Wednesday October 9th 2013, FAO Headquarters, Ethiopia Room 18:00 – 19:30
Measuring Food Insecurity
for Policy Guidance and Evaluation
Statistics Division - ESS
OPEN ISSUES AND POSSIBLE SOLUTIONS
Measuring food security
Analysts behaving badly
Carlo Cafiero, FAO ESS
Different views of “measurement”
• In physics, to measure means to provide rules to
associate numbers to phenomena
• Measuring tools (i.e. “rulers”) can be created relatively
easily
• Precision and reliability can be assessed by
experimenting
• Too narrow for the social sciences
• In a broader, philosophical sense, to measure is a
process towards understanding phenomena
• Development of the tool and assessment of its reliability
cannot be separated from the establishment of the
validity of the concept
A tension between data and
concepts
• Proceeding from data to concepts…
• Take stock of existing data -> Interpret them ->
Synthesize -> Communicate
• (examples: GHI, IPC)
• … or from concepts to data
• Conceptualize -> Analyze complexity -> Focus -> Define
the measurement tool -> Collect data
• (example: US – HFSSM scale)
Obviously, the process is circular
Definition of the metrics
Thinking,
Analysis,
Definitions
Synthesis,
Filtering,
Understanding
Concept
use of the measures for different
purposes (monitoring, targeting, etc.)
Data
The complementary roles of
statistics and communication
• Statistics
• particularly relevant when observation is partial,
affected by noise
• Communication
• particularly delicate when phenomena are complex and
evolving over time
The role of theory…
• Theory can be used to fill the data gap…
• …but you need a “good” theory, and here comes a
conundrum: you need data to validate the theory that
you need to fill data gaps…
• If theory is not sufficiently validated, using it may alter
the results of measurement, by contributing to
spreading myths
• Example: the Law of One Price in commodity markets
and the food price crisis of 2007/08
… and of methods
• Respect fundamental principles of logical inference
• Observed correlation does not imply causality
• A certain time sequence does not imply causality
• The possibility of concomitant, alternative causes should
always be explicitly considered and, if possible, ruled out
before drawing conclusions
• When data are noisy (and data are always noisy),
statistical inference is the only way to go
Analysts behaving badly
• Ad hoc interpretations of terms and concepts
underlying indicators
• Rhetoric of science used to support claims
• Miscommunicating the meaning and/or hiding the
(lack of) precision of estimates
• “There are 336,997 persons living in extreme poverty in
country X”
There is too much at stake …
• Halting or misdirecting research efforts
• International Symposium 2003/International Symposium 2012:
Ten years discussing methods, limited attention to improving
data quality.
• Encouraging possibly wrong policies
• Ban exports?
• Produce vegetables in Siberia?
• Blame the speculator?
… and we are under continuing
pressure
• The anxiety of measuring “something”, to provide
some number
• Political pressure
• Over-interpretation/Risk of misdirection
• The unresolved issue of the quality of the data
• Remember, models do not do miracles: garbage in,
garbage out
Our discussion today
• Who needs food
security measures,
and why?
• How to respond to
the demand, by
balancing rigor and
«usability»?
• What is FAO
Statistics offering?