This document discusses different approaches to measuring economic resilience. It notes that while resilience requires some redundancy which is inefficient, it also brings out an interesting trade-off between redundancy and efficiency. The document then evaluates the OECD's proposed measure of the "usefulness" of indicators for assessing resilience. It finds the OECD measure is similar but not identical to a more standard expected loss approach. It also discusses the OECD using quantile regressions to assess the impact of different factors on the tails of the GDP distribution, but notes concerns about identifying causal effects and justifying pooling across countries and periods.

1. How to measure the resilience of
economic systems?
Comments by Daniele Terlizzese (EIEF)
Strategic Forum, 2015
Measuring Economic, Social and Environmental
Resilience

2. Schizophrenic comments
On the one hand, some skepticism on the concept of
economic resilience
On the other hand, some nitty-gritty details on the
empirical approach (OECD; too late for Philipp!)
Outline

3. Shocks happen. An efficient economy adapts in a way
that cannot be improved for everybody
Does a resilient economy do something else? Is
resilience an additional requirement?
If shocks are the “usual suspect” (only big), resilience is
just a different name for efficient
More useful insights if we focus on “unusual” shocks:
natural disasters
unforeseen contingencies
Is resilience a useful concept?

4. The economic system is not “designed” to deal with
either kind of shocks, as they are:
either outside the realm of economics, or unamenable
to standard mechanism design
In both cases resilience seems to require idle capacity,
redundancy
But redundancy is ex-ante inefficient →
resilience brings out an interesting trade-off
Redundancy vs. Efficiency

5. Excessive leverage, excessive maturity mismatches,
excessive debt…excessive relative to what?
The analysis lacks a clear benchmark.
A (negative) shock would impose a cost even to a fully
efficient economy
How much larger is the cost actually borne?
Why is it larger? What are the market failures?
Distributive concerns? Need for a structural model
How to detect (lack of) resilience

6. OECD proposes a measure of “usefulness” of an indicator w
𝑢𝑢 𝑤𝑤 = min(Pr 𝐶𝐶 𝜗𝜗, Pr ¬𝐶𝐶 1 − 𝜗𝜗 ) −
𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 𝑖𝑖 𝑖𝑖 𝑤𝑤 𝑖𝑖𝑖𝑖 𝑛𝑛𝑛𝑛𝑛𝑛 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢
(𝜗𝜗Pr(𝐶𝐶) Pr(𝑤𝑤 = 0|𝐶𝐶)
𝑝𝑝𝑝𝑝(𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝐼𝐼 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒)
+ 1 − ϑ Pr(¬𝐶𝐶) Pr 𝑤𝑤 = 1 ¬𝐶𝐶
𝑝𝑝𝑝𝑝 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝐼𝐼𝐼𝐼 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒
𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 𝑖𝑖 𝑖𝑖 𝑤𝑤 𝑖𝑖𝑖𝑖 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢
)
Not standard (why should PM be concerned with T1 and T2
errors per se, rather than with actions?)
Compare with a more standard approach
Usefulness of indicators

7. Let
𝑎𝑎 1 = 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝐿𝐿 𝑎𝑎 𝐶𝐶 Pr 𝐶𝐶 𝑤𝑤 = 1 + 𝐿𝐿 𝑎𝑎 ¬𝐶𝐶 Pr ¬𝐶𝐶 𝑤𝑤 = 1
and
𝑎𝑎 0 = 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎{𝐿𝐿 𝑎𝑎 𝐶𝐶 Pr 𝐶𝐶 𝑤𝑤 = 0 + 𝐿𝐿 𝑎𝑎 ¬𝐶𝐶 Pr ¬𝐶𝐶|𝑤𝑤 = 0 }
The standard definition of the minimal expected loss from the
use of w is:
Pr 𝑤𝑤 = 1 [𝐿𝐿 𝑎𝑎 1 𝐶𝐶 Pr 𝐶𝐶 𝑤𝑤 = 1 + 𝐿𝐿 𝑎𝑎 1 ¬𝐶𝐶 Pr ¬𝐶𝐶|𝑤𝑤 = 1 ]
+
Pr 𝑤𝑤 = 0 [𝐿𝐿 𝑎𝑎 0 𝐶𝐶 Pr 𝐶𝐶 𝑤𝑤 = 0 + 𝐿𝐿 𝑎𝑎 0 ¬𝐶𝐶 Pr ¬𝐶𝐶|𝑤𝑤 = 0 ]
The loss from the use of w (1)

8. Assume/normalize 𝐿𝐿 𝑎𝑎(1) 𝐶𝐶 = 𝐿𝐿 𝑎𝑎(0) ¬𝐶𝐶 = 0 (with l.o.g.)
then the loss becomes:
Pr 𝑤𝑤 = 1 [𝐿𝐿 𝑎𝑎 1 ¬𝐶𝐶 Pr ¬𝐶𝐶|𝑤𝑤 = 1 ] +
Pr 𝑤𝑤 = 0 [𝐿𝐿 𝑎𝑎 0 𝐶𝐶 Pr 𝐶𝐶 𝑤𝑤 = 0 ]
= 𝐿𝐿 𝑎𝑎 1 ¬𝐶𝐶 Pr 𝑤𝑤 = 1, ¬𝐶𝐶 + 𝐿𝐿 𝑎𝑎 0 𝐶𝐶 Pr(𝑤𝑤 = 0, 𝐶𝐶)
Compare this with the OECD formulation:
1 − ϑ Pr(¬C)Pr 𝑤𝑤 = 1 ¬𝐶𝐶 + 𝜗𝜗 Pr 𝐶𝐶 Pr 𝑤𝑤 = 0 𝐶𝐶
= 1 − ϑ Pr(𝑤𝑤 = 1, ¬𝐶𝐶) + 𝜗𝜗Pr(𝑤𝑤 = 0, 𝐶𝐶)
Similar! But in general not true that
𝐿𝐿 𝑎𝑎 1 ¬𝐶𝐶 + 𝐿𝐿 𝑎𝑎 0 𝐶𝐶 = 1
The loss from the use of w (2)

9. Let 𝑎𝑎� = 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑛𝑛 𝐿𝐿 𝑎𝑎 𝐶𝐶 Pr(𝐶𝐶) + 𝐿𝐿 𝑎𝑎 ¬𝐶𝐶 Pr(¬𝐶𝐶)
Then the standard definition of the minimal expected loss
when w is not used is:
𝐿𝐿 𝑎𝑎� 𝐶𝐶 Pr(𝐶𝐶) + 𝐿𝐿 𝑎𝑎� ¬𝐶𝐶 Pr(¬𝐶𝐶)
Suppose instead that the PM, when not using w, chooses
either 𝑎𝑎𝐶𝐶
or 𝑎𝑎¬𝐶𝐶
, where
𝑎𝑎𝐶𝐶 = 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝐿𝐿(𝑎𝑎|𝐶𝐶) and
𝑎𝑎¬𝐶𝐶 = 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝐿𝐿(𝑎𝑎|¬𝐶𝐶).
The loss if w is not used

10. Then the minimal expected loss would be either
Pr C 𝐿𝐿 𝑎𝑎𝐶𝐶 𝐶𝐶 + Pr(¬C) 𝐿𝐿 𝑎𝑎𝐶𝐶 ¬𝐶𝐶 or
Pr C 𝐿𝐿 𝑎𝑎¬𝐶𝐶
𝐶𝐶 + Pr(¬C) 𝐿𝐿 𝑎𝑎¬𝐶𝐶
¬𝐶𝐶
If we assume that 𝐿𝐿 𝑎𝑎𝐶𝐶
𝐶𝐶 = 𝐿𝐿 𝑎𝑎¬𝐶𝐶
¬𝐶𝐶 = 0 (with l.o.g.)
the minimal expected loss would be
min(Pr C 𝐿𝐿 𝑎𝑎¬𝐶𝐶 𝐶𝐶 , Pr(¬C) 𝐿𝐿 𝑎𝑎𝐶𝐶 ¬𝐶𝐶 )
This is similar to the OECD formulation:
min(Pr 𝐶𝐶 𝜗𝜗, Pr ¬𝐶𝐶 1 − 𝜗𝜗 ). But:
(i) assumes a «bang-bang» behavior of PM;
(ii) 𝐿𝐿 𝑎𝑎¬𝐶𝐶 𝐶𝐶 and 𝐿𝐿 𝑎𝑎𝐶𝐶 ¬𝐶𝐶 do not add up to 1;
(iii) 𝐿𝐿 𝑎𝑎¬𝐶𝐶 𝐶𝐶 ≠ 𝐿𝐿 𝑎𝑎 0 𝐶𝐶 and 𝐿𝐿 𝑎𝑎𝐶𝐶 ¬𝐶𝐶 ≠ 𝐿𝐿 𝑎𝑎 1 ¬𝐶𝐶
The loss if w is not used (2)

11. Recent strand in OECD analysis: assess the impact of
different factors on the tails of the GDP distribution
Quantile regressions are a useful and natural tool for
this, and are more robust to outliers
(but outliers might contain a lot of info on the tails)
Two concerns:
• Correlation, not causal effect. How to identify the
latter?
• Pooling across many countries and many periods
might not be justified
Quantile regressions