Potential Growth in Asia and Its Determinants: - Recherche en IA spécialisée au MIT

Potential Growth in Asia and Its Determinants:
An Empirical Investigation
MATTEO LANZAFAME

∗

This paper contributes to the literature on growth in Asia in several respects. je
provide estimates of potential growth for 21 Asian economies using an aggregate
supply model with time-varying parameters and a Kalman ﬁltering methodology.
My estimates indicate that the actual growth slowdown experienced by many of
these economies in the 2000s is associated with a falling trajectory in potential
growth. Relying on Bayesian model averaging, I select robust determinants of
potential growth and ﬁnd that the latter is driven by the technology gap, trade,
tertiary education, and institutional quality, as well as by working-age population
growth. Effective reforms in these areas can help counterbalance declines in
potential growth in Asia. I also investigate the relationship between business
cycle features and potential growth, ﬁnding that higher volatility in actual growth
has signiﬁcantly negative effects on potential growth. Ainsi, stabilization policies
can have beneﬁcial effects on Asian economies’ long-term growth performance.

Mots clés: Asian economies, Kalman ﬁlter, potential growth
Codes JEL: C23, O40, O47

je. Introduction

Many Asian economies have enjoyed a remarkable growth performance over
the last 3 decades. With annual growth rates often close to 8%–10%, the People’s
Republic of China (RPC) and Southeast Asian economies, in particular, have been
referred to as growth miracles and their experience has fueled a growing debate in
the economic literature regarding the determinants of this performance and whether
it can be replicated in other emerging economies. Growth theory indicates that, dans
the long run, economies tend to grow at a rate consistent with the full utilization of
productive resources, which is known as the natural or potential growth rate (voir, pour
example, Blinder and Solow 1973, Le´on–Ledesma and Thirlwall 2002). Short-term
shocks can lead to temporary deviations from the potential growth rate that give
rise to changes in unemployment and inﬂation. Over time, these changes will be
corrected via the adjustment of relative prices, and growth will return to its potential

∗Matteo Lanzafame: Department of Economics, University of Messina, Italy. E-mail: mlanzafame@unime.it. Le
author would like to thank the participants at the Asian Development Outlook–Asian Development Review Conference
held in Seoul in November 2015, le rédacteur en chef, and an anonymous referee for helpful comments. He would also
like to thank Noli Sotocinal and Connie Bayudan–Dacuycuy for excellent research assistance. The usual disclaimer
applies.

Revue du développement en Asie, vol. 33, Non. 2, pp. 1–27

C(cid:3) 2016 Banque asiatique de développement
et Institut de la Banque asiatique de développement

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2 ASIAN DEVELOPMENT REVIEW

rate. There is wide agreement that the persistently high growth rates characterizing
Asian economies reﬂect their high potential growth rates.

Though still generally higher than that of other emerging and advanced
économies, average growth in Asia has declined in the last decade. In many cases,
growth performance seems to have deteriorated since the 2008–2009 global ﬁnancial
crise. This raises the question of whether such a decline reﬂects a transitory (though
persistent) deviation of actual growth from the potential growth rate, or if it signals
a fall in the potential growth rate itself. If Asian economies are entering a new phase
of permanently lower long-run growth, there will be implications for a number of
economic and social policies.

This paper proposes an empirical investigation of the issues discussed above.
I estimate the potential growth rate for a sample of 21 Asian economies, relying on
an unbalanced panel of annual data over the period 1960–2014. Given my interest in
the dynamics of potential growth in Asia, my approach is based on a time-varying
parameter aggregate supply (AS) model that is consistent with the concept of the
natural growth rate proposed by Harrod (1939) and its relation to Okun’s Law and
the Phillips curve. I consider different versions of the AS model and estimate it via a
Kalman ﬁltering methodology to obtain time-series estimates of Asian economies’
potential growth rates. I ﬁnd that the potential growth rate of most Asian economies
has been on a downward trajectory since the 2000s, and that this pattern either
continued or worsened during 2008–2014. In most cases, the estimated potential
growth rate was lower in 2014 than during 2000–2007. Given that actual growth
will tend to return to the potential growth rate in the long term, this outcome raises
concerns regarding the growth performance of Asian economies in the medium
to long term, and reinforces the need to investigate the determinants of potential
growth.

To carry out this task, I rely on a larger panel of annual data for 69 advanced
and emerging economies over the period 1960–2014. The objective is to obtain
more efﬁcient and reliable estimates, exploiting the additional information that
is available beyond the data in the sample of 21 Asian economies. From an
econometric viewpoint, my search for robust determinants of potential growth is
based on a recently proposed methodology for model selection: the Bayesian model
averaging (BMA) approach developed by Magnus, Powell, and Pr¨ufer (2010) for the
estimation of classical linear regression models with uncertainty about the choice
of the explanatory variables. Harrod’s natural growth rate is deﬁned as the sum
of the growth rates of labor productivity and the labor force. I introduce (trend)
working-age population growth (used as a proxy for labor force growth) dans le
model as a ﬁxed regressor, while letting BMA estimations select the additional
robust regressors. I ﬁnd that out of 35 variables considered, seven are considered to
be robust determinants of potential growth. My estimates conﬁrm that the growth
rate of the working-age population has a direct relationship with potential growth,
as suggested by theory, with a 1% increase in this variable leading a similar increase
in the potential growth rate. The implication is that, as with advanced economies,

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POTENTIAL GROWTH IN ASIA AND ITS DETERMINANTS 3

aging populations will gradually become a signiﬁcant drag on (potential) growth in
emerging Asian economies such as the PRC. Other variables that play a signiﬁcant
role in shaping the trajectory of the potential growth rate include a proxy for the
technology gap (as measured by an economy’s differences with the United States
[NOUS]), a measure of tertiary education levels, proxies for labor market rigidity, et
proxies for institutional quality (as measured by indexes for perceived government
efﬁciency and accountability). Integration with the world economy via trade and
ﬁnancial links is also important and has the expected positive impact on potential
growth. En particulier, the effects of ﬁnancial integration depend on the quality
of institutions. I ﬁnd that economies characterized by lower perceived regulatory
quality enjoy positive effects from greater connection to international ﬁnancial
marchés, but these gains gradually disappear as institutional quality increases.
Dans l'ensemble, the results indicate that effective economic policy interventions and, plus
generally, improvements in institutional quality (par exemple., more ﬂexible labor markets,
more efﬁcient and accountable government) have the potential to positively affect the
trajectory of potential growth, thus counteracting the effects of the recent slowdown
in many Asian economies.

Suivant, I turn to the question of whether the 2008–2009 global ﬁnancial crisis
left persistent, or even permanent, scars on the potential growth rates of Asian
économies. For this to be the case, potential growth should be endogenous with
respect to the actual growth rate (ou, more generally, business cycle features such
as deviations of the actual from the potential growth rate and growth volatility).
I investigate this hypothesis using two different approaches and ﬁnd that a higher
volatility of actual growth with respect to the potential growth rate has a signiﬁcantly
negative impact on the latter. This result suggests that policy measures leading to a
more stable macroeconomic and growth environment can have long-lasting positive
effects on growth performance. On the other hand, my estimates do not provide
evidence indicating that short-term deviations from actual growth affect the potential
growth rate, either positively or negatively. Taken at face value, this result indicates
that the effects of the 2008–2009 global ﬁnancial crisis on potential growth rates in
Asia, however deep and persistent, will not be permanent.

The remainder of the paper is organized as follows. The next section describes
the model and empirical methodology used to estimate the potential growth rates
of my sample of Asian economies. Section III is devoted to the investigation of
the determinants of potential growth. Section IV considers the relationship between
business cycle features and the dynamics of the natural or potential growth rate.
Section V concludes.

II. Model and Estimation Methodology

The concept of the natural rate of growth was formally introduced in growth
theory by Harrod (1939), who deﬁned it as the sum of the growth rates of the labor

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4 ASIAN DEVELOPMENT REVIEW

force and labor productivity, both of which were assumed to be exogenous. Ce
implies that Harrod’s natural rate of growth is the particular growth rate associated
with full employment and a stable inﬂation rate. En tant que tel, the role played by the
natural growth rate is twofold: it is both the trend growth rate of the economy and the
short-term upward limit to (noninﬂationary) growth that turns cyclical expansions
into recessions.

Since the natural growth rate is deﬁned as the sum of the growth rates
of labor productivity and the labor force, unemployment will rise whenever the
actual rate of growth (gt ) falls below the natural rate, and it will fall when gt rises
above g N ; c'est, the natural rate of growth is the particular growth rate consistent
with a nonchanging unemployment rate. Ainsi, a simple estimation framework to
pin down the value of g N is provided by the following speciﬁcation of Okun’s
Loi:

(cid:2)Ut = σ − ςgt

(1)

où (cid:2)Ut is the percentage change in the unemployment rate Ut , and gt is the
growth rate of output.

This speciﬁcation has been widely used in the literature to estimate g N
for economies and regions and, among other things, to investigate its possible
endogeneity (voir, Par exemple, Le´on–Ledesma and Thirlwall 2002, Lanzafame
2010). For my purposes, the speciﬁcation in equation (1) presents two drawbacks. Il
produces a single estimate of the natural or potential growth rate for the time period
under analysis, while I am interested in studying its evolution over time. Ainsi, je
rely on a time-varying-parameter approach to estimate a time series for g N
t ; on the
other hand, unemployment (including underemployment) and labor market data in
general are notoriously unreliable for some of the economies in my panel. To address
ce, I link Harrod’s deﬁnition of g N to the relationship between unemployment and
growth, and estimate the natural or potential growth rates of Asian economies relying
on an AS model. Since in the long run, unemployment will be constant when it is
equal to the nonaccelerating inﬂation rate of unemployment, the natural growth rate
t and, thus, (cid:2)Ut = 0. je
can be deﬁned as the growth rate consistent with Ut = U N
formalize this in Okun’s relation as

Ut = U N
t

− βt

(cid:3)

(cid:2)

gt − g N
t

(2)

where the Okun coefﬁcient
unemployment (U N
inﬂation and unemployment is given by the following Phillips curve in which

(βt ) and the nonaccelerating inﬂation rate of
t ) are assumed to be time varying. The relationship between

πt = π e
t

− γt

(cid:2)
Ut − U N
t

(cid:3)

(3)

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POTENTIAL GROWTH IN ASIA AND ITS DETERMINANTS 5

where πt and π e
a time-varying parameter. Combining equations (2) et (3), I get

t are, respectivement, the actual and expected inﬂation rates, while γt is

πt = π e
t

+ φt

(cid:3)

(cid:2)

gt − g N
t

(4)

where φt = βt γt . The speciﬁcation in equation (4) formalizes an AS model with
time-varying parameters.

To estimate the model in equation (4), I need an estimate of the expected
inﬂation rate, π e
t . Since there is very limited availability of time-series data for
t as a function of the actual inﬂation rate (πt ), assuming
expected inﬂation, I model π e
two possible speciﬁcations. The ﬁrst is in equation (5), where expected inﬂation in
time t is a time-varying function of actual inﬂation in t:

π e
t

= αt πt + εt

(5)

where αt is a time-varying parameter reﬂecting the public’s degree of accuracy in
forecasting inﬂation and εt is an independent normally distributed error term, avec
zero mean and constant variance. The estimated model in this case is

gt = g N
t

(1 − αt )
φt

πt + εt

(6)

The second speciﬁcation assumes an extreme form of adaptive expectations in which
expected inﬂation in t is equal to actual inﬂation in t − 1 plus a random error term:

π e
t

= πt−1 + εt

and the relative model is

gt = g N
t

+ 1
φt

(cid:2)πt + εt

(7)

(8)

Equations (6) et (8) can both be speciﬁed in state-space form. Speciﬁcally, le
measurement equations are

gt = μt + βt πt + εt

gt = μt + βt (cid:2)πt + εt

(6')

(8')

with μt = g N
t . Following standard practice in the literature (voir, Par exemple, Harvey
1989), to capture possible level breaks or trend patterns, the transition equations are

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6 ASIAN DEVELOPMENT REVIEW

assumed to follow a unit root:

μt = μt−1 + υt
βt = βt−1 + υt

(9)

(10)

Following Romer (1993), I take account of the possible effects of the degree of
openness on the slope of the Phillips curve, and thus of the AS models in equations
(6') et (8'), and also consider the following transition equation for βt :

βt = βt−1 + κmt + υt

(11)

where mt is the share of imports in gross domestic product (PIB). For each economy,
I select the most appropriate version of the model according to the signiﬁcance of
the estimated parameters and rely on the Akaike Information Criterion.1

My estimation is carried out relying on the Kalman ﬁlter recursive algorithm,
which is commonly used in the literature to obtain optimal estimates for state
variables in models with time-varying parameters (voir, Par exemple, Lanzafame and
Nogueira 2011). More speciﬁcally, to obtain a time series for the potential growth
rate g N
t , I apply the Kalman smoothing procedure, which uses all the information
in the sample to provide smoothed-state estimates. This procedure differs from
Kalman ﬁltering in the construction of the state series, as this technique uses only
the information available up to the beginning of the estimation period. Smoothed
series tend to produce more gradual changes than ﬁltered ones and, as discussed by
Sims (2001), they provide more precise estimates of the actual time variation in the
data.

Chiffre 1 below presents estimated potential growth rates and actual growth
rates for 12 Asian economies, including the region’s four most developed economies
and eight largest economies. The corresponding graphs for all other Asian economies
are included in Figure A.1 in the Appendix.

Dans l'ensemble, the Kalman smoother seems to perform well in ﬁtting the data, both in
terms of the signiﬁcance of the regressors and in providing a realistic approximation
for the long-run growth paths of Asian economies. The estimates show the potential
growth rate as being more stable than the actual growth rate, as well as being fairly
high and/or increasing in the 1980s and 1990s for most economies, which is in
line with expectations. It can also be seen that in most cases, the estimated potential
growth rate declined in the 2000s and with few exceptions, this trend either remained
stable or worsened during the period 2008–2014. Comparisons between the mean

1As an alternative, I also considered a different model augmented with ﬁnancial factors along the lines of
Felipe, Sotocinal, and Bayudan–Dacuycuy (2015). This turned out to be the most appropriate model only in the cases
of Thailand and Singapore.

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POTENTIAL GROWTH IN ASIA AND ITS DETERMINANTS 7

Chiffre 1. Actual and Potential Growth Rates in Select Asian Economies

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8 ASIAN DEVELOPMENT REVIEW

Chiffre 1. Continued.

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Source: Author’s calculations.

values for the 2000–2007 and 2008–2014 periods (et le 2014 estimates), lequel
are reported in Table 1, conﬁrm that this is the case.

As mentioned, a falling potential growth rate has signiﬁcant negative
consequences for an economy that can be particularly difﬁcult to cope with in an
emerging economy. Ainsi, an analysis of what drives potential growth and whether
its determinants may be inﬂuenced by policy interventions is of critical importance
for emerging Asian economies.

III. Determinants of Potential Growth

Having obtained time-series estimates for g N
t

for all Asian economies in my
panel, in this section, I turn to the investigation of the determinants of the potential
growth rate. My objective is to obtain robust and reliable estimates of variables that
are signiﬁcantly correlated with the potential growth rate. Pour y parvenir, I extend
the unbalanced panel to a group of 69 economies to include several other emerging
and advanced non-Asian economies. The list of economies included in the panel is
presented in Table A.1 in the Appendix.

My deﬁnition of potential growth is consistent with Harrod’s (1939) concept
of the natural growth rate represented as the sum of the growth rates of labor
productivity and the labor force; thus, in my search for the main drivers of g N
t , je

POTENTIAL GROWTH IN ASIA AND ITS DETERMINANTS 9

Tableau 1. Mean Estimates of Potential Growth Rates
(%)

Azerbaijan Bangladesh Cambodia
5.84
5.97
6.12

17.90
5.26
3.03

9.26
7.42
7.08

2000–2007
2008–2014
2014

RPC
9.90
8.80
7.91

Hong Kong,
Chine
4.83
3.01
2.25

India
7.03
6.97
6.29

Indonésie
4.95
5.81
5.01

2000–2007
2008–2014
2014

Japan
1.63
0.24
0.15

Republic

Kazakhstan of Korea Malaysia

8.77
6.81
5.05

5.58
3.49
3.33

5.27
5.02
5.81

Pakistan
4.04
4.93
5.40

Philippines Singapore

6.58
7.06
7.90

4.05
2.36
4.10

Sri Lanka Taipei,China Tajikistan Thailand Turkmenistan Uzbekistan Viet Nam
5.44
0.15
2.90

15.48
11.03
10.28

6.67
5.58
6.84

8.46
6.93
6.74

6.12
8.34
8.13

4.70
3.34
3.06

7.38
6.01
6.06

2000–2007
2008–2014
2014
PRC = People’s Republic of China.
Source: Author’s calculations.

need to take account of both of its components. As mentioned, labor market data
are not entirely reliable for Asian economies and emerging economies in general.
Donc, I proxy labor force growth using data on working-age population growth,
duly ﬁltered to purge short-term variability (par exemple., transitory migration ﬂows) et
obtain a better estimate for potential long-term labor force growth.2 However, le
search for the determinants of productivity growth is more complex and many
possible determinants are considered in the literature.

Faced with this issue, a number of recent studies have implemented various
model selection procedures to ascertain which variables have a robust association
with economic growth (voir, Par exemple, Fern´andez and Steel 2001; Sala-i-Martin,
Doppelhoffer, and Miller 2004). In this paper, I rely on the version of the BMA
approach developed by Magnus, Powell, and Pr¨ufer (2010) for the estimation
of classical linear regression models with uncertainty about the choice of the
explanatory variables. This estimator ﬁts the model nicely and the approach used in
this paper is based on a classical linear regression framework with two subsets of
explanatory variables: (je) The “focus regressors,” which are explanatory variables
always included in the model for theoretical reasons or other considerations about
the phenomenon under investigation. In my case, the growth rate of the working-age
population (gwap) is one such focus regressor; et (ii) The “auxiliary regressors,»
which are additional explanatory variables whose inclusion in the model is less
certain. The problem of model uncertainty and variable selection arises because

2I rely upon the frequency domain ﬁlter developed by Corbae, Ouliaris, and Philipps (2002) and Corbae and

Ouliaris (2006).

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10 ASIAN DEVELOPMENT REVIEW

different subsets of auxiliary regressors can be excluded from the model to improve
(in the mean-squared error sense) the unrestricted ordinary least squares estimates.
When there are k2 auxiliary regressors, the number of possible models to be
considered is 2k2. The BMA estimator provides a coherent method of inference
on the regression parameters of interest by taking explicit account of the uncertainty
due to both the estimation and the model selection steps. The BMA estimator uses
conventional noninformative priors on the focus parameters and the error variance,
and a multivariate Gaussian prior on the auxiliary parameters. The unconditional
BMA estimates are obtained as a weighted average of the estimates from each of the
possible models in the model space, with weights proportional to the marginal
likelihood of the dependent variable in each model. An auxiliary regressor is
considered to be robust if the t ratio on its coefﬁcient is greater than 1 in absolute
value or, equivalently, the corresponding one-standard error band does not include
zero. Alternativement, researchers can rely on their posterior inclusion probabilities.
Masanjala and Papageorgiou (2008) suggest that a posterior inclusion probability
de 0.5 corresponds approximately to a t ratio of 1 in absolute value.3

Despite being a useful tool for establishing a set of robust regressors given
a large set of possible explanatory variables, the BMA approach also has some
weaknesses. Ciccone and Jarocinski (2010), Par exemple, show that the results
of BMA estimations can be highly sensitive to measurement errors. Ghosh and
Ghattas (2015) show that high collinearity in three or more covariates tends to push
the posterior inclusion probabilities downward and that all collinear variables may
be falsely excluded.4 Sala-i-Martin, Doppelhofer, and Miller (2004) note that the
BMA approach’s emphasis on marginal measures of variable importance make it
difﬁcult (if not impossible) to detect dependence among explanatory variables. Ils
stress that the extent of interdependence between explanatory variables will affect
the posterior inclusion probability of any given model, as well as the form of the
posterior probability distribution of variables over the model space.

To deal with these issues, I implement a hybrid two-stage approach. In the
ﬁrst stage, I exploit the properties of the BMA methodology by Magnus, Powell,
and Pr¨ufer (2010) to assess the robustness of possible determinants of potential
growth. Since some of the variables in my data set have very high correlations, je
implement successive BMA estimation procedures to eliminate redundant indicators
from the analysis and further reduce the model space by removing variables that are
not robust. In doing this, I estimate multiple speciﬁcations of the model by adding
highly collinear robust determinants (par exemple., institutional quality variables) one at a

3While the BMA helps deal with model uncertainty, it does not deal with issues of causality. Ainsi, le
deﬁnition of regressors as robust should be intended as indicating that they are signiﬁcantly correlated with potential
growth.

4En particulier, Ghosh and Ghattas (2015) note that strong collinearity leads to a multimodal posterior
distribution such that if there are three or more highly collinear variables, the median probability model could
potentially discard all of them.

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POTENTIAL GROWTH IN ASIA AND ITS DETERMINANTS 11

temps. In the second stage, the robust determinants that are identiﬁed using the BMA
approach are used as regressors in a ﬁxed effects panel data regression to gauge
their effects on potential output growth. I also consider the possible presence of
nonlinearities and test separately for the signiﬁcance of interaction effects between
an index of ﬁnancial integration (selected as a robust determinant of potential growth
by the BMA methodology) and institutional quality variables, which is in line with
the literature on the nonlinearity of effects of ﬁnancial openness on economic growth.
I also examine whether there is a statistically signiﬁcant interaction effect between
institutional quality and the technology gap with the US, which is another regressor
deﬁned as robust by the BMA approach.

A detailed description of the two stages and the associated results are

presented in the next section.

UN.

BMA and Fixed Effects Results

I consider 35 potential determinants of g N

t , including the focus regressor
gwap. These potential determinants and their deﬁnitions and data sources are
presented in Table A.2 in the Appendix. Since the potential growth rate is the
particular rate toward which actual growth tends in the long run, the set of
determinants of potential growth I consider reﬂects a broad set of variables typically
deemed to affect actual growth in the long run. To control for the presence of ﬁxed
effects, I applied the forward-orthogonal-deviation transformation to the data before
implementing the BMA procedure. The number of possible determinants considered
is much larger than those included in a typical growth regression, but my data set
contains variables that can be considered to be close alternatives (par exemple., proxies for
éducation, openness, and institutional quality) and are highly correlated. Ainsi, comme
well as using all 35 variables at once, I also carried out BMA regressions using
subsets of the various proxies to reduce the computation burden and the number of
auxiliary regressors, as well as to lessen the impacts of the presence of correlated
regressors in the BMA analysis. I then excluded from the ﬁnal speciﬁcation the ones
that never turned out to be robust. Following this approach, I reduced the number
of possibly robust auxiliary regressors to 13. The BMA results for this speciﬁcation
are reported in Table 2.

As can be seen, out of the 13 auxiliary regressors, only seven are selected
by the BMA approach as robust determinants of potential growth with a posterior
inclusion probability equal to or greater than 0.5: (je) gross enrollment ratio in tertiary
éducation (es3enrot); (ii) technology gap vis-`a-vis the US (gap); (iii) degree of labor
market rigidity (lamrig); (iv) et (v) two indexes reﬂecting aspects of perceived
institutional quality (voice and accountability index, voa; government efﬁciency
index, goveff); (vi) trade-to-GDP ratio (trade); et (vii) a proxy of integration into
international ﬁnancial markets (ratio of overall ﬁnancial ﬂows with respect to GDP,
integr).

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12 ASIAN DEVELOPMENT REVIEW

Tableau 2. Bayesian Model Averaging Estimates

Coefﬁcient
0.697
0.002
−0.459
0.295
−0.006
0.004
−0.089
0.046
0.031
2.276
−0.002
−1.328
0.029
0.712

gwap
agrempsh
di16merdt
es10schom
es1enrop
es2enros
es3enrot
ﬁnref
gap
goveff
integr
lamrig
trade
voa
Model space: 8,192 models
pip = posterior inclusion probability, SD = standard deviation.
Note: Bold signiﬁes a pip exceeding 0.5.
Source: Author’s calculations.

t_stat
SD
2.560
0.273
0.013
0.160
0.683 −0.670
0.750
0.395
0.020 −0.310
0.011
0.380
0.021 −4.240
0.080
0.572
1.040
0.030
0.759
3.000
0.001 −2.550
1.245 −1.070
3.160
0.009
0.860
0.829

pip

1-SD Band

0.424
1.000
0.070 −0.011
0.380 −1.142
0.430 −0.101
0.130 −0.026
0.180 −0.007
1.000 −0.110
0.060 −0.526
0.001
0.610
1.517
0.970
0.940 −0.003
0.610 −2.573
0.020
0.980
0.500 −0.118

0.970
0.015
0.224
0.690
0.014
0.016
−0.068
0.618
0.061
3.035
−0.001
−0.083
0.038
1.541

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In the next step of my empirical investigation, I exclude from the analysis the
variables that turned out not to be robust using the BMA estimation and, relying on
the standard ﬁxed effects technique, estimate the following model for all economies
in my panel as well as for the subpanel of Asian economies:

g N
it

= ηi + θ1gwapit
+ θ5intergrit

+ θ2es3enrotit + θ3gapit
+ θ6lamrigit

+ θ7tradeit + θ8voait + ξit

+ θ4goveffit

(12)

To control for the effects of the possible presence of cross-sectional dependence
in the error term, I rely on Driscoll and Kraay (1998) for standard errors, lequel
assume the error structure to be heteroskedastic, autocorrelated (up to some lag),
and possibly correlated between the groups. En tant que tel, Driscoll–Kraay standard errors
are robust to very general forms of temporal dependence and/or cross-sectional
dependence due to, Par exemple, spatial correlation or time effects.

The results from equation (12), reported in the ﬁrst two columns on the
left-hand side of Table 3, are very much in line with expectations for all economies
and Asian economies, even though the latter are based on a fairly small sample size.
En particulier, the coefﬁcient on gwap, which is the elasticity of potential output with
respect to the working-age population, is signiﬁcant and very close to 1, suggérant
that a 1% increase in the working-age population leads to a 1% increase in potential
output growth. This is consistent with the deﬁnition of the natural or potential
growth rate used in this paper and indicates that gwap is a good proxy for the
potential long-run growth rate of the labor force. The signs of all other variables are
also as expected, with the possible exception of tertiary enrollment (es3enrot) et

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POTENTIAL GROWTH IN ASIA AND ITS DETERMINANTS 13

Tableau 3. Determinants of g N

t : Fixed Effects Estimations

All Economies Asian Economies

All Economies

1.002∗∗

0.867∗∗
0.158∗∗
−0.001∗∗
0.042∗
–
0.963∗
−0.003
–
−1.933∗∗
0.082∗∗

1.157∗∗
0.163∗∗
−0.002∗∗
0.073∗
−0.006∗
1.361∗∗
0.004∧
−0.003∗∗
−2.924∗∗
0.065∗∗
−0.0001∗∗ −0.0001∗∗

gwap
es3enrot
es3enrot_sq
gap
gap_ polstab
goveff
integr
integr_regq
lamrig
trade
trade_sq
voa
dummy0814
Constant
F-statistic for H0 : θ1 = 1
Non. of economies
Non. of observations
Remarques: ∗∗∗, ∗∗, and ∗ indicate signiﬁcance at the 1%, 5%, et 10% level, respectivement. Variables
instrumented with ﬁrst lag. Standard errors from Driscoll and Kraay (1998) used.
Source: Author’s calculations.

0.942∧
−0.155∗∗
–
0.097∧
–
2.824∗
−0.001
–
−8.757∗∗
0.063∗∗
–
−1.070∗
–
7.120
0.01
18
188

−0.052
–
0.069∗∗
–
1.147∗
−0.003∗∗
–
−1.619∗
0.054∗∗
–
1.456∗∗
–
−0.995
0.00
61
655

0.726∗
−2.432∗∗
−5.014∗∗
0.39
61
655

1.651∧
−2.727∗∗
−5.576∗∗
0.51
61
425

the ﬁnancial integration index (integr), both of which enter the regression with a
negative sign.

These last results are somewhat puzzling and warrant further investigation,
which I carry out by modifying the model in two steps to allow for some form
of nonlinearity.5 In the ﬁrst step, I start by assessing whether the regressors in
equation (12) may affect potential growth nonlinearly. Extending the model with
the introduction of various quadratic terms, I ﬁnd that this is the case for both
es3enrot and trade. Fait intéressant, the coefﬁcient on the quadratic term es3enrot_sq
is negative, but once this is included in the model, es3enrot enters with a signiﬁcantly
positive sign; c'est, enrollment in tertiary education affects g N
t positively, but its
impact decreases as es3enrot rises. The outcomes for trade and trade_sq are the
same. De plus, the evidence of a signiﬁcant downward shift in potential growth
during 2008–2014 due to the global ﬁnancial crisis points to the possibility of a
structural break in the g N
t series for many economies. To control for this, I introduce
an intercept dummy variable (dummy0814) equal to 1 for the period 2008–2014
and zero otherwise. The dummy turns out to be negative and strongly statistically
signiﬁcant. These changes to the benchmark model result in the speciﬁcation in the
third column in Table 3, where I can see that all other results remain fairly similar.
En particulier, the ﬁnancial integration index (integr) still enters with a negative sign,
even though it is not signiﬁcant.

5Because of the small panel size, the estimation of this extended model is not feasible for the subpanel of

Asian economies.

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14 ASIAN DEVELOPMENT REVIEW

In the second step of the benchmark model extension, I consider whether
institutional quality may affect potential growth not only directly, as indicated by the
signiﬁcant coefﬁcients on goveff, lamrig, and voa, but also via indirect channels. Il
has been suggested that the effects of integration on international ﬁnancial markets
may depend on institutional quality (voir, Par exemple, Kose et al. 2009). Similar
arguments have been advanced on the potential beneﬁts associated with technology
spillovers. To explore this possibility, I interact the gap and integr variables with
a number of proxies for the quality of institutions. If (on average) the impacts of
the technology gap and integration indexes depend on institutional quality, then the
interaction terms should turn out to be signiﬁcant. I ﬁnd that this is the case when the
gap variable is interacted with the index of political stability (gap_polstab), alors que
the effects of integr on the potential growth rate appear to depend signiﬁcantly on
regulatory quality (regq). Results are reported in the last column of Table 3.

The estimates show that once the interaction terms are introduced as
variables on the right-hand side, integr turns positive and is signiﬁcant at the
10% level, while gap remains positive and signiﬁcant too; the interaction terms
are always negative and signiﬁcant. The interaction terms are constructed as simple
products of the gap and integration indexes with the institutional quality indexes:
gap polstab = gap × polstab and integr regq = integr × regq. En tant que tel, the sign,
size, and signiﬁcance of the effect of gap and integr on the potential growth rate
will vary according to the value of the institutional quality index. Using regq
and integr as an example, if θ5 is the estimated coefﬁcient on integr and θ5 1 le
estimated coefﬁcient on integr_regq, then the overall impact of ﬁnancial integration
is given by (θ5 + θ5 1 × r egq). The size and signiﬁcance of this product will vary for
different values of regq. The institutional quality indexes range from –2.5 (weak)
à 2.5 (fort) to reﬂect governance performance. To test for the effect of the
ﬁnancial integration index, I can conduct a series of F-tests to determine whether
(θ5 + θ5 1 × r egq) is signiﬁcantly different from zero for different values of regq.
The F-tests conducted show that, when taking into account institutional
quality via the interaction term, the overall impact of ﬁnancial integration is positive
and signiﬁcant for r egq < (0.5). This indicates that ﬁnancial integration has larger positive effects on potential growth for economies with weaker institutions, thus acting as a substitute for high-quality institutions. For emerging economies with weaker institutions, including some of the Asian economies in my sample, the implication is that successful integration into international ﬁnancial markets may bring about signiﬁcant long-term growth beneﬁts by raising the potential growth rate. Meanwhile, the gap variable has a positive and signiﬁcant impact on potential growth for the entire range of polstab values, but again its effect is smaller for economies with better institutions (as proxied by greater political stability). This is not surprising since emerging economies, which can be expected to reap larger beneﬁts from technology spillovers, are also characterized by lower polstab values. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 3 2 1 1 6 4 2 9 3 2 a d e v _ a _ 0 0 0 7 0 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 POTENTIAL GROWTH IN ASIA AND ITS DETERMINANTS 15 The BMA procedure and the estimates reported in Table 3 depict a fairly clear picture of the main determinants of potential growth. From a policy viewpoint, the main result of this analysis is that institutional quality and other supply-side characteristics matter for potential growth. Structural reforms in these areas can signiﬁcantly improve long-term growth performance. In the next section, I address the question of whether demand-management policies can play a role too. IV. Business Cycles and the Endogeneity of Potential Growth Standard macroeconomic theory assumes business cycles and potential growth as separate phenomena. As a result, business cycle features such as the depth of a recession are deemed to have no signiﬁcant effects on long-term economic growth. Recent theoretical and empirical contributions, however, have challenged this view (see, for example, Christopoulos and Le´on–Ledesma 2014), while the length and signiﬁcant economic impacts of the 2008–2009 global ﬁnancial crisis have reignited the debate on the relationship between short- and long-term growth. This issue takes on particular importance in Asia as the PRC and other economies have seen their growth performance deteriorate between 2008 and 2014 to the point that it has been argued this may be the beginning of a “new normal” for growth patterns in Asia (see, for example, Asian Development Bank 2016). If potential growth is, at least to some extent, endogenous with respect to actual growth and its short-term cyclical features, this view may be shown to be correct and the growth slowdown in Asia may be structural. As mentioned, Le´on–Ledesma and Thirlwall (2002) develop an econometric framework that allows us to estimate Harrod’s natural growth rate (g N ) and test for its endogeneity with respect to the actual growth rate (g). The methodology is based on two steps. First, an estimate of g N , which is assumed to be constant over time, is produced using the version of Okun’s Law speciﬁed in equation (1) and the condition that gt = g N when (cid:2)Ut = 0. Next, a reversed version of the Okun’s Law relation is augmented with a dummy variable (Dgdev ) that takes the value of 1 when gt is t greater than the estimated g N (gdevt = gt − g N > 0) and zero otherwise. Ainsi, le
following model is estimated:

gt = η − ψ(cid:2)Ut + λDgdev

+ εt

(13)

If the estimated ˆλ is positive and signiﬁcant, then the actual growth rate needed
in boom periods (gt > g N or, equivalently,
to keep unemployment constant
gdevt > 0) has risen. C'est, the actual growth rate has pulled up the natural
growth rate.

Relying on the deﬁnition of g N

t used in this paper, I construct a test for
the endogeneity hypothesis, which is very much in line with the methodology of
Le´on–Ledesma and Thirlwall (2002). I start by noticing that since g N
is deﬁned as
t

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16 ASIAN DEVELOPMENT REVIEW

the particular growth rate consistent with a stable inﬂation rate, the estimated α in
equation (14) below is expected to be equal to zero, while the estimate of β should
be positive:

gdevt = α + β(cid:2)πt + εt

(14)

Rising inﬂation ((cid:2)πt > 0) should be associated with an actual growth rate higher
than the potential growth rate (gdevt > 0) so that β > 0; meanwhile, a stable
inﬂation rate ((cid:2)πt = 0) is expected to correspond to gdevt = 0, so that α = 0.
Introducing Dgdev

to equation (14), I obtain

gdevt = α + β(cid:2)πt + λDgdev

+ εt

(15)

A positive estimate of λ in equation (15) is expected as Dgdev
= 1 when gdevt > 0.
En outre, the size of gdevt in boom periods (gdevB) will be given by the sum
of the two estimates ˆα and ˆλ. Since gdevB is determined by the changes in actual
and potential growth during booms (gdevB = (cid:2)gB − (cid:2)g N
B ), I can test the null
hypothesis H0 : gdevB − (cid:2)gB = 0—if the latter is rejected, it follows that (cid:2)g N
B
is signiﬁcantly different from zero. C'est, rejection of the null indicates that the
potential growth rate rises when gt > g N
(ou, equivalently, gdevt > 0), which is
t
in line with the endogeneity hypothesis proposed by Le´on–Ledesma and Thirlwall
(2002). Allowing substantially more degrees of freedom than estimations based on
my benchmark model, this testing framework makes it feasible to obtain efﬁcient
estimates of the model parameters for the subpanel of Asian economies. En outre
to the usual ﬁxed effects estimator, I can also rely on the mean-group estimator
(Pesaran and Smith 1995) to allow for parameter heterogeneity.

I also investigate the endogeneity of the potential growth rate introducing the
dummy Dgdev
in my benchmark model. Just as in the testing framework proposed by
Le´on–Ledesma and Thirlwall (2002), a positive and signiﬁcant coefﬁcient on Dgdev
would support the hypothesis that potential growth is, at least to a certain extent,
endogenous to the actual growth rate. Concurrently, I also explore the possibility that
other business cycle features may play a role by including in the model as additional
regressors the following two variables: (je) gdev5t , which is the average deviation of
actual growth from the potential growth rate (gdevt = gt − g N
t ) over the previous
5 années; et (ii) gdev5sdt , which deﬁnes the standard deviation of gdevt over the
previous 5 years.6

Tableau 4 reports the estimates based on the ﬁrst approach to testing the
endogeneity hypothesis as formalized in equation (15). Independently of whether I

6I also considered the ﬁrst lag of gdevt (predetermined with respect to g N

t ) and the standard deviation of
actual growth over the previous 5 years as alternative variables to capture business cycle features. These turned out
not to be signiﬁcant in my estimations and therefore the results are not reported.

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POTENTIAL GROWTH IN ASIA AND ITS DETERMINANTS 17

Tableau 4. Tests of the Endogeneity Hypothesis

Fixed Effects Estimations

(cid:2)π
Dgdev
Constant
Estimate of gdevB
t-statistic on H0 : gdevB − (cid:2)gB = 0
Non. of economies
Non. of observations

0.015
2.209∗∗

Asian Economies
0.051∗
0.020
2.859∗∗
–
−1.138∗∗ −0.353∗∗ −1.669∗∗
1.189∗∗
–
–

All Economies
0.030
–
−0.096
–
–
69
2,456

1.071∗∗
0.031
69
2,456

−0.012
21
671

21
671

Mean Group Estimations

0.045∗
1.721∗∗

−0.891∗∗ −0.100

Asian Economies
0.054∗
–

All Economies
0.068∗∗
–
−0.034
–
–
69
2,456

(cid:2)π
Dgdev
Constant
Estimate of gdevB
t-statistic on H0 : gdevB − (cid:2)gB = 0
Non. of economies
Non. of observations
Remarques: ∗∗∗, ∗∗, and ∗ indicate signiﬁcance at the 1%, 5%, et 10% level, respectivement. Variables
instrumented with ﬁrst lag. Standard errors from Driscoll and Kraay (1998) used. To avoid undue
inﬂuence from high-inﬂation episodes, years with average inﬂation rates higher than 25% for the “All
Economies” speciﬁcations and higher than 45% for the “Asian Economies” speciﬁcations are excluded
from the estimations.
Source: Author’s calculations.

0.062∗∗
2.325∗∗
−1.306∗∗
1.019∗
−0.182
21
671

−0.210
69
2,456

0.830∗∗

21
671

–
–

refer to the ﬁxed effects or mean group estimates, the estimations for all economies
and Asian economies estimations both return clear-cut results. As expected, le
dummy variable Dgdev
turns out always to be positive and highly signiﬁcant, as does
gdevB. Cependant, the null hypothesis that gdevB is not signiﬁcantly different from
(cid:2)gB is never rejected, implying that there is no signiﬁcant evidence of an increase
in potential growth ((cid:2)g N
B

> 0) during boom periods.

Tableau 5 reports the results from my investigation of the endogeneity hypothesis
using the second approach outlined above that relies on my benchmark model. Comme
can be seen, Dgdev
turns out to be positive but not statistically signiﬁcant. Ce
outcome is consistent with the results in Table 4 as I again do not ﬁnd signiﬁcant
evidence supporting the endogeneity hypothesis for potential growth. De plus,
there is no signiﬁcant evidence that deviations of the actual from the potential growth
rate (proxied by gdev5t ) play a signiﬁcant role. This result implies that, cependant
deep and persistent, the decline in actual growth associated with the 2008–2009
global ﬁnancial crisis can be expected not to leave permanent scars on long-term
growth. An additional implication is that an economic policy intervention to boost
the latter
short-term growth above the potential growth rate will not affect
signiﬁcantly. En effet, my results indicate that, by increasing growth volatility,
this type of policy intervention may actually indirectly harm long-term growth

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18 ASIAN DEVELOPMENT REVIEW

Tableau 5. The Endogeneity Hypothesis: Fixed Effects Estimations

All Economies
1.229∗∗
0.124∗∗
−0.001∗
0.075∧
−0.008∧
1.194∗
0.004
−0.003∗
−2.812∗
0.081∗∗
−0.0001∗
2.017∧
−2.825∗∗
0.154
0.026
−0.191∗
−5.712∗∗
1.14
61
421

gwap
es3enrot
es3enrot_sq
gap
gap_ polstab
goveff
integr
integr_regq
lamrig
trade
trade_sq
voa
dummy0814
Dgdev
gdev5
gdev5sd
Constant
F-statistic for H0 : θ1 = 1
Non. of economies
Non. of observations
Remarques: ∗∗∗, ∗∗, and ∗ indicate signiﬁcance at the 1%, 5%, et 10% level, respectivement.
Variables instrumented with ﬁrst lag. Standard errors from Driscoll and Kraay (1998)
used.
Source: Author’s calculations.

Asian Economies
3.687∗∗
0.156
−0.004∗
0.013
0.005
−1.198
0.005
−0.010∧
−12.869∗∗
0.165∗∗
−0.0003∗
−0.836
−2.009∗∗
0.192
0.152
−0.314∗
7.964∧
13.22∗
18
121

performance. I ﬁnd that gdev5sd turns out to be robustly signiﬁcant and enters with
a negative sign, both in the “All Economies” and the “Asian Economies” estimations
in Table 5, which is in line with other evidence in the literature (voir, Par exemple,
Ramey and Ramey 1995).

Donc, the analysis carried out in this section suggests that the effects of
demand-management policies aimed at increasing actual growth above the potential
growth rate will not affect the trajectory of the latter and will be short-lived at best.
To have a positive impact on long-term growth performance, demand-management
policies should aim at stabilizing the actual growth rate as close as possible around
the path of potential growth.

V. Conclusions

Focusing on the performance of Asian economies over the period 1960–2014,
this paper contributes to the empirical literature on potential growth in several
respects. I provide estimates of potential growth for 21 Asian economies using
an AS state-space model with time-varying parameters and a Kalman ﬁltering
methodology. My estimates appear to ﬁt well with the growth experiences of Asian
economies and indicate that the actual growth slowdowns experienced by many

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POTENTIAL GROWTH IN ASIA AND ITS DETERMINANTS 19

of these economies in the 2000s can be associated with the falling trajectories
of their potential growth rates. Suivant, I investigate the determinants of potential
growth using a larger panel of 69 advanced and emerging economies and data for
35 possible growth determinants. Relying on the BMA selection procedure, I select
seven variables that, together with the growth rate of the working-age population,
can be considered robust determinants of potential growth. The results are in line
with expectations and indicate that potential growth is inﬂuenced by various aspects
of institutional quality, the technology gap with the US, trade, and tertiary education,
as well as by the growth rate of the working-age population. En particulier, I ﬁnd
that the effects of integration with international ﬁnancial markets are positive and
signiﬁcant only for economies with weak institutions.

With the objective of providing evidence regarding the possible effects of the
2008–2009 global ﬁnancial crisis on the dynamics of the potential growth rate, je
extend the benchmark model to include proxies for business cycle features. Moi aussi
carry out a new test of the endogeneity hypothesis proposed by Le´on–Ledesma
and Thirlwall (2002). My results indicate that deviations of actual growth from
the estimated potential growth rate do not have a signiﬁcant impact on potential
growth itself, which is in line with the hypothesis that recessions and booms do
not have long-lasting effects on long-term growth performance. On the other hand,
I ﬁnd that actual growth volatility with respect to potential growth does have a
signiﬁcant negative effect on g N
t . This indicates that policies aimed at stabilizing
actual growth in the proximity of the potential growth rate can have beneﬁcial effects
on an economy’s long-term growth performance.

Dans l'ensemble, the evidence gathered supports the hypothesis that Asian economies
may have entered a new era of slower potential and actual growth rates. My results
also suggest that appropriate changes in economic policies and institutions can play a
signiﬁcant role in lifting the potential growth rate. If these are carried out effectively,
the “new normal” in Asia may come to resemble previous growth patterns more than
would otherwise have been expected.

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POTENTIAL GROWTH IN ASIA AND ITS DETERMINANTS 21

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Appendix

Table A.1. Economies Included in the Analysis

Asian economies Azerbaijan; Bangladesh; Cambodia; People’s Republic of China; Hong Kong,
Chine; India; Indonésie; Japan; Kazakhstan; Republic of Korea; Malaisie;
Pakistan; Philippines; Singapore; Sri Lanka; Taipei,Chine; Tajikistan; Thaïlande;
Turkmenistan; Uzbekistan; Viet Nam

Other emerging
économies

Advanced

économies

Algeria, Argentina, Bolivia, Brazil, Colombia, Costa Rica, Dominican Republic,

Ecuador, Egypt, Hungary, Mexico, Morocco, Panama, Peru, Poland, Qatar, Saudi
Arabia, Afrique du Sud, Turkey, Uruguay, Venezuela

Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Estonia, Finlande,

France, Allemagne, Grèce, Iceland, Ireland, Israel, Italy, Luxembourg,
The Netherlands, Nouvelle-Zélande, Norway, Portugal, Slovak Republic, Slovenia,
Espagne, Sweden, Suisse, United Kingdom, États-Unis

Source: Author’s compilation.

Table A.2. Variables and Data Sources

Variable

Deﬁnition

Source

Potential growth rate estimate

Author’s estimates using WDI–IFS

data

g
gdev
gdev5
gdev5sd

Actual growth rate
g-gn
Average of g_dev over previous 5 années
Standard deviation of the g_dev5 over the

WDI–IFS data
Author’s calculations
Author’s calculations
Author’s calculations

previous 5 années

Gwap

Growth rate of working-age population

Author’s calculations using

(aged 15–64 years)

WDI–IFS data

Auxiliary regressors used for the BMA selection procedure

di16merdt R&D expenditures as a percentage of GDP CANA Database (v. Jan 2011) par
Castellacci and Natera (2011);
original source: UNESCO,
OECD, RICYT

Continued.

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22 ASIAN DEVELOPMENT REVIEW

Variable

Deﬁnition

Source

Table A.2. Continued.

2 es1enrop

Gross enrollment ratio (primaire): ratio of

total enrollment, regardless of age, to the
population of the age group that
ofﬁcially corresponds to the primary
level

3 es2enros

Gross enrollment ratio (secondaire): ratio

of total enrollment, regardless of age, à
the population of the age group that
ofﬁcially corresponds to the secondary
level

4 es3enrot

Gross enrollment ratio (tertiary): ratio of

total enrollment, regardless of age, to the
population of the age group that
ofﬁcially corresponds to the tertiary level

5 es10schom

Mean years of schooling: average number

of years of school completed in
population over the age of 14 années

6 es12educe

Public expenditure on education: current

and capital public expenditure on
éducation

7 es14teacr_ plus Primary pupil–teacher ratio: number of

8 ghc

pupils enrolled in primary school /
number of primary school teachers
Percentage growth rate in index of human
capital per person based on years of
schooling (Barro and Lee 2013) et
returns to education (Psacharopoulos
1994)

CANA Database (v. Jan 2011) par
Castellacci and Natera (2011);
original source: UNESCO

CANA Database (v. Jan 2011) par
Castellacci and Natera (2011);
original sources: Barro and Lee
(2001) and World Bank’s WDI
(national accounts data)

CANA Database (v. Jan 2011) par
Castellacci and Natera (2011);
original source: UNESCO
CANA Database (v. Jan 2011) par
Castellacci and Natera (2011);
original source: UNESCO
Author’s calculations using PWT

8.1 data

9 econglob

Index of Globalization: long distance ﬂows

KOF Index of Globalization

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10 overallglob

of goods, capital, and services, et
information, and perceptions that
accompany market exchanges measured
on a scale ranging from 0 (lowest) à 10
(highest)

Overall Globalization Index: weighted
average of econ_ glob, soc_ glob, et
pol_ glob on a scale ranging from 0
(lowest) à 100 (highest)

11 agrempsh

Employment in agriculture (% of total

employment)

12 indempsh

Employment in industry (% of total

employment)

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KOF Index of Globalization

International Labour Organization,
Key Indicators of the Labour
Market database; Global
Employment Trends Dataset
(2014)

Continued.

POTENTIAL GROWTH IN ASIA AND ITS DETERMINANTS 23

Variable

Deﬁnition

Source

Table A.2. Continued.

13 serempsh Employment in services (% of total
employment)

International Labour Organization,
Key Indicators of the Labour
Market database; Global
Employment Trends Dataset
(2014)

14 gap100

Technology gap variable: 1 minus the ratio

Author’s calculations using PWT

8.1 data

of the level of labor productivity
vis-`a-vis that of the US in purchasing
power parity terms, multiplied by 100;
labor productivity computed as a ratio
(rgdpo/emp), where rgdpo is output-side
real GDP at chained purchasing power
parity (in millions of 2005 US dollars)
and emp is number of persons engaged
(in millions); follows speciﬁcation
proposed by Le´on–Ledesma (2002)

15 gckemp

Percentage growth rate of the capital–labor

Author’s calculations using PWT

ratio

8.1 data

16 lmr

Index of labor market regulations

Gwartney et al. (2014) EFW 2014

Annual Report

World Bank’s Doing Business

database; LAMRIG database
from Campos and Nugent (2012)

17 lamrig

measuring economic freedom (par exemple.,
market forces determine wages and the
conditions of hiring and ﬁring,
government refrains from the use of
conscription) on a scale ranging from 0
(lowest) à 10 (highest)

Index of labor market rigidity on a scale
ranging from 0 (lowest) à 3 (highest);
data for 2004–2013 are from the World
Bank’s Doing Business database,
pre-2004 data are from the LAMRIG
database from Campos and Nugent
(2012); since the index exhibits very
little variation, annual values are
assumed constant over the 5-year periods
considered by Campos and Nugent
(2012); Campos and Nugent (2012) state
the LAMRIG index is consistent with the
World Bank’s Doing Business database

18 cocorr

Control of Corruption Index reﬂects

World Bank’s Worldwide

Governance Indicators (2014
Update)

perceptions of the extent to which public
power is exercised for private gain,
including both petty and grand forms of
corruption, as well as the capture of the
state by elites and private interests on a
scale measuring governance
performance ranging from −2.5 (weak)
à 2.5 (fort)

Continued.

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24 ASIAN DEVELOPMENT REVIEW

Variable

19 goveff

Table A.2. Continued.

Deﬁnition

Source

World Bank’s Worldwide

Governance Indicators (2014
Update)

Government Effectiveness Index reﬂects
perceptions of the quality of public
services, quality of the civil service and
the degree of its independence from
political pressures, quality of policy
formulation and implementation, et
credibility of the government’s
commitment to such policies on a scale
measuring governance performance
ranging from approximately –2.5 (weak)
à 2.5 (fort)

20 pf1corri Corruption Perception Index on a scale
measuring corruption ranging from 0
(highest) à 10 (lowest)

CANA Database (v. Jan 2011), par

Castellacci and Natera (2011), pour
pre-2008 data, Transparency
International for 2008–2014 data;
original source: Transparency
International

21 polstab

Political Stability (and Absence of

World Bank’s Worldwide

Violence and Terrorism) Index reﬂects
perceptions of the likelihood that the
government will be destabilized or
overthrown by unconstitutional or
violent means, including politically
motivated violence and terrorism on a
scale measuring governance
performance ranging from –2.5 (weak)
à 2.5 (fort)

Regulatory Quality Index reﬂects
perceptions of the ability of the
government to formulate and implement
sound policies and regulations that
permit and promote private sector
development on a scale measuring
governance performance ranging from
–2.5 (weak) à 2.5 (fort)

Governance Indicators (2014
Update)

World Bank’s Worldwide

Governance Indicators (2014
Update)

22 regq

23 rol

Rule of Law Index reﬂects perceptions of

World Bank’s Worldwide

Governance Indicators (2014
Update)

the extent to which agents have
conﬁdence in and abide by the rules of
society, particularly the quality of
contract enforcement, property rights,
the police, and the courts, as well as the
likelihood of crime and violence on a
scale measuring governance
performance ranging from –2.5 (weak)
à 2.5 (fort)

Continued.

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POTENTIAL GROWTH IN ASIA AND ITS DETERMINANTS 25

Variable

Deﬁnition

Source

Table A.2. Continued.

24 voa

25 trade

Voice and Accountability Index reﬂects
perceptions of the extent to which an
economy’s citizens are able to participate
in selecting their government, ainsi que
freedom of expression, freedom of
association, and a free media on a scale
measuring governance performance
ranging from –2.5 (weak) à 2.5 (fort)
Index of Openness: the sum of exports and
imports of goods and services measured
as a share of GDP

26 rmex

Raw materials as a share of total exports

World Bank’s Worldwide

Governance Indicators (2014
Update)

World Bank’s WDI (national
accounts data) and OECD’s
national accounts data

UN Comtrade Database, SITC
Aggregate 2, Revision 1. Pour
Taipei,Chine, Customs

27 fmex

Fuels and mining products as a share of

Administration of the Ministry of

total exports

Finance. https://portal.sw.nat.gov
.tw/APGA/GA03E

28 rmfmex

Sum of raw materials and fuel and mining

products as a share of total exports

30 ﬁnref

29 kaopen Kaopen Index measuring capital account
openness; normalized to be between 0
(less open) et 1 (more open)
Financial Reform Index measuring
ﬁnancial market liberalization;
normalized to be between 0 (less
liberalized) et 1 (more liberalized)
Portfolio Equity Integration Index reﬂects
the sum of the stocks of portfolio equity
assets and liabilities as a share of GDP;
follows suggestions in Kose et al. (2009)

31 peindex

Chinn and Ito (2006)

Abiad, Detragiache, and Tressel

(2010)

Updated and extended version of

data set constructed by Lane and
Milesi–Ferretti (2007)

32 fdiindex FDI Integration Index reﬂects the sum of

the stocks of FDI assets and liabilities as
a share of GDP; follows suggestions in
Kose et al. (2009)

33 integr

Integration Index reﬂects the sum of total

foreign assets and liabilities as a share of
PIB; follows suggestions in Kose et al.
(2009)

Net foreign assets as a share of GDP

34 nfagdp
BMA = Bayesian model averaging; EFW = Economic Freedom of the World; FDI = foreign direct investment;
GDP = gross domestic product; IFS = International Financial Statistics; OECD = Organisation for Economic
Co-operation and Development; PWT = Penn World Tables; RICYT = Red Iberoamericana de Indicadores de
Ciencia y Tecnolog´ıa; R.&D = research and development; UN = United Nations; UNESCO = United Nations
Educational, Scientiﬁc and Cultural Organization; WDI = World Development Indicators.
Source: Author’s compilation.

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26 ASIAN DEVELOPMENT REVIEW

Figure A.1. Potential Output Growth Rate Estimates and Actual Output Growth Rates

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POTENTIAL GROWTH IN ASIA AND ITS DETERMINANTS 27

Figure A.1. Continued.

Source: Author’s calculations.

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