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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. −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: − = , > 0 (1) where is broad money, is residual velocity controlling for interest and income effect, is the price level, is the real income, and 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: = + (2) where the real interest is rate and is the expected inflation rate, which can be defined as: ) = ln(+1 − ln( ) (3) Equilibrium in the money market requires that money demand equals money supply; hence, money supply and money demand are denoted by . 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: + = + − ( ) (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 (, ), which is the percentage change in prices, and is a function of the percentage change of money supply (%Δ, ), the percentage change of income ( %Δ, ), and the expected inflation rate (, ), 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: , = ∅%, + , − %∆, +∈, (5) ∈, is a regression error term defined as the sum of the unobservable change in the real interest rate (Δ ) and the change in the money shock (Δ ). 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: , = 0 + 1%Δ, +2 ,−1 + β3 %Δ, + 4 + 5 , + 6 , + ∈, (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 (−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: %∆, = 0 + ∅0 − 0 + ∞0 +∈, (7) where %∆, is the annual rate of GDP growth; 0 is the natural logarithm of initial GDP; 0 is the initial ratio of investment to real GDP; 0 is the initial ratio of government consumption to real GDP; 0 is the natural logarithm of total population; and ∈, 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: %∆, = 0 + 1 0 + 2 0 + 3 0 + 4 0 + 5 0 + 6 0 + 7 , + 8 0 + 9 0 +∈, (8) According to the equation (8) the five-year average rate of real GDP growth (%, ) 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. 0 is the initial GDP, and is expected to have a negative sign (conditional convergence). 0 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. 0 is the initial ratio of government consumption to real GDP. An increase in the growth of government consumption is expected to decrease GDP growth. 0 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. 0 represents the natural logarithm of initial total population, and is expected to have a positive sign. 0 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. 0 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. 0 is an interaction variable that captures the trade-off between growth and inflation under the proper exchange rate regime. Finally, , 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 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. 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