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DEPARTMENT OF ECONOMICS

ISSN 1441-5429

DISCUSSION PAPER 47/13

Will initiatives to promote hydroelectricity consumption be effective? Evidence

from univariate and panel LM unit root tests with structural breaks

Hooi Hooi Lean and Russell Smyth

Abstract This paper examines whether initiatives to promote hydroelectricity consumption are likely to be

effective by applying univariate and panel Lagrange Multiplier (LM) unit root tests to

hydroelectricity consumption in 55 countries over the period 1965 to 2011. We find that for the

panel, as well as about four-fifths of individual countries, that hydroelectricity consumption is

stationary. This result implies that shocks to hydroelectricity consumption in most countries will

only result in temporary deviations from the long-run growth path. An important consequence of

this finding is that initiatives designed to have permanent positive effects on hydroelectricity

consumption, such as large-scale dam construction, are unlikely to be effective in increasing the

share of hydroelectricity, relative to consumption of fossil fuels.

© 2013 Hooi Hooi Lean and Russell Smyth

All rights reserved. No part of this paper may be reproduced in any form, or stored in a retrieval system, without the

prior written permission of the author.

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Introduction

Hydroelectricity is a form of hydropower used to produce electricity via the kinetic energy of

moving water, which is converted to electrical energy by a water turbine driving a generator

(Bakis, 2007). Hydropower has been described “as the most technically mature and reliable

source of renewable energy” (Jia et al., 2012, p. 1357). It is the most important renewable

energy source for the generation of electricity worldwide, providing 97 per cent of all

electricity generated from renewable sources (Bakis, 2007). In 2008, hydropower generation

was responsible for approximately 20 per cent of the world’s electricity, and 40 per cent of the

electricity in developing countries, which was second only to fossil fuels (Jia et al., 2012). In

that same year, there were 16 countries that relied on hydropower for more than 90 per cent of

their energy supply; 49 countries that relied on hydropower for more than 50 per cent of their

energy supply and 57 countries that relied on hydropower for more than 40 per cent of their

energy supply (Jia et al., 2012).

The world’s demand for electricity is expected to increase. The United States Energy

Information Administration forecasts that world electricity consumption will almost double

between 2007 and 2035, while the International Energy Agency predicts that by 2040 world

energy consumption will be 30 per cent higher than in 2010 (Vandel, 2012). Fossil fuels are

the major cause of climate change and global warming (Stern 2006). Given that burning fossil

fuels is the major way of generating electricity, this raises serious concerns about how the

projected growth in world electricity consumption will effect the environment under steady

state assumptions.

It is in this context that it is often argued that hydropower represents a clean alternative source

of electricity to burning fossil fuels and that increasing the proportion of electricity generated

from hydropower would be beneficial for the environment (see eg. Bakis, 2007; Jia et al.,

2012; Vandal, 2012). There is plenty of scope to further develop hydropower use. As a

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proportion of potential availability, current electricity generated from hydropower is 11 per

cent in Africa, 25 per cent in Asia, 45 per cent in Oceania, 71 per cent in Europe, 65 per cent

in North America and 40 per cent in South America (Jia et al., 2012). Perhaps the main

benefit of increasing hydropower is that compared to burning coal, hydroelectricity produces

virtually no air pollution (Bakis, 2007; Chamberland & Levesque, 1996; Childress, 2009;

Rosa, 2001). Among the various sources of energy, hydropower has the highest energy

payback and lowest greenhouse gas emission (Jia et al., 2012).

The purpose of this study is to examine the integration properties of hydroelectricity

consumption for 55 countries/subregions using univariate and panel unit root tests, with and

without structural breaks.1 There has been a surge in studies that examine the unit root

properties of energy consumption, to the point that testing for a unit root in energy

consumption has been described “as a new branch of research in energy economics” (Narayan

et al., 2010, p. 1953). However, most studies have focused on aggregate energy consumption

or consumption of specific fossil fuels and have used data from the United States. This study

responds to calls for more studies that examine the unit root properties of disaggregated

renewable energy consumption and consider countries other than the United States (Smyth,

2013). The use of a relatively large number of countries has the advantage that it is possible

to use both a panel and allow for structural breaks, which has been recommended as providing

the most reliable evidence of the order of integration of energy variables (Smyth, 2013).

The issue of whether hydroelectricity consumption contains a unit root speaks directly to the

efficacy of policies, such as construction of large-scale water storage infrastructure,

promotion of small hydropower plants and investment in new hydro technologies such as

hydrokinetics, designed to increase the proportion of electricity generated from hydropower.

1 The sample consists of 50 countries plus five residual series/sub-regions corresponding to five of the six

regions considered (Other South and Central America, Other Europe and Eurasia, Other Middle East, Other

Africa and Other Asia-Pacific). For simplicity, hereafter, we refer to ‘countries’.

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Large-scale dams, in particular, often represent the single largest structure in terms of basic

infrastructure in all countries and become major landmarks in the countries in which they are

located (Bakis, 2007; Jia et al., 2012). Hence, each of these policies is designed to bring about

permanent changes in the electricity mix and are, by their vary nature, long-term because they

typically entail making large-scale investments. If hydroelectricity consumption contains a

unit root, a shock to the long run growth path of hydroelectricity consumption will have

permanent effects. This implies that policies involving large-scale investment, the purpose of

which is to engineer positive shocks to hydroelectricity consumption, will result in continuing

annual shocks and, as such, will be effective. On the other hand, if hydroelectricity

consumption is stationary, following a policy-induced shock, hydroelectricity consumption

will return to its long run growth path and policies designed to engineer permanent changes to

consumption will be ineffective.

Existing Literature

Beginning with Narayan and Smyth (2007), a large literature has evolved which tests for a

unit root in energy consumption. This literature has recently been comprehensively reviewed

in Smyth (2013). A subset of this literature has examined the unit root properties of

renewable energy or other alternative energy sources (Apergis & Tsoumas, 2011; Barros et

al., 2012, 2013a, 2013b; Lean & Smyth, 2013a, 2013b). There are two features of this

literature worth noting. The first is that with the exception of Lean and Smyth (2013a), who

examine the unit root properties of aggregate renewable energy generation across 115

countries, each of these studies is focused on the United States. The second is that with the

exception of Barros et al., (2013b), these studies do not examine the unit root properties of

hydroelectricity consumption, despite its importance as a renewable energy source and the

need to get a handle on the likely efficacy of policies to increase the proportion of

hydropower in the electricity mix. Barros et al. (2013b) found that hydropower exhibits

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nonstationarity, but mean reverting behaviour, in the United States. We address a gap in the

literature by not only considering the unit root properties of hydroelectricity consumption in

the United States, but other countries, and regions, as well.

Methodology

We apply the Lagrange Multiplier (LM) family of univariate and panel unit root tests with up

to two structural breaks. The LM univariate unit root tests consist of the Schmidt and Phillips

(1992) LM unit root test with no structural breaks and the Lee and Strazicich (2003) LM unit

root test with one and two structural breaks. The LM unit root test with one and two breaks

developed by Lee and Strazicich (2003) represent a methodological improvement over ADF-

type endogenous one and two break unit root tests proposed by Zivot and Andrews (1992) and

Lumsdaine and Papell (1997), which have the limitation that the critical values are derived

while assuming no break under the null hypothesis. Nunes et al. (1997) showed that this

assumption leads to size distortions in the presence of a unit root with structural breaks. As a

result, when utilizing ADF-type endogenous break unit root tests, one might conclude that a

time series is trend stationary, when in fact it is nonstationary with break(s), meaning that

spurious rejections might occur. In contrast to the ADF-type endogenous break tests, the LM

unit root test is unaffected by structural breaks under the null hypothesis (Lee & Strazicich,

2001). The panel LM unit root test with up to two structural breaks was proposed by Im et al.

(2005). The panel LM unit root test proposed by Im et al. (2005) has the major advantage

that, consistent with the univariate LM unit root tests, it allows for structural breaks under

both the null and alternative hypotheses. Given that the univariate and panel LM unit root

tests with up to two breaks are well known, we do not reproduce the methodology here, but

refer the reader to the original articles or other recent applications of the LM unit root tests to

energy consumption (see eg. Aslan & Kum, 2011; Aslan, 2011; Apergis & Payne, 2010;

Apergis et al., 2010; Lean & Smyth, 2013a; Narayan et al., 2008, 2010).

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In the univariate LM unit root case, we begin with the Schmidt and Phillips (1992) test with

no breaks, which provides a benchmark for later results. As first noted by Perron (1989), a

problem with the no-break test is that failure to incorporate a statistically significant structural

break will reduce the power to reject the null hypothesis of a unit root. Hence, it is important

to accommodate structural break(s). For the univariate unit root test with structural break(s),

we employ both the LM unit root test with one and two breaks in the intercept (Model A,

Model AA) (the so-called ‘crash model’) and the LM unit root test with one and two breaks in

the intercept and slope (Model C, Model CC) (the so-called ‘crash-cum-growth model’).

However, just as failure to accommodate structural breaks might result in loss of power to

reject the unit root null, as Ben-David et al. (2003) note, allowing for a break does not

necessarily produce more rejections of the unit root null hypothesis because the critical value

increases in absolute value. Thus, in the analysis below, we employ a rule of thumb where in

the event that the no-break case and one-break case give different results we prefer the one-

break case if the structural break is statistically significant and in the event that the one-break

case and two-break case give different results we prefer the two-break case if the second

structural break is statistically significant. This rule of thumb has been employed in previous

studies that have compared no-break and one-break results (see eg. Lean & Smyth 2013c,

2013d).

For the Lee and Strazicich (2003) LM tests, the break date is selected by searching for all

possible break dates over the trimming region (0.15T, 0.85T), where T is the sample size. We

present the results for the Schmidt and Phillips (1992) LM test for zero to four lags. For the

Lee and Strazicich (2003) LM tests the lag length is selected using the general-to-specific

approach suggested by Hall (1994) with a maximum of 4 lagged terms. Once the optimal lag

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length at each combination of breakpoints is decided, we determined the breaks where the LM

t-test statistic is at a minimum.

We apply the panel LM unit root test with up to two breaks to regional panels. Taylor and

Sarno (1998) suggested that rejection of the null hypothesis of a joint unit root using panel

data tests might be due to as few as one of the series being stationary. To address this issue,

we also apply the LM unit root test with no breaks and with one and two structural breaks to

smaller panels excluding those countries for which Models A, AA, C and CC rejected the unit

root null in hydroelectricity consumption.

Data

We have annual data on hydroelectricity consumption, measured in million tonnes oil

equivalent (mtoe), for 55 countries for the period 1965 to 2011. The data source is the BP

Statistical Review of World Energy. Table 1 shows the 55 countries, divided into six regions.

Countries in Europe/Eurasia and the Asia Pacific constitute about 70 per cent of the sample.

We computed the ratio of each region’s consumption as a proportion of world consumption.

Descriptive statistics are presented in Table 2. Europe/Eurasia has the highest mean

consumption, while the Middle East has the lowest mean consumption over the sample

period. The distribution for Africa and the Asia Pacific is significantly not normal at the 1 per

cent level.2 Table 3 presents descriptive statistics on total consumption of hydroelectricity for

each of the 55 countries. Mean total consumption is highest in the United States and Canada

and also exceeds 40 mtoe in Brazil and China. Mean total consumption is lowest in Denmark

The standard deviation, which can be seen as a proxy for volatility in consumption, is highest

2 Based on plots of the regional consumption ratios, the consumption ratio is decreasing for developed regions

like North America and Europe and Eurasia, while it is increasing for developing/emerging regions like South

and Central America, Middle East, Africa and the Asia Pacific.

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in China, followed by Brazil and Canada. The standard deviation is lowest in Denmark. There

are just 12 significant series, based on the Jarque-Bera statistic.

Results

Univariate unit root test with no breaks

We started through applying the Schmidt and Phillips (1992) LM unit root test to the data on

hydroelectricity consumption. The results are reported in Table 4. There are two test statistics,

Z(ρ) and Z(τ). There are 20 countries for which both statistics are significant for lags equal to

zero to four (Mexico, Bulgaria, Czech Republic, Denmark, Finland, France, Germany,

Greece, Hungary, Republic of Ireland, Italy, Portugal, Spain, Sweden, Switzerland, United

Kingdom, Algeria, Japan, Taiwan and Thailand). Thus, on the basis of the Schmidt and

Phillips (1992) LM unit root test, we reject the unit root null for just over one-third of the

sample countries.

Univariate unit root test with one break

Failure to reject the unit root null for the other approximately two-thirds of countries

potentially reflects failure to allow for a structural break in hydroelectricity consumption.

Tables 5 and 6 present the results for Lee and Strazicich’s (2003) Models A and C, which

allow for a structural break in the intercept and structural break in the intercept and slope

respectively. Model A provides slightly more evidence than the Schmidt and Phillips (1992)

test in favour of stationarity. Specifically, with Model A the unit root null is rejected for 26

countries (Mexico, Chile, Bulgaria, Czech Republic, Denmark, Finland, France, Germany,

Greece, Hungary, Republic of Ireland, Italy, Spain, Sweden, Switzerland, United Kingdom,

Iran, Algeria, Australia, China, Japan, Malaysia, South Korea, Taiwan, Thailand and Other

Asia Pacific). Thus, with Table A the unit root null is rejected for almost 50 per cent of

countries. Model C provides more evidence in favour of stationarity again. For model C, the

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unit root null is rejected for 41 countries (United States, Mexico, Chile, Ecuador, Belgium and

Luxembourg, Bulgaria, Czech Republic, Denmark, Finland, France, Germany, Greece,

Hungary, Republic of Ireland, Italy, Norway, Poland, Portugal, Romania, Slovakia, Spain,

Sweden, Switzerland, United Kingdom, Other Europe and Eurasia, Iran, Algeria, Egypt,

South Africa, Australia, Bangladesh, China, India, Japan, Malaysia, New Zealand, Pakistan,

Philippines, South Korea, Taiwan and Thailand), representing three quarters of the sample.

Given that the results of the no-break case and Model A and Model C differ, it is important to

determine which results are to be preferred. First, in terms of comparing Model A with Model

C, Sen (2003a, 2003b) showed that Model C produced more reliable estimates on the basis of

Monte Carlo simulations. Thus, we prefer Model C to Model A. Second, in choosing between

Model C and the no-break case, we prefer the results from Model C if the break in the

intercept and/or slope is significant. This was the case for each country for which Model C

and the no-break case gave a different result. Thus, we prefer the results for Model C over the

no-break case. Hence, after allowing for one break we conclude that the unit root null for

hydroelectricity consumption can be rejected for three quarters of the countries.

Univariate unit root test with two breaks

This still leaves one quarter of the countries for which we do not reject the unit root null for

hydroelectricity consumption. Failure to reject the unit root null for these countries could be a

by-product of failure to allow for a second break in the series. Tables 7 and 8 present the

results for Lee and Strazicich’s (2003) Models AA and CC, which allow for two structural

breaks in the intercept and two structural breaks in the intercept and slope respectively. Model

AA rejects the unit root null for 30 countries (Mexico, Chile, Ecuador, Bulgaria, Czech

Republic, Denmark, Finland, France, Germany, Greece, Hungary, Republic of Ireland, Italy,

Norway, Portugal, Slovakia, Spain, Sweden, Switzerland, Turkey, United Kingdom, Iran,

Algeria, China, Japan, Malaysia, Philippines, South Korea, Taiwan and Thailand), which is

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slightly more than Model A. Model CC rejects the unit root null for 44 countries (Canada,

Mexico, Brazil, Chile, Ecuador, Venezuela, Austria, Belgium and Luxembourg, Bulgaria,

Czech Republic, Denmark, Finland, France, Germany, Hungary, Republic of Ireland, Italy,

Norway, Poland, Portugal, Romania, Slovakia, Spain, Sweden, Switzerland, Turkey, United

Kingdom, Other Europe and Eurasia, Iran, Algeria, Egypt, South Africa, Australia,

Bangladesh, China, Japan, Malaysia, New Zealand, Pakistan, Philippines, South Korea,

Taiwan, Thailand and Vietnam). This represents 80 per cent of the sample and is a small

increase in the number of rejections on Model A.

We next reach a conclusion on a preferred set of results based on the no-break case as well as

Models A, C, AA and CC. First, in choosing between Models AA and CC, there is no Monte

Carlo evidence, of which we are aware, as in the one-break case. However, Model CC is

more general than Model AA in that it allows for breaks in the slope. On this basis, we prefer

Model CC over Model AA. This has generally been the approach taken in the literature (see

eg. Lean & Smyth, 2013b). Second, in choosing between Model CC and a no break/Model C

hybrid, we prefer Model CC if the second break in the intercept and/or slope is significant.

The results of this exercise are summarized in Table 9. The first column summarizes the

results comparing Model C with the no break case. As discussed above, for each country for

which Model C differs from the no-break case, the break in the intercept or slope is

significant. Thus, the no-break/Model C hybrid corresponds to Model C with the unit root null

rejected for 41 of 55 countries. The second column summarizes the results comparing Model

CC with the no-break/Model C hybrid. The results are similar to Model CC, with the

exception of Austria. Model CC suggests that the unit root null is rejected for Austria.

However, as neither the second break in the intercept nor slope is significant, the results for

Model C for Austria are preferred. Thus, overall we conclude that on the basis of a Model

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CC/Model C/no break hybrid that the unit root null is rejected for 43 countries, representing

78 per cent of the total.

We now turn to the question of why hydroelectricity consumption exhibits a unit root in some

countries, but not others. Narayan et al. (2008) hypothesised that energy consumption is more

likely to be non-stationary in countries in which energy consumption is more volatile,

reflected in a higher standard deviation. The rationale is that in those countries in which

energy consumption is more volatile, the level of persistence following a shock will be higher.

There is, at best, mixed support for this hypothesis. Of the 12 countries for which the

standard deviation in hydroelectricity consumption is greater than three, hydroelectricity

consumption is non-stationary in five countries (United States, Argentina, Colombia, Other

South and Central America and India). However, hydroelectricity consumption is stationary in

each of the three countries with the highest standard deviation (China, Brazil and Canada).

An alternative hypothesis, which has been proposed, is that energy consumption is more

likely to be non-stationary in countries that are large consumers (Narayan et al., 2008; Barros

et al, 2011; Maslyuk & Smyth, 2009). The rationale is that in such countries, shocks will

generate larger deviations from the long-run growth path. There is, however, little support for

this hypothesis. Of the 11 countries for which average hydroelectricity consumption exceeds

10 mtoe, hydroelectricity consumption is non-stationary in just two countries (India and the

United States).

Thus, overall, the order of integration of hydroelectricity consumption seems largely unrelated

to either the size of consumption or volatility in consumption. This is similar to the conclusion

reached by Lean and Smyth (2013a) for renewable energy generation for 115 countries, which

is the closest, large-scale multi-country study to this one in terms of the type of disaggregated

energy series considered. The one qualification to this conclusion is that, consistent with the

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result for hydropower in Barros et al. (2013b), we conclude that hydroelectricity consumption

in the United States is non-stationary. The United States has the highest mean consumption

of hydroelectricity and a sizeable standard deviation, which is in excess of eight.

As one would expect, given that we apply four models (Models A, AA, C, CC) to

hydroelectricity consumption across 55 countries, there is considerable variation in the

location of structural breaks. One set of breaks at the beginning of the 1980s, the late 1980s

and early 1990s coincide with spikes in oil prices, which were catalysts in the high-income

countries to find renewable energy alternatives to fossil fuels. A second set of breaks coincide

with the ratification of international treaties, such as the Kyoto Protocol, which provided the

stimulus for providing renewable alternatives to fossil fuels, or forums devoted to the

advancement of hydropower.

A third set of breaks, at the end of the sample period, correspond with international

agreements recognising the importance of hydropower. Resulting from the United Nations

Symposium on Hydropower and Sustainable Development in 2004, 44 countries signed the

“Beijing Declaration on Hydropower and Sustainable Development” which confirmed the

strategic importance of hydropower to sustainable development. Some of these agreements

provided particular impetus for the promotion of hydropower in specific regions of the world.

For instance, in 2008 a number of international organisations published the World Declaration

on Hydropower (Africa), stressing the importance of dam and hydropower development to the

continent. As a consequence, international organisations that signed, agreed to assist African

countries in joint efforts to promote hydropower development.

A fourth set of breaks are linked to policies in specific countries to promote renewable energy

as well as the construction of dams in specific countries. As an example of the former, the

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breaks in the United States in 1999 and 2000 coincided with various innovations at the state

level, such as the introduction of renewable portfolio standards, that required utilities to

generate, or purchase, minimum levels of renewable energy (Lean & Smyth, 2013b).

Examples of the latter include the breaks in the 2000s in China, which are associated with the

completion of Three Gorges in 2009, Xiluodu in 2009 and Longtan in 2001; the break in 1985

in Brazil, which is associated with the completion of Tucurui in 1984 and the breaks in 1987

and 1989 in Venezuela, which are associated with the construction of Guri in 1986.

Panel unit root test with up to two breaks

We finish by applying the LM panel unit root with up to two breaks, proposed by Im et al.

(2005). We first applied the LM unit root test with up to two breaks to a world panel of the 55

countries, as well as regional panels. The unit root null is rejected for the world panel in each

case. The unit root null is also rejected for each regional panel, at least in the no break or one

break case. In cases, such as South and Central America, in which the unit root null is rejected

with no breaks, but not one or two breaks, the likely explanation is that adding one or more

breaks when the unit root null is rejected weakens the power of the panel test to reject the unit

root null.

We also apply the panel unit root test to the world panel, excluding those countries which

alternative models (Model A, AA, C, CC, CC/C hybrid) suggest are stationary. The results of

this exercise suggest that stationary individual series are not driving the results for the world

panel. Excluding stationary individual series, we conclude that the panel unit root null is still

rejected, at least in the no-break or one break case.

Conclusion

Hydroelectricity is already responsible for one fifth of the world’s electricity consumption.

This, however, still trails fossil fuel combustion as a source of the world’s electricity supply

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by a relatively large margin. Concern about the adverse effects of fossil fuel combustion on

global warming has led to calls to increase the proportion of electricity generated from

hydropower. International resolutions, such as the United Nations sponsored Beijing

Declaration on Hydropower and Sustainable Development, see increasing the share of

hydroelectricity in the world electricity mix as an important platform of ensuring global

sustainable development.

This study has applied the LM unit root family of tests to the time series of hydroelectricity

consumption in 55 countries to examine the likely success of initiatives and policies designed

to increase the share of electricity coming from hydropower. The results suggest that the unit

root null is rejected for roughly four fifths of the sample, as well for world and regional

panels. Overall, this suggests that shocks to hydroelectricity consumption in most countries

will only result in temporary deviations from the long-run growth path. An important

consequence of this finding is that initiatives/policies designed to have permanent positive

effects on hydroelectricity generation through shocking the long-run growth path, such as

large-scale investment in dams and hydrokinetics, are unlikely to be effective in generating

continuing annual shocks. This rather pessimistic conclusion is similar to the assessment of

the potential for renewable energy in the United States by Apergis and Tsoumas (2011), but

differs from the more optimistic conclusions in Barros et al. (2012) and Lean and Smyth

(2013a, 2013b). The upside is that we conclude that hydroelectricity consumption is non-

stationary in some relatively large consumers of hydroelectricity, which are pushing policies

to further increase the share of hydroelectricity in electricity consumption (eg. Argentina,

India and the United States). The assessment of the potential for long-term permanent

investments in hydroelectricity to reap benefits in these countries is much more rosy.

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Sen, A. (2003b) Some aspects of the unit root testing methodology with application to real per capita

GDP, Manuscript, Xavier University, Cincinnati, OH.

Smyth, R. (2013) Are fluctuations in energy variables permanent or transitory? A survey of the

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371-8.

Stern, N. (2006) Stern Review on the Economics of Climate Change. London: UK Treasury.

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17

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18

Table 1: Countries in the sample by region

Country/Region Number

North America:

US, Canada, Mexico

3

South and Central America:

Argentina, Brazil, Chile, Colombia, Ecuador, Peru, Venezuela, Other South

and Central America

8

Europe and Eurasia:

Austria, Belgium and Luxembourg, Bulgaria, Czech Republic, Denmark,

Finland, France, Germany, Greece, Hungary, Republic of Ireland, Italy,

Norway, Poland, Portugal, Romania, Slovakia, Spain, Sweden, Switzerland,

Turkey, United Kingdom, Other Europe and Eurasia

23

Middle East:

Iran, Other Middle East

2

Africa:

Algeria, Egypt, South Africa, Other Africa

4

Asia Pacific:

Australia, Bangladesh, China, India, Indonesia, Japan, Malaysia, New

Zealand, Pakistan, Philippines, South Korea, Taiwan, Thailand, Vietnam,

Other Asia Pacific

15

Total countries 55

19

Table 2: Descriptive statistics on the regional consumption ratios

Countries Mean Std. Dev. Skewness Kurtosis Jarque-Bera

North America 29.1561 5.3251 -0.1296 1.8734 2.6171

South and Central America 14.5885 6.0573 -0.4000 1.6780 4.6760*

Europe and Eurasia 34.0935 5.4336 -0.2481 2.1218 1.9925

Middle East 0.4745 0.1724 0.5350 3.4592 2.6551

Africa 2.5625 0.4445 -1.1438 4.0260 12.3088***

Asia Pacific 19.1249 4.7104 1.4269 3.9625 17.7631***

* (**) *** denote statistical significance at the 10%, 5% and 1% levels respectively.

20

Table 3: Descriptive statistics for hydroelectricity consumption in each country

Mean Std. Dev. Skewness Kurtosis Jarque-Bera

US 62.9969 8.2439 -0.0333 2.9086 0.0251

Canada 62.0547 18.3914 -0.5184 1.8322 4.7754*

Mexico 5.0752 1.6097 0.2422 2.5461 0.8629

Argentina 4.6006 3.2406 0.1048 1.6616 3.5939

Brazil 44.1940 26.8561 0.1386 1.9018 2.5123

Chile 2.9337 1.6831 0.4201 1.8554 3.9484

Colombia 5.1722 3.0500 0.1399 1.7470 3.2280

Ecuador 0.9637 0.7272 0.1853 1.7632 3.2644

Peru 2.4470 1.2864 0.3039 1.8471 3.3268

Venezuela 8.3142 6.3973 0.3180 1.7112 4.0446

Other South and Central America 9.5706 7.3170 0.1509 1.3333 5.6182*

Austria 6.7817 1.7383 -0.4574 2.0068 3.5710

Belgium and Luxembourg 0.0664 0.0180 -0.0835 2.6023 0.3643

Bulgaria 0.6068 0.1829 0.9854 5.2946 17.9174***

Czech Republic 0.4577 0.1270 0.4897 2.7062 2.0476

Denmark 0.0056 0.0012 1.2889 3.2442 13.1310***

Finland 2.7580 0.4083 0.1487 2.7042 0.3447

France 13.8325 2.1212 -0.0130 1.9975 1.9694

Germany 4.1297 0.5191 -0.1696 3.2966 0.3978

Greece 0.7575 0.3037 0.9594 3.9497 8.9766**

Hungary 0.0358 0.0097 -0.4543 2.3205 2.5207

Republic of Ireland 0.1698 0.0268 -0.1935 2.3637 1.0861

Italy 9.4743 1.0165 0.1233 3.5410 0.6923

Norway 22.5794 5.9012 -0.4300 2.1129 2.9893

Poland 0.6766 0.2249 -0.7079 2.5201 4.3762

Portugal 2.0792 0.6851 0.5402 2.8966 2.3065

Romania 2.6608 1.1784 -0.5982 2.6485 3.0454

Slovakia 0.7032 0.3018 0.4290 1.6189 5.1772*

Spain 6.6660 1.5556 0.5363 2.6402 2.5062

Sweden 14.0823 2.1889 -0.3073 2.2925 1.7198

Switzerland 7.3435 0.8799 -0.1092 2.6898 0.2820

Turkey 4.8894 3.5037 0.2771 1.7636 3.5953

United Kingdom 1.0056 0.1392 -0.2656 2.3187 1.4616

Other Europe and Eurasia 25.9480 12.0140 0.9240 2.4217 7.3425**

Iran 1.4957 0.8404 1.2735 5.1134 21.4502***

Other Middle East 0.9038 0.6110 0.7564 3.6262 5.2499*

Algeria 0.0711 0.0357 0.6093 3.1227 2.9377

Egypt 2.1989 0.8409 -0.5907 2.5357 3.1553

South Africa 0.2546 0.1804 0.4274 2.6010 1.7430

Other Africa 10.1509 4.8675 0.3778 2.2359 2.2614

Australia 3.0691 0.5784 -1.0688 3.2842 9.1068**

Bangladesh 0.1584 0.1009 0.1630 2.2755 1.2361

China 40.6072 42.2484 1.5331 4.4716 22.6530***

India 14.2657 6.3417 0.5541 2.7607 2.5173

Indonesia 1.3232 0.9629 0.6936 2.6974 3.9473

Japan 18.2980 1.7275 0.1145 2.2783 1.1228

Malaysia 0.8812 0.5539 0.0955 1.6112 3.8488

New Zealand 4.5541 1.0988 -0.5752 2.1481 4.0132

Pakistan 3.4634 2.1262 0.1151 1.7383 3.2215

Philippines 1.1290 0.6371 0.0566 1.8369 2.6745

South Korea 0.6464 0.3020 0.3977 2.5497 1.6358

Taiwan 0.9836 0.1958 0.2358 2.5631 0.8094

Thailand 0.9894 0.4839 0.2289 1.8720 2.9022

Vietnam 1.8780 2.0511 1.0082 2.7658 8.0694**

Other Asia Pacific 4.7675 1.8054 0.5814 2.5159 3.1065 * (

**)

*** denote statistical significance at the 10%, 5% and 1% levels respectively.

21

Table 4: Results of LM unit root test with no break (Schmidt and Phillips, 1992)

Lags 0 1 2 3 4

Countries Z(ρ) Z(τ) Z(ρ) Z(τ) Z(ρ) Z(τ) Z(ρ) Z(τ) Z(ρ) Z(τ)

US -10.8748 -2.4531 -10.6530 -2.4280 -10.4879 -2.4091 -10.5065 -2.4112 -10.9071 -2.4568

Canada -3.2020 -1.2737 -3.0750 -1.2481 -3.4713 -1.3261 -3.8210 -1.3913 -4.2104 -1.4605

Mexico -18.3315**

-3.3383**

-16.0918* -3.1277

** -15.8338

* -3.1025

* -16.5482

* -3.1717

** -19.4682

** -3.4402

**

Argentina -3.4521 -1.3243 -4.1394 -1.4501 -4.0830 -1.4402 -4.2188 -1.4640 -4.3630 -1.4888

Brazil -1.0638 -0.7256 -1.4094 -0.8352 -1.8195 -0.9490 -2.1941 -1.0421 -2.5638 -1.1265

Chile -10.0823 -2.3509 -11.3962 -2.4994 -10.2431 -2.3696 -9.7354 -2.3101 -9.1109 -2.2348

Colombia -3.8041 -1.3929 -3.9333 -1.4163 -3.9272 -1.4152 -4.0955 -1.4452 -4.0342 -1.4344

Ecuador -7.4217 -1.9858 -7.3706 -1.9789 -6.8113 -1.9024 -6.9179 -1.9172 -6.7413 -1.8925

Peru -6.0924 -1.7855 -5.6569 -1.7205 -5.9076 -1.7582 -6.1364 -1.7919 -6.2187 -1.8039

Venezuela -1.9213 -0.9797 -2.6126 -1.1424 -3.0619 -1.2368 -3.5998 -1.3410 -4.0490 -1.4222

Other South and Central America -2.8147 -1.1916 -3.2641 -1.2832 -3.4886 -1.3266 -4.0990 -1.4380 -4.6376 -1.5295

Austria -7.2709 -1.9638 -6.3233 -1.8313 -6.3801 -1.8395 -5.7758 -1.7503 -5.4777 -1.7045

Belgium and Luxembourg -12.5549 -2.6629 -10.6719 -2.4551 -10.4777 -2.4327 -10.9475 -2.4866 -11.3452 -2.5314

Bulgaria -22.4784**

-3.8023***

-22.8197***

-3.8310***

-22.3194**

-3.7888***

-21.8150**

-3.7457***

-22.2149**

-3.7799***

Czech Republic -18.0816**

-3.3100**

-17.8153**

-3.2855**

-18.4401**

-3.3426**

-18.0178**

-3.3041**

-18.1602**

-3.3172**

Denmark -17.2524**

-3.2157**

-17.2955**

-3.2197**

-16.9318* -3.1856

** -16.6980

* -3.1636

** -15.2065

* -3.0190

*

Finland -39.5770***

-5.7837***

-40.7938***

-5.8720***

-36.4246***

-5.5486***

-35.2915***

-5.4616***

-36.8230***

-5.5789***

France -21.4331**

-3.6860**

-18.6722**

-3.4404**

-19.1237**

-3.4817**

-19.3624**

-3.5034**

-20.2801**

-3.5855**

Germany -26.8550***

-4.2893***

-26.4312***

-4.2553***

-27.2755***

-4.3227***

-27.2680***

-4.3221***

-27.1231***

-4.3106***

Greece -17.6297**

-3.2587**

-19.0998**

-3.3918**

-18.5323**

-3.3410**

-19.0673**

-3.3889**

-20.7870**

-3.5384**

Hungary -24.9244***

-4.0741***

-23.6112***

-3.9653***

-23.6385***

-3.9676***

-26.7409***

-4.2199***

-28.9539***

-4.3911***

Republic of Ireland -33.3303***

-5.0270***

-29.7631***

-4.7504***

-36.4332***

-5.2558***

-40.0554***

-5.5109***

-45.7114***

-5.8872***

Italy -25.4891***

-4.1369***

-27.3377***

-4.2843***

-26.5857***

-4.2250***

-23.9911***

-4.0135***

-20.2228**

-3.6848**

Norway -13.1776 -2.7386 -10.8103 -2.4805 -10.5516 -2.4506 -11.2514 -2.5306 -10.2131 -2.4110

Poland -6.7084 -1.8802 -5.6309 -1.7226 -5.0298 -1.6281 -5.2918 -1.6699 -5.7439 -1.7398

Portugal -33.2586***

-5.0187***

-32.3884***

-4.9526***

-31.5592***

-4.8888***

-39.0810***

-5.4403***

-42.9949***

-5.7062***

Romania -2.9503 -1.2209 -3.2086 -1.2732 -3.7343 -1.3736 -4.3067 -1.4751 -4.8398 -1.5637

Slovakia -11.2348 -2.4988 -9.4445 -2.2911 -9.5593 -2.3050 -9.4544 -2.2923 -10.1298 -2.3728

Spain -31.7352***

-4.8420***

-31.0300***

-4.7879***

-31.0525***

-4.7897***

-36.1564***

-5.1683***

-37.3352***

-5.2519***

Sweden -23.6243***

-3.9296***

-22.4571**

-3.8313***

-22.4518**

-3.8308***

-23.1806***

-3.8925***

-22.8770***

-3.8669***

Switzerland -33.4951***

-5.0463***

-33.7105***

-5.0625***

-35.5884***

-5.2016***

-34.3317***

-5.1089***

-33.0164***

-5.0101***

Turkey -9.4166 -2.2630 -9.1910 -2.2357 -9.2436 -2.2421 -9.1963 -2.2364 -9.1675 -2.2329

22

United Kingdom -48.1365***

-6.9484***

-46.8281***

-6.8533***

-46.1058***

-6.8002***

-44.8960***

-6.7104***

-42.5369***

-6.5317***

Other Europe and Eurasia -6.1669 -1.7971 -6.0093 -1.7740 -6.0593 -1.7814 -6.1587 -1.7960 -6.4841 -1.8428

Iran -11.6360 -2.5492 -14.0480 -2.8010* -13.5907 -2.7551 -12.8583 -2.6798 -12.5824 -2.6509

Other Middle East -2.4197 -1.1024 -3.1863 -1.2651 -3.5039 -1.3266 -3.6649 -1.3568 -3.8011 -1.3818

Algeria -23.3695***

-3.9013***

-19.6113**

-3.5738**

-22.5668**

-3.8337***

-23.5297***

-3.9146***

-25.3662***

-4.0645***

Egypt -2.1515 -1.0380 -3.1711 -1.2602 -3.8039 -1.3803 -4.2223 -1.4542 -4.5295 -1.5061

South Africa -5.4336 -1.6799 -5.6229 -1.7089 -5.4422 -1.6812 -5.8354 -1.7409 -5.9177 -1.7532

Other Africa -2.0478 -1.0121 -2.7106 -1.1645 -3.1224 -1.2498 -3.5273 -1.3284 -3.9452 -1.4049

Australia -3.5220 -1.3381 -4.4665 -1.5069 -4.6864 -1.5436 -5.4157 -1.6593 -6.1470 -1.7678

Bangladesh -5.5059 -1.6918 -5.4211 -1.6787 -5.7969 -1.7359 -5.7993 -1.7362 -5.9558 -1.7595

China -13.6817 -2.7992 -15.3060* -2.9608

* -16.2191

* -3.0478

* -17.6621

** -3.1805

** -17.7059

** -3.1844

**

India -5.4815 -1.6878 -6.5971 -1.8516 -6.7269 -1.8697 -6.7435 -1.8720 -6.3417 -1.8154

Indonesia -9.8702 -2.3231 -8.6783 -2.1783 -8.7883 -2.1921 -9.2990 -2.2549 -9.6551 -2.2976

Japan -44.8335***

-6.4766***

-45.0680***

-6.4935***

-48.8238***

-6.7587***

-46.1135***

-6.5684***

-42.2283***

-6.2856***

Malaysia -9.3848 -2.2587 -10.8109 -2.4243 -10.4755 -2.3864 -9.6841 -2.2945 -9.6040 -2.2850

New Zealand -8.0412 -2.0744 -6.6640 -1.8884 -7.2214 -1.9658 -6.6241 -1.8828 -6.9867 -1.9336

Pakistan -5.3330 -1.6633 -4.5069 -1.5291 -4.4447 -1.5185 -4.5095 -1.5295 -4.8834 -1.5917

Philippines -13.2041 -2.7418 -11.7331 -2.5846 -10.3670 -2.4295 -10.1864 -2.4082 -11.0411 -2.5072

South Korea -13.3967 -2.7650 -11.5919 -2.5721 -11.3360 -2.5435 -12.0137 -2.6184 -12.7157 -2.6939

Taiwan -31.8866***

-4.8595***

-29.4769***

-4.6723***

-32.8486***

-4.9322***

-35.1242***

-5.1002***

-38.0295***

-5.3070***

Thailand -29.5449***

-4.5919***

-28.9637***

-4.5465***

-29.9636***

-4.6243***

-30.6412***

-4.6763***

-29.1584***

-4.5618***

Vietnam -1.9377 -0.9840 -2.8217 -1.1874 -3.3086 -1.2858 -3.5449 -1.3309 -3.6863 -1.3572

Other Asia Pacific -5.3541 -1.6668 -6.9169 -1.8945 -7.7460 -2.0049 -8.1890 -2.0614 -8.6976 -2.1245

Note: * (

**)

*** denote statistical significance at the 10%, 5% and 1% levels respectively.

23

Table 5: Results of LM test with one break in the intercept (Model A)

Series TB K St-1 Bt

US 1986 0

-0.2748

(-2.7069)

-0.1618*

(-1.5589)

Canada 2000 0

-0.0859

(-1.4371)

-0.1047***

(-2.4768)

Mexico 1985 0

-0.4578**

(-3.6955)

-0.2385*

(-1.6298)

Argentina 1975 3

-0.1082

(-1.8634)

-0.2510

(-1.1604)

Brazil 2000 4

-0.0787

(-2.6065)

-0.1757***

(-4.5340)

Chile 2006 1

-0.4228**

(-3.6711)

-0.2182**

(-2.0940)

Colombia 1996 0

-0.1101

(-1.6372)

-0.1714**

(-2.2728)

Ecuador 1982 0

-0.3254

(-2.9897)

0.5749***

(3.2891)

Peru 1991 1

-0.1941

(-2.3864)

-0.2212***

(-4.8718)

Venezuela 1982 3

-0.0081

(-0.2357)

0.0934*

(1.4415)

Other South and Central America 1994 3

-0.0764

(-1.6215)

0.0701

(0.8559)

Austria 1978 0

-0.1983

(-2.2499)

0.1103

(1.2752)

Belgium and Luxembourg 1980 0

-0.3405

(-3.0721)

0.3604*

(1.5247)

Bulgaria 1984 0

-0.5867***

(-4.3697)

-0.3499*

(-1.5278)

Czech Republic 1994 0

-0.4573**

(-3.6928)

0.2515*

(1.3693)

Denmark 1984 0

-0.5417**

(-4.1338)

0.5508***

(3.7718)

Finland 1980 1

-1.1590***

(-5.9645)

0.3329***

(3.0140)

France 1976 0

-0.6171***

(-4.5307)

0.4594***

(4.1868)

Germany 1971 0

-0.6465***

(-4.6876)

-0.1820**

(-1.8445)

Greece 1982 0

-0.5097**

(-3.9662)

-0.3949**

(-1.7139)

Hungary 1991 0

-0.6989***

(-4.9711)

-0.1684

(-1.1277)

Republic of Ireland 1975 1

-0.5526*

(-3.3290)

-0.2257*

(-1.3295)

24

Italy 1993 1

-0.7227***

(-4.6034)

0.1131

(1.2225)

Norway 1995 4

-0.3865

(-2.1222)

-0.1764**

(-2.3455)

Poland 1979 4

-0.3335

(-3.0407)

0.3535***

(2.5522)

Portugal 2005 3

-0.4065

(-2.7786)

0.0743

(0.2525)

Romania 2006 3

-0.1005

(-1.8078)

-0.3436**

(-2.0717)

Slovakia 1994 0

-0.2899

(-2.7926)

0.2530*

(1.3113)

Spain 1988 0

-0.6542***

(-4.7289)

-0.6907***

(-2.9640)

Sweden 2002 0

-0.7321***

(-5.1540)

-0.3367***

(-3.5737)

Switzerland 1974 0

-0.7993***

(-5.5336)

0.1532**

(1.8156)

Turkey 1974 0

-0.3357

(-3.0461)

0.4142**

(2.2128)

United Kingdom 1994 0

-1.0891***

(-7.4161)

-0.0166

(-0.1318)

Other Europe and Eurasia 1987 3

-0.1537

(-2.0368)

1.5751**

(2.0226)

Iran 1996 1

-0.4487**

(-3.9580)

-0.3712**

(-1.8296)

Other Middle East 1973 1

-0.0747

(-1.5497)

0.8588***

(5.3447)

Algeria 2004 0

-0.6809***

(-4.8728)

1.0489**

(2.1929)

Egypt 1973 1

-0.0868

(-2.0271)

0.1698**

(2.0525)

South Africa 1975 3

-0.2010

(-2.4082)

1.6431*

(1.4252)

Other Africa 1999 1

-0.0658

(-1.5782)

-0.1042**

(-2.2784)

Australia 1971 3

-0.1901*

(-3.4361)

-0.0856*

(-1.6729)

Bangladesh 1972 0

-0.1374

(-1.8425)

0.8296

(0.9240)

China 2003 3

-0.6509***

(-4.5136)

0.0795*

(1.3059)

India 1974 1

-0.1757

(-2.3814)

0.1831**

(2.1594)

Indonesia 1985 0

-0.2780

(-2.7252)

0.4172*

(1.6054)

Japan 1980 0

-0.9860***

(-6.6878)

0.1056

(1.2106)

Malaysia 1983 1 -0.4043* 0.6953

***

25

(-3.5050) (4.8132)

New Zealand 1996 0

-0.2484

(-2.5542)

-0.1254**

(-1.8598)

Pakistan 1998 0

-0.1502

(-1.9324)

-0.1665*

(-1.4855)

Philippines 1972 0

-0.3638

(-3.1979)

0.1849

(0.9656)

South Korea 1993 0

-0.4821**

(-3.8221)

-0.6622***

(-2.9143)

Taiwan 1998 0

-0.8934***

(-6.0941)

-0.0153

(-0.0857)

Thailand 1997 0

-0.7494***

(-5.2501)

-0.3408*

(-1.4957)

Vietnam 1989 1

-0.0805

(-1.8107)

-0.1983

(-0.9932)

Other Asia Pacific 2003 4

-0.2673*

(-3.3527)

-0.0835**

(-2.0626)

Notes: Critical values for the LM test at 10%, 5% and 1% significance levels = -3.211, -3.566 and -4.239

respectively. Critical values for the dummy variable denoting the break date follows the standard asymptotic

distribution. * (

**)

*** denote statistical significance at the 10%, 5% and 1% levels respectively. TB is the break

date; K is the lag length; St-1 is the LM test statistic; Bt is the coefficient on the break in the intercept.

26

Table 6: Results of LM test with one break in the intercept and slope (Model C)

Series TB K St-1 Bt Dt

US 1974 0

-0.5629*

(-4.2448)

0.0579

(0.5965)

-0.1107***

(-2.8844)

Canada 1992 4

-0.8079

(-4.1680)

0.0500

(1.2479)

-0.1197***

(-4.7080)

Mexico 1987 0

-0.7468***

(-5.2356)

0.1202

(0.8806)

-0.2211***

(-3.8998)

Argentina 1977 3

-0.6137

(-4.0997)

0.0616

(0.3999)

0.2834***

(2.6003)

Brazil 1997 4

-0.1843

(-3.5509)

0.0507

(1.1471)

-0.1062***

(-4.6543)

Chile 2002 3

-1.0109***

(-5.2646)

-0.2239**

(-2.0693)

0.0809**

(1.8775)

Colombia 1990 0

-0.5490

(-4.1718)

0.0053

(0.0792)

-0.1259***

(-4.8921)

Ecuador 1982 0

-0.5699*

(-4.2813)

0.4535***

(2.7283)

0.0984**

(1.7449)

Peru 1983 0

-0.5492

(-4.1729)

0.0023

(0.0486)

-0.0517***

(-3.4664)

Venezuela 1987 3

-0.2040

(-2.7486)

0.0455

(0.7737)

-0.1243***

(-5.6915)

Other South and Central America 1992 3

-0.2914

(-3.2479)

0.0748

(1.0029)

-0.0322*

(-1.3324)

Austria 1985 0

-0.5225

(-4.0331)

-0.0731

(-0.8963)

0.0688**

(2.0342)

Belgium and Luxembourg 1978 0

-0.7723***

(-5.3796)

-0.2625

(-1.2176)

0.4709***

(4.3266)

Bulgaria 1984 0

-0.6064*

(-4.4741)

-0.4065**

(-1.7757)

0.0097

(0.1423)

Czech Republic 1974 0

-0.5856*

(-4.3639)

0.0838

(0.4686)

0.1516**

(2.1534)

Denmark 1983 3

-1.4443***

(-6.4768)

-0.4083***

(-2.7909)

0.4083***

(5.3557)

Finland 1976 4

-1.9308***

(-6.2950)

0.3288***

(3.1448)

-0.0980**

(-2.3356)

France 1975 0

-0.7998***

(-5.5366)

-0.4586***

(-3.7922)

0.2507***

(3.9207)

Germany 1972 0

-0.7043**

(-5.0005)

0.0779

(0.8700)

0.0451

(1.2494)

Greece 1973 0

-0.6366**

(-4.6346)

-0.1811

(-0.8089)

-0.3065***

(-3.2225)

Hungary 1974 0

-1.1894***

(-8.2154)

0.8380***

(8.3368)

-0.3707***

(-6.4254)

Republic of Ireland 1979 0

-1.0995***

(-7.4942)

0.0354

(0.2318)

0.2571***

(4.3604)

Italy 1993 2 -0.9915**

0.1571**

-0.0186

27

(-4.9661) (1.7108) (-0.6692)

Norway 1985 3

-1.5425***

(-5.9178)

-0.1906***

(-2.9967)

0.0489**

(2.1480)

Poland 1990 4

-1.0283***

(-6.0715)

-0.0635

(-0.5679)

-0.2267***

(-5.4846)

Portugal 2002 3

-1.1055**

(-4.7994)

1.0796***

(3.5654)

-0.8210***

(-4.3757)

Romania 1975 4

-1.3501***

(-5.4524)

-0.2534**

(-2.0788)

-0.0080

(-0.1091)

Slovakia 1993 0

-0.9408***

(-6.3923)

0.1024

(0.6872)

0.3781***

(4.8808)

Spain 2004 0

-0.7472***

(-5.2381)

-0.7857***

(-3.2643)

0.0898

(0.9227)

Sweden 1987 3

-1.5598***

(-5.5081)

-0.0270

(-0.3026)

0.0279

(1.0095)

Switzerland 1989 0

-0.8147***

(-5.6230)

-0.0886

(-1.0104)

-0.0269

(-1.0523)

Turkey 1996 0

-0.5070

(-3.9522)

0.0377

(0.1988)

-0.1633***

(-2.5987)

United Kingdom 1999 0

-1.0726***

(-7.2937)

0.1154

(0.8638)

-0.0156

(-0.3570)

Other Europe and Eurasia 1984 2

-0.9712**

(-5.0455)

-1.3837***

(-27.0303)

-0.0948***

(-4.8152)

Iran 1993 4

-0.8809**

(-4.6809)

-0.0321

(-0.1622)

-0.2801***

(-3.6006)

Other Middle East 1980 1

-0.3766

(-3.6374)

0.1884

(1.0644)

-0.2816***

(-4.5347)

Algeria 1994 0

-0.8878***

(-6.0598)

0.4913

(1.0728)

-0.5759***

(-3.3161)

Egypt 1974 1

-0.4394**

(-4.5777)

0.0066

(0.0949)

-0.1119***

(-4.2069)

South Africa 1973 1

-0.8882***

(-5.4354)

3.0303***

(3.3647)

0.1512

(0.3934)

Other Africa 1976 1

-0.4458

(-3.9143)

0.0401

(1.0802)

-0.1033***

(-6.3402)

Australia 1986 3

-0.2800**

(-4.6653)

-0.0908**

(-2.0663)

-0.0285**

(-2.1728)

Bangladesh 1972 2

-1.7529***

(-6.7498)

-1.7016

(-0.9725)

2.6930***

(3.8459)

China 2006 3

-0.7441**

(-4.8009)

-0.0780

(-1.1852)

0.1174***

(3.1373)

India 1980 1

-0.5206*

(-4.3475)

0.1463**

(1.8498)

-0.1599***

(-4.4820)

Indonesia 1982 0

-0.4351

(-3.5761)

0.2155

(0.8601)

0.1549**

(2.0041)

Japan 1988 0

-1.0305***

(-6.9923)

0.1503**

(1.7798)

-0.0748***

(-2.8516)

Malaysia 1983 1

-0.5543*

(-4.4347)

0.6214***

(4.6100)

-0.0732**

(-1.7664)

28

New Zealand 1989 2

-0.8798**

(-4.5427)

0.0319

(0.5376)

-0.0991***

(-4.2277)

Pakistan 1989 0

-0.5701*

(-4.2828)

-0.0615

(-0.6237)

-0.0611**

(-2.1229)

Philippines 1986 0

-0.8602***

(-5.8918)

-0.1485

(-0.9344)

-0.0696*

(-1.4951)

South Korea 1991 0

-0.8793***

(-6.0075)

0.0012

(0.0057)

-0.4071***

(-4.6480)

Taiwan 1988 0

-0.8661***

(-5.9277)

0.0434

(0.2397)

-0.0652

(-1.2190)

Thailand 1976 0

-0.8281***

(-5.7015)

-0.1261

(-0.5636)

-0.1123*

(-1.4604)

Vietnam 1987 1

-0.2156

(-2.7161)

0.0234

(0.1902)

0.2436***

(4.1599)

Other Asia Pacific 1994 4

-0.3451

(-4.0129)

0.1105***

(2.6539)

-0.0320***

(-2.4200)

Critical values

location of break, λ 0.1 0.2 0.3 0.4 0.5

1% significant level -5.11 -5.07 -5.15 -5.05 -5.11

5% significant level -4.50 -4.47 -4.45 -4.50 -4.51

10% significant level -4.21 -4.20 -4.18 -4.18 -4.17

Notes: The critical values are symmetric around λ and (1-λ).

Critical values for the dummy variables denoting the break date follows the standard asymptotic distribution.*

(**

) ***

denote statistical significance at the 10%, 5% and 1% levels respectively. TB is the break date; K is the

lag length; St-1 is the LM test statistic; Bt is the coefficient on the break in the intercept; Dt is the coefficient on

the break in the slope

29

Table 7: Results of LM test with two breaks in the intercept (Model AA)

Series TB1 TB2 K St-1 Bt1 Bt2

US 1986 2000 0

-0.3809

(-3.1434)

-0.1878**

(-1.8968)

-0.3436***

(-3.3547)

Canada 1997 2000 0

-0.1080

(-1.5485)

-0.0845**

(-2.0019)

-0.1036***

(-2.4576)

Mexico 1986 1996 0

-0.5608**

(-4.0455)

-0.1947

(-1.2999)

-0.1680

(-1.1261)

Argentina 1973 1975 3

-0.1516

(-1.9459)

0.3391*

(1.5404)

-0.3579*

(-1.6235)

Brazil 1973 2000 4

-0.0938

(-2.6310)

-0.0809*

(-1.4122)

-0.1747***

(-4.1441)

Chile 1981 2006 1

-0.4617*

(-3.6680)

0.0444

(0.4171)

-0.2095**

(-1.9240)

Colombia 1996 1999 0

-0.1470

(-1.8256)

-0.1801***

(-2.4187)

-0.1530**

(-2.0585)

Ecuador 1976 1982 0

-0.4585*

(-3.5343)

0.2721*

(1.5982)

0.5084***

(2.9734)

Peru 1991 2006 1

-0.2379

(-2.5255)

-0.2280***

(-4.8316)

-0.0840**

(-1.7858)

Venezuela 1975 1978 3

-0.0134

(-0.3391)

0.0969*

(1.3638)

0.1017*

(1.4585)

Other South and Central America 1994 2001 3

-0.0838

(-1.5999)

0.0754

(0.8454)

0.0620

(0.6894)

Austria 1976 1986 0

-0.2738

(-2.5808)

0.1736**

(2.0752)

0.1709**

(2.0382)

Belgium and Luxembourg 1980 1993 0

-0.3840

(-3.1590)

0.4001*

(1.6638)

0.3362*

(1.4033)

Bulgaria 1975 1984 0

-0.6376**

(-4.4334)

0.1877

(0.8018)

-0.3762*

(-1.6066)

Czech Republic 1994 1998 0

-0.4832*

(-3.6578)

0.2878*

(1.5177)

0.1143

(0.6018)

Denmark 1984 1994 0

-0.5856**

(-4.1702)

0.5629***

(3.7329)

0.1802

(1.1644)

Finland 1996 2002 0

-0.9970***

(-6.4614)

-0.1207

(-1.0175)

-0.4197***

(-3.3648)

France 1976 1993 2

-1.3846***

(-6.0737)

0.5809***

(5.7238)

0.3139***

(3.1924)

Germany 1976 1998 0

-0.8104***

(-5.3488)

0.1595**

(1.8460)

0.1026

(1.2047)

Greece 1982 1988 0

-0.6206**

(-4.3470)

-0.4456**

(-1.9408)

-0.3205*

(-1.3911)

Hungary 1971 1991 0

-0.7901***

(-5.2372)

0.0904

(0.6034)

-0.1644

(-1.0947)

Republic of Ireland 1978 1993 0

-0.9035***

(-5.8827)

0.1868

(1.0814)

0.3139**

(1.8051)

Italy 1989 1993 2

-0.9994***

(-4.7580)

-0.2098**

(-2.0939)

0.1565*

(1.6316)

Norway 1995 2002 0 -0.5734**

-0.2347***

-0.2435***

30

(-4.1089) (-3.2696) (-3.4223)

Poland 1972 1984 4

-0.3046

(-3.0679)

-0.4759**

(-2.1527)

0.3469**

(2.2330)

Portugal 1988 2001 0

-0.8482***

(-5.5617)

-0.8918***

(-3.3141)

-0.5578**

(-2.0799)

Romania 1977 2006 3

-0.1150

(-1.8444)

0.1966

(1.1266)

-0.3585**

(-2.0010)

Slovakia 1984 1992 0

-0.5050*

(-3.7665)

0.3070**

(1.8530)

0.4595***

(2.7656)

Spain 1980 1985 0

-0.8174***

(-5.3881)

-0.4210**

(-1.7785)

-0.0654

(-0.2760)

Sweden 1972 2001 0

-0.8555***

(-5.6028)

0.1547*

(1.6750)

-0.1225*

(-1.3243)

Switzerland 1982 2001 0

-0.8508***

(-5.5762)

0.0652

(0.7456)

-0.1004

(-1.1604)

Turkey 1974 2000 0

-0.5145*

(-3.8141)

0.3667**

(2.0242)

-0.4311**

(-2.3905)

United Kingdom 1971 1994 0

-1.1495***

(-7.5342)

-0.2248**

(-1.7232)

-0.0169

(-0.1324)

Other Europe and Eurasia 1984 1996 0

-0.1841

(-2.0633)

-1.3319***

(-21.0018)

-0.1124**

(-1.7962)

Iran 1971 1996 1

-0.5294**

(-4.2580)

0.2590

(1.2714)

-0.3698**

(-1.8086)

Other Middle East 1973 2005 1

-0.0825

(-1.6607)

0.8728***

(5.2586)

-0.2885*

(-1.5824)

Algeria 1993 2004 0

-0.8376***

(-5.5011)

-0.6350*

(-1.3814)

1.2558***

(2.6894)

Egypt 1973 1979 1

-0.0972

(-2.0945)

0.1735**

(1.9921)

0.0623

(0.6948)

South Africa 1971 1973 0

-0.2850

(-2.6416)

3.0831***

(2.8628)

2.2251**

(2.1605)

Other Africa 1973 1999 1

-0.0807

(-1.7541)

0.0962**

(2.1074)

-0.1086**

(-2.3607)

Australia 1971 1997 3

-0.2089

(-3.3878)

-0.0864*

(-1.5699)

0.0176

(0.3497)

Bangladesh 1972 1994 0

-0.1580

(-1.8977)

0.8555

(0.9246)

-1.0832

(-1.2024)

China 1978 2003 3

-0.7612***

(-4.6168)

0.0304

(0.4889)

0.0712

(1.1098)

India 1974 1986 1

-0.2048

(-2.4909)

0.1820**

(2.0978)

-0.1210*

(-1.4145)

Indonesia 1975 1985 0

-0.3714

(-3.0949)

-0.7434***

(-2.8928)

0.4558**

(1.8095)

Japan 1988 1991 0

-1.0254***

(-6.6478)

0.0929

(1.0851)

-0.1300*

(-1.5151)

Malaysia 1983 1996 1

-0.5227**

(-3.9297)

0.6702***

(4.8269)

-0.3629***

(-2.5580)

New Zealand 1996 2000 0

-0.3668

(-3.0714)

-0.1438**

(-2.2210)

-0.1557**

(-2.4022)

31

Pakistan 1996 1999 0

-0.2496

(-2.4471)

-0.3695***

(-3.8177)

-0.2737***

(-2.8277)

Philippines 1972 1977 0

-0.4793*

(-3.6385)

0.2491*

(1.3234)

0.3066*

(1.6304)

South Korea 1990 2000 0

-0.6010**

(-4.2477)

-0.4706**

(-2.1499)

-0.7122***

(-3.3154)

Taiwan 2001 2004 0

-1.0713***

(-6.9608)

-0.7156***

(-4.5836)

0.2353*

(1.5132)

Thailand 1976 1998 0

-0.8082***

(-5.3368)

0.0437

(0.1869)

-0.7155***

(-3.0268)

Vietnam 1989 2005 1

-0.0917

(-2.0383)

-0.3367*

(-1.5743)

0.2994**

(2.2400)

Other Asia Pacific 2001 2003 4

-0.3151

(-3.4845)

-0.0713**

(-1.7070)

-0.0906**

(-2.1428) Notes: Critical values for the LM test at 10%, 5% and 1% significance levels = -3.504, -3.842, and -4.545

respectively. Critical values for the dummy variables denoting the break dates follow the standard asymptotic

distribution. *(

**)

*** denote statistical significance at the 10%, 5% and 1% levels respectively. TB is the break

date; K is the lag length; St-1 is the LM test statistic; Bt1 is the coefficient on the first break in the intercept; Bt2 is

the coefficient on the second break in the intercept.

32

32

Table 8: Results of LM test with two breaks in the intercept and slope (Model CC)

Series TB1 TB2 K St-1 Bt1 Bt2 Dt1 Dt2

US 1986 1999 0

-0.7648

(-4.9766)

-0.1830**

(-1.9349)

-0.0689

(-0.7122)

-0.0722**

(-2.0073)

-0.1114***

(-2.5553)

Canada 1980 1992 4

-1.2240*

(-5.5792)

-0.0909**

(-2.2342)

0.0565*

(1.4648)

0.0241*

(1.5382)

-0.1275***

(-5.5422)

Mexico 1986 2000 0

-0.9977**

(-6.3100)

-0.1582

(-1.2081)

-0.0565

(-0.4229)

-0.1885***

(-3.4792)

-0.1648***

(-2.8582)

Argentina 1977 1989 3

-0.7403

(-4.5994)

-0.0360

(-0.2197)

0.2854**

(1.7722)

0.4166***

(3.2357)

-0.1933***

(-2.8824)

Brazil 1985 2000 4

-0.5882***

(-6.4742)

-0.0019

(-0.0699)

-0.1846***

(-6.7221)

-0.1116***

(-9.9621)

0.0200**

(1.8448)

Chile 1994 2003 3

-1.4424**

(-5.9011)

0.1404*

(1.4565)

-0.2227**

(-2.2684)

-0.0013

(-0.0374)

0.2776***

(4.0884)

Colombia 1981 1990 3

-1.3642

(-5.0020)

0.0320

(0.4574)

0.0365

(0.5514)

-0.1167***

(-3.3080)

-0.1008***

(-3.5572)

Ecuador 1981 1988 3

-1.7774***

(-6.6651)

-0.5116***

(-3.0921)

0.2203*

(1.4241)

0.5556***

(6.2232)

-0.7360***

(-7.3141)

Peru 1988 2000 0

-0.8022

(-5.1760)

-0.0017

(-0.0368)

0.0884**

(1.9444)

-0.1337***

(-4.9312)

-0.0014

(-0.0747)

Venezuela 1976 1989 3

-0.8584*

(-5.3291)

0.1071**

(2.0064)

-0.0872*

(-1.5674)

-0.2093***

(-6.2190)

0.0971***

(2.9423)

Other South and Central America 1981 1996 4

-0.5751

(-3.8544)

-0.1640**

(-2.1676)

0.0887

(1.2291)

0.0865***

(3.0489)

-0.2334***

(-5.5918)

Austria 1976 1996 2

-1.3164**

(-6.2054)

0.1451**

(2.0286)

-0.0071

(-0.1004)

0.1311***

(3.8157)

-0.0245

(-1.0480)

Belgium and Luxembourg 1979 1996 0

-0.8623*

(-5.5060)

0.0002

(0.0009)

-0.0113

(-0.0512)

0.3584***

(3.5889)

0.0831

(1.0619)

Bulgaria 1977 1993 4

-1.6785*

(-5.5750)

-0.5105**

(-2.2452)

-0.7293***

(-3.1965)

0.4739***

(3.4802)

0.3717***

(4.1255)

33

33

Czech Republic 1975 1991 2

-1.2882**

(-6.1356)

-0.8216***

(-4.0644)

0.2295*

(1.4085)

0.7876***

(5.4159)

-0.2111***

(-3.1278)

Denmark 1983 1992 3

-1.6945***

(-6.7702)

-0.3982***

(-2.6022)

-0.4337***

(-2.8602)

0.3982***

(4.8079)

0.0355

(0.6221)

Finland 1979 2002 1

-1.4649***

(-7.4381)

-0.4112***

(-3.4775)

-0.3719***

(-3.2336)

0.2532***

(5.1169)

0.0306

(0.7092)

France 1979 1990 0

-1.0254***

(-6.4875)

0.0031

(0.0267)

-0.1366

(-1.1753)

0.0453

(0.9572)

0.1621***

(3.3867)

Germany 1977 1998 0

-0.8553*

(-5.4671)

0.0235

(0.2570)

0.0845

(0.9091)

0.0749**

(2.0789)

0.0239

(0.7295)

Greece 1971 1991 1

-0.8802

(-5.2336)

0.1668

(0.7983)

-0.7073***

(-3.1062)

-0.3913***

(-3.3817)

0.3524***

(3.9534)

Hungary 1973 1976 0

-1.3645***

(-9.2679)

-0.7602***

(-5.9561)

-0.0158

(-0.1524)

0.3905***

(4.8274)

-0.6748***

(-8.2160)

Republic of Ireland 1977 2003 0

-1.2302***

(-7.9948)

-0.2932**

(-1.9013)

-0.0632

(-0.3972)

0.4996***

(6.2355)

-0.1082*

(-1.6502)

Italy 1995 2003 3

-1.4429*

(-5.6432)

0.1103

(1.1615)

0.3318***

(3.2360)

0.1030***

(2.4695)

-0.1641***

(-2.8986)

Norway 1975 1990 3

-1.9907***

(-6.9522)

0.1156**

(1.8652)

-0.2203***

(-3.5083)

-0.0681***

(-2.4232)

0.1168***

(4.1029)

Poland 1979 2001 4

-1.5673***

(-7.1437)

0.4062***

(3.9970)

-0.0270

(-0.2693)

-0.3232***

(-6.7315)

-0.0605*

(-1.6078)

Portugal 1980 2004 0

-1.0840***

(-6.8799)

-0.1385

(-0.5454)

-1.0922***

(-4.0268)

-0.1207*

(-1.4524)

0.3631***

(3.0634)

Romania 1979 1993 4

-1.1150**

(-5.7667)

0.3346***

(2.4404)

-0.1737

(-1.2616)

-0.5674***

(-6.4336)

0.2297***

(3.8594)

Slovakia 1973 1992 0

-1.1901***

(-7.6667)

0.6145***

(5.2924)

0.3159***

(2.6702)

0.0241

(0.5004)

0.2427***

(4.9474)

Spain 1980 2004 0

-1.0720***

(-6.7975)

-0.4076**

(-1.9716)

-0.8376***

(-3.8151)

-0.1368**

(-1.9663)

0.1510*

(1.6192)

Sweden 1992 1997 0

-0.9597**

(-6.0743)

0.1716*

(1.6443)

0.1053

(1.1136)

-0.1677***

(-3.1579)

0.1233**

(2.2400)

34

34

Switzerland 1976 1997 2

-1.2595**

( -5.9036)

0.3956***

(4.4695)

-0.0916

(-1.0282)

-0.1825***

(-4.0779)

0.1167***

(3.4864)

Turkey 1978 1997 4

-1.5253*

(-5.4539)

-0.0937

(-0.4757)

0.4404**

(2.2628)

0.3163***

(3.3516)

-0.3282***

(-4.1926)

United Kingdom 1971 1993 0

-1.2500***

(-8.1646)

-0.1799*

(-1.4499)

0.3736***

(2.9744)

0.1813***

(3.1697)

-0.3802***

(-6.3108)

Other Europe and Eurasia 1983 1989 4

-2.1004***

(-20.7741)

1.4774***

(19.7035)

-0.3514***

(-4.5050)

-1.5764***

(-25.3277)

1.2522***

(24.2665)

Iran 1989 1997 4

-1.2906**

(-6.0421)

-0.4010**

(-2.0555)

0.6531***

(3.2436)

-0.0332

(-0.4228)

-0.2593***

(-2.6981)

Other Middle East 1978 1999 1

-0.6176

(-4.8095)

0.2017

(1.2484)

0.0610

(0.3705)

-0.1169**

(-1.8890)

0.1052**

(1.7746)

Algeria 1983 2002 0

-1.1291***

(-7.2011)

0.5970*

(1.5392)

1.6287***

(4.0714)

-0.1756*

(-1.3917)

-0.1668

(-1.0026)

Egypt 1981 1995 2

-0.7095**

(-5.8009)

0.1556**

(2.1835)

-0.0509

(-0.7526)

-0.3630***

(-7.2425)

0.1291***

(3.9736)

South Africa 1972 1980 2

-1.2220**

(-5.7383)

-5.0639***

(-4.0497)

1.5602**

(1.8710)

3.4083***

(3.4873)

-1.9186***

(-4.2756)

Other Africa 1974 1993 1

-0.7500

(-5.0061)

-0.0517*

(-1.4652)

-0.0245

(-0.6842)

-0.0515***

(-3.3627)

-0.0071

(-0.6063)

Australia 1981 1999 4

-0.7568**

(-5.9436)

-0.1232***

(-2.9951)

-0.0360

(-0.8801)

-0.0822***

(-4.6172)

-0.0113

(-0.7134)

Bangladesh 1971 1974 3

-2.3645***

(-7.0429)

-1.2816

(-0.4935)

0.6999

(1.1636)

2.4014

(0.9220)

2.6397*

(1.4751)

China 1983 2002 3

-1.1763**

(-6.2153)

-0.1083**

(-2.0523)

-0.2814***

(-4.6152)

0.1280***

(4.2936)

0.1825***

(5.8264)

India 1975 2000 3

-0.9906

(-5.0479)

-0.0939

(-1.1865)

-0.1294*

(-1.6171)

-0.0167

(-0.5257)

-0.0325

(-1.0727)

Indonesia 1975 1992 4

-1.7466

(-5.2252)

-0.6888***

(-3.1910)

0.1012

(0.4635)

-0.4511***

(-2.4873)

-0.0760

(-1.0728)

Japan 1988 1996 0

-1.1023***

(-7.0082)

0.1274*

(1.4105)

0.0583

(0.6665)

-0.0532*

(-1.4512)

0.0319

(0.8148)

35

35

Malaysia 1975 1983 2

-1.3111***

(-6.3626)

0.0329

(0.2573)

0.6372***

(5.4022)

-0.3521***

(-4.3258)

0.0737*

(1.3988)

New Zealand 1974 1995 2

-1.3607**

(-6.3523)

0.1901***

(3.3541)

-0.0150

(-0.2794)

-0.1278***

(-4.5194)

-0.0445***

(-2.5366)

Pakistan 1985 1997 4

-1.0125*

(-5.3767)

-0.2663***

(-2.6063)

0.4514***

(3.9302)

0.1535***

(2.9431)

-0.2735***

(-4.4759)

Philippines 1971 1990 2

-1.7835***

(-6.5717)

-0.6659***

(-3.7850)

-0.1399

(-0.9887)

0.6258***

(5.1451)

-0.2842***

(-5.1103)

South Korea 1984 1992 1

-1.2734**

(-6.2012)

0.2084

(0.9909)

0.7416***

(3.4895)

-0.0086

(-0.0985)

-0.6750***

(-4.7455)

Taiwan 1999 2003 0

-1.1371***

(-7.2602)

0.0263

(0.1392)

-0.1108

(-0.6311)

-0.2047**

(-2.0724)

0.2936***

(2.5739)

Thailand 1979 1986 3

-2.1134***

(-7.2209)

-1.5094***

(-7.5194)

-0.3053**

(-1.7655)

0.2309***

(2.7546)

-0.5684***

(-6.4518)

Vietnam 1987 1995 3

-1.4766***

(-9.1417)

-0.3901***

(-4.7557)

0.3196***

(4.2348)

0.7437***

(12.2123)

-0.6605***

(-11.3417)

Other Asia Pacific 1983 1994 4

-0.5037

(-4.5815)

-0.0059

(-0.1505)

0.1492***

(3.2602)

0.0591***

(3.2008)

-0.1081***

(-4.1032)

Critical values for the LM test

λ2 0.4 0.6 0.8

λ1 1% 5% 10% 1% 5% 10% 1% 5% 10%

0.2 -6.16 -5.59 -5.27 -6.41 -5.74 -5.32 -6.33 -5.71 -5.33

0.4 - - - -6.45 -5.67 -5.31 -6.42 -5.65 -5.32

0.6 - - - - - - -6.32 -5.73 -5.32

Notes: λj denotes the location of breaks. Critical values for the dummy variables denoting the break dates follow the standard asymptotic distribution. *(

**)

*** denote statistical

significance at the 10%, 5% and 1% levels respectively. TB is the break date; K is the lag length; St-1 is the LM test statistic; Bt1 is the coefficient on the first break in the

intercept; Bt2 is the coefficient on the second break in the intercept; Dt1 is the coefficient on the first break in the slope; Dt2 is the coefficient on the second break in the slope.

36

36

Table 9: Summary of the LM univariate unit root test results

Model C/No break hybrid Model CC/Model C hybrid

Series No breaks versus Model C Model CC vs No break/Model C hybrid

US S NS

Canada NS S

Mexico S S

Argentina NS NS

Brazil NS S

Chile S S

Colombia NS NS

Ecuador S S

Peru NS NS

Venezuela NS S

Other South and Central America NS NS

Austria NS NS

Belgium and Luxembourg S S

Bulgaria S S

Czech Republic S S

Denmark S S

Finland S S

France S S

Germany S S

Greece S NS

Hungary S S

Republic of Ireland S S

Italy S S

Norway S S

Poland S S

Portugal S S

Romania S S

Slovakia S S

Spain S S

Sweden S S

Switzerland S S

Turkey NS S

United Kingdom S S

Other Europe and Eurasia S S

Iran S S

Other Middle East NS NS

Algeria S S

Egypt S S

South Africa S S

Other Africa NS NS

Australia S S

Bangladesh S S

China S S

India S NS

Indonesia NS NS

Japan S S

Malaysia S S

New Zealand S S

Pakistan S S

Philippines S S

37

37

South Korea S S

Taiwan S S

Thailand S S

Vietnam NS S

Other Asia Pacific NS NS

TOTAL 41/55 stationary 43/55 stationary

38

38

Table 10: Panel LM unit root test with up to two structural breaks

Panels No break One break Two breaks

Panel of All Countries -7.335***

-16.9987***

-16.221***

Panel of North America -1.081 -1.8210**

-0.502

Panel of South and Central America 2.398***

-0.8370 0.294

Panel of Europe and Eurasia -8.054***

-15.4528***

-15.647***

Panel of Middle East -0.568 -1.7989**

-1.478*

Panel of Africa 0.505 -2.4860***

-3.116***

Panel of Asia Pacific -5.405***

-10.0691***

-9.540***

Model A ‘29 countries’ 2.649***

-2.3658***

-2.941***

Model C ‘14 countries’ 2.239**

-0.3347 0.975

Model AA ‘25 countries’ 2.006**

-1.8129**

-0.369

Model CC ‘11 countries’ 0.036 -2.0916**

-1.003

Model CC/Model C hybrid ’12 countries’ 0.041 -2.1327**

-0.979 Notes: The 1%, 5% and 10% critical values for the panel LM unit root tests with structural breaks are -2.326, -1.645 and -1.282 respectively.

*(

**)

*** denote statistical

significance at the 10%, 5% and 1% levels respectively.