THE EFFECTS OF EXCHANGE RATE REGIMES ON INFLATION AND GROWTH
IN DEVELOPING COUNTRIES
A Thesis
Presented to the Faculty of the Department of Economics
California State University Sacramento
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF ARTS
in
Economics
by
Ertug Misirli
SPRING
2012
THE EFFECTS OF EXCHANGE RATE REGIMES ON INFLATION AND GROWTH
IN DEVELOPING COUNTRIES
A Thesis
by
Ertug Misirli
Approved by:
__________________________________, Committee Chair
Yan Zhou, Ph.D.
__________________________________, Second Reader
Tim Ford, Ph.D.
___________________________
Date
ii
Student: Ertug Misirli
I certify that this student has met the requirements for format contained in the University
format manual, and that this thesis is suitable for shelving in the Library and credit is to
be awarded for the thesis.
____________________________, Graduate Coordinator _____________________
Kristen Kiesel, Ph.D.
Date
Department of Economics
iii
Abstract
of
THE EFFECTS OF EXCHANGE RATE REGIMES ON INFLATION AND GROWTH
IN DEVELOPING COUNTRIES
by
Ertug Misirli
This thesis revisits the effect of exchange rate regimes on inflation and growth in
developing countries under the most recent financial crises. The data used in this paper
covers 102 developing countries over the period 1970-2009. This paper finds that
developing countries under the fixed exchange rate regime have lower inflation rate
compare to those under the flexible exchange rate regime. This negative relationship
between fixed regimes and inflation becomes higher in non-emerging markets.
Furthermore, empirical findings also indicate that fixed regime is associated with higher
economic growth relative to the flexible regime in developing countries. However, this
positive relationship between fixed regime and economic growth only remains significant
in emerging markets since these countries are more integrated in global financial markets.
__________________________________________, Committee Chair
Yan Zhou, Ph.D.
________________________
Date
iv
ACKNOWLEDGEMENTS
I want to thank my family, Engin, Sema, Tugba, Altug, and Jennifer. I also want
to thank my life-time partner, Dr. Jeanine Walter. I also want to say thank you to my
professors, Yan Zhou and Tim Ford for their help and guidance in writing this paper.
Without all of your support I would not have been able to do it.
v
TABLE OF CONTENTS
Page
Acknowledgements……………………………………………………………………. ...v
List of Tables……………………………………………………………...……………. vii
Chapter
1. INTRODUCTION………………………………………………………………...….. 1
2. LITERATURE REVIEW………………………………………………………...…... 7
3. DATA…………………………………………………………………………………13
4. METHODOLOGY AND MODEL……………………………………………..……16
4.1. Exchange Rate Regime Classification…...……....………………………………16
4.2. Theoretical Justification………………………………………………………… 18
4.2.1. Inflation Model……………………………………………………………. 18
4.2.2. Growth Model...…………………………………………………………… 21
4.3. Methodology...………………………………………………………………….. 22
5. EMPIRICAL FINDINGS…………………………………………………………….25
5.1. Inflation Performance…...……………………………………………………….25
5.1.1. Robustness Check…...…………………………………………………….. 31
5.2. Growth Performance...…………………………………………………………...35
6. CONCLUSION……………………………………………………………………….41
Appendix…List of Countries…………………………………………………………....44
References………………………………………………………………………………..45
vi
LIST OF TABLES
Table
Page
3.1
Variables of Definitions and Sources……………………………………………14
3.2
Summary Statistics………………………………………………………………15
5.1
The Inflation Performance….……………………………………………………26
5.2
First Stage Regression: Probit Model……………………………………………33
5.3
Two Stage Least Squares………………………………………………………...34
5.4
The Growth Performance………………………………………………………...36
vii
1
CHAPTER 1
INTRODUCTION
The impacts of the exchange rate regimes in developing nations has been a hotly
debated issue and remains one of the most important questions in international
economics. Past financial crises, including those in Chile (1982), Mexico (1995), East
Asia (1997), Argentina (2001), and Turkey (2001), have shown that a change or a shift
in an exchange rate regime has led developing countries to have high inflation rates and
low/negative growth rates. Since then a discussion has followed over how the choice of
exchange rate regimes might have contributed to macroeconomic instability, and how a
change in exchange rate regime could fix that macroeconomic turmoil in developing
countries.
In the early 1990s, a number of developing countries have moved to a fixed
exchange rate regime1 to sustain price stability. By pegging the currency to an anchor
currency (U.S dollar), developing countries could import credibility and confidence into
their economies. Adopting a fixed regime could eliminate uncertainty in exchange rates
and prices. Therefore, monetary authorities could gain confidence from the public,
financial institutions, and businesses. This credibility effect of fixed regimes could also
influence expected inflation, and would lower inflation rates. According to Cobb and
Field (2008), fixed exchange rates provide the discipline needed in economic policy to
prevent high inflation rates. Similarly, Yeyati and Sturzenegger (2001) indicate that a
1
A fixed exchange rate regime is defined as the value of a currency that is pegged relative to the value of
one other currency (anchor currency) so that the exchange rate is fixed in terms of the anchor currency.
2
fixed exchange rate system forces this type of policy of action by sustaining pricestability; hence lowering the inflation rate. For instance, Argentina adopted a fixed
regime to fight against its high inflation rate in the beginning of the 1990s. Three years
after adopting of the regime, Argentina reduced its inflation from 800% to 5%.
Fixed regimes were also used in developing countries to increase economic
growth during the 1990s. It is expected that a fixed regime promotes openness to trade in
developing countries and leads to an increase in economic growth. Calvo and Mishkin
(2003) state that countries seeking to expand trade naturally place a higher value on some
form of a fixed regime with a trading partner and have shown higher growth rates. Most
East Asian countries, such as Thailand and South Korea, chose to adopt fixed exchange
rate regime in order to be competitive in international trade markets and to increase their
economic growth. In the early years of the arrangement, countries showed high growth
rates through a fixed regime’s trade channel.
There are a few shortcomings of the use of a fixed regime. Developing countries
under fixed regimes are more open to experience speculative attacks. For instance,
Thailand, South Korea, and Indonesia faced speculative attacks after the adoption fixed
regimes. Investors sold such countries’ currencies and bought the U.S. dollar, as there
were increases in interest rates in the United States. In the presence of a speculative
attack, such currencies lose their value against the U.S. dollar and countries enter an
economic recession. At the end of the recession, Thailand’s GDP dropped 11%, South
Korea’s 6%, and Indonesia’s GDP decreased by 13%. Another shortcoming of a fixed
3
regime is that central banks lose control of domestic monetary policy since they follow
changes in monetary policies in an anchor currency’s country. Finally, it is expensive to
keep a fixed regime since it requires sizeable international reserves.
Another exchange rate arrangement common in developing nations is the flexible
exchange rate regime.2 Flexible exchange rate regimes act as a shock absorber. In the
presence of external shocks, central banks with flexible regimes can use monetary
policies independently for domestic considerations, unlike central banks under fixed
regimes. Edwards (2011) indicates that a flexible regime is expected to allow for price
adjustments in the face of external shocks reducing output volatility. However, one of the
shortcomings of flexible regimes is that there might be high volatility in exchange rates
due to an increase in unstable financial markets. Especially high volatility in exchange
rates brings uncertainty for public and financial institutions, which leads to high inflation
rates in developing countries. For example, in Turkey (2001), the government was forced
to allow its currency to float by IMF to protect the Turkish lira. However, switching from
a fixed to a flexible regime caused devaluation in the Turkish lira. At the end of the year,
one U.S. dollar was equal to 1,500,000 Turkish liras. Turkey struggled with high inflation
rates and unemployment rates in the following years.
Recent episodes of financial turmoil have shown that exchange rate regimes have
an impact on inflation and economic growth in developing countries. Therefore, it is
reasonable to look at which exchange rate regime may be optimal for developing
2
A Flexible exchange rate regime is defined as the value of currency which is allowed to fluctuate against
all other currencies so that the exchange rate is determined by market forces.
4
countries and which regime may fix macroeconomic instability in developing nations in
the presence of global and unstable financial markets.
There is no consensus about which regime works best for developing countries.
Different exchange rate regimes are likely to be appropriate for different nations. There
are many developing countries that have been struggling with high inflation rates;
therefore, their main macroeconomic goal is to sustain price stability. On the other hand,
there are developing countries that have focused only on economic growth. Thus, each
developing country needs different type of exchange rate arrangement based on their
macroeconomic interests.
For decades, economists have investigated the impacts of optimal exchange rate
regimes on macroeconomic performance. However, in today’s turbulent markets, the
effects of exchange rate regimes become even more important in developing countries.
Hence, the purpose of this paper is to revisit the effects of exchange rate regimes on
inflation and economic growth in developing countries under the most recent unstable
financial markets and external shocks.
This paper differs from previous studies in three dimensions: First, the paper
covers data between 1970 and 2009. This time-span has not been reviewed in any
previous study. The importance of this time-span is that it covers the effects of past
financial crises in developing countries, and it also covers the effects of the most recent
financial turbulence in developing countries. Second, this paper investigates the effects of
regimes in all developing countries (102) where data is available. A list of countries is
5
given in the Appendix. Third, the paper separates developing nations into two categories:
emerging markets and non-emerging markets. This distinction makes it clear to see the
impacts of regimes on inflation and growth for each category. Because these two
categories differ from each other, each market is expected to have different impacts of
regimes. Emerging markets are more open to trade and have sizeable foreign capital
inflows and outflows. Therefore, they are more likely to have financial crises and
currency collapses. On the other hand, non-emerging countries are poorer than emerging
countries. They are close to trade, and are less likely to have financial crises and currency
collapses relative to emerging economies. However, non-emerging countries have been
struggling with high inflation rates; thus, the effects of regimes are also very important
for these countries since they have been looking for some sort of a channel that creates
price-stability and lowers inflation rates.
Empirical findings for inflation performance suggest that developing countries
with fixed exchange rate regimes experience significantly lower inflation rates than those
with flexible exchange rate regimes. Moreover, the impact of fixed regimes on inflation
is higher in non-emerging markets. A non-emerging market under a fixed regime has
significantly lower inflation rates compared to an emerging market under a fixed regime.
According to the growth performance, the findings of this paper indicate that
developing nations with fixed rate regimes show significantly higher economic growth
relative to those with flexible rate regimes. Furthermore, the impact of fixed regimes is
6
only significant for emerging markets. This paper finds no significant effect of any
exchange rate regimes on growth in non-emerging markets
The paper is organized as follows: In Chapter 2, previous studies are reviewed.
Chapter 3 describes the data. Chapter 4 explains the methodology and model that
includes the exchange regime classification, theoretical justification, and methodology.
Chapter 5 gives the main empirical findings for inflation and growth performances.
Finally, Chapter 6 concludes the paper.
7
CHAPTER 2
LITERATURE REVIEW
The debate of choosing an alternative exchange regime gets more complicated
when characterizing the classification of regime. In the literature, there are two types of
classification options. The first option is a “de jure” classification, which is also IMF’s
classification that is based on the publicly stated commitment of the central bank. Every
IMF member country is required to report and publish its exchange rate arrangement
every year to the IMF. The second is a “de facto” classification, which is based on the
observed behavior of the exchange rate. Calvo and Reinhart (2002) stated that the central
banks of many countries, which have claimed to be floaters, intervene heavily in
exchange rate markets to reduce exchange rate volatility. They suggest that there is a
mismatch between statements and actions, because a de jure classification method is
unable to capture the effects of changes to exchange rates within a given year, and that a
government’s declarations to the IMF of the exchange rare regimes in place are not
accurate. Thus, the de facto classification is used in this paper.
The dominant view in the literature about the relationship between inflation and
exchange rate regimes is that pegged regimes are seen as an important anti-inflationary
tool for monetary policy makers. Romer (1993) states that fixed rates are able to reduce
inflation due to its role as a mechanism for monetary authorities. Similarly, in their book,
Cobb and Field (2008) indicate that fixed regimes are needed to provide such effects to
monetary policies when a country struggles with high inflation rates.
8
Recent empirical studies have also found a negative relationship between fixed
regimes and inflation. Yeyati and Sturzenegger (2001) study impact of the currency
policies on inflation. Their paper is very important in international economics, because
the authors were the first to create their own de facto classification method (based on
observed behavior) and use it in their analysis. Their paper covered data over time period
1974-1999. Based on their findings for developing countries, pegged regimes (only the
ones lasting five or more years) have lower inflation rates compared to countries under
flexible regimes with the cost of slower growth. In addition, Abbott and De Vita (2011)
investigated the trade-off between inflation and growth under an alternative exchange
rate by using a de facto classification regime over time period 1980-2004. The authors’
findings show that fixed regimes are associated with lower inflation rates, compared to
flexible and intermediate regimes. Yeyati and Sturzenegger’s (2001), and Abbott and De
Vita’s (2011) empirical findings for inflation performance are consistent with this paper,
even though the authors used different classification models and data with shorter timespan than this paper.
By looking at previous studies with different de facto schemes, it can be seen that
pegged regimes reduce inflation rates in developing countries. Shambaugh and Klein
(2004) use his own de facto method over the time 1980-1999. They found that fixed
regimes have lower inflation rates compared to flexible regimes. Reinhart and Rogoff
(2004) find the same relationship between pegged regimes and inflation by using black
market rates (unofficial rates) instead of official exchange rates over the time 1946-2001.
9
They believe that the collapse of the Bretton Woods era had less impact on exchange rate
regimes than is popularly believed. Therefore, the authors employ data on unofficial
exchange rates going back to 1946 for 153 countries including developed and developing
nations. As mentioned, this paper studies the effects of official exchange rates on
inflation and economic growth. Thus, the paper focuses on the impact of currency
regimes after 1970, since the Bretton Woods era was dominant in developing countries
before 1970, and a fixed regime was the one only used due to political reasons during that
era. Furthermore, Bleaney and Francisco (2005) compare the results using these two
major de facto schemes for robustness, covering more 91 developing countries over the
time frame 1984-2001. According to their results, fixed regime variables have a
significantly negative coefficient in inflation equation.
Pegged regimes also play an anti-inflationary role, when the regimes are classified
by a de jure method (based on the stated intentions of central banks). For instance, Ghosh
et al. (2002) used a de jure classification method over the time period 1970-2000.
According to their empirical results, fixed regimes are associated with the best inflation
results when a treatment effects model is applied to control the issue of endogeneity
problem between pegged regimes and inflation. Although the classification of regimes is
different, the negative relationship between inflation and fixed regimes always remains
the same. Hence, based on previous studies, the results suggest that fixed rates are
expected to reduce inflation rates in developing nations.
10
The effects of currency policies on long-term growth have received recent
attention in the literature. It is believed that choosing the optimal exchange rate regime
may have a positive impact on economic growth; however, there is no consensus among
economists. According to its supporters, fixed rates increase economic growth.
Shambaugh and Klein (2008) find that countries with pegged regimes follow the interest
rate movements of the base nation more closely than countries with non-pegged regimes;
thus countries with pegged regimes have an interest rate advantage under fixed exchange
regimes, which leads to higher growth. Similarly, Dornbusch (2001) investigates the
regime’s impacts on economic growth by using the data from 1970-1999 and finds that
fixed regimes are associated with lower interest rates, and thus increases growth in the
developing world. Rose and Wincoop (2001) use a de jure classification model by using
data from 1970-1995. Their findings suggest that fixed regimes promote trade and
increase economic growth. Finally, Husain and Rogoff (2004) find that fixed rates have
higher growth than flexible rates using unofficial exchange rates in their paper. The
authors look at the effects of regimes on macroeconomic performances in developed and
developing countries. These previous studies believe that pegged regimes increase
economic growth through its trade and real interest rate channel. These results are
consistent with the findings of this paper.
Some economists indicate that flexible exchange rates are expected to increase
economic growth in developing countries in the presence of unstable capital markets.
Edwards (2011) analyzes exchange rate regimes and its ability to absorb major external
11
shocks in emerging nations without experiencing currency crises or balance of payment
crises. His empirical findings suggest that under flexible exchange rate regimes, countries
have lower output volatility in the existence of real interest rates and terms of trade
shocks than those countries with fixed exchange rate regimes. Yeyati and Sturzenegger
(2001) found that flexible exchange rate regimes reduce output fluctuation in the face of
external shocks and increase economic growth. In both studies, the authors used changes
in terms of trade and in real interest rates to capture the effect of external shocks on
economies. Edwards (2011) studied the impact of exchange rate regimes in countries in
Latin America and Asia whereas Yeyati and Sturzenegger (2001) focused on the effects
of exchange rate regimes in both developed and developing countries. Calvo and Mishkin
(2003) looked at the relationship between real shocks, which arise from changes in
productivity, real interest rates, and terms of trade, and an alternative exchange rate
regime. Their results suggest that if an economy faces a real shock, the use of a flexible
regime allows for adjustments with changes in factors, and that flexible rates could
reduce fluctuation in output. Thus, according to its supporters, flexible rates allow
necessary price adjustments in the presence of external shocks, and improve economic
growth. These findings differ from the findings of this paper. First, a different
classification regime model is used in this paper. Second, this paper covers data with a
larger time-span, which also covers the past and the most recent financial crises. Thus,
the findings are different from each other.
12
Some studies suggest that there is no effect of any exchange rate regimes on longterm growth. Miles (2006) criticizes past studies that found significant effects of
exchange rates on long-term growth in developing countries. He uses the de facto
classification model over the time 1976-2000, and suggests that previous studies have not
accounted for policy distortions and certain imbalances, which arise from weak monetary
and fiscal institutions, in their analyses. Therefore, he states that the effects of exchange
rates on growth are likely to be biased in magnitude in past studies, and adds the black
market premium (the difference between the unofficial and official price of foreign
currency) to capture the effect of market distortions in his analysis. The author finds no
significant impact of currency policies on long-term growth. As mentioned, however,
Husain and Rogoff (2004) use the same black market premium and find a significant
effect of fixed rates on growth. Another critique about the significant effects of fixed
regimes on growth is in regards to its trade channel. Eichengreen (2001) indicates that
fixed regimes cannot promote trade for a country by itself. Trade depends on many other
policies and attributes, and some of them may be hard to observe or measure, so the
estimated effect of trade on growth implied by an alternative exchange rate is likely to be
biased. However, the problem in these statements is that most of the previous studies
have captured effect of the trade policies by using openness to trade and terms of trade
measurements. In addition, most studies have used country fixed and time fixed effects
models to control for unobservable effects or distortions in the economy.
13
CHAPTER 3
DATA
The paper covers annual observations for 102 developing countries over the time
period 1970-2009. Regime classification, as stated above, is based on the de facto
scheme. This paper excludes transition countries due to a lack of available data.
Transition countries were socialist economies in the 1970s and 1980s and in a transitional
state during the 1990s. This paper also ignores developed nations because the consensus
view in previous studies that there is no significant effect of exchange rate regime on
developed countries’ macroeconomic outcomes.
The data for the inflation and growth model, and other control variables, were
taken from IMF (International Financial Statistics), World Bank, and Penn World
Database. Table 3.1 represents the definitions and the sources of the variables.
This paper covers 4,081 annual observations. Table 3.2 reports the summary statistics for
inflation and growth depending upon the chosen country’s exchange rate policies. The
raw means indicate that approximately 44% of developing countries have adopted a fixed
exchange rate regime while approximately 56% of developing countries have chosen a
flexible exchange rate regime. Based on Table 3.2, the median values indicate that a
country with a fixed currency policy has 2% less inflation rate compared to a country
with a flexible exchange rate regime. On the other hand, flexible regimes show 1% lower
growth rate relative to the fixed regimes.
14
Table 3.1 Variables of Definitions and Sources
Variables
Definitions and Sources
%ΔGDP
Rate of growth of real GDP ( Penn World Table)
π
Annual percentage change in the Consumer Price Index (IFS)
GC
Government consumption to GDP ratio ( Penn World Table)
IGDP
Ratio of investment to real GDP (Penn World Table)
%ΔM
Rate of growth of Money Supply (IFS)
OPEN
Openness ratio of the sum of the Exports and Imports to real GDP (World Bank)
POP
Natural logarithm of total population (IFS)
TOT
Terms of Trade; the ratio of the price exports to price of imports (World Bank)
FIXED
A binary variable. Takes the value 1 if a country has a fixed regime; takes the value 0 if
a country has a flexible regime.
INFFIXED
An interaction variable that consists of inflation and fixed.
INGDP
Natural logarithm of initial real GDP.
𝜋𝑡−1
One year lagged value of inflation rate.
______________________________________________________________________
15
Table 3.2 Summary Statistics
FIXED REGIME
44%
Variables
N
Mean
Min
Median
Max
S.D
π
1546
0.10
-1.00
0.07
449.00
0.20
%ΔGDP
1712
0.04
-1.02
0.05
0.70
0.08
%ΔM
1636
0.18
-1.00
0.16
544.00
0.20
OPEN
1808
85.94
2.41
76.01
441.22
55.66
TOT
1353
0.87
0.12
0.85
2.31
0.28
GC
1808
13.03
1.20
11.11
86.34
7.91
IGDP
1808
25.14
0.74
23.18
86.34
12.41
POP
1808
15.04
10.60
15.30
21.01
2.24
Max
S.D
FLEXIBLE REGIME
56%
Mean
Min
Median
Variables
N
π
1918
0.39
-1.00
0.09
117.50
3.56
%ΔGDP
2266
0.03
-0.67
0.04
0.83
0.08
%ΔM
2050
0.52
-1.00
0.17
125.13
4.15
OPEN
2272
69.76
7.19
59.50
443.18
45.20
TOT
1806
0.82
0.05
0.81
2.34
0.33
GC
2272
12.66
0.90
10.07
58.59
9.13
IGDP
2272
22.76
-10.84
20.85
85.17
11.89
POP
2272
15.50
10.68
15.76
20.88
2.01
16
CHAPTER 4
METHODOLOGY AND MODEL
4.1. Exchange Rate Regime Classification
As mentioned previously, a de facto classification model is used in this paper.
There are three well-known de facto classification schemes in literature. These schemes
are measured by Yeyati and Sturzenegger (2001), Reinhart and Rogoff (2004), and Klein
and Shambaugh (2008). Each scheme has been widely used by other researchers.
Yeyati and Sturzenegger (2001), also known as the LYS de facto classification
(hereafter LYS), includes three categories: fixed regimes, intermediate regimes, and
flexible regimes. The LYS scheme uses cluster analysis to generate one observation per
calendar year based on the volatility of an exchange rate, as measured by the average
absolute monthly percentage change. The volatility of exchange rate changes is measured
as the standard deviation of monthly percentage changes; the volatility of foreign
exchange reserves is measured as the average absolute monthly percentage change in net
dollar international reserves, relative to the dollar value of monetary base in the previous
month. According to the LYS scheme, if a country has exchange rate volatility, but large
reserve volatility in one year, it is categorized as a fixed regime. If a country has a
relatively constant, but not non-zero rate of change, and a high rate of change in reserves,
then it is categorized as an intermediate regime. Finally, if a country has a high level of
exchange volatility but a low level of reserve volatility, then a country’s exchange rate
arrangement is considered as a flexible exchange rate regime. The LYS scheme is the
17
first de facto scheme classification in international economics. It is used by many
researches; however, the scheme does not give a clear picture of what is defined as a peg
or a non-peg. Furthermore, it is hard to obtain data on reserves to make the classification.
Reinhart and Rogoff (2004) (hereafter RR) created exchange rate regimes based
on the behavior of parallel (unofficial, which is market determined exchange rates) rather
than the official exchange rate. According to the RR scheme, observations are
categorized based on the odds of the parallel exchange rates being outside a band over a
five-year window. They use a five-year rolling window to avoid spurious switches in
exchange rate regimes due to devaluations. The RR scheme is best-suited analyses to
focus on transactions taking place in unofficial exchange rates. The biggest problem with
this scheme, however, is that it is not easy to obtain data on black market rates to create
the scheme.
Finally, Klein and Shambaugh (2008) (hereafter KS) created their own de facto
classification and used two categories: a fixed regime and a floating regime. The KS
scheme is based on bilateral (national currency/ U.S. dollar) and annual country/ year
observations. A country is considered as having a fixed exchange rate regime if its endof-month official bilateral exchange rate stays within +/- 2% band, both each month and
over course of that year. This requires that a currency is within the same +/- 2% band at
the end of each month, for full year. Since the coding is annual, the peg must last for a
full calendar year for a country to be classified as a pegged regime in that year. Pegs that
last less than a full year are classified as non-pegs.
18
This paper uses the KS scheme as a core classification. The KS scheme is most
straightforward, and the newest de facto classification in the literature. The use of official
exchange rate data has a wider coverage, and is easier than other classifications that
require black market rates, central banks’ reserves, and interest rates. In addition, a 2%
band makes the distinction between pegged regimes and non-pegged regimes clear.
According to Shambaugh (2008), the KS scheme matches the historical definitions of
pegs such as gold points in the gold standard and the bands in the Bretton Woods’s era.
4.2. Theoretical Justification
4.2.1. Inflation Model
According to its supporters, fixed exchange rates are associated with lower
inflation rates. As mentioned above, a pegged regime may play a role as an antiinflationary tool for developing countries. In addition, the literature focuses on a
credibility effect of a pegged rate on inflation expectations that may stabilize the money
velocity and price fluctuations in the developing world. In theory, a fixed exchange rate
regime is expected to have an impact on the link between prices and money. Since a
pegged regime is expected to affect the relationship between prices and money, this paper
uses a standard money demand theory as a core model to explain inflation performance.
Therefore, the base model takes the form of a time series as the following:
𝑀𝑡 𝑉𝑡
−𝛽
= 𝑌𝑡𝛼 𝑖𝑡 𝛼, 𝛽 > 0
𝑃𝑡
(1)
where 𝑀𝑡 is broad money, 𝑉𝑡 is residual velocity controlling for interest and income
effect, 𝑃𝑡 is the price level, 𝑌𝑡 is the real income, and 𝑖𝑡 is the nominal interest rate at time
19
t. Money demand increases with real income and decreases with nominal interest rate.
Nominal interest rate can be formed by the Fisher equation, as the following:
𝑖𝑡 = 𝑟𝑡 + 𝜋𝑡𝑒
(2)
where 𝑟𝑡 the real interest is rate and 𝜋𝑡𝑒 is the expected inflation rate, which can be defined
as:
𝑒 )
𝜋𝑡𝑒 = ln(𝑃𝑡+1
− ln(𝑃𝑡 )
(3)
Equilibrium in the money market requires that money demand equals money
supply; hence, money supply and money demand are denoted by 𝑀𝑡 . By taking the
natural logarithm, and representing all variables but the real interest rate by lower case
letters (i.e. lnZ=z), this paper uses the following equation:
𝑚𝑡 + 𝑣𝑡 = 𝑝𝑡 + 𝛼𝑦𝑡 − 𝛽(𝑖𝑡 )
(4)
which can be formed and expressed as a percentage of change terms by taking the first
difference of each variable. After rearranging the equation and using the Fisher equation
in the equation (4), the paper finds inflation (𝜋𝑖,𝑡 ), which is the percentage change in
prices, and is a function of the percentage change of money supply (%Δ𝑚𝑖,𝑡 ), the
𝑒
percentage change of income ( %Δ𝑦𝑖,𝑡 ), and the expected inflation rate (𝜋𝑖,𝑡
), which is the
lagged dependent variable, “inflation”, to capture the effect of past policies on current
expectations. Therefore, a core regression equation is based on:
𝑒
𝜋𝑖,𝑡 = ∅%𝑚𝑖,𝑡 + 𝛽𝜋𝑖,𝑡
− 𝛼%∆𝑦𝑖,𝑡 +∈𝑖,𝑡
(5)
∈𝑖,𝑡 is a regression error term defined as the sum of the unobservable change in the real
interest rate (Δ𝑟𝑡 ) and the change in the money shock (Δ𝑣𝑡 ).
20
To capture the effects of currency policies on inflation, this paper includes a
regime dummy variable (FIXED). FIXED takes the value of one when a developing
country is classified as a fixed exchange rate regime, and takes the value of zero when a
country is classified as a flexible exchange rate regime. A dummy variable FIXED is
measured by using the KS de facto scheme. Thus, the regression framework for inflation
performance is the following:
𝜋𝑖,𝑡 = 𝛽0 + 𝛽1%Δ𝑚𝑖,𝑡 +𝛽2 𝜋𝑖,𝑡−1 + β3 %Δ𝐺𝐷𝑃𝑖,𝑡 + 𝛽4 𝐹𝐼𝑋𝐸𝐷 + 𝛽5 𝑂𝑃𝐸𝑁𝑖,𝑡 + 𝛽6 𝑇𝑂𝑇𝑖,𝑡 +
∈𝑖,𝑡
(6)
Based on the equation (6), the annual percentage change in inflation for country i
(i=1, 2, …, 102) over time period t, with t= 1970, 1971, …, 2009, depends upon other
explanatory variables. OPEN is the openness to trade; it is the ratio of the sum of exports
and imports to real GDP. OPEN is included in to capture the effect of international trade
on inflation in developing countries. OPEN is expected to be associated with lower
inflation rates, because greater openness to trade creates incentives for adopting stable
macroeconomic policies. Stable macroeconomic policies reduce fluctuation in prices.
Moreover, an increase in openness to trade leads to a great variety in consumption, which
could also reduce price volatility in developing economies. Another explanatory variable
in the inflation model is the terms of trade (TOT). TOT is the ratio of a country’s price of
exports to its price of imports. TOT is included in the model to control the effect of
external shocks. TOT is expected to be negatively related to inflation as long as the terms
21
of trade rise for a country. Finally, to capture for the effect of past policies of inflation on
current expectations, the lagged variable of the dependent variable is also used (𝜋𝑡−1 ).
4.2.2. Growth Model
In the paper, a simple growth model is used to explain the effects of exchange rate
regimes on economic growth. Therefore, a growth model is based on:
%∆𝐺𝐷𝑃𝑖,𝑡 = 𝛿𝐺𝐷𝑃0 + ∅𝐼𝐺𝐷𝑃0 − 𝜕𝐺𝐶0 + ∞𝑃𝑂𝑃0 +∈𝑖,𝑡
(7)
where %∆𝐺𝐷𝑃𝑖,𝑡 is the annual rate of GDP growth; 𝐺𝐷𝑃0 is the natural logarithm of
initial GDP; 𝐼𝐺𝐷𝑃0 is the initial ratio of investment to real GDP; 𝐺𝐶0 is the initial ratio of
government consumption to real GDP; 𝑃𝑂𝑃0 is the natural logarithm of total population;
and ∈𝑖,𝑡 is the error term.
The effect of the proper exchange rate regime on growth is explained using by the
five-year average panel model. The sample includes 102 developing countries over the
period 1970-2009. The baseline growth regression equation is formed as the following:
%∆𝐺𝐷𝑃𝑖,𝑡 = 𝛽0 + 𝛽1 𝐺𝐷𝑃0 + 𝛽2 𝐼𝐺𝐷𝑃0 + 𝛽3 𝐺𝐶0 + 𝛽4 𝑂𝑃𝐸𝑁0 + 𝛽5 𝑃𝑂𝑃0 + 𝛽6 𝑇𝑂𝑇0
+ 𝛽7 𝐹𝐼𝑋𝐸𝐷𝑖,𝑡 + 𝛽8 𝐼𝑁𝐹0 + 𝛽9 𝐼𝑁𝐹𝐹𝐼𝑋𝐸𝐷0 +∈𝑖,𝑡
(8)
According to the equation (8) the five-year average rate of real GDP growth
(%𝛥𝐺𝐷𝑃𝑖,𝑡 ) for country i (i=1,2,…102) over time period t, with t= 1970-1974, 19751989, 1990-1994, 1995-1999, 2000-2004, and 2005-2009, depends upon several
additional control variables. 𝐺𝐷𝑃0 is the initial GDP, and is expected to have a negative
sign (conditional convergence). 𝐼𝐺𝐷𝑃0 is the initial ratio of investment to real GDP, and
its coefficient is expected to have a positive sign since higher investment rates leads to
22
higher economic growth. 𝐺𝐶0 is the initial ratio of government consumption to real GDP.
An increase in the growth of government consumption is expected to decrease GDP
growth. 𝑂𝑃𝐸𝑁0 is the initial rates of openness to trade. It is measured as the ratio of the
sum of export and import to GDP. It is expected to have a positive relationship with
economic growth. 𝑃𝑂𝑃0 represents the natural logarithm of initial total population, and is
expected to have a positive sign. 𝑇𝑂𝑇0 is the initial terms of trade, and is measured as the
ratio of a country’s price of exports to its price of imports. It is expected to have a
positive sign. 𝐼𝑁𝐹0 is the initial percentage change in inflation, and is expected to have a
negative relationship with GDP growth based on the money demand and money supply
equation in previous section. 𝐼𝑁𝐹𝐹𝐼𝑋𝐸𝐷0 is an interaction variable that captures the
trade-off between growth and inflation under the proper exchange rate regime. Finally,
𝐹𝐼𝑋𝐸𝐷𝑖,𝑡 is a binary variable that takes the value of 1 if a country adopts a fixed
exchange rate regime, and the value of 0 if a country has a flexible exchange rate regime.
In this section, an observation requires a total of four years of a peg in to be categorized
as fixed in a five-year panel, based on the KS regime scheme. FIXED is the main variable
that looks at the impact of currency policies on economic growth.
4.3. Methodology
The paper uses two econometrics models to explain the effects of currency
policies on inflation and growth: the country fixed effects model, and the Arellano Bond
model. The Arellano Bond model is used only in inflation performance to capture the
effects of past policies of inflation on current inflation since a standard fixed effects
23
model is unable to control for the impact of the lagged value of the dependent variable,
and gives inconsistent estimates in the analysis. Hence, by using the Arellano Bond
model, this paper can control for the impact of past policies on current inflation without
having any biased estimates.
The country fixed effect model is used to control for unobserved or difficult to
measure country characteristics in panel data when such variables vary across countries
but do not change over time in both inflation and growth models. For instance, cultural or
historical ties could play a role the choice of currency policies that do not change
dramatically over time, but differ across developing countries. Therefore, by using the
country fixed effects model, the paper can capture the effects of these unobserved omitted
variables on inflation and growth, and eliminate the omitted variable bias in analysis.
Moreover, clustered standard errors are used in country fixed effects regressions.
Clustered standard errors allow for heteroskedasticity and for autocorrelation within a
county, but are uncorrelated across entities. Therefore, clustered standard errors are valid
whether or not there is heteroskedasticity, autocorrelation, or both.
Additionally, time dummies are used in inflation and growth models. The reason
is that common shocks across countries (such as spikes in oil prices or fluctuations in the
U.S. dollar) influence all economies beyond the effects channeled through observed
variables. Therefore, time dummies can control for unobserved or difficult to measure
variables that are constant across countries but evolve over time. Finally, a two- stage
24
least squares method (2SLS) is used for a robustness check since there might be problem
of endogeneity between fixed regime and inflation in the inflation model.
25
CHAPTER 5
EMPIRICAL FINDINGS
5.1. Inflation Performance
Before analyzing the regression results in the inflation model, one might suspect
that the variable “ inflation” has a unit-root since time series are more likely to have unitroots and are non-stationary (i.e. lagged value of inflation is used as an independent
variable). Based on the Fisher test for panel root using an Augmented Dickey Fuller Test
with one lag, the null hypothesis (which is that “inflation” has a unit root) is rejected,
since the probability of Chi2 is less than 1%. Therefore, the variable “inflation” does not
have a unit root and is stationary.
Table 5.1 reports the regression results. Countries that have more than a 50%
inflation rate are ignored to eliminate the outlier effect in the models. As mentioned, time
dummy variables are used in all the regressions to control the effects of common
unobservable shocks across countries. Table 5.1 divides developing nations into two
categories: emerging markets and non-emerging markets. This paper analyzes inflation
models across all developing nations, emerging markets, and non-emerging markets to
see the differences of regime impact on inflation between emerging economies and nonemerging markets. The emerging markets group is defined using the Standard & Poor
classification.
26
Table 5.1 The Inflation Performance
ALL DEVELOPING COUNTRIES
EMERGING MARKETS
NON-EMERGING MARKETS
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
VARIABLES
NO CFE
CFE
A-BOND
NO CFE
CFE
A-BOND
NO CFE
CFE
A-BOND
%ΔM
0.165***
(0.0433)
0.104***
(0.0269)
0.110*
(0.0636)
0.270***
(0.0680)
0.168***
(0.0469)
0.201***
(0.0241)
0.155***
(0.0439)
0.0971***
(0.0271)
0.0973***
(0.0126)
%ΔGDP
-0.0209
(0.0904)
0.0191
(0.0900)
-0.0362
(0.0702)
-0.365***
(0.0921)
-0.219***
(0.0785)
-0.462***
(0.0800)
0.0159
(0.0973)
0.0392
(0.0968)
-0.0196
(0.0354)
-0.0362***
(0.00420)
-0.0237**
(0.0112)
0.0258
(0.0526)
-0.0396***
(0.00791)
0.0112
(0.0132)
0.00435
(0.0169)
-0.0379***
(0.00467)
-0.0240*
(0.0143)
0.0369**
(0.0185)
0.00581
-0.00801
0.0123
0.0317*
0.0411**
0.0577***
0.00861
-0.0171
(0.00840)
(0.0127)
(0.0277)
(0.0162)
(0.0207)
(0.0210)
(0.00965)
(0.0141)
0.000223
(0.0174)
-0.0188***
-0.0318***
-0.0353***
-0.0246***
-0.0194***
-0.00481
-0.0183***
(0.00347)
(0.00406)
(0.00932)
(0.00688)
(0.00662)
(0.00781)
(0.00410)
0.0380*** 0.0365***
(0.00519) (0.00674)
OPEN
TOT
FIXED
πt-1
0.0261***
(0.00601)
Constant
0.0462***
(0.00232)
0.0226***
(0.00972)
0.0773***
(0.0175)
0.0656**
(0.0286)
0.0813
(0.0499)
0.0954***
(0.0219)
-0.0398
(0.0278)
0.00542
(0.0496)
0.0640***
(0.0214)
0.0696*
(0.0362)
0.0631
(0.0578)
Observations
2,680
2,680
2,551
486
486
468
2,194
2,194
2,083
Adj Rsquared
0.220
0.38
0.41
0.60
0.20
0.35
_____________________________________________________________________________________________
Notes: ***, **,* represent 99, 95, 90 percent significance, respectively. Heteroskedasticity and autocorrection robust standard
errors are shown in parenthesis below coefficients
27
Columns 1, 2, and 3 represent results for all developing nations. Column 1 shows
results for a regression without country fixed effects. It reports that the regime dummy
FIXED is statistically significant and has the expected sign. Results indicate that a
developing nation, under a fixed exchange rate regime, has 1.9% less of an inflation rate
relative to a country under a flexible exchange rate. Coefficients for other explanatory
variables in column 1 show that the growth of money supply (%ΔM) is significant and
positively related to inflation, and that openness to trade ratio (OPEN) is significant and
negatively related to inflation, as expected. On the other hand, the terms of trade (TOT) is
not significant with a positive coefficient. GDP growth (%ΔGDP) is negatively related to
inflation but it is not significant. Column1 explains the variation in inflation by
approximately 22%.
Once country fixed effects (hereafter CFE) is included (column2), the adjusted Rsquared has become 38%, and the magnitudes of the coefficients on explanatory variables
have changed, meaning that the omission of the CFE has caused the omitted variable bias
into column 1. Based on the results, fixed regimes are associated with a 3.2% lower
inflation rate than flexible regimes, and %ΔM and OPEN are significant and have the
expected signs, whereas TOT is insignificant but has a negative sign. GDP growth is
insignificant and positively related to inflation. Column 3 gives the results for the
Arellano-Bond model (hereafter A-Bond), which is the model that allows paper to control
the past policies on inflation. Based on the results, the regime dummy is again significant
and has a negative coefficient. The results suggest that a country with a fixed exchange
28
regime has 3.5% less of an inflation rate compared to a country with a flexible exchange
rate regime. Moreover, the lagged dependent variable, and %ΔM are also significant and
have the expected signs.
Columns 4, 5, and 6 show the results for emerging markets. Emerging markets are
more likely to open to trade, and have more income relative to non-emerging markets.
Based on Column 4 (no CFE), the regime dummy has a significant, negative relationship
with inflation, as expected. An emerging market under a fixed regime has 2.5% of a
lower inflation rate relative to emerging markets under a flexible regime. In addition,
%ΔM, OPEN, and GDP growth are significant, with the expected signs; the overall fit of
the equation is approximately 41%. When CFE is added into the regression (Column5),
the regime dummy stays significant with the negative coefficient. Its coefficient indicates
that an emerging market with a fixed regime is associated with 1.9% lower inflation
compared to an emerging market with a flexible regime. Once again, CFE has improved
the variation in inflation. A regression with the country fixed effects explains the
variation in inflation by 60%. By adding the lagged, dependent variable, and changing the
specification in Column 6 (with A-Bond), the regime dummy becomes insignificant but
stays negatively related to inflation. The magnitude of FIXED also drops in the A-Bond
model. Results for emerging markets suggest that the effects of currency regimes are less
on inflation since the ratio of openness to trade, the growth of real GDP, and the growth
rate of money supply, picks up almost all of the effect.
29
As stated earlier, emerging markets are inclined to have more open market
policies compared to non-emerging markets. Emerging markets have also more income
and less of a balance of payment crises relative to non-emerging markets. Moreover,
monetary and fiscal institutions are stronger than those in non-emerging markets. These
could be main the reasons why the impact of regime on inflation is lower in emerging
markets.
Columns 7, 8, and 9 report the results for non-emerging markets. A regression
without country fixed effects (Column 7) has a low adjusted R- squared value,
approximately 20%; however, FIXED is again significant and negatively related to
inflation rate. FIXED decreases inflation by 1.8% compared to flexible regimes. Other
explanatory variables, %ΔM and OPEN, are significant except for GDP growth and TOT.
Once the unobserved, omitted variables are captured by the country fixed effects, the
regime dummy becomes highly significant with the expected sign. Results suggest that
fixed regimes are associated with 3.8% lower inflation rates versus flexible regimes.
Finally, based on the A-Bond results, once the lagged dependent variable picks up the
effect, the regime dummy loses its magnitude by 0.2% compared to the regression with
the CFE. According to the A-Bond model, the results indicate that under a fixed regime, a
non-emerging market has a 3.6% lower inflation rate relative to those under a flexible
exchange rate regime. The lagged dependent variable, %ΔM, and OPEN are significant
with expected signs; GDP growth and TOT are insignificant with the expected signs.
30
As mentioned in the introduction, fixed rate regimes are associated with lower
inflation rate compared to flexible rates regimes. The results in Table 5.1 show that
inflation rate is negatively related to the regime dummy FIXED, based on eight out of
nine different specifications in the inflation model. A significantly negative relationship
between fixed exchange rate regimes and inflation is found by using the country fixed
effects with the Arellano-Bond model, and with time dummy variables. Results also
indicate that fixed regimes have a larger impact on non-emerging markets compared to
emerging markets. A non-emerging market under a fixed regime has significantly lower
inflation rates (2% lower in the country fixed effects model, and almost 3% lower in the
A-Bond model) compared to an emerging market under a fixed regime.
These results are consistent with findings from previous studies. Yeyati and
Sturzenegger (2001) found that a developing nation under the fixed regime has a 2.95%
lower inflation rate compared to a country with a flexible exchange rate regime.
Shambaugh and Klein (2004) found that a fixed regime reduces inflation by 3.08%, and
Husain and Rogoff (2004) found similar results, showing 2.8% less inflation in countries
with a fixed regime relative to those with flexible regimes.
The main findings from previous studies and this paper indicate that there is a
significantly negative relationship between fixed exchange rate regimes and inflation.
Therefore, it is reasonable to suggest that developing countries (especially non-emerging
countries) that are fighting against high inflation could adopt a fixed exchange rate
regime as a currency policy, and therefore sustain price stability.
31
5.1.1. Robustness Check
Based on the previous empirical study, fixed regimes may lead to a lower
inflation rate. However, a developing nation adopts or maintains a pegged regime
because a pegged regime itself might depend upon on its inflation performance. For
instance, if a country that has a low inflation rate is more likely to adopt or maintain a
fixed regime, then the regime dummy FIXED is simultaneity biased in previous inflation
performance.
To address this issue, this paper uses the two-stage least squares method (2SLS).
The instruments for the regime dummy are: the ratio of domestic credit over GDP (DC);
the ratio of the country’s GDP over that of the United States (SIZE); the binary variable
EMERGING takes the value of 1 if a country is an emerging market, 0 otherwise; and the
lagged value of inflation. Yeyati and Sturzeggener (2001) used DC in their 2SLS model.
The authors suggest that DC is significantly associated with the choice of an exchange
rate regime. SIZE is associated with choice of the currency policies, since smaller
countries are more likely to open to trade and are more inclined to choose pegged
regimes. The binary variable EMERGING is used to capture the effects of different
markets on the choice of regime. Finally, the lagged variable of inflation is used to see
the effect of past policies on the choice of the rate regimes.
A standard probit regression on the regime dummy FIXED with these control
variables is used to check instrument relevance. Based on the F-test, the instrument
variables are different from 0, at 5% level of significance, indicating that the instruments
32
are not weak and relevant in the model. Table 5.2 reports the probit model estimation for
the regime dummy FIXED.
33
Table 5.2 The First Stage Regression: Probit Model
VARIABLES
FIXED
EMERGING
-0.0757***
(0.0256)
SIZE
0.838***
(0.0797)
πt-1
-0.217***
(0.0642)
DC
-0.531***
(0.158)
%ΔM
-0.122**
(0.0534)
%ΔGDP
0.696***
(0.175)
TOT
-0.0431
(0.0360)
OPEN
-0.000167
(0.000275)
Year
Dummies
YES
Observations
2,813
Pseudo R0.115
squared
Notes: ***, **, * represent 99, 95, 90 percent significance, respectively. Heteroskedasticity and
autocorrelation robust standard errors are shown in parenthesis below coefficients.
34
Table 5.3 Two Stage Least Squares
𝜋
VARIABLES
FIXED
-0.0571***
(0.0152)
%ΔM
0.162***
(0.0435)
%ΔGDP
-0.00125
(0.0899)
OPEN
-0.0311***
(0.00491)
TOT
0.00931
(0.00853)
Constant
0.109***
(0.0231)
YEAR DUMMIES
YES
Observations
2,660
R-squared
0.199
Adj. R-squared
0.185
Notes: ***, **, * represent 99, 95, 90 percent significance, respectively.
Heteroskedasticity and autocorrelation robust standard errors are shown in parenthesis
below coefficients.
Instruments: The ratio of domestic credit over GDP (DC), the ratio of a country’s GDP
over the U.S. GDP (SIZE), the lagged value of inflation, the dummy variable
“EMERGING”.
35
Table 5.3 represents inflation performance with the 2SLS model. As mentioned,
the regime dummy FIXED is controlled by relevant instrument variables. The results
show that the regime dummy FIXED is still negatively related to inflation and is
significant at all levels of significance. Its negative coefficient indicates that developing
under a fixed regime has a 5.7% lower inflation rate compared to under a flexible regime.
In addition, findings show that %ΔM and OPEN are significant, with the expected signs.
On the other hand, %ΔGDP is insignificant with the expected sign, and TOT is
insignificant with the unexpected sign. Thus, Table 5.3 confirms that a negative
relationship between inflation and a pegged regime still remains and is statistically
significant after the paper accounted for the problem of endogeneity.
5.2. The Growth Performance
Based on the past studies, there is no consensus on the impact of the proper
exchange rate regime on economic growth. Hence, this section investigates whether or
not exchange rate regimes affect growth. As discussed, the effect of the proper exchange
rate regime on growth is explained by using the five-year average panel with the country
fixed effects model (time dummy variables are included in all regressions). In this
section, an observation requires a total of four years of a peg to be categorized as a fixed
in a five-year panel, based on the KS regime scheme. The regime dummy FIXED is the
main variable that looks at the impact of currency policies on economic growth. Table
5.4.represents the empirical findings for growth performance.
36
Table 5.4 The Growth Performance
ALL DEVELOPING
EMERGING
NON-EMERGING
(1)
(2)
(3)
(4)
(5)
(6)
NO CFE
CFE
NO CFE
CFE
NO CFE
CFE
-0.00679**
-0.0426***
-0.0101***
-0.0499***
-0.00765**
-0.0399***
(0.00274)
(0.00854)
(0.00364)
(0.0105)
(0.00348)
(0.0111)
IGDP
0.0772***
(0.0281)
0.0693**
(0.0338)
0.0896***
(0.0277)
0.127***
(0.0475)
0.0601*
(0.0315)
0.0652*
(0.0383)
GC
-0.00540
(0.0284)
-0.111
(0.0740)
0.00539
(0.0866)
-0.338**
(0.165)
-0.0251
(0.0314)
-0.116
(0.0756)
OPEN
0.00561*
(0.00322)
0.00308
(0.00789)
0.00257
(0.00613)
-0.0163
(0.0109)
0.00774**
(0.00351)
0.00625
(0.00957)
POP
0.00841***
(0.00230)
0.0209
(0.0179)
0.0137***
(0.00368)
0.0512
(0.0363)
0.00700**
(0.00286)
0.0191
(0.0191)
TOT
0.00159
(0.00523)
0.00570
(0.00821)
0.00951
(0.0113)
0.0145
(0.0117)
-8.65e-06
(0.00589)
0.00445
(0.00966)
FIXED
0.00798***
(0.00290)
0.00951**
(0.00443)
0.0182***
(0.00575)
0.0132**
(0.00543)
0.00406
(0.00320)
0.00860
(0.00573)
INF
-0.000011
(0.00009)
-0.00000286
(0.0000958)
-0.000112
(0.000166)
-0.00000899
(0.000153)
-0.000102**
(0.0000396)
-0.000126*
(0.0000711)
INFFIXED
-0.0155
(0.0119)
-0.0169
(0.0169)
-0.0146
(0.0307)
-0.0103
(0.0273)
-0.00631
(0.0122)
-0.0178
(0.0180)
Constant
0.0643**
(0.0324)
0.737**
(0.359)
0.0287
(0.0593)
0.423
(0.788)
0.124***
(0.0423)
0.715*
(0.408)
YES
YES
YES
YES
YES
YES
0.143
0.2824
0.3970
0.5739
0.1417
0.2596
537
537
105
105
432
432
VARIABLES
INGDP
YEAR
DUMMIES
Adj. Rsquared
Observations
Notes: ***, **, * represent 99, 95, 90 percent significance, respectively. Heteroskedasticity and autocorrelation robust
standard errors are shown in parenthesis below coefficients.
37
According to Table 5.4, Columns 1 and 2 report the results for all developing
nations. In both columns, initial GDP has a negative coefficient, indicating conditional
convergence in developing countries. Investments to GDP ratio is also significant and has
the expected signs in two specification models. The findings in the regression without the
country fixed effects (Column1) indicate that under a fixed exchange rate, a country has
significantly (0.7%) higher economic growth compared to a country under a flexible
regime. According to the results, OPEN and POP are significant and positively related to
GDP growth; GC, TOT, INF, and the interaction variable, INFFIXED, have the expected
signs, but are not significant in the growth model. Moreover, Column 1 explains the
variation in the growth model by 14%.
When the country fixed effects (CFE) is included in Column 2 to capture the
unobserved omitted variables that vary across the countries, but stay constant over time,
adjusted R-squared increases by 14% compared to Column1, becoming 28%. The fixed
exchange rate regimes’ impact on GDP growth also increases by 0.2%, significantly. This
result indicates that fixed regimes are associated with 0.9% higher GDP growth than
flexible regimes. The relationship between growth and inflation is the same as in the
money equation, remaining negative. However, this negative relationship is insignificant
in Column 2 as well. Other explanatory variables, such as GC, OPEN, TOT, and POP,
have the expected signs, but are insignificant in Column 2.
Columns 3 and 4 look at the relationship between GDP growth and exchange rate
regimes for emerging markets. Based on the results, the initial GDP and the investment to
38
GDP ratio are statistically significant, and have the expected signs in two different
specifications. In the case of an emerging market, fixed exchange regimes are
significantly associated with higher GDP growth than are flexible regimes, in two
models. According to the results, fixed regimes have: (1) 1.8% higher GDP growth in a
regression without the CFE; and (2) 1.3% higher GDP growth in a regression with the
CFE. Column 3 (no CFE) explains the variation in GDP growth by 40% whereas Column
4 (with CFE) explains the variation in GDP growth by 57%.
The last two columns, Columns 5 and 6, represent the results for non-emerging
markets. In Column 5 (no CFE) and in Column 6 (CFE), the regime dummy FIXED is
positively related to GDP growth. However, the results show that there is no significant
effect of exchange rate regime on economic growth in non-emerging markets. The results
are reasonable, since non-emerging markets are more likely to adopt closed-market
policies and do not participate in international trade relative to emerging countries.
Moreover, as mentioned, external shocks are less relevant in countries with closedmarket policies. Hence, the impact of exchange rates on economic growth is not
significant in non-emerging countries. In both columns, initial GDP, investment to GDP
ratio, and inflation are significant, with the expected signs. Additionally, in Column 5 (no
CFE), OPEN and POP are significant and have positive signs, as expected. The adjusted
R-squared values are 14% and 25% in Columns 5 and 6, respectively.
Table 5.4 suggests that developing countries have higher GDP growth under fixed
exchange regimes compared to those under flexible exchange rate regimes, but only in
39
richer economies (emerging markets). In four out of six regressions, the regime dummy
FIXED is significant and positively related to GDP growth. The growth performance
shows results that are consistent with past studies which support the positive effects of
fixed exchange regimes on GDP growth. For instance, Husain and Rogoff (2004) have
found that developing countries under a fixed regime have 1.9% higher economic growth
than those under a flexible regime. Similarly, Shambaugh and Klein (2004) also found
that a fixed regime is associated with 0.8% higher growth relative to a flexible regime.
However, the main findings for growth performance differ from other studies.
Yeyati and Sturzenegger (2001) indicate that fixed regimes reduce economic growth by
0.7% in developing countries, and Bleaney and Francisco (2007) also found that fixed
regimes decrease growth by 1.02% compared to flexible regimes. Ghosh et al. (2002) and
Miles (2006) did not find any significant effects of exchange rate regimes on economic
growth. This paper’s results differ from these previous studies because this paper covers
data over a larger time-span, and includes more developing countries than do previous
studies. Moreover, the results differ from each other based on the use of regime
classification.
According to its supporters, a fixed regime is expected to increase economic
growth through its trade channels, because the adoption of a fixed regime promotes trade
in developing countries. Since emerging markets are more open to trade relative to nonemerging markets, it is reasonable to suggest that developing countries that pursue
40
export-oriented strategies can increase their economic growth by adopting a fixed
exchange rate regime.
41
CHAPTER 6
CONCLUSION
The past financial crises have shown that exchange rate regimes impacted on
inflation and economic growth. For decades, economists have studied the effects of
exchange rate regimes on macroeconomic outcomes and have tried to find the optimal
exchange rate regime for developing countries. Empirical studies, with improvements in
the de facto classification scheme, suggest that the optimal exchange rate regime has a
significant effect on these outcomes.
This paper revisits the relationship between exchange rate regimes and
macroeconomic outcomes under the most recent financial crises and external shocks. The
paper has included data from more than one hundred developing nations, from 19702009. This span of time has not been covered in previous studies and includes both past
and recent financial turbulences in developing nations. This paper also used the Klein and
Shambaugh (2008) de facto classification scheme, which is the newest model in the
literature, and looked only at fixed or flexible exchange rate regimes effects on inflation
and economic growth in developing countries.
This paper included two additional distinctions. It divided developing countries
into two categories: emerging markets and non-emerging markets. This distinction gave a
better picture of the effect of exchange rate regimes on inflation and growth for each
category. Based on the empirical studies, the findings suggest that developing countries
with fixed exchange rate regimes experience significantly lower inflation rates than those
42
with flexible exchange rate regimes. Moreover, a non-emerging market under a fixed
regime has significantly lower inflation rates -2% lower in the country fixed effects
model, and almost 3% lower in the A-Bond model- when compared to an emerging
market under a fixed regime.
In growth performance, developing nations with fixed rate regimes show
significantly higher economic growth relative to those with flexible rate regimes in both
specification models. Overall, fixed regimes have significantly (0.9%) higher economic
growth than flexible regimes in developing nations. The impact of a fixed regime on
growth increases for richer countries (emerging markets), and becomes significant, at
1.3%. This positive link between fixed regimes and GDP growth can occur through a
fixed regime’s price stability effect. Countries with fixed exchange rate regimes might
have lower real interest rates, since fixed regimes act as an anti-inflationary tool for
monetary policy makers. Thus, low real interest rates lead to an increase in investment,
and in the end, a high level of investment leads to higher levels of economic growth.
Moreover, adopting a fixed regime can promote trade for emerging countries and lead an
increase in economic growth. However, this positive relationship becomes insignificant
for non-emerging markets, since those countries are more likely to implement closedmarket policies and focus on sustaining price stability.
In many developing countries, adopting an exchange rate regime is heavily
influenced by macroeconomic conditions and goals. Based on empirical findings, a fixed
exchange rate arrangement should be the preferred policy option for developing countries
43
that choose to sustain price stability as a key macroeconomic goal. Furthermore, fixed
exchange rate regime may be the optimal policy option for developing countries who
want to pursue export-oriented strategies to increase economic growth.
44
APPENDIX
List of Countries
Emerging Markets
Argentina
Brazil
China
Colombia
Egypt
Hungary
India
Indonesia
South Korea
Malaysia
Mexico
Morocco
Peru
Philippines
South Africa
Algeria
Bahamas
Bahrain
Barbados
Belize
Bhutan
Bolivia
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Central Africa
Rep.
Chad
China
Comoros
Congo, Rep
Costa Rica
Cote d`Ivoire
Djibouti
Dominica
Non-Emerging Markets
Dominican Rep.
Laos
Ecuador
Lesotho
El Salvador
Liberia
Equatorial Guinea
Madagascar
Ethiopia
Malawi
Fiji
Mali
Gabon
Mauritius
Gambia, T
Mozambique
Ghana
Namibia
Grenada
Nepal
Guatemala
Nicaragua
Guinea
Niger
Guinea-Bissau
Nigeria
Guyana
Oman
Haiti
Pakistan
Honduras
Panama
Hong-Kong
Papua New Guinea
Iran
Paraguay
Iraq
Romania
Jamaica
Rwanda
Jordan
Samoa
Kenya
Sao Tome and
Principe
Thailand
Turkey
Senegal
Seychelles
Singapore
Solomon Islands
Sri Lanka
St. Kitts & Nevis
St. Lucia
St. Vincent &
Grenadines
Sudan
Suriname
Syria
Tanzania
Togo Tonga
Trinidad &Tobago
Tunisia
Uganda
Uruguay
Vanuatu
Venezuela
Zambia
45
References
Abbott, A. & De Vita, G. (2011). Revisiting the relationship between inflation and
growth: A note on the role of exchange rate regimes. Economic Issues, Vol. 16,
Part.
Andres, J. & Hernando, I. & Kruger, M. (1996). Growth, inflation and the exchange rate
regime. Economics Letters, 53, 61-65.
Appleyard, D. & Field, A. & Cobb, S. (2008). International economics. McGraw-Hill
Irwin, 6th Edition.
Barro, R. & X. Sala-i-Martin (2004). Economic growth, MIT Press, Cambridge,
Massachusetts
Calvo, G. & Reinhart, C. (2002). Fear of floating. Quarterly Journal of Economics, 117,
379-408.
Calvo, G., & Mishkin F. (2003).The mirage of exchange rates for emerging market
countries. NBER Working Paper, 9808.
Dornbusch, R. (2001). Fewer monies, better monies. American Economic Review, 91,
238-42.
Edwards, S. (2011). Exchange rates in emerging countries: Eleven empirical regularities
from Latin America and East Asia. National Bureau of Economic Research,
Working Paper 17074.
Eichengreen, B. & Hausmann, R. (1999). Exchange rates and financial fragility. NBER
Working Paper, 7418.
46
Fischer, S. (2001).Exchange rate regimes: Is the bipolar view correct?” Finance &
Development, Vol. 38 (June), pp. 18–21.
Gosh, A. & Gullde, A. & Ostry, J. & Wolf H. (1997). Does the nominal exchange rate
regime matter? NBER Working Paper No. 5874.
Ghosh, A. & Gulde, A. & Wolf, H. (2002). Exchange rate regimes: Classification and
consequences. Centre of Economic Performance.
Husain, A.M. & Mody, A. & Rogoff, G. S. (2002). Exchange rate regime durability and
performance in developing versus advanced economies. Journal of Monetary
Economics 52, 35-64.
Jones, L. & Manuelli, R. (1995). Growth and the effects of inflation. Journal of
Economic Dynamics and Control, 19, 1405-1428.
Levy-Yeyati, E. & Sturzenegger, F. (2001). Exchange rate regimes and economic
performance. IMF Staff Papers, Vol. 47, Special Issue.
Miles, W. (2006). To float or not to float? Currency regimes and growth. Journal of
Economic Development, Vol.31, Number 2.
Klein, W. & Shambaugh, J. (2006). The nature of the exchange rate regimes. NBER
Working Paper.
Reinhart, C. & Rogoff, K. (2004). The modern history of exchange rate arrangements: A
reinterpretation. Quarterly Journal of Economics 119, 1-48.
Romer, D. (1993). Openness and inflation: Theory and evidence. Quarterly Journal of
Economics, Vol. 108, pp. 869–903.
47
Rose, A. (2000).One money, one market? The effects of common currencies on
international trade. Economic Policy, 15, 7-46.
Shambaugh, J. (2004). The effect of fixed exchange rates on monetary policy. Quarterly
Journal of Economics 119, 301-52.
Shambaugh, J. & Klein, M. (2008). Exchange regimes in the modern era. MIT Press,
Cambridge, Massachusetts.