding strategic grain reserves to address food price hikes has received renewed
attentions in recent years. This paper examines such a program in Ethiopia that has been
successful in addressing several emergencies since the 1990s. The analysis suggests that the key
ingredients behind the success are a unique institutional design, coordination during emergencies
with food-based safety net programs, and keeping the grain stocks to a minimum. Institutional
design is unique because, unlike similar agencies in other countries, Ethiopia’s Emergency Food
Security Reserve Administration (EFSRA) is independent of price stabilization and hence is not
engaged in buying and selling of grain. The paper also demonstrates that scaling up school
feeding programs will generate additional food demand and an effective outlet for stock rotation;
and that increasing the stock level for price stabilization will adversely affect both grain markets
and the performance of the EFSRA.
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Shukla, B. D. (1988). Overview of grain drying and storage problems in India. Eschborn,
Germany, GASGA, Group for Assistance on Systems Relating to Grain after Harvest, GTZ.
In India, about 70% of farm produce is stored by farmers for their own consumption.
Farmers store grain in bulk, using different types of storage structures made from locally
available materials. The pre-treatment necessary for better storage life is cleaning and drying of
the grain, but storage structure design and its construction also play a vital role in reducing or
increasing the losses during storage. Storage losses constitute a major share of food grain loss in
postproduction operations
Statistics Canada (2013c). "Survey of Commercial Stocks of Corn and Soybeans." CANSIM.
from
http://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&lang=en&db=imdb&adm=8&di
s=2&SDDS=3464
This survey collects data on commercial elevator stocks of corn and soybeans and the
industrial use of corn. The estimates produced are used in national supply-disposition analyses to
verify production and farm stocks. The data are also used by Agriculture and Agri-Food Canada
and by grain analysts in the public and private sectors.
Statistics Canada (2013f). "Field Crop Reporting Series." CANSIM. from
http://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&SDDS=3401&lang=en&db=im
db&adm=8&dis=2
.
This is a series of six data collection activities which are used in the release of estimates
at pre-scheduled, strategic times during the crop year. These data are meant to provide accurate
and timely estimates of seeding intentions, seeded and harvested area, production, yield and farm
stocks of the principal field crops in Canada at the provincial level. The crops surveyed include
wheat, oats, barley, rye, flaxseed, canola, corn for grain, soybeans, sunflower seed, dry beans,
dry field peas, lentils, mustard seed, Canary seed and chick peas. The data are used by
Agriculture and Agri-food Canada and other federal departments to develop and administer
agricultural policies. This information is also used by provincial departments for production and
price analysis and for economic research.
Statistics Canada (2013m). "Miller's Monthly Report." CANSIM. from
http://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&SDDS=3403&lang=en&db=im
db&adm=8&dis=2
This is a census of the large Canadian millers. The data collected by this survey are part
of supply-disposition statistics of major grains and allow the calculation of the domestic
disappearance of grains for human and industrial uses. They are also required to verify grain
production and farm stocks data. The information is used by the federal and provincial
governments, as well as by grain millers, farmers and other private businesses for the purpose of
market research and consultation.
Statistics Indonesia (2013). "Food crops." from http://www.bps.go.id/eng/tnmn_pgn.php?kat=3.
Data on area, yield and production only. No stocks data.
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Stephens, E. C. and C. B. Barrett (2011). "Incomplete Credit Markets and Commodity Marketing
Behavior." Journal of Agricultural Economics 62(1): 1-24.
We use a simple theoretical model of seasonal market participation in the presence of
liquidity constraints and transaction costs to explain the ‘sell low, buy high’ puzzle in which
some households do not take advantage of inter-temporal price arbitrage through storage and sell
output postharvest at prices lower than observed prices for purchases in the subsequent lean
season. We test our model with data from western Kenya using maximum likelihood estimation
of a multivariate sample selection model of market participation. Access to off-farm income and
credit indeed seem to influence crop sales and purchase behaviours in a manner consistent with
the hypothesized patterns.
Trostle, R. (2009). "Fluctuating Food Commodity Prices: A Complex Issue With No Easy
Answers." Amber Waves 6(5): 11-17.
Rising food demand, increased energy costs, a weak U.S. dollar, and other factors
contributed to the rapid escalation of food commodity prices until July 2008.
UNHLTF (2009). Progress Report, April 2008-October 2009. New York, UN High Level Task
Force on the Global Food Security Crisis.
United Nations (2009). "United Nations Millennium Development Goals." from
http://www.un.org/millenniumgoals/
The eight Millennium Development Goals (MDGs) – which range from halving extreme
poverty to halting the spread of HIV/AIDS and providing universal primary education, all by the
target date of 2015 – form a blueprint agreed to by all the world’s countries and all the world’s
leading development institutions. They have galvanized unprecedented efforts to meet the needs
of the world’s poorest. The Millennium Project was commissioned by the United Nations
Secretary-General in 2002 to develop a concrete action plan for the world to achieve the
Millennium Development Goals and to reverse the grinding poverty, hunger and disease
affecting billions of people. In 2005, the independent advisory body headed by Professor Jeffrey
Sachs, presented its final recommendations to the Secretary-General in a synthesis volume
“Investing in Development: A Practical Plan to Achieve the Millennium Development Goals.”
Wiggins, S. and S. Keats (2009). Grain stocks and price spikes. London, Overseas Development
Institute.
This paper reviews the role stocks played in 2007/08 spike in world food prices and their
potential for mitigating future food price volatility. It reviews available information on cereals
stocks internationally; considers the role of stocks in the formation of the price spike; discusses
historical experiences of price stabilisation schemes involving buffer stocks; and assesses current
proposals to stabilise prices internationally. The data used come from published statistics, mainly
those from FAO and USDA; academic and professional literature; and from interviews with key
informants at FAO, the grain trade, and the International Grains Council (IGC).
World Agricultural Outlook Board (WAOB) (2013). World Agricultural Supply and Demand
Estimates (WASDE). Washington DC, United States Department of Agriculture.
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The World Agricultural Outlook Board (WAOB) serves as USDA’s focal point for
economic intelligence and the commodity outlook for U.S. and world agriculture. The Board
coordinates, reviews, and approves the monthly World Agricultural Supply and Demand
Estimates (WASDE) report, houses OCE's Joint Agricultural Weather Facility, and coordinates
USDA's Agricultural Outlook Forum.
World Bank (2013). "Living Standards Measurement Study (LSMS)." from
http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/EXTLSMS/0,,co
ntentMDK:21610833~pagePK:64168427~piPK:64168435~theSitePK:3358997,00.html
The Living Standards Measurement Study (LSMS) and the Living Standards
Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) were established by the
Development Research Group (DECRG) to explore ways of improving the type and quality of
household data collected by statistical offices in developing countries. The goal is to foster
increased use of household data as a basis for policy decision making.
Wright, B. (2001). Storage and Price Stabilization. Handbook of Agricultural Economics. B
Garnder and G. Rausser. Eds., Baltimore, MD, Hopkins University: 817-861.
Commodity storage models, developed first within agricultural economics in the tradition
of Gustafson (1958), are valuable in helping us understand how prices of storable commodity
markets behave, and how they respond to policy interventions. They show that the policyrelevant dynamic effects of storage-increasing policies are quite different from comparative
statics, and generally less favorable to consumers. They help us understand the implications of
price controls, price supports, buffer stocks, speculative attack, and "convenience yield," and
have great potential for assessing various econometric methodologies used for studying market
efficiency and bias, and supply response. However, more attention should be paid to appropriate
commodity market interventions in times of rapid productivity change, and in extremely
depressed markets such as those of the 1930s, that influenced the course of agricultural policy in
the United States over the next half-century.
Wright, B. D. (2011). "The Economics of Grain Price Volatility." Applied Economic
Perspectives and Policy 33(1): pp. 32-58.
Recent volatility of prices of major grains has generated a wide array of analyses and
policy prescriptions that reveal the inability of economists to approach a consensus on the nature
of the phenomenon and its implications for policy. This review of market events and their
economic interpretations finds that recent price spikes are not as unusual as many discussions
imply. Further, the balance between consumption, available supply, and stocks seems to be as
relevant for our understanding of these markets as it was decades ago. Though there is much to
be learned about commodity markets, the tools at hand are capable of explaining the main forces
at work, and of giving good guidance to policymakers confronted with a bewildering variety of
expensive policy prescriptions.
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