Transcript Appendices to: Exchange Rate Regimes Jeffrey Frankel Harpel Chair, Harvard University IMF Institute * April 27, 2011 *

Appendices to:
Exchange Rate Regimes
Jeffrey Frankel
Harpel Chair, Harvard University
IMF Institute
* April 27, 2011 *
Appendices
I)
Tables comparing economic
performance of different regimes
II)
Do exchange rate regimes have real effects?
III)
The case of the euro’s effect on trade
IV)
Emigrants’ remittances
V)
More on the synthesis technique
for estimating de facto exchange rate regimes.
VI)
Proposal to Peg the Export Price
Appendix I
Tables comparing economic
performance of different regimes:
– Ghosh, Gulde & Wolf
– Sturzenegger & Levy-Yeyati
– Reinhart & Rogoff
Which category
experienced the
most rapid
growth?
Ghosh, Gulde
& Wolf:
currency boards
LevyYeyati &
Sturzenegg
er:
floating
Reinhart &
Rogoff:
limited
flexibility
Source: Levy-Yeyati
and Sturzenegger
(2001).
Sample: yearly
observations 19741999.
Source: Levy-Yeyati
and Sturzenegger
(2001).
Sample: yearly
observations 19741999.
Appendix II:
Do exchange rate regimes make a “real” difference?
• I.e., do nominal regimes affect real variation?
• Some theoretical models say they don’t,
that they only determine whether real shocks show up
in the form of nominal exchange rates or prices.
• History says they do.
-- e.g., Mussa (1986):
• The final nail in the coffin was Alan Taylor’s (2002)
century-of-PPP study.
VOLATILITY OF Q DEPENDS ON REGIME
Is it coincidence? No, it can’t be. Every time a move to
increased flexibility raises variability of nominal exchange
rates, it also raises variability of real exchange rates.
• Pre- and post-1973
•Inter-war period (Eichengreen 1988):
1922-26 float vs. 1927-31 fix
• Post-war regimes (Mussa 1986):
- Canadian float in the 1950s
- Ireland: 1957-70 $ peg; 1973-78 union with £; 1979-99 Eur. ERM
•A Century of PPP (Alan Taylor 2002):
1870-1914 Gold standard
1914-45 Interwar
1946-71 Bretton Woods
1971-96 Float
Taylor spliced together 100 years
of data for 20 currencies.
Exchange rate variability across a century of regimes
Each observation is a country-regime. Adapted from A.Taylor (2002).
Variability
of real
exchange
rate
Variability of nominal exchange rate
Appendix III:
The case of the euro’s effect on trade
Frankel, “The Estimated Effects of the Euro on Trade: Why are They Below Historical Evidence on
Effects of Monetary Unions Among Smaller Countries?”in Europe and the Euro, edited by A.Alesina & F.Giavazzi, 2010.
1. Gravity estimates of effect of € on intra-EMU trade
in the first decade show the coefficient steady ≈ 15% .
2. << estimates of other Monetary Unions’ effects (x2 or x3)
3. No evidence that the gap is explained by a MU effect that
1.
2.
diminishes with country size, or
is subject to long lags.
Why is the estimated effect in euro-land so much smaller
than monetary unions among small developing countries?
A natural experiment:
The effects of the French franc’s conversion to €
on bilateral trade of African CFA members.
•
The long-time link of CFA currencies to the French franc
has clearly always had a political motivation.
–
So CFA-France trade could not reliably be attributed to currency link,
•
•
perhaps even after controlling for common language, former colonial status, etc.
But in Jan. 1999, 14 CFA countries suddenly found themselves with
the same currency link to Germany, Austria, Finland, etc.
– No economic/political motivation. A natural experiment.
– If CFA trade with these other countries has risen,
that suggests a € effect that we can declare causal.
16
Results of CFA experiment
• The dummy variable
representing when one partner is
a CFA country and the other a € country
has a highly significant coefficient of .57.
• Taking the exponent, the point estimate
is that the euro boosts bilateral trade between
the relevant African and € countries by 76%.
17
Bottom line on discrepancy in € effect
• The large effect of monetary unions on
developing countries is real.
• Tentative conclusion:
– Although monetary unions don’t have larger effects
on small countries per se,
– They do have larger effects on poor countries per se.
Appendix IV. Emigrants’ remittances:
Brief literature summary
• Theory
– Chami et al (2008): remittances are macroeconomically
stabilizing.
– Martin (1990): steady flow of remittances can undermine the
incentive for governments to create a sound institutional
framework – a sort of natural resource curse for remittances.
• Bilateral Data
– Ratha & Shaw (2005), in the absence of hard bilateral data, allocate the
totals across partners.
– Schiopu & Siegfried (2006) created bilateral data set between some EU
countries & neighbors.
– Jiménez-Martin, Jorgensen, & Labeaga (2007) estimate bilateral
workers’ remittance flows from all 27 members of the EU.
– Lueth & Ruiz-Arranz (2006, 08) have largest bilateral data set to date.
Literature review: cyclicality of remittances
• Evidence on cyclicality
–
–
–
–
–
–
World Bank: p.c. remittances respond significantly to home country p.c.income.
Clarke & Wallstein (2004) & Yang (2007): receipts rise in response to natural disaster.
Kapur (2003): they go up in response to an economic downturn.
Lake (2006): remittances into Jamaica respond to the US-local income difference
Yang and Choi (2007): they respond to rainfall-induced economic fluctuations.
IMF finds less countercyclicality.
• Sayan (2006): 12-developing-country study finds no countercyclicaty.
• Lueth & Ruiz-Arranz (2006, 2008): similarly.
• Evidence on the Dutch Disease.
– On the one hand, Rajan & Subramanian (2005): although the Dutch Disease analogy
does extend to foreign aid (leading to real appreciation & slow growth), it does not
extend to remittances.
– On the other hand, Amuendo-Dorantes & Pozo (2004): an increase in remittances to
LACA countries leads to real appreciation, a major symptom of Dutch Disease.
• OCA
– Singer (2008): counter-cyclical remittances are a determinant of the currency decision.
Are Bilateral Remittances Countercyclical?
Frankel (2011a)
• I combine the three substantial data sets
on bilateral remittances:
• Lueth & Ruiz-Arranz (2006, 2008), for an eclectic set of countries
(mostly in Europe & Asia), thanks to their generosity in supplying the data.
• Jiménez-Martin, Jorgensen, & Labeaga (2007) for EU sending countries.
• For Central American receiving countries (incl. DR, El Salvador & Panama)
• Result: evidence of countercyclicality.
• Highly significant positive coefficient on cyclical
difference between home & sending countries.
Frankel (2011a)
Dependent Variable:
Table 3: Cross-Section 2003-04 -Composite data set (merging three sources)
Ln (Stock migrants 2000 )
Cyclical Difference (Ln (Real GDP/ Trend GDP))
Sender relative to recipient
Ln Remittances 2003-04 between Countries
(1)
(2)
(3)
(4)
0.762***
0.741***
1.061***
1.233***
(0.040)
(0.041)
(0.088)
(0.152)
16.199***
16.099***
14.723***
13.983***
(2.905)
(2.765)
(3.390)
(3.927)
0.039***
0.028*
0.022
(0.015)
(0.016)
(0.019)
1.345***
0.087
-0.590
(0.222)
(0.389)
(0.632)
OLS
2SLS
2SLS
border/language/
islands/colonial
border/language
GDP per capita Sender
Currency Union
Estimation Method
OLS
Instrumental variables
Observations
R2
331
328
328
328
0.526
0.546
0.463
0.351
Statistical significance: * 10% level, ** 5% level, *** 1% level
Three sources of remittance data for 2003-04: Central America data, FOMIN and the Central Banks; EU data: Jiménez-Martín, S., Jorgensen, N.
and Labeaga, J. M. (2007); IMF data: Lueth, E. and Ruiz-Arranz, M. (2006).
Appendix V:
Synthesis Technique for Estimating
De Facto Exchange Rate Regimes
Frankel & Xie (AER, 2010),
which adds endogenous break points to Frankel & Wei (IMF Staff Papers, 2008)
Shambaugh (2007) again finds:
the de facto classification schemes tend to agree with each
other even less than they agree with the de jure scheme.
Percentage agreement of methodologies to code who pegs
De
Jure
Jay S.
LY-S
De
Jure
100%
Jay S.
86%
100%
LY-S
74%
80%
100%
R-R
81%
82%
73%
R-R
100%
The IMF has its own “de facto classification” -still close to official IMF one.
Bénassy-Quéré et al (2004): correlation (BOR, IMF) = .76
Synthesis technique to estimate de facto exchange rate regime
Δ log Ht =
c + w(1) Δlog$ t + w(2) Δlog¥t + w(3) Δlog€t
+ w(4) Δlog£t + ß {Δ EMPt} + ut
where Δ EMPt ≡ ΔlogH t + (ΔRest /MBt.).
• We impose ∑ w(j) = 1, implemented by treating £ as the last currency.
• If the exchange rate is governed by a strict peg,
• we should recover the true weights, w(j), precisely;
• and the equation should have a perfect fit.
• Flexibility in the exchange rate around the central parity
should be captured by ß > 0 .
Finally, we introduce the Bai-Perron technique
for endogenous estimation
of m possible structural break points
k
 log H t  ci   wi , j  log X i , j ,t  i  EMPt  ut
j 1
(6)
t  Ti1  1,...,Ti ; T0  0; Tm1  T ; i  1,...,m  1
For further details, see NBER WP, Dec. 2009.
Illustration using 5 currencies
• These are 5 emerging market currencies of interest
all of which now make available their data on
reserves on a weekly basis
(which is necessary to get good estimates, if structural changes
happen as often as yearly)
• Mexico (monetary base is also available weekly)
• Chile, Russia, Thailand, India
(although reserves available weekly, denominator must be
interpolated from monthly monetary base data)
Overview of findings
• For all five, the estimates suggest managed floats
during most of the period 1999-2009.
• This was a new development for emerging
markets.
• Most of the countries had had some variety of a
peg before the currency crises of the 1990s.
• But the Bai-Perron test shows statistically
significant structural breaks for every currency,
• even when the threshold is set high, at the 1%
level of statistical significance.
Table 1A reports estimation for the Mexican peso
• 5 structural breaks
• The peso is known as a floater.
• To the extent Mexico intervenes to reduce exchange rate
variation, $ is the primary anchor, but some weight on €
also appears, starting in 2003.
• Aug.2006 - Dec.2008, coefficient on EMP is essentially 0,
surprisingly, suggesting intervention around a $ target.
• But in the period starting Dec.2008, the peso once again
moved away from the currency to the north,
as the full global liquidity crisis hit and $ appreciated.
Table 1A. Identifying Break Points in Mexican Exchange Rate Regime
M1:1999-M7:2009
(1)
(2)
(3)
(4)
(5)
(6)
1/21/19999/2/2001
9/9/20013/18/2003
3/25/20037/29/2006
8/5/20061/28/2008
2/4/200812/15/2008
12/22/20087/29/2009
0.92***
0.88***
0.62***
1.11***
0.96***
0.20
(0.09)
(0.12)
(0.07)
(0.10)
(0.19)
(0.22)
0.14
-0.09
0.30***
0.20*
0.51***
0.51***
(0.08)
(0.14)
(0.09)
(0.11)
(0.16)
(0.18)
-0.05
0.22***
0.08
-0.34***
-0.33**
0.18
(0.06)
(0.07)
(0.06)
(0.06)
(0.12)
(0.13)
0.14***
0.32***
0.17***
0.02
0.07
0.28***
(0.03)
(0.03)
(0.03)
(0.02)
(0.07)
(0.04)
0.00
-0.00***
-0.00*
-0.00
-0.00
0.00
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
Observations
131
78
168
76
46
29
R-squared
0.62
0.86
0.69
0.67
0.54
0.78
Br. Pound
-0.01
-0.01
-0.01
0.02
-0.14
0.11
VARIABLES
US dollar
euro
Jpn yen
△EMP
Constant
Tables 1B-1E
• Chile (with 3 estimated structural breaks) appears a managed
floater throughout.
– The anchor is exclusively the $ in some periods,
but puts significant weight on the € in other periods.
• Russia (3 structural breaks) is similar,
except that the $ weight is always significantly less than 1.
• For Thailand (3 structural breaks), the $ share in the anchor
basket is slightly > .6, but usually significantly < 1.
– The € & ¥ show weights of about .2 each Jan.1999-Sept. 2006.
• India (5 structural breaks) apparently fixed its exchange rate
during two of the sub-periods, but pursued a managed float in
the other four sub-periods.
– $ was always the most important of the anchor currencies, but the € was also
significant in four out of six sub-periods, and the ¥ in two.
Future research
• Results for the other currencies,
– often requiring weekly interpolation
between monthly reserve figures
– are reported in Frankel & Wei (2008)
– or, with endogenous break points, will be forthcoming.
• Next econometric extension:
Threshold Autoregression for target zones.
Appendix VI:
Original proposal to Peg the Export Price (PEP)
Intended for countries with volatile terms of trade,
particularly those specialized in the production of
mineral or agricultural commodity exports.
Proposal in its pure form:
The authorities peg the currency to a basket or
price index that includes the price of their leading
commodity export (oil, gold, copper, coffee…),
rather than to the $ or € or CPI.
The regime is intended to combine the best of both worlds:
(i) The advantage of automatic accommodation to terms of
trade shocks, together with
(ii) the advantages of a nominal anchor and integration.
How would it work operationally,
say, for a Gulf oil-exporter?
• Each day, after noon spot price of oil in
London S($/barrel), the central bank
announces the day’s exchange rate, according
to the formula:
• E (dirham/$) = fixed target price P
(dirham/barrel) / S($/barrel). It intervenes in $
to hold this exchange rate for the day
• The result is that P (dirham/barrel) is indeed
fixed from day to day.
Does floating give the same answer?
• True, commodity currencies tend to appreciate
when commodity markets are strong, & vice versa
– Australian, Canadian & NZ $ (e.g., Chen & Rogoff, 2003)
– South African rand (e.g., Frankel, 2007)
– Chilean peso and others
• But
– Some volatility under floating appears gratuitous.
– Floaters still need a nominal anchor.
The Rand, 1984-2006:
Fundamentals (real commodity prices,
real interest differential, country risk premium, & l.e.v.)
can explain the real appreciation of 2003-06 – Frankel (SAJE, 2007).
200.000
180.000
160.000
140.000
120.000
100.000
80.000
60.000
40.000
Actual
vs
Fitted
vs.
20.000
FundamentalsProjected Values
Q
Q
2
19
8
1 4
19
Q 85
4
19
Q 85
3
19
Q 86
2
19
Q 87
1
19
Q 88
4
19
Q 88
3
19
Q 89
2
19
Q 90
1
19
Q 91
4
19
Q 91
3
19
Q 92
2
19
Q 93
1
19
Q 94
4
19
Q 94
3
19
Q 95
2
19
Q 96
1
19
Q 97
4
19
Q 97
3
19
Q 98
2
19
Q 99
1
20
Q 00
4
20
Q 00
3
20
Q 01
2
20
Q 02
1
20
Q 03
4
20
Q 03
3
20
Q 04
2
20
Q 05
1
20
06
0.000
RERICPIactual
RERICPIFitted
RERICPIProjected
Why are PEP & PPT better than CPI-targeting
for countries with volatile terms of trade ?
Better response to adverse terms of trade shocks:
• If the $ price of imported commodity goes up, CPI
target says to tighten monetary policy enough to
appreciate currency. Wrong. (E.g., oil-importers.)
• If the $ price of the export commodity goes up, PEP
(or PPT) says to tighten monetary policy enough to
appreciate currency. Right. (E.g., Gulf currencies.)
PEP, in its strict form, has some disadvantages
• Passes every fluctuation in world commodity prices
straight through to domestic-currency prices of other
Traded Goods, creating high volatility
– Even for countries where non-commodity TGs are a small
share of the economy, some would like to nurture this sector,
• so as to encourage diversification in the long run.
• Exposing it to full volatility could shrink non-commodity TG sector
– The volatility is undesirable, in particular, for those short-term
fluctuations that are likely to be reversed.
Moderate versions of the proposal
• Target a broader Export Price Index (PEPI).
• 1st step for any central bank dipping its toe in these waters:
compute monthly export price index.
• 2nd step: announce that it is monitoring the index.
• Target a basket of major currencies ($, €, ¥) and minerals.
• The still more moderate version is PPT:
target a monthly index of product prices.
• Key point:
exclude import prices from the index,
& include export prices.
• Flaw of CPI target:
it does it the other way around.