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The impact of foreign direct investment, portfolio investment and other

investment on real effective exchange rates in East Asia and Pacific.

Abstract

This paper studies the effect of different capital flows on the real effective exchange rate. The three

types of capital flows distinguished are foreign direct investment, portfolio investment and other

investment. An autoregressive distributed lag model has been used to study annual data from

countries in East Asia and Pacific from the year 1982 to 2014. Total net capital flows, portfolio

investment and other investment have a positive and significant impact on the REER in the short run,

while no long run impact is measured. Foreign direct investment appears to be unable to explain

REER movements in either the short- or the long run.

JEL Classification: F21 International Investment; Long-Term Capital Movements, F31 Foreign Exchange

Keywords: International Capital Flows, Real Exchange Rates, East Asia and Pacific

Name: Annemarije Santman

Student number: 11111844

Programme: MSc ECO

Date: 21 January 2017

Supervisor: Boe Thio

Second supervisor: Dirk Veestraeten

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Statement of Originality

This document is written by Student Annemarije Santman who declares to take full responsibility for

the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other

than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the

work, not for the contents.

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1. Introduction

The real effective exchange rate (REER) can be seen as a measure of international competitiveness of

a country. An appreciation of the REER is equivalent to a more expensive domestic currency of a

country which could worsen the county’s international competitiveness. This could lead to lower

demand for the country’s exports, which could stagnate economic growth. Large REER fluctuations

thus increase uncertainty and are a threat to sustainable economic growth. A potential trigger for REER

fluctuations are capital flows. Extensive research has been done on the causal effect of aggregate

capital flows on the REER, but relatively few papers have differentiated among different types of

capital flows in their empirical analysis. Capital controls implemented by policy makers are generally

based on aggregate capital flows (Combes et al, 2011), while a number of studies (Combes et al, 2011;

Wiboonchutikala et al, 2011; Bakardzhieva et al, 2010; Shen & Wang, 2001; Ahlquist, 2006; Athukorala

& Rajapatirana, 2003) provide evidence that macroeconomic impact differs among capital flows.

This paper will add to economic understanding of the impact of different capital flows on the

REER. The three categories that are most often distinguished and that will be used in this paper as well

are foreign direct investment (FDI), portfolio investment and other investment. These capital flows

combined with reserve assets comprise the financial account of the balance of payments. Based on

existing literature it is expected that short-run effects are similar for the three types of capital flows as

all indicate an increase in demand for the domestic currency which would thus lead to an appreciation.

Long run effects however are expected to differ significantly as a result of the different drivers of the

different capital flows which could thus be accompanied by different macroeconomic implications

(Moore & Pentecost 2006). As there have not been many studies on the causal effect of different types

of capital flows on the REER and as results of the studies that have been done on this relationship

feature different results there is room for further research on this topic.

According to the World Bank Group, the region East Asia and Pacific has been, and still is, one

of the main drivers of economic growth in the global economy. Capital inflows are generally large in

fast growing economies as fast growth is generally accompanied by opportunities to gain high rewards

on investment. These capital inflows encourage further development of the recipient country and

could give economic growth a further boost. A negative side effect is loss of international

competitiveness. This could reduce demand for tradable goods and thus limit growth. The region East

Asia and Pacific has experienced large fluctuations in capital flows making it an interesting region to

study. Large capital flows have been pointed out as main cause of the Asian crisis that erupted in 1995

by for instance Wiboonchutikala et al (2011), Khan et al (2005), Khan (2004) and Radelet & Sachs

(1998). Does this mean that large capital flows are a threat to economic stability? Based on research

by for instance Combes et al (2010), Bakardzhieva et al (2011) and Athukorala & Rajapatirana (2003) it

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can be said that this is highly dependent on the composition of these capital flows. The impact of the

composition of capital flows on the REER is what will be studied in this paper, leading to the research

question: how do foreign direct investment, portfolio investment and other investment affect the REER

in East Asia and Pacific? The method that will be used for this study is the fundamentals approach,

which will be explained in section 2.1 and 3.1.

The structure of this paper will be as follows. The next section presents an overview of

literature related to the research question in this paper. In section three the empirical model will be

specified and in section four the methodology is explained. Chapter five provides an overview of the

data used and the results will be discussed in chapter six. Implications of the results and limitations of

this paper will be discussed in section seven and finally a conclusion is given in section eight.

2. Literature

2.1 Capital flows and the REER

Capital inflows lead to an appreciation of the domestic currency and, by the definition used in this

paper, an increase of the REER. This argument is generally accepted in the field of economics and is

supported by several macroeconomic models, such as the Dornbusch (1976) and Frenkel & Rodrigues

(1982) models of exchange rate overshooting and the Portfolio Balance Model. An increase of capital

inflows can be seen as an increase in demand for the domestic currency and thus the currency

appreciates. This is however only the short-run effect, the long run effect may be different as other

factors play an important role as well. Moore & Pentecost (2006) argue that if the nominal currency

appreciation reduces international competitiveness and leads to a lower export level, this may cause

the domestic price level to fall which would return the real exchange rate to its old level. On the other

hand they argue that if the capital inflows were induced by financial liberalization, the liberalized

interest rates could be accompanied by liberalized prices which could actually lead to an increase in

the domestic price level and thus a further increase of the real exchange rate. Financial liberalization

refers to the elimination of restrictions on financial markets and -institutions, which stimulates

economic growth and improves the investment climate.

FDI, portfolio investment and other capital flows have different determinants. Dunning’s

eclectic OLI theory for instance states that a multinational enterprise will engage in FDI if it faces

ownership-, location-, and internalization advantages by doing so (Faeth, 2009). Portfolio investment

on the other hand is mainly triggered by return on equity and is much more volatile (Taylor & Sarno

1997). Furthermore, John Ahlquist (2006) states that while political stability is an important FDI

determinant, portfolio investment is more sensitive to the government’s economic policy. Moore &

Pentecost (2006) pointed out that different drivers of capital flows can lead to different behaviour of

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the real exchange rate, which supports the hypothesis that different capital flows could indeed have

significantly different effects on the real exchange rate.

An approach to explain REER movements that has been used by for instance Clark &

MacDonald (1998), Athukorala & Rajapatirana (2003), Combes et al (2011) and Bakardzhieva et al

(2010) is the fundamentals approach. This approach estimates the equilibrium REER as a function of a

country’s macroeconomic fundamentals. Combes et al (2011) use the fundamentals approach to

estimate the effect of different capital flows on the REER in 42 developing countries. They found that

portfolio investment has a much more severe impact on the REER than direct investment and that

direct investment in turn had a greater impact than bank loans. They argue that FDI is mostly directed

towards productivity increases and is a relatively stable capital flow, explaining its minimal impact on

the REER. Portfolio investment on the other hand is more volatile and speculative, which they argue is

a trigger of macroeconomic instability. Hence, portfolio investment leads to relatively large REER

fluctuations. Combes et al (2011) use the pooled mean group estimator by Pesaran et al (1999) to

estimate an ARDL model, with the assumption that long run effects of the capital flows and

fundamentals on the REER are homogeneous. Their results provide evidence that this is indeed the

case. A similar approach will be used in this paper to determine the extent to which REER movements

can be explained by different capital flows in the region East Asia and Pacific.

Bakardzhieva et al (2010) studied the impact of different capital flows, aid and remittances on

the REER of 57 countries across six different regions, including South East Asia, using the Generalised

Method of Moments (GMM) estimator. Their results show a significant positive relationship between

net capital flows and the REER in most regions, with the exception of Central and Eastern Europe.

Furthermore, the authors found evidence for a significant positive relationship between FDI and the

REER in Africa. However, no significant impact of FDI on the REER was found for the other regions. This

leads the authors to conclude that FDI does not affect competitiveness of a country. The causal

relationship between portfolio investment and the REER appears to differ per region. It is significant

and positive in South East Asia, Latin America, the Gulf Cooperation Council and Central and Eastern

Europe, but is significant and negative in the Middle East and North Africa while it is not significant at

all in Africa. The argument provided by Bakardzhieva et al (2010) to explain the insignificant impact of

portfolio investment on the REER is that the level of portfolio investment in the region is low. The

negative relationship in the Middle East and North Africa is explained by the relatively young capital

markets in the countries included in the studies. They explain that portfolio investment in the region

mainly comprises the privatisation of public enterprises, which closely resembles FDI.

Athukorala & Rajapatirana (2003) performed a comparative analysis of capital flows in Latin

America and Asia and found that the impact of total capital flows on the real exchange rate (RER) in

Latin America was much larger than in Asia. The method used by the authors is a two stage least

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squares instrumental analysis, using macroeconomic indicators as control variables. They found that

capital flows other than FDI where jointly determined with the RER and used a range of instruments

to correct for this endogeneity problem. Athukorala & Rajapatirana (2003) found that non-FDI capital

flows lead to a RER appreciation, while FDI leads to a RER depreciation. They argue that FDI is mainly

directed towards export oriented production, limiting pressure on prices in the non-traded goods

sector compared to other types of capital flows and thereby leading to a depreciation of the RER.

Capital flows in Asia comprise a larger share of FDI than capital flows in Latin America, which explains

that capital flows in Asia have less impact on the RER than those in Latin America.

Moore & Pentecost (2006) used a vector auto regression (VAR) model to find the determinants

of the real exchange rate appreciation after the liberalization of financial markets in 1997 in India. In

VAR models endogenous variables are specified with respect to their lags, in order to avoid

endogeneity. Moore & Pentecost (2006) focused on the difference between real and nominal shocks

and found that real shocks were the main determinants of fluctuations in the real interest rate. This

refers to the second case mentioned earlier, where the increase in the real exchange rate due to the

nominal currency appreciation is offset by a decrease in the domestic price level. These results show

that when looking at the effect of capital flows on the real exchange rate, the determinants of these

capital flows have a significant effect on the long run real exchange rate changes.

Shen & Wang (2001) distinguish between FDI, portfolio investment and other investment in

their empirical analysis where they look at the impact of central bank policy on the real exchange rate.

Although the focus of their study lies on the role of the central bank and the implications of different

inflation regimes, their results are interesting to look at for the purposes of this paper. Shen & Wang

(2001) use Tsay’s arranged auto regression to study the effect of capital flows on the exchange rate

under different inflation regimes by the central banks in selected Asian economies, looking at actual

inflation. They found significant differences in the effects of FDI, portfolio investment and other

investment within and among countries and under different inflation regimes. However, they do not

elaborate on the causes and relationships of these differences as the aim of their study is to determine

the impact of intervention of the central bank on real exchange rates.

Wiboonchutikala et al (2011) study the effect of net capital flows on several macroeconomic

indicators including the REER in Thailand. In their study they also recognize that different capital flows

might have different effects, so they also run a regression in which they distinguish between FDI,

portfolio investment in equity, portfolio investment in debt investments, long-term loans, and short-

term loans. Like Moore & Pentecost (2006) the authors use a VAR model to deal with endogeneity.

They use the Akaike information criterion to determine the optimal lag length, which is twelve periods

for their model. They study the effect of the different capital flows per gross domestic product (GDP)

on the nominal and the real bilateral exchange rate, the real effective exchange rate, equity prices,

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housing prices, domestic foreign exchange reserves, money supply, and the inflation rate. Each of

these variables is incorporated in the model one at a time in order to estimate the effect of each of

the capital flows on the selected macroeconomic indicators separately. The short run effects of FDI

and portfolio investment in equity on the real exchange rate are similar, both lead to an immediate

appreciation. In the long run however, the real exchange rate stabilizes after FDI inflows while it

fluctuates afters portfolio investment in equity inflows. Portfolio debt inflows trigger a short-run

depreciation after which the currency appreciates with persistence to future periods. An increase in

both long- and short-term loans induces a steady and persistent appreciation of the currency. By using

several dependent variables and entering them in the model one by one, the model of

Wiboonchutikala et al (2011) lacks control variables. The effects estimated by the authors might thus

be unreliable due to omitted variable bias. Singling out one dependent variable and using the others

as control variables might generate a more accurate estimation.

The papers discussed in this section show that the impact of capital flows on the REER differs

per region or country. Studies by Shen & Wang (2001), Combes et al (2011), Bakardzhieva et al (2010)

and Athukorala & Rajapatirana (2003) distinguish capital flows similar to the ones distinguished in this

paper, but provide deviating results and conclusions. For instance, Shen & Wang (2001) and Combes

et al (2011) find a positive relationship between all types of capital flows and the REER.

Wiboonchutikala et al (2011) find that both FDI and portfolio investment lead to an immediate

appreciation of the REER, but that the REER returns to its old level in the case of FDI and portfolio

investment in equity and leads to a long run depreciation in the case of portfolio investment in debt.

Bakardzhieva et al (2010) compare the relationship between capital flows and the REER in six different

regions and find that while a certain capital flow may lead to a REER appreciation in one region, it may

lead to a depreciation in another region. Athukorala & Rajapatirana (2003) compare the impact of

capital flows on the RER in Latin America and Asia and also find the results to differ per region.

Furthermore, they find FDI inflows to lead to a depreciation of the RER, while non-FDI flows lead to an

appreciation. Although the methodology of Bakardzhieva et al (2010) is different from the

methodology used in this paper, the authors study the impact of capital flows on the REER in several

regions including South East Asia. They use the same group of East Asian countries studied in this paper

and use annual data over a similar timespan making it reasonable to expect the results found in this

study to be in line with the results found by Bakardzhieva et al (2010). Total capital flows and portfolio

investment are thus expected to have a positive and significant relationship with the REER, which

implies that net inflows of total capital or portfolio investment lead to an appreciation of the REER. FDI

is expected to have an insignificant impact on the REER. Bakardzhieva et al (2010) define other

investment as debt and find a positive significant impact on the REER. This relationship is also expected

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for other investment in this paper, implying that net inflows of other investment lead to an

appreciation of the REER.

2.2 The Asian crisis

The period taken into account in this study includes the Asian Crisis. As a result of rapid economic

growth and financial liberalization in the early nineties many Asian economies experienced large

capital inflows at the time. The Asian crisis erupted in Thailand when economic growth slowed down

in 1995. Roll-over of loans was largely discontinued and extensive capital outflows were experienced.

This led to the collapse of the peg of the Thai Baht to the US Dollar in 1997 when the Thai government

was forced to devaluate the Baht (Khan et al, 2005). The value of foreign debt increased significantly

as a result of the devaluation which triggered the Thai currency and financial crisis. The crisis spread

to other Asian countries in a similar fashion and led to the Asian crisis. Large capital inflows followed

by large capital outflows have been named as a prime cause for the Asian crisis by for instance

Wiboonchutikala et al (2011), Khan et al (2005), Khan (2004) and Sach & Radelet (1998).

Wiboonchutikala et al (2011) explain that until the early nineties the only capital flows that entered

Thailand were related to direct investment and these inflows increased at a substantial pace in

accordance with an improvement of the terms of trade. As a result of this prosperity the Thai

government decided to allow for other types of investment as well and due to the attractive

investment climate the country experienced large surges of capital inflows. These capital inflow surges

led to a real appreciation of the currency which had a negative effect on the terms of trade leading to

a current account deficit of eight percent. A substantial share of the capital inflows was made up out

of short terms loans and portfolio investment, two types of capital flows that can change of owner

quickly and are thus easily reversible, Wiboonchutikala et al (2011) explain. This is exactly what

happened in 1997 when the large current account deficit was accompanied by increased inflation.

Investors could easily refuse to roll-over loans and direct portfolio investment out of Asia towards

other economies. These volatile types of capital flows are thus accompanied by much greater

macroeconomic risk than foreign direct investment that is generally more difficult to reverse. This

leads to the conclusion that if a government were to impose capital controls it would be important to

distinguish between long term and short term types of investment. A distinction that is also

emphasized by Desai et al (2006), although they mention that this is difficult to implement.

Not all research papers distinguish between different capital flows in determining the causes

of the Asian crisis. Khan et al (2005) explain that the peg of Asian currencies to the US Dollar that was

appreciated at the time, led to an appreciation of these currencies as well that negatively affected the

export levels leading to large current account deficits. The result were over valuated currencies which

led to a collapse of the pegs to the US Dollar. While the large outflows of short term investment did

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play an important role in the Asian crisis, the cause was the peg of several Asian currencies to a single

major currency like the US Dollar Khan et al (2005) explain. In order to prevent a similar situation from

occurring again capital controls would be unnecessary, as long as the exchange rate would be able to

adjust in accordance to the domestic market and not be bound by a single large economy’s behaviour.

As the Asian crisis is assumed to have impacted REER movements significantly, a dummy variable is

included in this research to prevent biased results. The dummy will take a value of one if the time

period falls within the Asian Crisis and zero otherwise. Irregular behaviour of the REER during the crisis

will then be captured by the dummy variable, without distorting the relationship of the REER with the

control- and determining variables.

3. Model specification

3.1 Fundamentals approach

The long run relationship between the REER and the macroeconomic fundamentals is specified as

follows:

𝑅𝐸𝐸𝑅𝑖𝑡 = 𝜃0 + 𝜃1𝑇𝑂𝑇𝑖𝑡 + 𝜃2𝑃𝑅𝑂𝐷𝑖𝑡 + 𝜃3𝑇𝑅𝐴𝐷𝐸𝑖𝑡 + 𝜃4𝐶𝐴𝑃𝐼𝑇𝐴𝐿𝑖𝑡 + 𝜈𝑖𝑡 (1)

𝑖 = 1,2, … , 𝑁 ; 𝑡 = 1,2, … , 𝑇

The different countries are denoted by i and t indicates the time period. 𝑅𝐸𝐸𝑅𝑖𝑡 represents the real

effective exchange rate, 𝑇𝑂𝑇𝑖𝑡 the terms of trade, 𝑃𝑅𝑂𝐷𝑖𝑡 the productivity gap, 𝑇𝑅𝐴𝐷𝐸𝑖𝑡 trade

openness, and 𝐶𝐴𝑃𝐼𝑇𝐴𝐿𝑖𝑡 the aggregate capital flows. The 𝜃s represent the long-run coefficients of

the fundamentals. The derivation of this model will be explained in the methodology section. The

choice to use the terms of trade, the productivity gap and trade openness as control variables has been

made following the example of Combes et al (2011), as the empirical estimation method used in this

paper closely resembles their method. The REER used in this paper is the weighted average of a

country’s consumer price index (CPI) based real exchange rate and is defined as:

𝑅𝐸𝐸𝑅𝑖𝑡 = 𝑁𝐸𝐸𝑅𝑖𝑡 ∗ ∑ (𝐶𝑃𝐼𝑖𝑡

𝐶𝑃𝐼𝑗𝑡)

𝑤𝑗𝑁𝑗=1 (2)

𝑁𝐸𝐸𝑅𝑖𝑡 = ∑(𝑁𝐵𝐸𝑅𝑗𝑡)𝑤𝑗

𝑁

𝑗=1

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𝑅𝐸𝐸𝑅𝑖𝑡 is the real effective exchange rate of country i at time t, 𝑁𝐸𝐸𝑅𝑖𝑡 represents the nominal

effective exchange rate, 𝐶𝑃𝐼𝑖𝑡 and 𝐶𝑃𝐼𝑗𝑡 represent the consumer price index of country i and j

respectively, 𝑤𝑗 is the weight of country j which is based on the share of trade and 𝑁𝐵𝐸𝑅𝑗𝑡 is the

nominal bilateral exchange rate. An increase of the REER index reflects an appreciation of the domestic

currency.

Terms of trade is an indicator of the value of exports as a percentage of the value of imports.

A deterioration of the terms of trade affects the real exchange rate through the substitution- and

income effect (Ostry, 1988). The substitution effect refers to the shift from expensive imported goods

to less expensive non-traded goods which leads to an increase in demand for the domestic currency

and thus an appreciation. The income effect on the other hand reflects that consumers can buy less of

everything as a result of the terms of trade deterioration. This indicates a decrease in demand for the

domestic currency and ceteris paribus this leads to a real depreciation. It is assumed that the income

effect dominates the substitution effect which means that a deterioration of the terms of trade leads

to a real depreciation of the domestic currency (Combes et al, 2011). A positive relationship is expected

to be found between the terms of trade and the REER.

The productivity gap refers to the Harrod-Balassa-Samuelson effect. The effect is based on the

assumption that productivity grows faster in the tradable sectors that in non-tradable sectors. The

productivity increase in the tradable sectors results in higher wages in these sectors while relative

prices do not change. Workers in non-tradable sectors will demand higher wages as well, but as these

wage increases are not accompanied by a proportional productivity increase relative prices of goods

in the non-tradable sector increase. The CPI increases relative to other countries and this is reflected

by an appreciation of the REER (Lothian & Taylor, 2008) and thus, the productivity gap is expected to

be positively related with the REER.

Trade openness reflects changes in a country’s trade policy. An increase of trade liberalization

leads to an increase in the total amount of trade and is especially associated with an increased amount

of imports at a lower relative price due to for instance abolition of tariffs. The lower price level is

reflected in the consumer price index, which leads to a depreciation of the REER. This implies a negative

relationship between trade openness and the REER. Trade openness is measured as the ratio of exports

and imports to GDP.

Aggregate capital flows reflect the financial account of the balance of payments, excluding

reserves. An increase of net capital flows generally results in an appreciation of the REER. Capital

inflows are usually associated with an appreciation of the REER, as a result of increased demand for

the currency (Calvo et al, 1993; Shen & Wang, 2001; Wiboonchutikala et al, 2011). A positive

relationship between capital flows and the REER is thus expected to be found. The capital flows used

in this research are in accordance with the financial account of the balance of payments. The capital

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flows are recorded according to the sixth edition of the International Monetary Fund’s (IMF) balance

of payments and international investment position manual (BPM6). All transactions between domestic

entities and entities abroad involving financial assets are recorded on the financial account. The

transactions are divided into three categories; foreign direct investment, portfolio investment and

other investment. Reserves also make up part of the financial account, but for the purpose of this

research they are not included in the model.

3.2 Composition of capital flows

The balance of payments is a method to record all international monetary transactions and

theoretically it should be equal to zero. The balance of payments is divided into three categories; the

current account, capital account and financial account. The current account records the inflows and

outflows of goods and services, the capital account records international capital transactions of non-

financial assets and the financial account records transactions of financial assets and liabilities that

take place between domestic residents and foreigners. The capital flows distinguished in this paper

are FDI, portfolio investment and other investment. As the data used in this paper has been extracted

from the Balance of Payments Statistics database of the IMF, the definitions of the different capital

flows will be in accordance with definitions used in BPM6. The sum of these capital flows comprises

the financial account of the balance of payments, excluding reserves.

3.2.1 Foreign direct investment

Foreign direct investment is defined as investment in which a foreign firm establishes a subsidiary in a

foreign country or acquires a significant interest of a foreign firm with control over management. This

control can be established via immediate direct investment where at least ten percent of the voting

power is obtained or via indirect direct investment where voting power is obtained in an enterprise

that has voting power in another enterprise.

3.2.2 Portfolio investment

Portfolio investment is defined as investment in debt or equity securities across countries, other than

the transactions included in foreign direct investment or reserve assets. Securities are assets or

liabilities that are negotiable and change owner relatively easy. Acquisition of stocks of an enterprise

worth less than ten percent of the total company value would for instance be reported as portfolio

investment. Equity in another form than securities however, is not included in portfolio investment

but is recorded as foreign direct investment or other investment.

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3.2.3 Other investment

Other investment comprises of financial positions or transactions that are not included in foreign direct

investment, portfolio investment and financial derivatives and employee stock options. Financial

derivatives and employee stock options are recognised as a separate category of the financial account

by the IMF, but due to the low volume of these capital flows they have been added to other investment

in this paper. The residual transactions are categorised into seven categories: other equity in other

forms than securities; currency and deposits; loans; nonlife insurance technical reserves, life insurance

and annuities entitlements, pension entitlements and provisions for calls under standardized

guarantees; trade credit and advances; other accounts receivable or payable and special drawing rights

(SDR) allocations.

4. Methodology

Three approaches are commonly used to estimate dynamic panel data models (Combes et al, 2011).

The first is the mean group (MG) estimator approach that averages the coefficients of parameters

based on the individual coefficients of each cross-section unit. Pesaran & Smith (1995) showed that

this method gives consistent estimates, however, homogeneity between the different units is not

considered. The second approach assumes that variables in different cross-section units are

homogeneous and estimates the coefficients by pooling the data. Some models that use this approach

are the dynamic fixed effects model and the random effects model (Hill et al, 2008, pp. 391-406).

Pesaran et al (1999) introduced a third intermediate approach based on the mean group estimator and

the dynamic fixed effects approach called the pooled mean group estimation method for dynamic

heterogeneous models which allows variables to be heterogeneous in the short-run, while it assumes

long-term coefficients to be identical for each cross-section unit. This is the model that has been used

by Combes et al (2011) to determine the effect of different capital flows on the REER. For all three

estimation methods the model will take the form of an autoregressive distributed lag (ARDL) (p,q)

model in order to deal with endogeneity and the possibility of stationary and non-stationary variables

in a single equation.

This section describes the model as it has been developed by Combes et al (2011). Movements

of the REER and macroeconomic fundamentals are assumed to differ across countries in the short-run,

while they converge in the long-run. The pooled mean group estimation allows for the REER and REER

determinants’ behaviour to be heterogeneous across countries in the short-run, while the coefficients

are assumed to converge to a homogeneous equilibrium in the long-run. Whether this is indeed the

case can be verified by performing a Hausman test which compares the results of the pooled mean

group estimator with results of the mean group estimator. For the dynamic fixed effects estimator as

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well as the pooled mean group estimator and the mean group estimator the model takes the form of

an ARDL (p,q) model:

𝛥𝑦𝑖𝑡 = 𝛼𝑖𝑦𝑖,𝑡−1 + 𝛽𝑖𝑥𝑖,𝑡−1 + ∑ 𝜆𝑖𝑗𝛥𝑦𝑖,𝑡−𝑗

𝑝−1

𝑗=1

+ ∑ 𝛿𝑖𝑗𝛥𝑥𝑖,𝑡−𝑗 + 𝜇𝑖 + 휀𝑖𝑡

𝑞−1

𝑗=0

(3)

𝑦𝑖𝑡 is the dependent variable, 𝑥𝑖𝑡 is the matrix of regressors, 𝜇𝑖 represents the fixed effects, 𝛼𝑖 is the

coefficient on the lagged dependent variable, 𝛽𝑖 is the vector of coefficients of the lagged regressors,

𝜆𝑖𝑗 the vector of coefficients of the lagged first differences of the dependent variable, 𝛿𝑖𝑗 the vector of

coefficients of the lagged first differences of the regressors. 𝑃 and 𝑞 represent the number of lags of

the dependent variable and the explanatory variables respectively. The error term, 휀𝑖𝑡, is assumed to

be normally and independently distributed across i and t with a mean equal to zero, variances greater

than zero and finite fourth moments. Under the assumption that 𝛼𝑖 is smaller than zero, indicating

that the model is stable as shocks die out over time, a long term relationship between 𝑦𝑖𝑡 and 𝑥𝑖𝑡 exist

that looks like:

𝑦𝑖𝑡 = 𝜃𝑖𝑥𝑖𝑡 + 𝜂𝑖𝑡 (4)

𝜃𝑖 = −𝛽𝑖

𝛼𝑖

𝜃𝑖 represents the long-run relationship between the regressors and the dependent variable. The error

term, 𝜂𝑖𝑡 is assumed to be stationary. Based on equation (4), equation (1) can be rewritten for the

long-run as:

𝛥𝑦𝑖𝑡 = 𝛼𝑖𝜂𝑖,𝑡−1 + ∑ 𝜆𝑖𝑗𝛥𝑦𝑖,𝑡−𝑗 + ∑ 𝛿𝑖𝑗𝛥𝑥𝑖,𝑡−𝑗 + 𝜇𝑖 + 휀𝑖𝑡

𝑞−1

𝑗=0

𝑝−1

𝑗=1

(5)

𝜂𝑖,𝑡−1 has been estimated using equation (4), and 𝛼𝑖 represents the speed at which the dependent

variable converges to its long-run equilibrium. The three estimators use the maximum likelihood

technique to derive the parameters. The parameters are defined as 𝛼�̂�, 𝛽�̂�, 𝜆𝑖�̂�, 𝛿𝑖�̂�, and 𝜃�̂�, the

estimators are defined as:

�̂� =∑ �̂�𝑖

𝑁𝑖=1

𝑁, �̂� =

∑ �̂�𝑖𝑁𝑖=1

𝑁, 𝜃 =

∑ 𝜃𝑖𝑁𝑖=1

𝑁 (6)

�̂�𝑗 =∑ �̂�𝑖𝑗

𝑁𝑖=1

𝑁, 𝑗 = 1, … , 𝑝 − 1, 𝛿𝑗 =

∑ 𝛿𝑖𝑗𝑁𝑖=1

𝑁, 𝑗 = 0, … , 𝑞 − 1 (7)

This system of equations is used to establish the long-run relationship between the macroeconomic

fundamentals and the REER given in equation (1). The long run coefficients are derived by taking the

14

average of the coefficients for each of the cross-section units. In case of the fixed effects estimator

and the pooled mean group estimator, homogeneity among countries occurs for all- or the long term

parameters respectively and thus the weighted average of these parameters is equal to each of the

parameters itself (∑ �̂�𝑖

𝑁𝑖=1

𝑁= �̂�𝑖 = �̂�).

Before constructing the model, the variables are tested on stationarity and cointegration and

the number of lags to be included is determined. A variable is stationary if it has a constant mean and

variance over time and if the covariance between two values depends only on the time between the

observation moments, but not on the moment of observation (Hill et al, 2008). If any of these

conditions is violated a variable is said to have a unit root. Multiple unit root tests should be performed

for the results to be reliable. Many tests can be used to determine whether a variable contains a unit

root, the tests used in this paper are the Im et al (2003) and Levin et al (2002) tests for a model with

constant, but without trend. The following test is performed:

𝛥𝑦𝑡 = 𝛼 + 𝛾𝑦𝑡−1 + 𝜈𝑡 (8)

𝐻0: 𝛾 = 0

𝐻1: 𝛾 < 0

To test the hypothesis, equation (8) is estimated by ordinary least squares (OLS) and the τ-statistic for

the hypothesis that 𝛾 = 0 is analysed. The τ-statistic is the t-statistic with an adjusted distribution to

take into account changes in the variance is the null hypothesis is true and the variable is

nonstationary.

Stationary variables and nonstationary variables that are integrated of order 1, namely I(1),

can both be used in the ARDL model, but have to be treated differently. A variable is I(1) if its level

values are nonstationary, but its first differences are stationary. If nonstationary variables are treated

as stationary in a regression the estimated coefficients of unrelated variables could appear to be

significant which is called a spurious regression. An exception to this problem are nonstationary

variables that are cointegrated which implies that the variables share a similar stochastic trend.

Cointegration can be tested by testing the least square residuals (�̂�𝑡 = 𝑦𝑡 − 𝑏1 − 𝑏2𝑥𝑡) for a unit root.

An endogeneity problem is expected to occur with FDI, portfolio investment and other capital

flows as determining variables of the REER. These capital flows do not only affect the real exchange

rate but they are also affected by changes in the real exchange rate causing reverse causality which

would lead to biased results if not dealt with properly. The ARDL model corrects for endogeneity,

including reverse causality, by taking lags of the different variables. When determining the optimal lag

length for a model a trade-off is made between adding more information to the model and losing

15

efficiency. The lag length in most existing literature where the REER is estimated lies between zero and

two (Combes et al 2011, Clark & MacDonald 1998). The lag length to be used in this paper will therefore

be determined using the Akaike Information Criterion with a maximum lag length of two.

Next a separate ARDL(p, q, q, q, q) for each country must be run in order to establish whether

a long-run relationship between the REER and the macroeconomic fundamentals, including the capital

flows, exists. For this to be the case it is required that 𝛼𝑖 ≠ 0. Next, the estimation can be performed.

Under the assumption that the variables are I(1) and cointegrated the ARDL(p, q, q, q, q) model looks

like:

𝑅𝐸𝐸𝑅𝑖𝑡 = 𝜇𝑖𝑡 + 𝛿10𝑇𝑂𝑇𝑖𝑡 + ∑ 𝛿11

𝑞

𝑗=1

𝑇𝑂𝑇𝑖,𝑡−𝑗 + 𝛿20𝑃𝑅𝑂𝐷𝑖𝑡 + ∑ 𝛿21

𝑞

𝑗=1

𝑃𝑅𝑂𝐷𝑖𝑡,𝑡−𝑗 (9)

+ 𝛿30𝑇𝑅𝐴𝐷𝐸𝑖𝑡 + ∑ 𝛿31

𝑞

𝑗=1

𝑇𝑅𝐴𝐷𝐸𝑖,𝑡−𝑗 + 𝛿40𝐶𝐴𝑃𝐼𝑇𝐴𝐿𝑖𝑡 + ∑ 𝛿41

𝑞

𝑗=1

𝐶𝐴𝑃𝐼𝑇𝐴𝐿𝑖,𝑡−𝑗 + 휀𝑖𝑡

And the equilibrium error correction representation of an ARDL (p, q, q, q, q) model for the relationship

between the REER and the macroeconomic fundamentals is:

𝛥𝑅𝐸𝐸𝑅𝑖𝑡 = 𝛼𝑖[𝑅𝐸𝐸𝑅𝑖,𝑡−1 − 𝜃0𝑖 − 𝜃1𝑖𝑇𝑂𝑇𝑖,𝑡−1 − 𝜃2𝑖𝑃𝑅𝑂𝐷𝑖,𝑡−1 − 𝜃3𝑖𝑇𝑅𝐴𝐷𝐸𝑖,𝑡−1 (10)

− 𝜃4𝑖𝐶𝐴𝑃𝐼𝑇𝐴𝐿𝑖,𝑡−1] − ∑ 𝛿0,𝑗−1,𝑖

𝑝

𝑗=2

𝛥𝑗𝑅𝐸𝐸𝑅𝑖𝑡 − ∑ 𝛿1𝑗𝑖

𝑞

𝑗=1

𝛥𝑗𝑇𝑂𝑇𝑖𝑡

− ∑ 𝛿2𝑗𝑖

𝑞

𝑗=1

𝛥𝑗𝑃𝑅𝑂𝐷𝑖𝑡 − ∑ 𝛿3𝑗𝑖

𝑞

𝑗=1

𝛥𝑗𝑇𝑅𝐴𝐷𝐸𝑖𝑡 − ∑ 𝛿4𝑗𝑖

𝑞

𝑗=1

𝛥𝑗𝐶𝐴𝑃𝐼𝑇𝐴𝐿𝑖𝑡 + 휀𝑖𝑡

With

𝜃0𝑖 =𝜇𝑖

1 − 𝜆𝑖, 𝜃1𝑖 =

∑ 𝛿2𝑗𝑖𝑞𝑗=0

1 − 𝜆𝑖, 𝜃2𝑖 =

∑ 𝛿3𝑗𝑖𝑞𝑗=0

1 − 𝜆𝑖, 𝜃3𝑖 =

∑ 𝛿4𝑗𝑖𝑞𝑗=0

1 − 𝜆𝑖, 𝜃4𝑖 =

∑ 𝛿5𝑗𝑖𝑞𝑗=0

1 − 𝜆𝑖,

𝛼𝑖 = −(1 − 𝜆𝑖)

Where the 𝜃s and 𝛿s represent the long-run coefficients and 𝛼𝑖 the short-run coefficient. The error

correction component in this equation is 𝑅𝐸𝐸𝑅𝑖,𝑡−1. 𝐶𝐴𝑃𝐼𝑇𝐴𝐿𝑖𝑡 will be disaggregated into the before

mentioned three categories in order to determine the long-run relationship between the different

capital flows and the REER.

16

5. Data

Annual data from 1983 to 2014 is used for seven countries in the region East Asia and Pacific: China,

Indonesia, the Republic of Korea, Malaysia, the Philippines, Singapore and Thailand. This timespan and

the countries have been selected based on data availability of different capital flows.

Data for the REER has been extracted from a Bruegel (2016) database. Data for the terms of

trade, trade openness and the productivity gap have been extracted from the World Development

Indicator Databank of the World Bank Group. The terms of trade have been calculated as the

percentage of the export value index to the import value index. Trade openness has been defined as

the sum of imports and exports as a share of GDP. The productivity gap is measured as domestic GDP

per capita to the weighted average GDP per capita of the country’s ten largest trade partners. Data for

the trade partners has been extracted from the Direction of Trade Statistics database of the IMF. The

weights per country are included in Table 2 in the appendix. Data for the capital flows has been

extracted from the Balance of Payments Statistics database of the IMF. In this research capital flows

are weighted against GDP in order to make the flows in each country comparable to flows in other

countries.

The data of the REER, terms of trade, trade openness and productivity feature kurtosis

significantly different from the normal distribution kurtosis of three, as is shown in Table 3 and Figures

1 to 4 in the appendix. To impose normality the data is transformed by taking natural logarithms which

will be used in the regression. The development of the log of these variables can be observed in Figures

5 to 8 in the appendix, which shows the average of each variable for the countries included in this

study. Development of the average of the different capital flows is depicted in Figures 9 to 11 and

development of average aggregate capital flows is shown in Figure 12. If the figures are positive, the

region experiences net inflows and if the figures are negative the region experiences net outflows.

Comparing development of the REER and aggregate capital flows shows that the REER features

depreciation in periods where capital inflows are negative while it appreciates when capital inflows

are positive. This is consistent with existing economic models (Dornbusch 1976, Frenkel and Rodrigues

1982). FDI and portfolio investment follow almost identical paths, which is not what would typically

be expected. Portfolio investment is much more volatile than FDI and development of these flows

would therefore be expected to feature more fluctuations than FDI flows.

6. Results

First the Im et al (2003) and Levin et al (2002) unit root tests are applied to the variables in order to

verify that none of the variables are integrated of order 2 as this would invalidate the model. The

results are depicted in Table 4 in the appendix and show that indeed all variables are either stationary

or integrated of order 1. This confirms that an ARDL model is the appropriate model to use as this type

17

of econometric model features the ability to regress stationary and non-stationary variables in the

same regression. A prerequisite is however, that the non-stationary variables are cointegrated.

Running a regression with non-stationary variables that are not cointegrated could lead to a spurious

regression where a significant causal relationship is estimated between variables that are in fact

unrelated or vice versa. The four cointegration tests for panel data by Westerlund (2007) are used to

determine whether the non-stationary variables the REER, terms of trade and productivity gap are

cointegrated. The results of these tests are included in Table 5 of the appendix. Ga and Gt test the null

hypothesis of no cointegration for each individual cross-section unit. A rejection of the null suggests

that the variables are cointegrated for at least one of the cross-section units. The Pa and Pt tests test

whether the variables for the panel as a whole are cointegrated. At a five percent level of significance

the Gt and Pt tests reject the null hypothesis of no cointegration, while the Ga and Pa tests fail to

reject. The evidence for cointegration is thus not very strong, however, following the example of

Persyn & Westerlund (2008) and Fedeli (2012) the rejection of the null hypothesis by two of the four

tests is interpreted as evidence for the existence of cointegration.

The optimal number of lags to use is estimated using the Akaike Information Criterion (AIC)

with a maximum of two lags. The results are shown in Table 6 of the appendix. Figure 5 shows the

development of the REER with a clear structural break from 1997 when the peg of the Thai Baht to the

US Dollar collapsed to 2000. In order to capture the effect of the Asian crisis on the REER, the

observation time span is divided into three periods: pre-crisis, crisis and post-crisis. The reasoning

behind differentiating between pre- and post-crisis years is that before the crisis erupted the Asian

currencies were pegged to the US Dollar, while this peg collapsed at the beginning of the crisis.

Behaviour of the REER can therefore be expected to be different after the crisis compared to the pre-

crisis years. Two dummies are included for the pre-crisis and crisis periods, the third is omitted to avoid

multicollinearity. The pre-crisis dummy takes a value of one from 1982 to 1996 and zero otherwise,

the crisis dummy takes a value of one from 1997 to 1999 and zero otherwise. In addition to these

constant dummies, interactive dummies can be included if there are reasons to expect that one or

more variable(s) will have affected the REER differently depending on the period. For instance, one of

the channels through which capital flows affect an economy is via policy makers who may choose to

mitigate fluctuations of the REER by adjusting monetary policy (Calvo et al, 1993). If policy makers

would have altered their responses to capital flows after the crisis indicating that a capital flow of one

pre-crisis would have a different effect on the REER than a capital flow of one post-crisis, ceteris

paribus, this would be a reason to include interactive dummies in the regression. However,

Wiboonchutikala et al (2011) explain that Asian policy makers have adjusted capital controls after the

crisis which will have an effect on the volume of capital flows, but not on the way that these flows

affect the REER. As the difference in exchange rate policy is captured by the pre- during- and post-crisis

18

dummies and there is no reason to expect such changes in the relationship between the REER and the

other variables, no interactive dummies are used. This gives the ARDL (2,2,1,2,1,1,1) UECM model:

𝛥𝑅𝐸𝐸𝑅𝑖𝑡 = 𝛼𝑖(𝑅𝐸𝐸𝑅𝑖,𝑡−1 − 𝜃0𝑖 − 𝜃1𝑖𝑇𝑂𝑇𝑖,𝑡−1 − 𝜃2𝑖𝑇𝑅𝐴𝐷𝐸𝑖,𝑡−1 − 𝜃3𝑖𝑃𝑅𝑂𝐷𝑖,𝑡−1 (11)

− 𝜃4𝑖𝐹𝐷𝐼𝑖,𝑡−1 − 𝜃5𝑖𝑃𝐼𝑖,𝑡−1 − 𝜃6𝑖𝑂𝐼𝑖,𝑡−1) − 𝛿01𝑖𝛥2𝑅𝐸𝐸𝑅𝑖𝑡 − 𝛿11𝑖𝛥𝑇𝑂𝑇𝑖𝑡

− 𝛿12𝑖𝛥2𝑇𝑂𝑇𝑖𝑡 − 𝛿21𝑖𝛥𝑇𝑅𝐴𝐷𝐸𝑖𝑡 − 𝛿31𝑖𝛥𝑃𝑅𝑂𝐷𝑖𝑡 − 𝛿32𝑖𝛥2𝑃𝑅𝑂𝐷𝑖𝑡 − 𝛿41𝑖𝛥𝐹𝐷𝐼𝑖𝑡

− 𝛿51𝑖𝛥𝑃𝐼𝑖𝑡 − 𝛿61𝑖𝛥𝑂𝐼𝑖𝑡 + 𝛽1𝑝𝑟𝑒 + 𝛽2𝑐𝑟𝑖𝑠𝑖𝑠 + 휀𝑖𝑡

Estimating this model with the pooled mean group estimator, the mean group estimator and the

dynamic fixed effects approach provides the results shown in Table 7 in the appendix. The first column

of each estimator shows the results of the regression where total capital flows are recognised, while

the regression in the second column distinguishes between the different capital flows. A Hausman test

is used to determine which of the three approaches is the consistent one to use. The mean group

estimator is assumed to be consistent in any case, so the results of the other two estimators are

compared to the results of the mean group estimator. If the differences are significant, the mean group

estimator should be used. If not, the pooled mean group estimator or dynamic fixed effects estimators

are consistent. Contrary to Combes et al (2011), for both estimators the null hypothesis that the

differences are insignificant is rejected at a five percent level of significance indicating that the mean

group estimator is the best estimator to use for this regression. Both the long- and short-term effects

are thus heterogeneous across the countries included in this sample. The adjustment speed is negative

indicating that there is no omitted variable bias (Combes et al, 2011). The results of the mean group

estimation are depicted in Table 1. The results indicate that total capital flows, portfolio investment

and other investment have a positive impact on the REER in the short run, which makes sense. Net

inflows of these types of investment trigger an immediate appreciation of the REER. FDI appears to be

insignificant when looking at the impact on the REER in the short run. None of the capital flows appears

to be able to explain REER movements in the long run. The terms of trade are insignificant in the short-

and long run, however, the second lag appears to have a significant negative impact on the REER. Trade

openness has a negative significant impact on the REER at a one percent level of significance in the

short run, while this relationship is insignificant in the long run. The productivity gap has a positive

significant effect on the REER in the short run, while the lag of the productivity gap is also significant

but has a negative impact on the REER. In the long run the productivity gap affects the REER

significantly negative. The Asian crisis has a significant negative impact on the REER in the regression

where the different capital flows are distinguished, but appears to be insignificant if only total capital

flows are taken into account. The difference between pre- and post-crisis estimation results appears

to be insignificant.

19

Table 1: Composition of capital flows and the REER

1 2 Long run

Terms of trade -6.208 (5.594)

-0.274 (1.017)

Trade openness -0.780 (1.047)

-0.228 (0.441)

Productivity gap -1.194 (2.095)

-1.105* (0.641)

Total capital 7.570

(11.187)

FDI 0.923

(6.757)

Portfolio investment -2.192

(2.561)

Other investment -1.945

(2.549)

Short run

Ec -0.031 (0.085)

-0.025 (0.066)

D2 REER 0.402*** (0.059)

0.370*** (0.052)

D terms of trade 0.048

(0.056) -0.054 (0.106)

D2 terms of trade -0.075***

(0.027) -0.034 (0.043)

D trade openness -0.179***

(0.042) -0.186***

(0.063)

D productivity gap 0.544*** (0.093)

0.500*** (0.088)

D2 productivity gap -0.254***

(0.075) -0.212***

(0.074)

D total capital 0.330*** (0.118)

D FDI -0.147

(0.733)

D portfolio investment 0.394*

(0.221)

D other investment 0.284*

(0.150)

pre -0.003 (0.022)

-0.016 (0.021

crisis -0.009 (0.016)

-0.023** (0.010)

Constant 0.227

(0.643) 0.035

(0.637) Observations

217

217

Standard errors in parentheses *p<0.10, **p<0.05, ***p<0.01

20

Table 8 in the appendix shows the results of the mean group estimation with results for each

country where in the first column total capital flows are taken into account while the different capital

flows are recognised in the second column. In the long run significant coefficients are neither found

for the different capital flows nor for the total capital flows indicating long run movements of the REER

cannot be explained by capital flows. In the short run the similar results are found for most of the

countries. Only movements of the REER in the Republic of Korea can be partly explained by portfolio

investment and the REERs in Malaysia and Thailand by FDI flows. Other investment appears to be

insignificant for all countries. In addition to these results, it also appears that the Asian crisis has had

no significant effect on the REER in any of the countries included in the study. The terms of trade

cannot explain behaviour of the REER in any of the seven countries, trade openness only in China and

Thailand and the productivity gap only in Thailand.

The results imply that the REER in most countries included in this study moves independently

of capital flows and of the macroeconomic fundamentals used in this model. Implications of these

results are discussed in the next section.

7. Discussion

The results per country in Table 8 show that FDI has a positive and significant impact on the REER in

Malaysia and Thailand in the short run, while no significant relationship is found in the long run. This

implies that FDI inflows lead to an immediate appreciation of the domestic currency, but that this

appreciation is deferred in the long run. Potential explanations are that the appreciation of the local

currency reduces the country’s international competitiveness which could trigger a decrease of exports

(Moore & Pentecost, 2006) or that the capital inflows stimulate productive growth which lead to

capital outflows by an increase of the level of capital goods imports (Bakardzhieva et al, 2010), both

triggering the REER to depreciate back to its old level. This is consistent with the expectations formed

at the beginning of this research. The other five countries included in the study and the region as a

whole feature no causal relationship between FDI and the REER in either the short- or the long run.

The absence of an immediate effect could be a result of FDI being directed towards productivity

enhancement in the tradable goods sector. This would require import of capital goods, offsetting the

appreciating effect of increased exports.

A positive and significant causal relationship of portfolio investment on the REER is found in

the region East Asia and Pacific as a whole and in the Republic of Korea in the short run, but contrary

to what was expected this relationship is insignificant in the long run. For none of the other six

countries a significant causal relationship between portfolio investment and the REER is found. As

these countries face a relatively high level of centralisation, it could be that portfolio investment is a

result of financial liberalisation and is mainly driven by the privatisation of public companies. This type

21

of portfolio investment features similar behaviour as FDI (Bakardzhieva et al, 2010), explaining the

absence of impact on the REER. The same arguments that were used to explain the diminishing of an

initial currency appreciation as a result of FDI flows can be applied to portfolio investment as well.

The final type of capital flow recognised in this paper, other investment, appears to be

insignificant both in the short- and the long run for all the individual countries studied in this paper. In

the region East Asia and Pacific other investment has a positive and significant effect on the REER in

the short run, but no significant impact in the long run. Similarly to portfolio investment, this is contrary

to what was expected based on the results from Bakardzhieva et al (2010). This category comprises all

capital flows not included in FDI and portfolio investment. If the major share of other investment would

comprise financial transactions directed towards productivity enhancement, other investment shows

similar behaviour as FDI. As was the case with portfolio investment, this can explain the insignificant

relationship between other investment and the REER.

Total capital flows have a positive and significant impact on the REER in the region East Asia

and Pacific in the short run. A one unit increase of total capital flows leads to a 0.33 unit increase of

the REER. In the long run the impact of total capital flows appears to be insignificant. This is not what

was expected, but as the long run impact of all the different capital flows on the REER is insignificant

as well it makes sense that this also holds for total capital flows.

The Asian crisis has a significant negative impact on the REER in the regression where the

distinction between the different capital flows is made, but is insignificant in the regression featuring

total capital flows. The negative significant estimate implies that the crisis led to a depreciation of the

REER which is a confirmation of what was observed in that period. The absence of a significant

relationship between the crisis and the REER in the regression that only takes into account total capital

flows suggests that the impact of the crisis is reflected in the net capital flows and control variables.

In the short run the terms of trade and trade openness both have a negative and significant

impact on the REER, while the long run effects are insignificant. For both this implies that the

substitution- and income effect neutralise one another in the long run. The first lag of the productivity

gap is significant and positive in the short run and the second lag is significant and negative in the short

run. The long run impact of the productivity gap is significant and negative, indicating that an increase

in the productivity gap initially leads to an appreciation of the REER while it leads to a depreciation in

the long run.

Some of the results found in this paper are surprising as studies by for instance Combes et al

(2011), Wiboonchutikala et al (2011), Bakardzhieva et al (2010), Shen & Wang (2015) and Khan (2004)

show that capital flows do have a significant impact on the REER in the long run. Combes et al (2011)

take into account the exchange rate regime in their regression, while this variable has not been taken

into account in this paper. This could explain the differences in results. The absence of a long run causal

22

relationship between capital flows and the REER found in this paper for the individual countries and

for the region as a whole could also be related to the Asian region in specific. Athukorala & Rajapatirana

(2003) studied the impact of FDI and other capital flows in Latin America and East Asia and found that

the impact of other capital flows on the REER was much smaller in East Asia than in Latin America.

Bakardzhieva et al (2010) study the impact of capital flows in, among other regions, South and East

Asia and do not find a causal relationship between FDI and the REER. These results combined with the

results found in this paper indicate that capital flows in the Asian region might have different

implications in this region than in the rest of the world. This would be interesting to study in further

research.

The results of this research should be interpreted with caution. A limitation of this research is

for instance the relatively small dataset. Only seven countries have been taken into account, while the

region East Asia and Pacific comprises of many other economies for which insufficient data was

available for the purpose of this paper. The same holds for the relatively short time span of the data

that was chosen based on data availability. This time span has been reduced further by the use of an

ARDL model in which degrees of freedom are lost as a result of the use of lags. The ARDL model has

been selected for its ability to deal with endogeneity and the use of I(1) and I(0) variables

simultaneously. However, the loss of degrees of freedom for a dataset that was already small is a large

disadvantage and should be considered when interpreting the results.

Another limitation of this paper lies in the transformation of the data. The natural logarithms

are taken of the terms of trade, trade openness and productivity gap, but not of the different capital

flows. The reason to take the natural logarithms of the control variables is clear; the distribution of all

variables features skewness and/or kurtosis. The capital flows however are also featured with kurtosis,

but this data is not transformed as some values of the capital flows are negative the natural logarithm

cannot be taken of the original values. A possible solution would be to add a constant to all values

equal to the highest negative value in order to make all values positive. However, this would alter the

data significantly and could make the regression unreliable so it has been decided to follow the

example of Wiboonchutikala et al (2011), Combes et al (2011) and Bakardzhieva et al (2010) not to

transform the data of the capital flows even though the distribution is similar to the distribution of the

other variables.

8. Conclusion

The purpose of this paper has been to study the effect of different capital flows on the REER in the

region East Asia and Pacific. Capital control measures are often based on aggregate capital flows, while

the composition of these flows might have a significant impact on how the capital flows affect the

23

REER (Combes et al, 2011; Bakardzhieva et al, 2010). Several studies (Wiboonchutikala et al, 2011;

Shen and Wang, 2001; Ahlquist, 2006) have been performed on the effect of capital flows on the REER

and a few have differentiated between different capital flows. The studies have had different research

questions and have focused on different geographical regions, but they have in common that almost

all have found a significant relationship between capital flows and the REER. The exception being

Bakardzhieva et al (2010) who found no significant relationship between FDI and the REER.

The final dataset used for empirical analysis in this paper comprises of seven countries from

the region East Asia & Pacific. An ARDL model is used to regress the REER on several macroeconomic

fundamentals and the different capital flows following the example of Combes et al (2011). FDI appears

to be insignificant when it comes to the explanation of REER movements in the short- as well as the

long run. This implies that policy makers do not have to impose capital controls directed towards FDI,

as this type of capital flow leads to sustainable growth and does not harm competitiveness of the

country. Total capital flows, portfolio investment and other investment do have a significant positive

effect on the REER in the short run, but appear to have no significant impact in the long run either.

One could therefore argue that capital controls are unnecessary as the loss of competitiveness affects

the country only in the short run while this effect dies out in the long run. However, evidence suggests

that the Asian crisis has been caused by excessive capital flows (Wiboonchutikala et al, 2011; Khan et

al, 2005; Khan, 2004; Sach & Radelet, 1998) so this argument is not very strong. Short run effects could

have long run consequences if the shock is large enough, even if the direct impact appears to be

insignificant in the long run. The Asian crisis erupted as a result of sudden large capital outflows, which

are more likely to occur in the form of portfolio investment than FDI. Capital restrictions are therefore

not redundant, but should be directed towards the more volatile capital flows. FDI should be

encouraged as it leads to productivity enhancement which leads to sustainable growth.

For further research it would be interesting to study the causes for the absence of a long run

impact of capital flows on the REER in the region East Asia and Pacific, as this relationship does appear

to be present in other regions (Combes et al, 2011; Bakardzhieva et al, 2010; Athukorala &

Rajapatirana, 2003). This paper focused on the impact of the different capital flows on the REER, but

the explanation for the results has been based on existing literature and has not been studied for the

region in specific. An interesting variable to take into account is the exchange rate regime of each of

the Asian countries included in this study, as Combes et al (2011) find a significant negative relationship

between flexible exchange rates and REER appreciation. Another study that would be interesting

would be to look at portfolio investment in more detail. In some countries portfolio investment

appears to be highly volatile with severe impact on the REER, while in other countries portfolio

investment closely resembles FDI. This suggests that some types of portfolio investment contain a

larger trade-off between economic growth and stability than others. Policy makers would be able to

24

impose capital controls more effectively if more knowledge would be available on the nature of

portfolio investment.

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27

Appendix

Table 2: Trade partners and weights

China Indonesia Korea, Rep Japan 0,22 Japan 0,21 China, P.R.: Mainland 0,31 Korea, Republic of 0,26 Singapore 0,16 United States 0,19 United States 0,14 China, P.R.: Mainland 0,16 Japan 0,18 Germany 0,09 United States 0,12 Saudi Arabia 0,06 Australia 0,08 Korea, Republic of 0,09 China, P.R.: Hong Kong 0,05 Thailand 0,04 Malaysia 0,08 Germany 0,05 Malaysia 0,05 Thailand 0,06 Australia 0,05 Saudi Arabia 0,05 India 0,06 Singapore 0,04 Russian Federation 0,04 Australia 0,04 Indonesia 0,04 Brazil 0,03 Germany 0,03 United Arab Emirates 0,04

Malaysia Philippines Singapore Singapore 0,19 United States 0,22 Malaysia 0,19 United States 0,17 Japan 0,21 China, P.R.: Mainland 0,15 Japan 0,16 China, P.R.: Mainland 0,12 United States 0,15 China, P.R.: Mainland 0,16 Singapore 0,11 Indonesia 0,11 Thailand 0,07 China, P.R.: Hong Kong 0,08 Japan 0,10 Korea, Republic of 0,06 Korea, Republic of 0,07 China, P.R.: Hong Kong 0,09 China, P.R.: Hong Kong 0,05 Thailand 0,06 Korea, Republic of 0,07 Indonesia 0,05 Malaysia 0,05 Thailand 0,05 Germany 0,04 Netherlands 0,05 India 0,04 Australia 0,04 Germany 0,04 Australia 0,04

Thailand Japan 0,25 China, P.R.: Mainland 0,18 United States 0,15 Malaysia 0,09 Singapore 0,08 Indonesia 0,05 United Arab Emirates 0,05 China, P.R.: Hong Kong 0,05 Australia 0,05 Korea, Republic of 0,05

28

Table 3: Summary statistics: real effective exchange rate, terms of trade, trade openness and the

productivity gap

99% 2.296185 2.338038 Kurtosis 5.518355

95% 1.829255 2.330506 Skewness 1.897989

90% 1.453722 2.296185 Variance .3287338

75% .4609559 2.268135

Largest Std. Dev. .5733531

50% .1301895 Mean .3994696

25% .0548481 .0212953 Sum of Wgt. 231

10% .0423961 .0211899 Obs 231

5% .0287053 .02051

1% .0211899 .0204468

Percentiles Smallest

PROD

99% 4.223305 4.396567 Kurtosis 4.083755

95% 3.615913 4.303576 Skewness 1.513293

90% 3.253934 4.223305 Variance 1.106543

75% 1.40437 4.062921

Largest Std. Dev. 1.051923

50% .7355135 Mean 1.217543

25% .5161184 .2286198 Sum of Wgt. 231

10% .4358661 .1661985 Obs 231

5% .330013 .1446278

1% .1661985 .1439353

Percentiles Smallest

TRADE

99% 118.1625 132.1371 Kurtosis 2.431536

95% 111.1721 118.2333 Skewness -.271734

90% 107.3808 118.1625 Variance 238.0012

75% 101.5166 116.0825

Largest Std. Dev. 15.42729

50% 89.52453 Mean 89.29216

25% 78.98589 55.90446 Sum of Wgt. 231

10% 67.17267 55.51052 Obs 231

5% 60.77424 55.42536

1% 55.51052 54.69106

Percentiles Smallest

TOT

99% 204.9419 258.6827 Kurtosis 11.46724

95% 156.5864 208.7859 Skewness 2.397087

90% 129.2679 204.9419 Variance 654.7192

75% 112.7762 204.6079

Largest Std. Dev. 25.58748

50% 103.7342 Mean 106.9825

25% 93.34971 72.87826 Sum of Wgt. 231

10% 84.83589 70.56959 Obs 231

5% 78.43083 67.22163

1% 70.56959 51.14095

Percentiles Smallest

REER

29

Figure 1 Figure 2

Figure 3 Figure 4

020

40

60

Perc

ent

0 200 400 600 800 1000REER

Distribution REER

05

10

15

20

Perc

ent

0 100 200 300TOT

Distribution TOT

05

10

15

20

25

Pe

rce

nt

0 1 2 3 4TRADE

Distribution TRADE

020

40

60

Pe

rce

nt

0 .5 1 1.5 2 2.5PROD

Distribution PROD

30

Figure 5 Figure 6

Figure 7 Figure 8

Figure 9 Figure 10

Figure 11 Figure 12

4.3

4.4

4.5

4.6

4.7

Me

an

lo

g(T

OT

)1980 1990 2000 2010 2020

Year

Development of the terms of trade

4.4

4.6

4.8

5

Me

an

lo

g(R

EE

R)

1980 1990 2000 2010 2020Year

Development of the REER

-.6

-.4

-.2

0.2

Mean log(T

RA

DE

)

1980 1990 2000 2010 2020Year

Development of trade openness-2

.2-2

-1.8

-1.6

-1.4

-1.2

Me

an

lo

g(P

RO

D)

1980 1990 2000 2010 2020Year

Development of the productivity gap

-.0

4-.

03

-.0

2-.

01

0

.01

Me

an

FD

I/G

DP

1980 1990 2000 2010 2020Year

Development of FDI

-.0

5

0

.05

.1

Me

an

OI/

GD

P

1980 1990 2000 2010 2020Year

Development of other investment

-.1

-.05

0

.05

Mean C

AP

ITA

L/G

DP

1980 1990 2000 2010 2020Year

Development of total capital flows

-.02

0

.02

.04

Mean P

I/G

DP

1980 1990 2000 2010 2020Year

Development of portfolio investment

31

Table 4: Im, Peseran & Shin (2003) and Levin, Lin & Chu (2002) unit root tests

IPS LLC IPS LLC

Level Level First difference First difference

REER 0.93 0.54 0.00 0.00 Terms of trade 0.00 0.00 0.00 0.00 Trade openness 0.30 0.07 0.00 0.00 Productivity gap 1.00 1.00 0.00 0.00 Capital 0.00 0.00 0.00 0.00 Foreign direct investment 0.00 0.00 0.00 0.00 Portfolio investment 0.00 0.00 0.00 0.00 Other investment 0.00 0.00 0.00 0.00

Note: Numbers reported are p-values. The null hypothesis is the presence of a unit root.

Table 5: Westerlund (2007) cointegration tests

Note: The null hypothesis is no cointegration

Table 6: Lag selection

Lags AIC

REER 0 6.97 1 6.21 2 6.18*

TOT 0 7.34 1 6.29 2 6.29*

TRADE 0 -0.69 1 -2.54* 2 -2.50

PROD 0 -1.83 1 -4.43 2 -4.45*

FDI 0 -6.33 1 -6.58* 2 -6.52

PI 0 -5.68 1 -5.94* 2 -5.87

OI 0 -4.35 1 -4.64* 2 -4.60

Pa -1.698 0.433 0.667

Pt -5.259 -2.092 0.018

Ga -1.279 2.199 0.986

Gt -2.176 -2.005 0.023

Statistic Value Z-value P-value

32

Table 7: Composition of capital flows and the REER

MG PMG DFE 1 2 1 2 1 2

Long run

Terms of trade -6.208 (5.594)

-0.274 (1.017)

4.634 (11.174)

5.382 (14.980)

-1.280 (1.024)

-1.122 (0.900)

Trade openness -0.780 (1.047)

-0.228 (0.441)

-2.515 (5.079)

-1.829 (4.147)

-0.406 (0.345)

-0.398 (0.312)

Productivity gap -1.194 (2.095)

-1.105* (0.641)

-4.900 (11.458)

-5.423 (14.843)

0.562** (0.272)

0.505** (0.233)

Total capital 7.570

(11.187) 5.395

(12.367) 0.009

(1.691)

FDI 0.923

(6.757) 5.179

(15.346) -1.165

(2.951)

Portfolio investment -2.192

(2.561) 6.280

(16.268) 0.988

(1.888)

Other investment -1.945

(2.549) 6.695

(17.999) -0.687

(1.904)

Short run

Ec -0.031 (0.085)

-0.025 (0.066)

0.009** (0.004)

0.008** (0.003)

-0.043* (0.023)

-0.049** (0.023)

D2 REER 0.402*** (0.059)

0.370*** (0.052)

0.410*** (0.026)

0.412*** (0.030)

0.381*** (0.039)

0.374*** (0.040)

D terms of trade 0.048

(0.056) -0.054 (0.106)

0.042 (0.049)

0.013 (0.036)

0.038 (0.052)

0.035 (0.052)

D2 terms of trade -0.075***

(0.027) -0.034 (0.043)

-0.059 (0.044)

-0.046 (0.036)

-0.077** (0.032)

-0.078** (0.032)

D trade openness -0.179***

(0.042) -0.186***

(0.063) -0.166***

(0.058) -0.159** (0.067)

-0.176*** (0.036)

-0.182*** (0.037)

D productivity gap 0.544*** (0.093)

0.500*** (0.088)

0.524*** (0.111)

0.499*** (0.098)

0.552*** (0.041)

0.547*** (0.042)

D2 productivity gap -0.254***

(0.075) -0.212***

(0.074) -0.218** (0.092)

-0.198** (0.088)

-0.242*** (0.041)

-0.238*** (0.042)

D total capital 0.330*** (0.118)

0.057 (0.063)

0.019 (0.076)

D FDI -0.147

(0.733) -0.110

(0.369) -0.001

(0.138)

D portfolio investment 0.394*

(0.221) 0.103

(0.121) -0.014

(0.093)

D other investment 0.284*

(0.150) 0.057

(0.137) 0.056

(0.086)

pre -0.003 (0.022)

-0.016 (0.021

-0.003 (0.012)

-0.003 (0.012)

-0.008 (0.011)

-0.008 (0.012)

crisis -0.009 (0.016)

-0.023** (0.010)

-0.001 (0.010)

0.001 (0.011)

-0.003 (0.011)

-0.004 (0.012)

Constant 0.227

(0.643) 0.035

(0.637) 0.148*** (0.055)

0.171*** (0.060)

0.477*** (0.181)

0.499*** (0.185)

Observations

217

217

217

217

217

217

Standard errors in parentheses *p<0.10, **p<0.05, ***p<0.01

33

Table 7: Compostition of capital flows and the REER

China Indonesia Republic of Korea Malaysia Philippines Singapore Thailand

(1) (2) (1) (2) (1) (2) (1) (2) (1) (2) (1) (2) (1) (2)

Long run

Terms of trade -0.73 -1.02 1.58 1.94 -39.09 -3.00 2.13 -3.57 -0.22 -0.31 -6.95 4.16 -0.18 -0.13

(0.49) (0.68) (1.02) (2.86) (444.56) (3.48) (2.61) (6.94) (0.65) (1.49) (24.20) (26.86) (0.23) (0.41)

Trade openness -0.64** -0.69* 1.78 2.05 -6.65 0.25 -0.34 -1.36 -0.31 -0.74 1.36 -1.17 -0.66*** 0.07

(0.25) (0.33) (1.83 (3.38) (77.05) (0.57) (0.87) (2.03) (0.73) (2.03) (6.68) (6.14) (0.12) (0.90)

Productivity gap 0.13 0.14 0.47 0.44 -13.27 -0.54 -0.43 -4.48 0.22 -0.30 4.19 -2.05 0.33** -0.94

(0.09) (0.11) (0.56) (0.64) (154.23) (0.87) (1.01) (6.81) (0.70) (2.16) (13.50) (13.94) (0.12) (1.74)

Total capital 0.18 -16.74 73.24 0.99 -2.65 -2.29 0.25

(3.44) (14,97) (846.38) (2.60) (2.30) (8.22) (0.54)

FDI 1.41 -27.64 08.04 31.33 -0.84 -0.28 10.52

(5.50) (52.63) (14.10) (47.56) (12.33) (4.35) (13.62)

Portfolio investment 1.39 -13.02 5.74 -4.98 -8.42 1.26 2.69

(6.64) (18.57) (5.15) (7.47 (18.09) (7.47) (2.63)

Other investment 1.35 -16.15 5.10 -0.31 -2.68 0.80 -1.72

(4.34) (24.03) (9.09) (5.57) (3.82) (7.11) (2.36)

Short run

Linear -0.34 -0.32 -0.10 -0.10 0.01 0.14 0.07 0.05 -0.20 -0.15 -0.04 0.05 0.38 0.16

(0.19) (0.22) (0.09) (0.14) (0.09) (0.20) (0.08) (0.08) (0.22) (0.30) (0.13) (0.28) (0.21) (0.18)

D2 REER 0.18 0.18 0.27* 0.28 0.47*** 0.57*** 0.45*** 0.34* 0.33 0.37 0.47** 0.53* 0.65** 0.31

(0.12) (0.12) (0.11) (0.15) (0.14) (0.13) (0.13) (0.14) (0.18) (0.23) (0.16) (0.23) (0.22) (0.19)

D terms of trade 0.09 0.27 0.02 -0.02 -0.21 -0.44 0.13 -0.37 0.24 0.27 0.14 0.04 -0.06 -0.12

(0.25) (0.28) (0.16) (0.21) (0.18) (0.23) (0.19) (0.34) (0.15) (0.19) (0.21) (0.36) (0.11) (0.09)

D2 terms of trade -0.08 -0.16 -0.13 -0.13 -0.04 0.03 -0.12 0.07 -0.17* -0.17 -0.01 0.03 0.03 0.10*

(0.14) (0.16 (0.08) (0.11) (0.12) (0.11) (0.09) (0.14) (0.09) (0.11) (0.11) (0.17) (0.05) (0.04)

D trade openness -0.28 -0.29 -0.26* -0.28 -0.25*** -0.28*** -0.27* -0.37** -0.01 -0.00 -0.11 -0.15 -0.07 0.07

(0.15) (0.17 (0.11) (0.16) (0.07) (0.07) (0.11) (0.13) (0.12) (0.17) (0.09) (0.14) (0.10) (0.10)

D productivity 0.27 0.42 0.74*** 0.73*** 0.75*** 0.59*** 0.36** 0.13 0.75** 0.79** 0.22 0.32 0.71*** 0.52**

(0.20) (0.26) (0.09) (0.20) (0.10) (0.11) (0.11) (0.17) (0.20) (0.27) (0.16) (0.32) (0.17) (0.18)

D2 productivity 0.08 0.02 -0.25* -0.24 -0.55*** -0.51*** -0.14 -0.00 -0.35 -0.40 -0.20* -0.24 -0.36* -0.11

(0,16) (0.18) (0.12) (0.15) (0.09) (0.08) (0.09) (0.11) (0.19) (0.23) (0.10) (0.15) (0.17) (0.18)

D capital 0.52 0.83 0.35 -0.02 0.49 0.00 0.13

(0.85) (0.56) (0.25) (0.11) (0.40) (0.11) (0.18)

pre -0.12 -0.14 0.04 0.05 0.04 -0.00 -0.02 -0.01 0.01 -0.01 -0.02 -0.02 0.04 0.01

(0.06) (0.07) (0.07) (0.08) (0.03) (0.03) (0.04) (0.04) (0.06) (0.09) (0.03) (0.04) (0.04) (0.04)

crisis -0.04 -0.04 -0.01 -0.04 0.04 -0.00 -0.04 -0.01 -0.06 -0.07 -0.01 -0.00 0.05 0.01

(0.06) (0.08) (0.06) (0.08) (0.02) (0.02) (0.03) (0.03) (0.04) (0.05) (0.02) (0.03) (0.03) (0.03)

D FDI -2.05 1.67 -3.57 1.02* 0.84 -0.07 1.13***

(1.88) (1.32) (2.24) (0.50) (1.11) (0.19) (0.33)

D portfolio investment 0.14 0.87 1.18** -0.34 0.93 -0.01 0.00

(1.71) (2.47) (0.45) (0.19) (0.82) (0.17) (0.31)

D Other investment 0.74 0.72 0.18 -0.12 0.61 -0.02 -0.14

(0.92) (0.68) (0.28) (0.17) (0.67) (0.18) (0.14)

Constant 2.66 2.98 -0.05 -0.20 -1.47 -2.48 0.37 -0.85 1.20 0.72 1.21 0.63 -2.34 -0.56

(1.61) (1.77) (0.37) (0.86) (0.91) (1.69) (0.61) (1.06) (1.44) (1.97) (1.15) (2.41) (1.46) (1.30)

Standard errors in parentheses

* p<0.05, ** p<0.01, *** p<0.001

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