p160 baylie testing the balassa hypothesis in low and middle income countries

7/25/2019 P160 Baylie Testing the Balassa Hypothesis in Low and Middle Income Countries

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TESTING THE BALASSA HYPOTHESIS IN LOW AND

MIDDLE INCOME COUNTRIES: External Version

Fentahun Baylie 1

(Professor Scott Hacker, Professor Par Sjolander, Dr. Girma Estiphanos)2

March/2016

Addis Ababa,

Ethiopia.

Abstract: This study analyses the long run relationship between economic growth and real

exchange rate for a group of 15 low and middle income countries for the period 1950-2011.

Cointegration between growth and exchange rate are established by means of an augmented

pooled mean group estimation method (which controls for heterogeneity and cross sectional

dependence). Unlike previous studies, cross sectional dependence is accounted for which

implies that the productivity effect of the Balassa term is expected to be estimated consistently

and without bias. Moreover, our results indicate that the Balassa hypothesis relies more onlevel of development than on the rate of economic growth. In general, the strength of the

effect is stronger for higher level income countries in the long run. The study clearly indicates

that the Balassa hypothesis holds only for middle income countries-while productivity growth

does not lead to the appreciation of real exchange rate for low income countries. However, in

the short run, fiscal policy and exchange rate volatility rather clearly explains the variation in

real exchange rate.

Key words: Productivity growth, Real exchange rate, Balassa hypothesis.

1

PhD Student in JIBS based in Ethiopia, ([email protected])2Advisors from JIBS and Addis Ababa University ([email protected], [email protected], [email protected] respectively)


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1. Introduction

The Balassa hypothesis tests the impact of productivity growth on real exchange rate. It states that

for a growing economy, the real exchange rate is expected to appreciate in the long run. This study

tests the hypothesis for a group of 15 growing economies. These countries are well performing

economies (in terms of rate of economic growth) in the past decade according to different reports of

the World Bank (World Bank (2014; 2015)). The sample countries represent nearly half the world

population [3.7 billion people] and one-third of the world GDP from four different continents; Asia,

Europe, Africa and Latin America. The countries grow with mean growth rate of 5% for past decade

(IMF, 2013). This group of countries are heterogeneous in many aspects but share common feature

regarding the features of their economic growth.

This study is based on a finding in Baylie (2008). It shows that real effective exchange rate is an

important policy parameter and among the most determining factors of growth in Ethiopia. Though

it recommended depreciation of the domestic currency to promote economic growth in the short run,

it discovered that it is healthier to allow appreciation in the long-run to encourage sustainable

economic growth. Therefore, it provided [an exchange rate] policy recommendation which

promotes appreciation of the domestic currency for sustainable growth in the long run.

Both depreciation and appreciation are not welcomed effortlessly by monetary authority.

Depreciation in particular, as suggested in Baylie (2008), is not favoured by the monetary authority

as it increases the burden on importing capacity for a developing country like Ethiopia. By the time

a country is recommended to appreciate, all the advantage of depreciation are assumed to be

exhausted and prospects of appreciation are awaiting. Depreciation may initially help to promote

exports and generate sufficient foreign earnings. Once this objective is met, there arises a need to

promote the import of capital goods to establish import-substituting industries, transform the

economy and sustain development in a developing economy by allowing appreciation. The only

question left unanswered in this case would be the timing to switch policy. The solution to this

dilemma is provided byBalassa hypothesis.

The time when the Balassa hypothesis holds in a particular economy, appreciation is gainful. In

short, it states that if economic growth is accompanied by appreciation of the domestic currency

[Balassa hypothesis], the monetary authority should not constrain the appreciation for just simple

reason that it may discourage exports. If economic growth by itself brings appreciation, it can be

sustained as the later further put inertia onto the former. There is a possibility for one to derive the

other in the long-run when the hypothesis holds.


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The Balassa-Samuelson effect, first formulated by Harrod in 1934 and later by Balassa and

Samuelson in 1964 separately, describes that the distortion in purchasing power parity (PPP) is the

result of international differences in relative productivity growth between the tradable goods sector

(manufacturing and agriculture) and the non-tradable goods sector (services). Accordingly, during

the development process, productivity tends to increase more quickly in the tradable goods sector

than the non-tradable goods sector. Given that the prices of tradable goods are set by international

competition, an increase in productivity in this sector leads to an increase in wages, which is not

harmful to competitiveness but leads to a rise in relative prices in the non-tradable goods sector,

where productivity has not grown at the same pace. Given that the price index is an average of these

two sectors, there is an increase in the prices of domestic goods relative to those from abroad, which

results in an appreciation of the real exchange rate (Tica and Druzic (2006), Herberger (2003),

Coudert (2004)).

In short, the hypothesis states that the impact of growth on exchange rate is positive i.e. appreciation

of the domestic currency. The main purpose of this study is, therefore, to show whether this analysis

can be extended to a group of low and middle income countries from various continents.

While there are many evidences in favour of the hypothesis, there are also certain anti-Balassa

results in some studies. The negative results could be due to certain reasons such as lack of quality

or long period data, or the difficulty to guarantee assumptions of the model. Examples of anti-

Balassa studies include Chuah (2012), Genius and Tzouvelekas (2008), Canzoneri, Cumby and Diba

(1997), Funda, Lukinic and Ljubaj (2007), Gubler and Sax (2008), Wilson (2010), Drine and Rault

(2003), and Asea and Mendoza (1994) among others.

Tica and Druzic (2006) survey shows that since its discovery in 1964, the theory has been tested 58

times in 98 countries in time series or panel analyses and in 142 countries in cross-country analyses.

In these analyzed estimates, country specific Balassa Hypothesis coefficients have been estimated

164 times in total, and at least once for 65 different countries. The conclusion shows the difficulty to

ignore the significance of the Balassa-Samuelson theory.

The first empirical test of the theory was carried out by Balassa (1964). It was a cross-country

analysis of nine countries' data sets for 1955. Balassa (1964) made an OLS estimate with the ratio of

purchasing power parity and nominal exchange rate as a dependent variable and GNP per capita as

an independent variable. He found a significant and positive relationship between the two.


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De Gregorio, Giovannini and Wolf (1993) made a study on OECD for the period 1970-1985 and

came to the conclusion that [in addition to demand shift to nontradables] higher productivity growth

in tradables are the main causes of inflation for the nontradables. Similarly, De Gregorio and Wolf

(1994) added the Penn effect to the Balassa hypothesis by introducing terms of trade parameter for a

sample of fourteen OECD countries. They arrived on the same conclusion. A relatively faster

productivity growth in the tradable sector [and an improvement in the terms of trade (TOT)] induce

a real appreciation.TOT, which is assumed to be constant in the hypothesis, is not only significant

but also found to have a role to play on the interaction between real exchange rate and productivity

differentials if allowed to vary.

In general, one may, therefore deduce from the population of literature on Balassa-Samualson effect

that the results of empirical studies depends on the level of development of countries in sample

studies, period of analysis and type of Econometrics methods employed. Thus, more powerful

results from the test are associated with longer period and panel data type of analysis.

2. Theoretical Framework of the model

2.1. The Balassa-Samuelson Hypothesis3

Balassa hypothesis demonstrates the relationship between exchange rate, purchasing power parity

[PPP] and inter-country income comparisons in general. The hypothesis emanates from the PPP. It

explains why the PPP theory of exchange rate is imperfect. In the absence of all frictions, the prices

of a common basket of goods in two countries measured in the same currency should be the same at

all times for the absolute PPP to hold i.e. / = 1. The Balassa-Samuelson hypothesis providesproductivity growth differential between tradable and nontradable sectors and across nations as one

of the reasons why this does not hold.

The hypothesis is put in the first line in the list of modifications for the long run PPP by Kenneth

Rogoff. In contrast to the purchasing power parity theory, price levels are found to be higher in richcountries than the poor ones when converted to a common currency. This is associated to higher

productivity growth in the tradable sector in rich countries (Rogoff 1996).

The Balassa hypothesis explains the fact that there is no reason for price differentials under perfect

competition (Prusa, 2007). Given this assumption, Balassa (1964) considers the fact that price

3 Though the idea has been mentioned by several authors [like David Ricardo (1911), Roy Harrod (1933), Jacob Viner (1937), Paul A. Samuelson (1964) and Bela Balassa (1964)], the contribution

of other authors is not as bold as Paul A. Samuelson and Bela Balassa and hence the name Balassa-Samuelson hypothesis (Tica J. and Druzic I. 2006). The name Balassa Hypothesis may be also

used in this study.


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increase in the nontradable sector is the same as money wage transferred from producers to workers

in the traded sector. The PPP theory holds if and only if these productivity changes and wage

adjustments are identical and there exists neutral production and consumption effects in every

country. In the absence of which general price levels in each country follow the path of

technological improvements and wage adjustments. Samuelson (1964) also suggests that countries

should narrow their technological gap to have a relatively similar value of currency.

The Balassa hypothesis has two versions; the external and internal versions. The external version

analyzes the impact of productivity on real exchange rate. The internal version analyzes the impact

of productivity on relative price. By combining the two one can realize whether the hypothesis is

functioning through the internal version or the external version. If one finds a significant

relationship between relative price, real exchange rate and productivity, it is most likely that the

hypothesis is functioning through the internal version. The main purpose of this study is to discuss

the external version of the hypothesis.

The original Balassa hypothesis assumes a fully employed small open economy; a 2X2X2 economic

model (two-countries, two-commodities, two-factors); an inter-sector mobile labour factor [scarce]

and inter-nation mobile capital; law of one price for factors within a nation and for tradables across

nations; a constant return to scale production frontier; perfect competition in both (goods and

factors) markets; neural technical progress; and constant terms of trade (Podkaminer, 2003).

2.2. Model Specification

The derivation of the Balassa-Samualson model may be considered as a three-stage process. The

first is to derive the relationship between productivity differential and relative price. This is referred

to as Internal Version of Balassa hypothesis. The second is to derive the relationship between

relative price differential and exchange rate. The third is to derive the relationship between

productivity differential and exchange rate. This is referred to as External Version of Balassa

hypothesis

STEP 1:

The original Balassa-Samuelson model is framed on the basis of the traditional Ricardian trade

model (Asea and Corden, 1994). It was a supply side model defined by a constant return to scale

Cobb-Douglas style production functions in two sectors as follows (Podkaminer, 2003).

= (2.1)= (2.2)


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Where T and N refers to the traded and nontraded sectors, and and represent the share oflabour in each sector respectively.

In a perfectly competitive market, factor prices must equal their respective value of marginal

products at equilibrium for both sectors.

= (2.3)

(1 ) = (2.4)

= (2.5)

(1 ) = (2.6)

Combing equation (2.3) and (2.4) and taking the logarithm of both sides, yields the following

results, equation (2.7).

=

!!"#

= $()"!# =

= $()

%&' = (1 )%&'

(1 )

%&'(*)+,-.() + ,-.() = (1 ) %&'()(1 ),-.(1 )(1 ),-.() (1 ),-.()

%&'(*) = (1 ) %&'()(1 ),-.(1 ) ,-.() ,-.(),-.()=(1 ) %&'()(1 ),-.(1 )+ %&'(*) ,-.() (2.7)

Similarly, Combing equation (2.5) and (2.6) and taking the logarithm of both sides, yields the

following results, equation (2.8).

,-.()=(1 ) %&'()(1 ),-.(1 ) +%&'(*) ,-.() (2.8)Recall the assumption that price of tradables and interest rate [not technology] are fixed.

Differentiation of equations (2.7) and (2.8) with respect to time yields equations (2.9) and (2.10)

respectively.

2() () = 3 =

4() 5()

6() () (2.9)

2()

() = 4()

5() 6()

() (2.10)


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Substituting the relation in equation (2.9) into (2.10) gives the final result that defines relative price

of nontradables in terms of productivity differentials.

2() () =

6() ()

6() () (2.11)

78= 9 9 (2.12)Where a hat represents growth rate of the variables.

For a foreign economy (*), equation (2.12) can be arranged as follows.

78 = :9 9;....................................................................................(2.13)

Taking the difference between equation (2.12) and (2.13) helps to define price differentials across

countries as well.

78 78 = =?

In log-linear form: @ = + 7 7 (2.15)Price indices may be defined as a weighted average of prices in the tradable and nontradable sectors

in both domestic and foreign.

= A

A

In log-linear form: 7 = B7+(1 B)7 (2.16)= C(C)

In log-linear form: 7= D7 +(1 D)7 (2.17)Band Drepresents the share of nontradables in consumer basket at home and foreign respectively.Substituting equations (2.16) and (2.17) into equation (2.15) helps to define exchange rate as a

function of price differential.

@ = + 7 7


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@ = + D7 + (1 D)77 (1 )7@ = + 7 7+E(7 7) (7 7 )F (2.18)

In relative PPP terms, equation (2.18) may be defined as:

G@ = G + G7 G7+E(G7 G7) D(G7 G7 )F (2.19)For the PPP to hold, thenG + G7 = G7. Equation (2.19) will be:

G@ =E(G7 G7) D(G7 G7 )F---------------------------------------- (2.20)In absolute PPP terms, equation (2.20) may be defined as:

@ =E

(7 7) D(7

7

)F---------------------------------------- (2.21)

STEP 3:

Equation (2.21) defines exchange rate indirectly as a function of price differential between

countries. Substituting equation (2.14) into equation (2.21) helps to defined exchange rate directly

as a function of productivity differential. We assume that the share of nontradables in consumer

basket of foreign(H) is the same as at home (I). Hence,

@J =


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Therefore, the econometric model which is used in this study and includes other factors is derived

from equation (2.22)6.

(%KL)MN= M + M%K(O/O)MN+ PM%K(Q/Q)MN+ RMS-,(T)MN+ UMN------------------------- (2.23)Q andEare real and nominal exchange rate. %K(>)MNrepresents log of real exchange rate for eachcountry measured against the US dollar. An increase in real exchange rate implies appreciation. it

refers to the ith

country in periodt. %K(O/O)MN: refers to log of real GDP per capita relative to the USeconomy, the reference point. It is a proxy for Productivity growth differential in each country.

%K(Q/Q)MNis log of real government expenditure relative to the US economy. It is a proxy for fiscalpolicy in each country. vol(E) is exchange rate volatility measured as the absolute value of

percentage change in nominal exchange rate

7

.

3.Econometric Methodology

VW

X/ YY0Z[\]^ K_ 0`[a[Kb[K\[0c[Y]0

Cross sectional dependence is a problem associated with panel data that mix information from

different cross sections and results in a difficulty to interpret individual effects of each section. It

may arise from various sources such as neighbourhood or socioeconomic network effects, spatial

effects, the influence of a dominant unit or the influence of common unobserved factors. When

Cross sectional dependence is ignored, estimates may be badly biased and tests for unit roots and

cointegration may be misleading (Shin, 2014).

Factor models are used to study unobserved common factors which represent cross sectional

dependence.

dMN= eNM+ MfMN+ gMhN+ MN.................................................................( 3.1)where dMN is a scalar dependent variable, eN is a kzx1 vector of variables that do not differ overunits, e.g. intercept and trend,fMNis a kxx1 vector of observed regressors which differ over units, hNis an rx1 vector of unobserved factors, which may influence each unit differently and which may be

correlated with the fMN, and MNis an unobserved disturbance with mean zero and constant variancefor each cross section i, which is independently distributed across iand (possibly) t. The covariance

between the errors MN= gMhN+ MNis determined by the factor loadings gM(Shin, 2014).We use the Pesaran cross sectional independence test in this study as it is the most powerful test

(Eberhardt, 2011). It is given by:

6 See Appendix A for derivation.7 See Appendix A for variable measurement.


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ij =k P() lm m noMppqMr sJMpMq t ............................................................................( 3.2)Where ijuv(3w1).

VV W

x_K[ 0yK^]0z ]0c[Y]Y0

Six types of panel unit root tests are available. These are: Levin-Lin-Chu (LLC), Hariss-Tzavalis

(HT), Breitung test, Im-Pesaran-Shin (IPS), Fisher type and Hadri LM tests. The panel data of this

study are unbalanced and Nis fixed and smaller relative to T. It also assumes that autoregressive

parameter, {wis panel specific. Hence, the candidate panel unit root tests that fit these criteria arethe IPS and Fisher type tests. Another advantage of these tests is they can be used to test a series

which is not serially independent across cross sections. However, the panel unit root test must be

preceded by a test of cross sectional independence.

a) ImPesaranShin test

The following is a panel unit root test as proposed by Pesaran (2007) with the presence of cross

sectional dependence. The standard augmented DickeyFuller (ADF) regressions are further

augmented with cross section averages of lagged levels and first-differences of the individual

series. Let yit be the observation on the ith

cross-section unit at time t and suppose that it is

generated according to the simple dynamic linear heterogeneous panel data model:

dMwN =(1 |M)}M+ |MdMwN+~(3.3) ~MN= gMhN+ ; = 1 v = 1 o .The initial value, yi0, has a given density function with a finite mean and variance, and the error

term, uit, has the single-factor structure. ft is the unobserved common effect, and it is the

individual-specific (idiosyncratic) error. It is also convenient to write the model as:

GdMwN = M+ MdMwN+ gMhM+(3.4)where M=(1 |M)}Mw M= (1 |M) GdMwN = dMwN dMwN. The unit root hypothesis ofinterest is expressed as:

M= 3 h- ,, _'_^KY] ][ a&YY^% [][&'[K[&Y _%][K_]^[Y M 3w = 1w w W vw M= 3w = v+ 1w vP+ w W w v

v/v, the fraction of the individual processes that are stationary, is nonzero and tends to the fixedvalue such that 0 < < 1 as v . This condition is necessary for the consistency of the panelunit root tests.


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b) Fisher-type tests

Maddala and Wu (1999) provide a fisher-type of panel unit root test which accounts for cross

sectional dependence. This test originates from Fisher (1932). Like the IPS test, the Fisher test is a

way of combining the evidence on the unit-root hypothesis from the Nunit-root tests performed on

theNcross-section units. Fisher-type test make this approach more explicit.

The Fisher-type panel unit-root test combines the p-values from the panel-specific unit-root tests

using four methods. Three of the methods differ in whether they use the inverse chi-square,

inverse-normal, or inverse-logit transformation of p-values, and the fourth is a modification of the

inverse chi-square transformation. Simulation results suggest that the inverse normal Z statistic

offers the best trade-off between size and power(www.stata.com).

Let Mw be a unit root test statistic for the ithgroup and assume that as Ti, then Mw= M.Let pibe the p-value of a unit root test for cross-section i, i.e., pi= 1 F(Mw), where F() is thedistribution function of the random variable M. The Fisher type test is given as follows (Chen M.2013).

= m %K 7MMq ........................................................(3.5)P is distributed as 2with 2N degrees of freedom as T for all N. When

7Mcloses to 0,

%K 7M

closes to so that large value P will be found and then the null hypothesis of existing panel unit

root will be rejected. In contrast, when 7Mcloses to 1, %K 7Mcloses to 0 so that small value P will befound and then the null hypothesis of existing panel unit root will not rejected.

VVVW

x_K[ 0X ^K]['/_]^ K 0c[Y]Y0

There are two possibilities to deal with nonstationary variables in a given model after the

stationarity test. One is, to test if the linear combination of nonstationary variables is stationary by

using cointegration test. If they are cointegrated, then we proceed to a long run analysis with

nonstationary variables. Otherwise, we difference the stationary variables for a short run analysis.

Granger noted (Gujarati, 2004) that a test for cointegration can be thought as a pre-test to avoid

spurious regression situations. A regression of one nonstationary variable over another

nonstationary variable may yield a stationary series and if so, it is known as cointegrating

regression and the slope parameter in such a regression is known as cointegrating parameter.

We employ a residual-based Pedroni cointegration test which is simply unit root tests applied to theresiduals obtained from a cointegrating regression. If the variables are cointegrated then the


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residuals should be I(0). On the other hand if the variables are not cointegrated then the residuals

are not I(0) (Eviews 9 Users Guide; 2015). The test allows for heterogeneous intercepts and trend

coefficients across cross-sections. It is based on a residual obtained from a regression,

dMN= M+ BM + MfMwN+ PMfPMwN+W W W+MfMwN+ MwN............................................(3.6)for = 1w W w o; = 1w w ; = 1w w where fand dare assumed to be integrated of orderone, I(1). The parameters Mand BMare individual and trend effects.Pedroni has proposed seven different statistics to test panel data cointegration, namely the Panel v-

Statistic, Panel rho-Statistic, Panel PP-Statistic, Panel ADF-Statistic, Group rho-Statistic, Group

PP-Statistic and Group ADF-Statistic. Out of these seven statistics, the first four are based on

pooling, what is referred to as the Within dimension, and the last three are based on the

Between dimension. Both kinds of tests focus on the null hypothesis of no cointegration.

However, the distinction comes from the specification of the alternative hypothesis. For the tests

based on Within dimension, the alternative hypothesis is i = < 1 for all i, while the tests

based on Between dimension, the alternative hypothesis is i< 1 for all i (Bangake and Eggoh

2012).

IV. Estimation Method

The choice of estimation method mainly depends on the nature of data. Analysis of nonstationary

variables calls for the use of long-panel related methods. The type of macro-econometrics method

used is also associated with assumption regarding technology parameters and factor loadings.

Therefore, we are not supposed to consider traditional estimators such as Pooled OLS, fixed effect

and first difference OLS models which are mainly used for micro data with large N and small T in

homogeneous technology parameters and factor loadings. Eberhardt et. al. (2011) and others

suggested the use of pooled mean group estimation method for the analysis of nonstationary

variables which are cointegrated in long panel setting. This method is helpful in particular when Tis large relative to N for heterogeneous technology parameters and factor loadings.

Pooled mean group (PMG) estimator involves averaging and pooling. It restricts the long-run

coefficients to be homogenous over the cross-sections, but allows for heterogeneity in intercepts,

short-run coefficients (including the speed of adjustment) and error variances. It is argued that

country heterogeneity is particularly relevant in short-run relationships, given that countries may be

affected by over lending, borrowing constraints, and financial crises in short-time horizons. On the

other hand, there are often good reasons to expect that long-run relationships between variables are


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homogeneous across countries due to budget or solvency constraints, arbitrage conditions or

common technologies (Cavalcanti et. al., 2011).

The relationship in pooled mean group estimation may be defined by an ARDL model as follows:

G@MN= M+ MGfMN+ MlDfMwN @MwNt + MN...............................................................(3.7)Where @ = ,>and f = ,. M, are short-run parameters, which like MPdiffer across countries.Error-correction termMalso differs across i, long-run parameter Hhowever is constantacross thegroups. This estimator is quite appealing when studying small sets of arguably similar countries.

In I(1) panels, this estimator allows for mix of cointegration (M 3) and noncointegration (M=3). fMwN represent the set of explanatory variables defined in equation (2.23).

To account for cross sectional dependence which may result from any common unobserved factor

incorporated in the error term, we follow the Common Correlated Effect approach by Pesaran

(2013). The approach can handle multiple factors which can be correlated with the regressors, and

serial correlation in the errors and lagged dependent variables (Shin, 2014).

The procedure consists of approximating the linear combinations of the unobserved factors by cross

sectional averages of the dependent and explanatory variables, and then running standard panel

regressions augmented with these cross section averages. It does not require an a prioriknowledgeof the number of unobserved common factors and can be applied to dynamic panels with

heterogeneous coefficients and weakly exogenous regressors (Pesaran, 2013).

The PMG estimator for a cross sectionally dependent series may be defined as follows:

G@MN= M+ MGfMN+ MlDfMwN @MwNt + gMNhN+MN...................................................(3.8) gMhN+ MN= MN

hN is a vector of unobserved common shocks, which captures source of error term dependenciesacross countries. It may be stationary or nonstationary. The impacts of these factors on eachcountry are governed by the idiosyncratic loadings in gMN. The individual-specific errors, MN, aredistributed independently across i and t; they are not correlated with the unobserved common

factors or the regressors; and they have zero mean, variance greater than zero, and finite fourth

moments. The common factors, or the heterogeneous time effects, may be captured by adding cross

sectional averages of the observables to our regressions (Cavalcanti et. al., 2011).


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The augmented Pooled mean group estimator is, therefore, defined by substituting cross sectional

averages for the unobserved common factors.

G@MN= M+ MGfMN+ MlDfMwN @MwNt + m BewNq +MN...................................(3.9)Where ewN represents the set of cross sectional averages of the dependent and independentvariables and their lagged values which approximate/proxy the unobserved common factors (hN).The focus of this estimator is on obtaining consistent estimates of the parameters related to the

observable variables, while the estimated coefficients on the cross-section averaged variables are

not interpretable in a meaningful way: they are merely present to alter out the biasing impact of the

unobservable common factor (Eberhardt, 2012).

V. Error Correction Mechanism (ECM)

If two/more variables are cointegrated or proved to have a long run relationship one needs to go for

an error correction mechanism. Error correction mechanism (ECM) is a method used to correct any

short run deviation of variables from their long run equilibrium i.e. it corrects for disequilibrium.

An important theorem, known as the Granger representation theorem, states that if two variables Y

and X are cointegrated, then the long term or equilibrium relationship that exists between the two

can be expressed as ECM (Gujarati, 2004). This means one shall go for the construction of an error

correction model if the two variables are cointegrated. The ECM can be given by:

@MN= M+ MfMN+ D~MwN+ MN............................................(3.10)denotes the first difference operator, MN is a random error term, and ~MwN= (@MNfMN)is one-period lagged value of the error term from the cointegrating regression.

This ECM equation states that @MNdepends on fMNand also on the equilibrium error term. If thelatter [error term] is nonzero, the model is out of equilibrium. Suppose fMN is zero and ~MwN ispositive. This means @MwN is too high [above] to be in equilibrium. Since D is expected to benegative, the term D~MwN is negative and, therefore, @MN will be negative to restore theequilibrium. That is, if @MN is above its equilibrium value, it will start falling in the next period tocorrect the equilibrium error; hence the name ECM. By the same token, if ~MwNis negative (i.e.,@MN is below its equilibrium value), D~MwNwill be positive, which causes @MN to be positive,leading @MN to rise in period t. Absolute value of D determines how quickly the equilibrium isrestored (Gujarati, 2004).


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4. Empirical Results

4.1. Data type and Collection methods

This study relies on a macro panel data. Data for all countries and variables are from the World

Bank database. The variables include exchange rate, per capita GDP and government expenditure.

The final version [Penn World Table version 8.0] of the World Bank database provides annual time

series data for the period 1950-2011. While the choice of study period for each country depends on

data availability, our sample countries are selected on the basis of their economic performance (rate

of economic growth). Basically, the sample includes 15 countries from two income groups; middle

(BRICS) and low with best performance in terms of economic growth since the beginning of the

21stcentury. The World Bank and reports of other international financial institution show that the

countries in our sample are among the fastest growing economies in the world (World Bank; 2015).

The countries in our sample are Angola, Brazil, China, Ethiopia, Ghana, Indonesia, India, Kenya,

Nigeria, Philippines, Russia, Rwanda, Tanzania, Uganda, South Africa plus the USA (our reference

point).

4.2. Test Results

The analysis begins by performing different econometric tests. Since not all unit roots provide the

appropriate results, cross sectional independence test was performed to decide the type of panel

unit root test to be considered.

Using the Pesaran CD test, and possibly all other tests, we reject the null of cross sectional

independence for the raw data. Hence, the series for our data are cross sectionally dependent

initially. However, after the data is augmented for cross sectional averages to eliminate the

common factors, the Pesaran CD test, and possibly three other tests, fails to reject the null of cross

sectional independence. Test results are available in table 2B, annex B.

Panel unit root tests which account for cross sectional dependence are used; the IPS and Fisher tests.

The results of the tests with different assumptions are given in table 3B, annex B. All variables are

nonstationary at levels at 1% level of significance.

The next step is to test for cointegration-whether there is a long run relation between our

nonstationary variables. The test for cointegration is residual based. We use two Pedroni type tests

(ADF and PP tests) and the IPS test. In all cases, we strongly reject the null of no cointegration for

both types of models (augmented and non-augmented). Augmented models include cross sectional

averages of dependent and independent models which account for cross sectional dependence.


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In the final analysis of our estimation, we consider three types of augmented models; model I (with

one explanatory variable), model II (with two explanatory variables) and model III (with three

explanatory variables). In the first model, log of real exchange rate (,>)is defined as a function ofproductivity (

%K (/

)). In the second Model, we include government expenditure (

%K (/

)). In

the third model, we add exchange rate volatility (&% (?)). Even though the model selection criterionsuggests that the model with three variables is our best model in terms of log likelihood ratio and

akaike information criteria (table 5B, annex B), we present the results of other models for

comparison purposes. However, both the log likelihood test and information criteria shows that an

ARDL(1, 1, 1, 1) model best explains/represents our relationship. The dimensions represent,>N,%K (/), %K (/) and &% (?) respectively. Therefore, we focus on the results of model III [theshaded rows in each group] in the discussion that follows.

4.3. Findings

Since the objective in this study is to find country specific coefficients for each variable, the

assumption of homogeneous parameter is not valid. Pesaran H., Shin Y., and Smith R. (1998)

suggested the use of pooled mean group [PMG] estimation for the analysis of nonstationary

variables as T gets larger if they are cointegrated. PMG estimator involves both pooling and

averaging. It allows the intercepts, short run coefficients and error variances to differ across groups

while the long run coefficients are constrained to be the same.

Table 4.1 shows the long run results of panel cointegration estimation using augmented PMG

estimator for different groups of countries and models in the sample. We follow the tradition of

presenting the estimated coefficients of only observable variables as the cross-section averaged

variables are not directly interpretable in a meaningful way. The estimated coefficients of full

model (observable and unobservable variables) are reported in annex C.

Basically, we consider three types of groups in comparing the three types of models; ALLCOUNTRIESgroup (15 countries); Country groups by income-middle income countries (MICs; 5

countries) and low income countries (LICs; 10 countries); and Country groups by region (AFRICA

(9 countries) and ASIA (4 countries)). For each group, the first row shows a model with one

explanatory variable (productivity-%K(/)); the second row shows a model with two explanatoryvariables (productivity and government expenditure-%K (/),); the third row shows a model withthree explanatory variables (productivity, government expenditure and exchange rate volatility-

&% (?)). The results for model III [shaded rows] are discussed for each group below.


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Table 4.1: Panel Cointegration Estimation: Augmented PMG Estimator

Sample[# of countries]

Type ofModel

Long-run coefficients

,> = 7 S,

%K (/

)

%K (/

)

&%(?)

All countries[15]

Model I 0.378296***(0.108540)

Model II 0.246642***(0.075056)

0.170621***(0.037414)

Model III 0.388355***

(0.092802)

0.110547**

(0.045996)

-0.021531**

(0.010345)

MICs (BRICS)

[5]

Model I 0.109014

(0.151776)

Model II 0.382324**

(0.160930)

0.220887*

(0.120288)

Model III 0.344657**

(0.149000)

0.252061***

(0.092608)

0.024845**

(0.012602)

LICs

[10]

Model I 0.320488*

(0.132415)

Model II 0.211173**

(0.098594)

0.140663***

(0.041793)

Model III -0.287286***

(0.086673)

0.010920

(0.049502)

-3.21610***

(0.419248)

Africa

[9]

Model I 0.366216**

(0.144702)

Model II 0.239056**

(0.103439)

0.168407***

(0.043046)

Model III -0.247591***

(0.071625)

0.077565**

(0.038439)

-2.56978***

(0.285754)

Asia

[4]

Model I 0.248016

(0.258474)Model II 0.519768**

(0.216328)

-0.179409**

(0.078622)

Model III 0.771434*

(0.439197)

-0.339890

(0.207397)

-7.71735***

(2.826821)Q andEare real and nominal exchange rate, Y /Y*= real GDP of home relative to foreign (US), G and G*=real government

expenditure of home relative to foreign (US), vol(E) exchange rate volatility.

***, ** and * refer to significance level at 1%, 5% and 10%. Standard errors in parentheses.

MICs: refers to middle income countries of the BRICS group (Brazil, Russia, India, China and South Africa)

LICs: refers to low income countries (Angola, Ethiopia, Ghana, Indonesia, Kenya, Nigeria, Philippines, Rwanda, Tanzania and

Uganda)

Africa refers to African countries (Angola, Ethiopia, Ghana, Kenya, Nigeria, Rwanda, Tanzania, Uganda, and South Africa)

Asia refers to Asian countries (China, India, Indonesia, Philippines)

The results in table 4.1, in general, show that the Balassa hypothesis holds for all countries as a

group in the long-run i.e. a 1% improvement in productivity leads to an appreciation of domestic

currencies of the countries in the group by 0.388% on average. We find a different result, however,

when we categorize our sample into different groups. When categorized by level of per capita

income, the results show the Balassa hypothesis holds only for middle income countries (MICs).

The same fact holds when countries are categorized by region i.e. Balassa hypothesis holds only for

Asiancountries. This may be related to the fact that most middle income countries are from Asia

and poor countries are from Africa in our sample. In both cases, a 1% increase in productivity


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appreciates the domestic currencies of the countries in MICsandAsiagroups nearly by 0.34% and

0.77% respectively (though only at 10% level of significance for the later). For LICs and Africa

groups, On the other hand, a 1% increase in productivity depreciates the domestic currencies of the

countries in the groups nearly by 0.287% and 0.247% respectively. With type II model, however,

the outcomes are uniform for all cases and are in line with the Balassa hypothesis.

The long run relationship between government expenditure and real exchange rate shows that

Expansionary fiscal policy results in appreciation of the domestic currencies in all cases except for

theLICsandAsiagroups. This may not be surprising as the major countries with big economies

in both groups are almost the same (Indonesia and Philippines are members of both groups).

Exchange rate volatility has the impact of depreciating real exchange rate for all countries in all

groups except the middle income group in the long run.

Table 4.2 presents the results for short run dynamics of the same groups of countries and models in

Table 4.1. The discussions that follow focus on model III [the shaded rows].

The short-run dynamics shows that the impact of productivity on real exchange rate is also

significant though negative i.e. it has the impact of depreciating real exchange rate for all countries

in all groups in the short run. Fiscal policy does not significantly explain the variation in real

exchange rate. Exchange rate volatility significantly and negatively impacts real exchange rate in

the short run in all groups except the LICsand Africa group. In the later groups it has no impact at

all in the short run. This may be due to the reason that LICsdo not use exchange rate as a policy

variable i.e. it is mainly fixed or highly managed.

An error correction term links short-run periods to the long-run period. It adjusts short-run

dynamics to achieve a long-run equilibrium. The [negative] signs and statistical significances of the

error correcting terms show that the system is stable. A stable cointegrating relationship adjusts the

short-run deviations by the extent of the error correcting term. The rate of adjustment is, however,

higher (12%) inMICsthanLICs(8%). This meansMICshave faster rate of adjustment and achieve

equilibrium earlier thanLICs.


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Table 4.2: Short-run dynamics of Panel Cointegration Estimation: Augmented PMG Estimator

Sample[# of countries]

Type ofModel

Adjustmentcoefficient

Short-run coefficients

,> = 7 S,

%K (/

)

%K (/

)

&%(?)

All countries[15]

Model I -0.109894***(0.022462)

-0.327721***(0.081918)

Model II -0.142981***(0.032498)

-0.359401***(0.091545)

-0.046910*(0.027791)

Model III -0.122432***

(0.037203)

-0.397465***

(0.082479)

-0.057736*

(0.030909)

-0.161279***

(0.038693)

MICs (BRICS)

[5]

Model I -0.167326***

(0.068251)

-0.300195***

(0.082509)

Model II -0.193645***

(0.102726)

-0.408364***

(0.133144)

-0.082710

(0.073367)

Model III -0.12243***

(0.037203)

-0.3974***

(0.082479)

-0.057736*

(0.030909)

-0.161279***

(0.038693)

LICs

[10]

Model I -0.111716***

(0.027206)

-0.352239***

(0.118025)

Model II -0.141304***

(0.036735)

-0.364848***

(0.136587)

-0.042973

(0.032613)

Model III -0.086261***

(0.024822)

-0.423972***

(0.099213)

-0.016775

(0.028716)

-0.020308

(0.031332)

Africa

[9]

Model I -0.098028***

(0.031963)

-0.427619***

(0.104199)

Model II -0.131601***

(0.041812)

-0.427570***

(0.122564)

-0.058400**

(0.031572)

Model III -0.107015***

(0.032358)

-0.408247***

(0.100776)

-0.032190

(0.029437)

-0.034526

(0.046803)

Asia

[4]

Model I -0.127473***

(0.041693)

-0.213683

(0.193959)Model II -0.165803*

(0.092356)

-0.229174

(0.192154)

0.035224

(0.067468)

Model III -0.052518***

(0.019297)

-0.458148***

(0.162181)

-0.013347

(0.067688)

-0.044258*

(0.023795)Q andEare real and nominal exchange rate, Y /Y*= real GDP of home relative to foreign (US), G and G*=real government

expenditure of home relative to foreign (US), vol(E) exchange rate volatility, =first difference.***, ** and * refer to significance level at 1%, 5% and 10%. Standard errors in parentheses.

MICs: refers to middle income countries of the BRICS group (Brazil, Russia, India, China and South Africa)

LICs: refers to low income countries (Angola, Ethiopia, Ghana, Indonesia, Kenya, Nigeria, Philippines, Rwanda, Tanzania and

Uganda)

Africa refers to African countries (Angola, Ethiopia, Ghana, Kenya, Nigeria, Rwanda, Tanzania, Uganda, and South Africa)

Asia refers to Asian countries (China, India, Indonesia, Philippines)

Table 4.3 and Table 4.4 present the short run dynamics for individual countries in two income

groups (MICsandLICs). The results are presented for type-III model given our criteria.

Table 4.3 presents the short run dynamics forMICs-BRICS (Brazil, Russia, India, China and South

Africa). The short-run dynamics shows that the impact of productivity on real exchange rate is

significant and negative for all countries except Brazil and South Africa. Productivity does not have

an impact on real exchange rate in these countries. Expansionary Fiscal policy results in

depreciation of the real exchange rate in Brazil, Russia and China. The role of Exchange rate

volatility is significant in all countries. However, the effect is exceptionally positive in Russia.


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Table 4.3: Short-run dynamics by Country: Middle Income Group (BRICS): Model III

Q andEare real and nominal exchange rate, Y /Y*= real GDP of home relative to foreign (US), G and G*=real

government expenditure of home relative to foreign (US), vol(E) exchange rate volatility, =first difference.***, ** and * refer to significance level at 1%, 5% and 10%. Standard errors in parentheses.MICs: refers to middle income countries of the BRICS group (Brazil, Russia, India, China and South Africa)

The coefficients of error correction terms are negative and statistically less than one in absolute

value for all countries. The rate of adjustment is however, highest (56.25%) in Russia followed by

Brazil (22.76%). This may be associated with the size and nature of their economies. These are the

biggest economies in the group which account for 40% and 20% of the US economy. Faster rate of

adjustment means they can achieve equilibrium earlier than other countries.

Table 4.4, on the other hand, presents the short run dynamics for LICs. The short-run dynamics

shows that the impact of productivity on real exchange rate is significant and negative for all

countries except Indonesia and Uganda. Productivity does not impact real exchange rate in the

short run in these countries. The role of Fiscal policy is significant in all countries even though the

effect is different. Increase in government expenditure results in depreciation of the real exchange

rate in all countries but in Ghana, Kenya, Indonesia and Philippines. The strongest impact of fiscal

policy is shown by Uganda (0.15%). The impact of Exchange rate volatility is significant in all

countries except Indonesia.

The coefficients of error correction terms are negative and statistically significant for all countries.

The rate of adjustment is highest (24.89%) in Kenya followed by Philippines (14.58%) and

Ethiopia (13.63%). They achieve equilibrium earlier than other countries.

Cases Adjustment

coefficient


,> = 7 S,%K (/) %K (/) &%(?)

Allcountries

-0.12243***(0.037203)

-0.3974***(0.082479)

-0.057736*(0.030909)

-0.161279***(0.038693)

Brazil -0.227627***

(0.004377)

0.228636*

(0.096920)

-0.088359***

(0.006094)

-0.002061***

(1.62E-05)

China -0.064666***

(0.000587)

-0.5913***

(0.012107)

-0.101536***

(0.006333)

-0.354891***

(0.007472)

India -0.046854***

(0.000797)

-0.4483***

(0.026819)

0.015571

(0.008586)

-0.301000***

(0.006721)

Russia -0.562507***

(0.015174)

-0.4439***

(0.045163)

-0.451463***

(0.010053)

0.006537***

(3.27E-05)

South

Africa

-0.078684***

(0.001472)

-0.194967

(0.151551)

0.039537

(0.058783)

-0.373313***

(0.009758)


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Table 4.4: Short-run dynamics by Country: Low Income Group

Cases Adjustmentcoefficient


,> = 7 S,

%K (/

)

%K (/

)

&%(?)

Allcountries

-0.086261***(0.024822)

-0.423972***(0.099213)

-0.016775(0.028716)

-0.020308(0.031332)

Angola -0.001548***(4.27E-07)

-0.372509***(0.025896)

-0.171725**(0.003968)

-0.005968***(7.06E-06)

Ethiopia -0.136337***

(0.000650)

-0.816907***

(0.015748)

-0.03830***

(0.002945)

0.065656***

(0.007485)

Ghana -0.094831***

(0.000562)

-0.691301***

(0.035033)

0.03351***

(0.002317)

-0.042398***

(0.002733)

Indonesia 0.000108***

(1.02E-05)

-0.006073

(0.086557)

0.06723***

(0.010901)

-0.017159

(5.17E-05)

Kenya -0.248868***

(0.001492)

-0.121422***

(0.012865)

0.11217***

(0.000995)

0.164651***

(0.003452)

Nigeria -0.042414***

(0.000194)

-0.632917***

(0.009143)

-0.02030***

(0.000807)

-0.125337***

(0.002453)Philippines -0.145762***

(0.000584)

-0.647025***

(0.022619)

0.05421***

(0.003975)

-0.089533***

(0.002379)

Rwanda -0.075394***

(0.000518)

-0.345743***

(0.005333)

-0.012847**

(0.002458)

-0.028748***

(0.002997)

Tanzania -0.107271***

(0.000337)

-0.665093***

(0.013354)

-0.04128***

(0.002109)

-0.177574***

(0.003046)

Uganda -0.010293***

(5.76E-05)

0.059272

(0.032875)

-0.15042***

(0.007855)

0.053328***

(0.002087)

lnQ= log of real exchange rate differenced, lnY/Y*=log of real GDP relative to foreign (US) differenced, lnG/G*=log ofreal government expenditure relative to foreign(US) differenced, vol(E) exchange rate volatility differenced.


LICs: refers to low income countries (Angola, Ethiopia, Ghana, Indonesia, Kenya, Nigeria, Philippines, Rwanda, Tanzania andUganda)

5. Conclusions and Implications

5.1. Conclusions

In general, the results of our study confirm that the relationship between real exchange rate and

productivity does exist and is stronger for higher income countries in the long run. The level of

development [measured by real per capita income] matters more than the rate of economic growth

in explaining the effect of the Balassa term in our study. This may be related to what is stated in the

Balassa hypothesis. If a sustainable economic growth is derived by productivity growth in the

tradable sector, it will transform an economy to a higher level of per capita GDP. In this case,

productivity growth appreciates the real exchange rate. This may be the reason why the Balassa

hypothesis holds for MICs and why it does not hold in for LICs in general.

In the short run, however, we find almost uniform results. Productivity growth (mainly of

nontradables), expansionary fiscal policy and high exchange rate volatility results in real exchangerate depreciation. More specifically,


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=>Improvements in productivity and expansionary fiscal policies both have the impact of

depreciating real exchange rate in almost all countries both middle and low income.

=>The impact of exchange rate volatility is significant only in MICs. This may be associated with

the type of exchange rate policy/regime adopted. It is mainly fixed (unchanged) inLICsin which

case it may not be able to explain a change in real exchange rate in the short run.

5.2. Policy Recommendations

We recommend the following policy options for MICs and LICs on the basis of our findings.

=> Since the Balassa hypothesis does not hold for LICs, economic growth does not lead to real

exchange rate appreciation in these countries. Hence, countries in this group may continue to

grow by promoting productivity growth in the non-tradable sector.

=>The Balassa hypothesis does hold for the MICs. Economic growth does lead to real exchange

rate appreciation in these countries. Hence, countries in this group may promote growth by

increasing productivity in the tradable sector.

=>The role of fiscal policy may not last long inLICsbut inMICs. Therefore, it should be used only

as a short run tool to alter movements in real exchange rate in LICs. The same is true for

exchange rate volatility.


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Appendices

Annex A

1A: Model Derivation (Hackers contribution)

Suppose that the growth rate of real exchange rate is defined as a function of productivity

differential between the nontradable and tradable sectors as in equation (2.22),

>= 78 78 .This is the same as equation (1A),

>= = + l9 9 t (2A)In levels form this is equivalent to:

> = (/ )! (3A)and in log-levels it is

@ = + ,(/ ) (4A)Where @ %K > %K

We proxy/ with /where Yis the Home real GDP per capita and Y* is theforeign (US) real GDP per capita, so we get equation (2.23).

@ = + (,/) (5A)


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Annex B

Table 1B: Descriptive Statistics (all countries)

Table 2B: Cross Sectional Dependence Tests

Tests Non-augmented Model Augmented Model

Breusch-Pagan LM 235.4183*** 145.5989***

Pesaran scaled LM 7.964616*** 1.766490*

Bias-corrected scaled LM 7.837498*** 1.639371

Pesaran CD 11.56950*** -0.781383Null hypothesis: No cross-section dependence (correlation)

Table 3B: Panel unit root tests (IPS and Fisher tests)

*** indicates the rejection of the null hypothesis (unit root) at 1%.

Table 4B: Results of Cointegration tests

Model

IPS test ADF test PP test

Non-

Augmented

Augmented Non-

Augmented

Augmented Non-

Augmented

Augmented

Model I -19.1510*** -22.1827*** 356.986*** 427.724*** 353.953*** 430.157***

Model II -18.1349*** -22.0030*** 339.992*** 426.947*** 351.516*** 499.481***

Model II -17.3608*** -20.7525*** 314.362*** 392.643*** 317.275*** 409.215***

*** indicates rejection of the null hypothesis (unit root/no cointegration) at 1%.

Table 5B: Model Selection Criteria

Model Type LogL AIC* BIC HQ Specification

I 822.924667 -1.821332 -1.372511 -1.649096 ARDL(1, 1)

II 858.646483 -1.830431 -1.197479 -1.587536 ARDL(1, 1, 1)

III 920.977772 -1.980019 -1.244362 -1.697463 ARDL(1, 1, 1, 1 )

%K(>) %K (/) %K (/) &%(?)Mean -0.673730 -2.701146 0.946177 0.472646

Median -0.664635 -2.721925 1.181342 0.042409

Maximum 0.423540 -0.434831 2.860480 45.55217

Minimum -1.626351 -4.691372 -2.248809 0.000000

Std. Dev. 0.367519 0.790716 0.979527 2.854727

Skewness 0.229321 0.058521 -0.706851 11.76179

Kurtosis 2.768579 2.519203 2.946596 164.6432

Jarque-Bera 9.159802 8.498842 69.46539 909407.2

Probability 0.010256 0.014272 0.000000 0.000000

Sum -561.2175 -2250.055 788.1657 386.6244

Sum Sq. Dev. 112.3785 520.1929 798.2815 6658.114

Observations 833 833 833 818

Variables Specifications Pesaran statistics Fisher statistics Order of

integration

%K(>) Constant -1.05668 32.7336I(1)

Constant and trend 1.61611 21.1285

%K(>) Constant -21.0684*** 401.234***I(0)Constant and trend -20.0749*** 344.399***

%K (/) Constant -0.31331 34.8417I(1)Constant and trend 3.39295 21.6995

%K (/)Constant -16.2246*** 296.619***

I(0)Constant and trend -19.4344*** 327.624***

%K (/) Constant -0.92447 32.5055I(1)Constant and trend 0.61201 26.5756

%K (/) Constant -19.9922*** 377.622***I(0)Constant and trend -19.2329*** 350.826***

&%(?) Constant -13.7445*** 246.719***I(0)Constant and trend -16.0748 230.487


28/30

27

Annex C

Table 1C: Panel Cointegration Estimation: Pooled Mean Group Estimator (MICs)

a) Model I: 5 countries and one explanatory variableCases Adjustment

coefficientLong-run coefficients

%K(>)= 7 S,%K(>) %K (/) %K (/) All countries 0.798599

(0.392870)

0.109014

(0.151776)

-0.279218

(0.408663)


%K(>)= 7 S,%K(>) %K (/) %K (/)

All countries -0.167326***

(0.068251) 1.184039*** (0.297654)

-0.300195***

(0.082509)

0.193001

(0.143309)

Brazil -0.094035***(0.002577)

1.517754*** (0.092884) 0.080101(0.081196)

-0.134599(0.097405)

China -0.062517***

(0.001160)

0.731379*** (0.044042) -0.544820***

(0.016645)

0.172248*

(0.061033)

India -0.088281***

(0.001965)

0.651760*** (0.023531) -0.424671***

(0.029397)

0.116570**

(0.026727)

Russia -0.432825***(0.012245)

2.202588*** (0.107770) -0.200435***(0.030829)

0.727058*(0.308526)

South Africa -0.158971***

(0.005060)

0.816714*** (0.058186) -0.250946*

(0.106019)

0.083730

(0.075642)

b) Model II: 5 countries and two explanatory variablesCases Adjustment

coefficient


%K(>)= 7 S,%K(>) %K (/) %K (/) %K (/) %K (/)

All

countries

2.572100***

(0.618219)

0.382324**

(0.160930)

-2.035545** (0.784899) 0.220887*

(0.120288)

-1.001433***

(0.281870)


%K(>)= 7 S,%K(>) %K (/) %K (/) %K (/) %K (/)

All

countries

-0.193645***

(0.102726)

0.911459***

(0.179075)

-0.408364***

(0.133144)

0.280367

(0.233861)

-0.082710

(0.073367)

0.186317***

(0.061233)Brazil -0.145779***

(0.004389)1.251202***(0.114768)

0.015243(0.092681)

-0.112581(0.096360)

-0.009625(0.005991)

0.045260(0.043487)

China -0.093285***

(0.001064)

0.514951***

(0.041654)

-0.565473***

(0.014315)

0.112595

(0.052182)

-0.13009***

(0.007605)

0.278712***

(0.029306)

India -0.048770***(0.001090)

0.623423***(0.030155)

-0.391839***(0.032739)

0.117295** (0.027114) 0.002926(0.009092)

0.105934***(0.012866)

Russia -0.599758***

(0.017277)

1.420242***

(0.223360)

-0.784496***

(0.057028)

1.200264** (0.317115) -0.34638***

(0.012417)

0.376870**

(0.101346)

SouthAfrica

-0.080631***(0.001471)

0.747475***(0.062188)

-0.315255(0.146280)

0.084263(0.077302)

0.069632(0.042599)

0.124810**(0.035594)

c) Model III: 5 countries and three explanatory variable)Cases Adjustment

coefficient


%K(>)= 7 S,%K(>) %K (/) %K (/) %K (/) %K (/) &%(T)

Allcountries

3.073043***(0.585842)

0.344657**(0.149000)

-2.291279***(0.706127)

0.252061***(0.092608)

-0.885854***(0.253193)

0.024845**(0.012602)


%K(>)= 7 S,%K(>) %K (/) %K (/) %K (/) %K (/) &%(T)

Allcountries

-0.12243***(0.037203)

0.842451***(0.104108)

-0.3974***(0.082479)

0.195265**(0.086941)

-0.057736*(0.030909)

0.0647640.066303()

-0.161279***(0.038693)

Brazil -0.227627***

(0.004377)

1.030612***

(0.104719)

0.228636*

(0.096920)

-0.087487

(0.086499)

-0.088359***

(0.006094)

0.295247**

(0.052857)

-0.002061***

(1.62E-05)

China -0.064666***(0.000587)

0.546403***(0.034465)

-0.5913***(0.012107)

0.194748**(0.043771)

-0.101536***(0.006333)

0.224381***(0.024206)

-0.354891***(0.007472)

India -0.046854***

(0.000797)

0.58133***

(0.026750)

-0.4483***

(0.026819)

0.144519***

(0.022928)

0.015571

(0.008586)

0.083573***

(0.011171)

-0.301000***

(0.006721)

Russia -0.562507***(0.015174)

1.35472***(0.171888)

-0.4439***(0.045163)

1.173511**(0.302887)

-0.451463***(0.010053)

0.323706**0.(064650)

0.006537***(3.27E-05)

South

Africa

-0.078684***

(0.001472)

0.64524***

(0.054775)

-0.194967

(0.151551)

-0.002830

(0.067864)

0.039537

(0.058783)

0.225998***

(0.030591)

-0.373313***

(0.009758)



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28

Table 2C: Panel Cointegration Estimation: Pooled Mean Group Estimator (LICs)

a) Model I: 10 countries and one explanatory variableCases Adjustment

coefficientLong-run coefficients

%K(>)= 7 S,%K(>) %K (/) %K (/) All countries 0.316777

(0.223733)

0.320488*

(0.132415)

0.832994***

(0.212990)


%K(>)= 7 S,%K(>) %K (/) %K (/)


(0.027206)

0.764732*** (0.100349) -0.352239***

(0.118025)

0.287913**

(0.131336)

Angola -0.102129***(0.012986)

0.855895** (0.208818) -0.253948**(0.044527)

0.617786**(0.337611)

Ethiopia -0.336409***

(0.003452)

0.501835*** (0.033156) -0.971438***

(0.018734)

0.383020***

(0.041881)

Ghana -0.122792***

(0.003199)

0.311695** (0.072224) -0.778050***

(0.043337)

0.783258***

(0.093796)

Indonesia -0.138577***

(0.004322)

0.809850*** (0.128066) 0.297808**

(0.063096)

0.577364**

(0.162552)Kenya -0.103606***

(0.002487)

1.058192*** (0.039619) -0.460566***

(0.034020)

0.179325** (0.045056)

Nigeria -0.036397***

(0.000244)

0.846841*** (0.088541) -0.492910***

(0.011218)

0.859689*** (0.121913)

Philippines -0.108977***

(0.003604)

0.673336*** (0.075928) -0.243259**

(0.071328)

-0.198562*

(0.083026)

Rwanda -0.066496***

(0.000788)

0.850908*** (0.048690) -0.427341***

(0.006880)

-0.007950

(0.053100)

Tanzania -0.050526***

(0.001991)

0.373877*** (0.057828) -0.292225***

(0.015115)

-0.005919

(0.065919)

Uganda -0.051252***

(0.000494)

1.364888*** (0.055526) 0.099539*

(0.032548)

-0.308875** (0.066104)


b) Model II: 10 countries and two explanatory variablesCases Adjustment

coefficient


%K(>)= 7 S,%K(>) %K (/) %K (/) %K (/) %K (/)

All countries 0.687212***

(0.225947)

0.211173**

(0.098594)

0.385414

(0.251218)

0.140663***

(0.041793)

-0.350725***

0.095940()


%K(>)= 7 S,%K(>) %K (/) %K (/) %K (/) %K (/)


(0.036735)

0.763468***

(0.103235)

-0.364848***

(0.136587)

0.265293**

(0.135126)

-0.042973

(0.032613)

-0.014364

(0.109360)

Angola -0.183476***

(0.010533)

1.043815***

(0.167278)

-0.289023***

(0.034866)

0.620600**

(0.262359)

-0.132596***

(0.004975)

-0.716392***

(0.099287)

Ethiopia -0.411325***(0.003066)

0.447039***(0.029137)

-1.107956***(0.016134)

0.444276***(0.033366)

-0.139015***(0.002781)

0.112293***(0.016147)

Ghana -0.230456***(0.004595)

0.262230**(0.069607)

-0.885434***(0.051758)

0.643068***(0.087026)

-0.089409***(0.003402)

0.171048**(0.042679)

Indonesia -0.157128***

(0.005046)

0.807029***

(0.130655)

0.386360**

(0.076477)

0.492586*

(0.163478)

0.045406**

(0.010344)

-0.053054

(0.074396)

Kenya -0.119677***

(0.003269)

1.026971***

(0.037416)

-0.404290***

(0.033373)

0.169240**

(0.044295)

0.074036***

(0.003699)

-0.153491***

(0.021064)

Nigeria -0.027739***

(0.000211)

0.796104***

(0.083229)

-0.507388***

(0.010632)

0.946687***

(0.113823)

-0.015557***

(0.001098)

0.501909***

(0.043609)

Philippines -0.113822***(0.004514)

0.710150***(0.072457)

-0.381052**(0.071595)

-0.142261(0.082116)

0.132843***(0.014086)

-0.382858***(0.046927)

Rwanda -0.100080***

(0.001786)

0.745821***

(0.055946)

-0.433120***

(0.007076)

-0.064469

(0.054039)

-0.009768*

(0.003485)

-0.020404

(0.031347)

Tanzania -0.035869***

(0.002403)

0.440817***

(0.057186)

-0.090052**

(0.026334)

-0.062814

(0.065823)

-0.142793***

(0.005772)

0.100866**

(0.030611)

Uganda -0.033473***(0.000409)

1.354706***(0.052564)

0.063473(0.031394)

-0.393982***(0.065113)

-0.152872***(0.006981)

0.296444***(0.022795)



30/30

c) Model III: 10 countries and three explanatory variablesCases Adjustment

coefficient


%K(>)= 7 S,%K(>) %K (/) %K (/) %K (/) %K (/) &%(T)

All countries 1.066903***(0.212739)

-0.287286***(0.086673)

0.272016(0.255193)

0.010920(0.049502)

-0.018202(0.128177)

-3.216100***(0.419248)


%K(>)= 7 S,%K(>) %K (/) %K (/) %K (/) %K (/) &%(T)All countries -0.086261***

(0.024822)

0.563134***

(0.129416)

-0.423972***

(0.099213)

0.189612

(0.116225)

-0.016775

(0.028716)

0.017332

(0.095085)

-0.020308

(0.031332)

Angola -0.001548***

(4.27E-07)

0.936938***

(0.125498)

-0.372509***

(0.025896)

0.422490

(0.181194)

-0.171725**

(0.003968)

-0.585533**

(0.088576)

-0.005968***

(7.06E-06)

Ethiopia -0.136337***

(0.000650)

0.365378***

(0.027949)

-0.816907***

(0.015748)

0.34775***

(0.034343)

-0.03830***

(0.002945)

0.13374***

(0.019193)

0.065656***

(0.007485)

Ghana -0.094831***

(0.000562)

0.093121

(0.047023)

-0.691301***

(0.035033)

0.310028**

(0.062187)

0.03351***

(0.002317)

-0.079616*

(0.025340)

-0.042398***

(0.002733)

Indonesia 0.000108***

(1.02E-05)

0.807090***

(0.124859)

-0.006073

(0.086557)

0.620197**

(0.160934)

0.06723***

(0.010901)

0.029467

(0.068542)

-0.017159

(5.17E-05)

Kenya -0.248868***

(0.001492)

0.261664***

(0.013310)

-0.121422***

(0.012865)

-0.038091*

(0.012417)

0.11217***

(0.000995)

-0.05375***

(0.007361)

0.164651***

(0.003452)

Nigeria -0.042414***

(0.000194)

0.732966***

(0.061339)

-0.632917***

(0.009143)

0.77990***

(0.085053)

-0.02030***

(0.000807)

0.59288***

(0.037783)

-0.125337***

(0.002453)

Philippines -0.145762***

(0.000584)

0.319883***

(0.022588)

-0.647025***

(0.022619)

0.106018**

(0.023882)

0.05421***

(0.003975)

-0.044644*

(0.015794)

-0.089533***

(0.002379)Rwanda -0.075394***

(0.000518)

0.708706***

(0.036318)

-0.345743***

(0.005333)

-0.161643**

(0.041584)

-0.012847**

(0.002458)

0.027850

(0.023495)

-0.028748***

(0.002997)

Tanzania -0.107271***

(0.000337)

0.066797**

(0.019542)

-0.665093***

(0.013354)

-0.127586**

(0.023788)

-0.04128***

(0.002109)

-0.12341***

(0.010226)

-0.177574***

(0.003046)

Uganda -0.010293***

(5.76E-05)

1.338799***

(0.054160)

0.059272

(0.032875)

-0.362955**

(0.067288)

-0.15042***

(0.007855)

0.27633***

(0.027592)

0.053328***

(0.002087)


p160 baylie testing the balassa hypothesis in low and middle income countries

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