# Economic Transition and Growth Dynamics in Asia: Harmony or Discord?

## Abstract

This paper studies whether the economic transitional and growth dynamic experiences of Asian economies are in harmony or discord so as to serve as a catalyst for, or a hindrance to, fostering regional economic cooperation and integration. Empirical evidence is derived from the application of a nonlinear time-varying factor convergence approach in conjunction with nonparametric distribution dynamics techniques. Results are in favor of growth convergence, but not level convergence, among the economies along their transition dynamic growth paths, and provide insights on issues that have significant bearing on the prospect of deepening regional economic cooperation and integration.

## Keywords

Asia Convergence Growth Regional integration Transition## JEL Classifications

C23 O47 O53## Introduction

In the new world economic order after the Second World War, three waves of regionalism in the world trading system can be distinguished (Goto 2002; Pomfret 2005). The first wave of regionalism was unveiled in the 1950s when European economies undertook a series of economic cooperation and integration activities that led to the ultimate formation of the European Union (EU). The second wave began in North America in the 1980s and peaked with the establishment of the North American Free Trade Agreement. The third wave currently underway is led by Asian economies. Asia used to be the least regionalized region in the world, due to its high degree of dependence on international trade and investment relationships with the USA instead of intra-regionally (Schott 1991). More progress in Asian economic regionalism occurred in the 1990s when the Association of Southeast Asian Nations (ASEAN) initiated the ASEAN Free Trade Agreement (FTA). Triggered by the 1997–1998 Asian Financial Crisis, the 2000s saw a proliferation of regional trade agreements being proposed, under negotiation and concluded (Ahn and Cheong 2007; Kawai 2007).

Geographic distribution and income classification of economies in 2016.

*Source*: World Development Indicators

World Bank income classification | Region | |||
---|---|---|---|---|

Northeast Asia | Southeast Asia | South Asia | Australasia | |

High | Hong Kong (HKG) | Brunei (BRN) | Australia (AUS) | |

Japan (JPN) | Singapore (SGP) | New Zealand (NZL) | ||

Korea (KOR) | ||||

Macao (MAC) | ||||

Taiwan (TWN) | ||||

Upper middle | China (CHN) | Malaysia (MYS) | ||

Thailand (THA) | ||||

Lower middle | Cambodia (KHM) | India (IND) | ||

Indonesia (IDN) | ||||

Laos (LAO) | ||||

Myanmar (MMR) | ||||

Philippines (PHL) | ||||

Vietnam (VNM) |

There has been a growing literature in recent years that investigates whether the income convergence hypothesis holds in Asia, partly because of the connection between the equality of income across economies and the success of economic integration among them as mentioned above (Park 2000a; Moon 2006). However, studies vary substantially in their coverage of economies under investigation due to overlapping economic groupings in the region and/or availability of data. For instance, while Lim and McAleer (2004) and Jayanthakumaran and Verma (2008) examine the ASEAN-5 economies (including Indonesia, Malaysia, the Philippines, Singapore and Thailand), Solarin et al. (2014) focus on ASEAN-9 (excluding Myanmar), and Park (2000a) and Tam (2017) investigate ASEAN-10. Heng and Siang (1999) and Moon (2006) study ASEAN-5 plus selected Northeast Asian economies (such as Japan, Hong Kong and South Korea). Felipe (2000), Evans and Kim (2005), Ito (2016) and Aulia (2017) consider a selection of ASEAN, Northeast Asian and South Asian economies (such as India, Pakistan and Sri Lanka), whereas Park (2000b) and Wang (2012) analyze ASEAN-10 together with some Northeast Asian and Australasian economies (Australia and New Zealand) countries. With the endeavor to evaluate the prospect of RCEP, this paper pioneers the study of income convergence among the RCEP economies with adaptations discussed in the data description section. Moreover, unlike the extant literature, this paper applies the log *t* convergence test of Phillips and Sul (2007) in conjunction with the conditional density estimation and visualization methods of Hyndman et al. (1996) that are particularly suitable for analyzing the transitional and growth dynamic experiences of Asian economies. Merits of these econometric methods are discussed in the methodology section.

Despite being an important and popular indicator of economic development, income is an imperfect measure of well-being or living standards (Sen 1987; Dasgupta 1990; Khan 1991). Income is only one of the means, whereas consumption is the end (Wan 2005), and household consumption is a plausible demand-side measurement of economic well-being (Cuffaro et al. 2006). Therefore, assessing consumption convergence is vital in the broader context of economic development for the maintenance of regional cohesion (Ševela 2004; Liobikienė and Mandravickaitė 2013) and henceforth promising economic integration in the long run. In fact, more attention has been paid to the testing of consumption convergence in the EU region in recent years, especially with the accession of transition economies as new members. Examples of these studies are Ševela (2004), Fiaschi and Lavezzi (2005), Liobikienė and Mandravickaitė (2011) and Dudek et al. (2013). Empirical evaluation of consumption convergence is also of particular relevance to RCEP, given the presence of a number of less developed economies as shown in Table 1, but has largely been overlooked in the Asian context.

On top of the above-mentioned innovations, this paper also contributes by probing into the issue of fiscal convergence among the RCEP economies. Investigating fiscal convergence is of practical interest for the EU, since member states have to fulfill certain fiscal convergence criteria (along with other criteria) for entry into the Eurozone. Some relevant works in this vein include Afxentiou and Serletis (1996), Esteve et al. (2000) and Kočenda et al. (2008), which use different fiscal measures in their tests. Although the creation of a monetary union is not in the scope of RCEP, it is commonly held that such a monetary architecture would be a long-term objective for the Asian economies (de Brouwer et al. 2006). With this in mind, this paper also assesses the readiness of these Asian economies for monetary unification. To do so, government expenditure convergence (or divergence) is examined as in Afxentiou and Serletis (1996), de Bandt and Mongelli (2000), Blot and Serranito (2006), Apergis et al. (2013) and Perović et al. (2016). This type of convergence is theoretically supported by Skidmore et al.'s (2004) model that is consistent with the macroeconomic growth literature and therefore with the transitional and growth dynamic experiences of the Asian economies.

The remainder of the paper is organized as follows. The next section sets out the econometric methodologies, namely the log *t* convergence test and the conditional density estimation and visualization. The subsequent section describes the data used in analysis and outlines the observational patterns. The penultimate section presents the econometric results that trace out the transitional dynamic growth experiences of Asian economies and discusses the factors that drive the results. The final section concludes the paper and draws implications derived from the empirical findings.

## Econometric Methodology

This section sets out the methodologies of the nonlinear time-varying factor approach and the nonparametric distribution dynamics techniques employed in this paper. Given their respective merits, a growing number of empirical studies has employed either methodology in recent years. As will be made clear below, while the former method models the trajectories of transition growth paths of individual economies, the latter approach studies the trajectory of cross-economy distributions. They can therefore be regarded as complementary methods and can provide a multi-dimensional analysis of the transition growth dynamics of economies when used in combination, which is taken up in this paper.

### Log *t* Convergence Test

A main advantage of the log *t* convergence test of Phillips and Sul (2007) is that it is designed for use under technological heterogeneity. It is based on a nonlinear time-varying factor model that embodies both common and idiosyncratic components. Under this model, the process by which convergence takes place over time is identified by the individual-specific time-varying factor loadings, thus providing measures of individual economic transition. Consequently, this formulation is flexible to accommodate a wide spectrum of transition growth paths for individuals and is therefore particularly suitable for the present analysis since it is highly probable that the heterogeneous RCEP economies experience convergence to varying extents. In contrast, under the technological homogeneity assumption that is extensively used in growth studies, the convergence test approaches based on the conventional augmented Solow regression model (Barro and Sala-i-Martin 1992; Mankiw et al. 1992) and standard unit root and cointegration testing (Bernard and Durlauf 1996; Carlino and Mills 1993; Evans and Karras 1996) become inappropriate methods according to Phillips and Sul (2009). Other merits of the log *t* convergence test include its robustness with respect to the stationarity properties of the variables under consideration and its power against cases of club convergence.

*z*

_{i,t}, with

*i*= 1,…,

*N*and

*t*= 1,…,

*T*, where

*i*and

*t*index the cross section and time dimension, respectively, while

*N*and

*T*are the number of economies and number of time observations in the sample, respectively. Suppose that

*z*

_{i,t}can be decomposed as

*a*

_{i,t}and

*e*

_{i,t}are, respectively, the systematic component and transitory component. Assume that in this panel of economies, there exists in

*z*

_{i,t}a common trend component such as world technology,

*f*

_{t}, which follows either a non-stationary stochastic trend with drift or a trend stationary process. Then, Eq. (1) may be transformed into

*b*

_{i,t}measures the relative deviation of economy

*i*from

*f*

_{t}, with

*f*

_{t}determining the common steady-state growth path. The growth dynamic experience is heterogeneous across economies and can be described by the relative transition parameter,

*h*

_{i,t}, constructed as

This measures the transition element *b*_{i,t} for economy *i* relative to the panel average at time *t.* The evolution of *h*_{i,t} over time traces out the trajectory of each economy relative to the average, which also measures the relative divergence of the economy from the common steady-state growth path. Despite possible transient diverging relative patterns, long-run convergence among economies is possible if \(\mathop {\lim}\nolimits_{t \to \infty } (z_{i,t} /z_{j,t} ) = 1\) for all *i* \(\ne\) *j*, or equivalently \(\mathop {\lim}\nolimits_{t \to \infty } b_{i,t} = b\) for all *i*. In other words, convergence occurs when \(\mathop {\lim}\nolimits_{t \to \infty } h_{i,t} = 1\) for all *i*.

*i*against the alternative of non-convergence for some

*i*, the following time series regression is estimated

*T*

_{0}= [

*ϰT*] for some

*ϰ*> 0, so that the first

*ϰ*% of the time series data is discarded before carrying out regression. Considering the sample size in this study,

*ϰ*is set to be 0.3 according to the simulation results in Phillips and Sul (2007). Under the null of growth convergence,

*γ*≥ 0, whereas

*γ*< 0 under the alternative. The null hypothesis is tested based on a robust

*t*-statistic calculated on the slope coefficient

*γ*in Eq. (4),

*t*

_{k}. This is called the log

*t*test due to the log

*t*regressor. Level convergence among all

*i*can also be tested under this framework by using the null hypothesis of

*γ*≥ 2 versus the alternative of

*γ*< 2.

Since the log *t* test has power against cases of club convergence, Phillips and Sul (2007) also propose a clustering algorithm to test for this phenomenon. The main steps of the algorithm are outlined as follows. First, economies are ordered according to the values of their last observations. Second, the core group is identified. This is done by selecting the first *k* highest economies to form the subgroup, and then, the log *t* regression is run to obtain the log *t*-statistic *t*_{k}. The core group size *k** is chosen by maximizing *t*_{k} over *k* subject to min {*t*_{k}} > − 1.65. Third, individuals are sieved for club membership. Each of the remaining economy is added separately to the chosen core group and the log *t* regression run. The individual is included in the convergence club if *t*_{k} > *c*, where *c* is set to zero as suggested by Phillips and Sul (2007). The final step searches the remaining economies in the panel for other convergence clubs by repeating the above steps. If no such clusters can be found, it can be concluded that these economies diverge.

### Conditional Density Estimation and Visualization

The distribution dynamics approach advanced by Quah (1993) studies the trajectory of cross-economy distribution, tracks shifts of distribution, as well as examines intra-distribution dynamics (Quah 1997). In empirical work on distribution dynamics, one seeks a law of motion that describes the evolution of the distribution across economies. The discretized version of the law of motion amounts to the construction of a transition probability matrix (Quah 1993), which is replaced by the estimation of a stochastic kernel in the continuous case (Quah 1996).

*X*and

*Y*denote the relative transition parameter at time

*t*(the initial year of the sample period) and time

*t*+

*τ*(the final year of the sample period), respectively. Let

*f*(

*x*,

*y*) be their joint density,

*f*(

*x*) the marginal density of

*X*and

*g*(

*y|x*) =

*f*(

*x*,

*y*)/

*f*(

*x*) the conditional density of

*Y*|(

*X*=

*x*). The kernel estimator of

*g*(

*y|x*) is

*f*(

*x*,

*y*) and

*f*(

*x*). Notice that

*h*

_{x}and

*h*

_{y}are bandwidth parameters controlling the smoothness of fit, while \(\| {_{{}}^{{}} \cdot_{{}}^{{}} }\|_{x}\) and \(\| {_{{}}^{{}} \cdot_{{}}^{{}} } \|_{y}\) are distance metrics on the spaces of

*X*and

*Y*, respectively. The kernel function,

*K*(

*u*), is assumed to be real, integrable, nonnegative, and even on ℜ concentrated at the origin such that \(\int_{\Re } {K(u){\text{d}}u = 1}\), \(\int_{\Re } {uK} (u){\text{d}}u = 0\) and \(\int_{\Re } {u^{2} K(u){\text{d}}u < \infty }\). The conditional density estimator in Eq. (5) can then be rewritten as

Hyndman et al. (1996) also propose two graphical methods for visualizing the estimated conditional densities, the stacked conditional density (SCD) plot and the high density region (HDR) plot. Since they highlight the conditioning, they are much more informative than the traditional three-dimensional perspective plots and contour plots. The SCD plot displays a number of densities side by side in a perspective fashion. For a given income level at time *t*, the probability density of the income level at time *t* + *τ* is traced, thus clearly showing the evolution of changes in the shape of the income distribution at time *t* + *τ* over the range of the income levels at time *t*. An HDR is the smallest region of the sample space covering a specified probability. The HDR plot consists of vertical strips, each representing the projection on the *xy* plane of the conditional density of *y* on *x*. Within each strip, the 50, 95 and 99% HDRs, in increasingly lighter-shaded regions, are reported. The mode for each conditional density is shown as a bullet. Each strip is thus essentially a visual summary of the characteristics of a probability distribution.

Strong convergence toward equality, analogous to level convergence, is supported if the probability distribution mass is clustered at unity on the horizontal axis in the SCD plot. This manifests in the 50% HDRs being crossed by the horizontal line at unity in the HDR plot. These patterns suggest that any existing deviation across *i* at time *t* disappears at time *t* + *τ*. Weak convergence toward equality is evidenced when the horizontal line at unity crosses the 95 and 99% HDRs. Furthermore, low persistence and some intra-distribution mobility is present if the diagonal crosses only the 95 and 99% HDRs. These features corroborate the notion of growth convergence. By contrast, high persistence is indicated by the mass of distribution being concentrated along the diagonal line in the SCD plot, and the 50% HDRs being crossed by the main diagonal in the HDR plot. In this case, existing deviations across *i* at time *t* continue to exist at time *t* + *τ*.

## Data Description

Annual data for the period of 1970–2014 are collected from the Penn World Table 9.0. Data on gross domestic product measured in US dollars are at constant 2011 national prices. This version of income is suitable for use in cross-country growth comparison (Feenstra et al. 2015) and is also found to be an appropriate measure of economic activities (Pinkovskiy and Sala-i-Martin 2016). Data on consumption expenditure and government expenditure measured in US dollars at constant 2011 national prices are derived from their local currency counterparts. These three aggregate variables are converted into the per capita basis using the population data and are denoted as GDP, CEXP and GEXP, respectively. They are expressed in logarithms in analysis in the next section. The data set covers a total of 19 economies. In addition to the 16 prospective RCEP members, Hong Kong, Macao and Taiwan are also included due to their intimate economic trade and investment relationships with the RCEP economies, notably with China under the Closer Economic Partnership Arrangement regarding Hong Kong and Macao, and the Economic Cooperation Framework Agreement in the case of Taiwan. The significant relationship between China and these three economies in the context of income convergence has been demonstrated in Lei and Tam (2010, 2013).

Per capita gross domestic product (GDP), 1970–2014.

*Source*: Penn World Table 9.0

Economy | Amount (US$) | Growth rate (%) | ||||
---|---|---|---|---|---|---|

1970 | 1980 | 1990 | 2000 | 2014 | 1970–2014 | |

AUS | 22,520 | 26,411 | 30,582 | 38,758 | 47,544 | 1.71 |

BRN | 92,785 | 150,293 | 85,693 | 83,116 | 75,420 | − 0.47 |

CHN | 1150 | 1489 | 2311 | 4181 | 12,524 | 5.58 |

HKG | 7589 | 14,069 | 23,522 | 29,603 | 46,641 | 4.21 |

IDN | 1772 | 2977 | 4448 | 5792 | 10,009 | 4.01 |

IND | 1195 | 1274 | 1801 | 2536 | 5534 | 3.55 |

JPN | 14,666 | 20,305 | 30,287 | 32,946 | 36,250 | 2.08 |

KHM | 1434 | 716 | 906 | 1292 | 2933 | 1.64 |

KOR | 2609 | 5279 | 11,836 | 21,538 | 34,540 | 6.05 |

LAO | 1031 | 1221 | 1681 | 2433 | 5249 | 3.77 |

MAC | 12,677 | 27,526 | 38,825 | 42,094 | 139,989 | 5.61 |

MMR | 673 | 797 | 741 | 1292 | 4793 | 4.56 |

MYS | 3541 | 6898 | 9277 | 14,332 | 21,737 | 4.21 |

NZL | 19,025 | 21,023 | 23,488 | 27,894 | 33,966 | 1.33 |

PHL | 3359 | 4509 | 4078 | 4298 | 6774 | 1.61 |

SGP | 8297 | 16,964 | 28,564 | 43,761 | 64,624 | 4.78 |

THA | 2392 | 3581 | 6383 | 8986 | 14,642 | 4.20 |

TWN | 3712 | 7731 | 14,772 | 25,833 | 41,378 | 5.63 |

VNM | 925 | 1074 | 1473 | 2597 | 5440 | 4.11 |

Per capita consumption expenditure (CEXP), 1970–2014.

*Source*: Penn World Table 9.0

Economy | Amount (US$) | Growth rate (%) | ||||
---|---|---|---|---|---|---|

1970 | 1980 | 1990 | 2000 | 2014 | 1970–2014 | |

AUS | 11,114 | 13,638 | 15,876 | 20,089 | 25,245 | 1.88 |

BRN | 17,359 | 7631 | 7764 | 7663 | 11,159 | − 1.00 |

CHN | 674 | 753 | 1088 | 1763 | 4598 | 4.46 |

HKG | 4542 | 9125 | 15,376 | 19,751 | 30,877 | 4.45 |

IDN | 835 | 1477 | 2267 | 3539 | 5526 | 4.39 |

IND | 832 | 918 | 1151 | 1521 | 3154 | 3.07 |

JPN | 8846 | 12,510 | 17,533 | 19,439 | 21,817 | 2.07 |

KHM | 1584 | 792 | 972 | 1092 | 2277 | 0.83 |

KOR | 2336 | 3749 | 7296 | 12,206 | 17,115 | 4.63 |

LAO | 830 | 984 | 1336 | 2030 | 2736 | 2.75 |

MAC | 4793 | 10,406 | 14,090 | 16,844 | 29,333 | 4.20 |

MMR | 583 | 632 | 584 | 736 | 2456 | 3.32 |

MYS | 1745 | 3353 | 4095 | 5486 | 10,884 | 4.25 |

NZL | 11,403 | 11,637 | 13,173 | 15,196 | 20,073 | 1.29 |

PHL | 2283 | 2728 | 2793 | 3234 | 4866 | 1.74 |

SGP | 5441 | 9051 | 13,166 | 19,342 | 22,838 | 3.31 |

THA | 1516 | 2149 | 3292 | 4500 | 7826 | 3.80 |

TWN | 2383 | 4723 | 9528 | 17,053 | 22,394 | 5.22 |

VNM | 840 | 976 | 1313 | 1771 | 3593 | 3.36 |

Per capita government expenditure (GEXP), 1970–2014.

*Source*: Penn World Table 9.0

Economy | Amount (US$) | Growth rate (%) | ||||
---|---|---|---|---|---|---|

1970 | 1980 | 1990 | 2000 | 2014 | 1970–2014 | |

AUS | 3577 | 4671 | 5718 | 6800 | 8224 | 1.91 |

BRN | 5200 | 10,873 | 11,782 | 11,571 | 15,679 | 2.54 |

CHN | 122 | 194 | 297 | 628 | 1652 | 6.11 |

HKG | 894 | 1480 | 2388 | 3165 | 4155 | 3.55 |

IDN | 136 | 326 | 438 | 406 | 879 | 4.33 |

IND | 115 | 141 | 224 | 331 | 602 | 3.84 |

JPN | 2276 | 3350 | 4606 | 6078 | 7430 | 2.73 |

KHM | 39 | 19 | 35 | 56 | 163 | 3.32 |

KOR | 835 | 1309 | 2109 | 2952 | 5084 | 4.19 |

LAO | 173 | 204 | 253 | 339 | 935 | 3.91 |

MAC | 870 | 1878 | 4577 | 5550 | 9916 | 5.69 |

MMR | 139 | 150 | 139 | 200 | 796 | 4.06 |

MYS | 377 | 853 | 968 | 1263 | 2817 | 4.68 |

NZL | 3575 | 4424 | 4846 | 4900 | 6400 | 1.33 |

PHL | 403 | 595 | 517 | 484 | 685 | 1.21 |

SGP | 1007 | 1747 | 2619 | 5054 | 6215 | 4.22 |

THA | 314 | 569 | 766 | 1255 | 2429 | 4.76 |

TWN | 1319 | 1987 | 3836 | 5295 | 6068 | 3.53 |

VNM | 52 | 61 | 90 | 136 | 336 | 4.33 |

The data presented in Tables 2 through 4 suggest that the sample of economies as a whole reveals signs of converging behavior. Those starting off with higher initial levels of GDP, CEXP and GEXP tended to grow more slowly than those with lower initial levels. However, there still exist substantial disparities across the board of economies in their respective levels of GDP, CEXP and GEXP. Economies that had relatively high levels in the beginning of the sample period stayed put at the end of the period. The reverse also holds true. These observations are indicative of the presence of growth convergence but not level convergence across economies, which is explored econometrically in the next section.

## Econometric Results

Whether the Asian economies are converging in terms of GDP, CEXP and GEXP is formally tested using the log *t* convergence test. The distribution dynamics of these variables across economies over time are also examined with the estimated conditional densities visualized using SCD and HDR plots. In this section, empirical results for GDP, CEXP and GEXP are first reported, followed by a discussion of the empirical findings.

### Income

*t*convergence test results for GDP are reported in Table 5. The null of growth convergence for all economies is first tested. As indicated by the

*t*-statistic, the null hypothesis cannot be rejected at any of the conventional significance levels. Although the notion of growth convergence is supported, little evidence is found for level convergence. The estimated coefficient of the log

*t*regressor for all economies is significantly below 2, and the null hypothesis of level convergence for the economies is strongly rejected.

Log *t* convergence test results for GDP, 1970–2014

Group | Coefficient |
| Converging economies |
---|---|---|---|

| |||

All | 0.110 | 2.049 | All |

High-level club | 0.399 | 5.811 | AUS, BRN, HKG, JPN, KOR, MAC, MYS, NZL, SGP, TWN |

Low-level club | 3.371 | 2.120 | CHN, IDN, IND, KHM, LAO, MMR, PHL, THA, VNM |

Growth club 1 | 0.283 | 3.626 | AUS, BRN, CHN, HKG, IND, JPN, KOR, MAC, MMR, MYS, NZL, SGP, THA, TWN, VNM |

Growth club 2 | 0.564 | 5.124 | IDN, KHM, LAO, PHL |

| |||

All | 0.110 | − 35.204*** | – |

High-level club | 0.399 | − 23.331*** | – |

Low-level club | 0.371 | − 9.323*** | – |

Growth club 1 | 0.283 | − 21.971*** | – |

Growth club 2 | 0.564 | − 13.030*** | – |

*t*growth convergence test result found above.

Notwithstanding, the SCD plot shows that there is a high concentration of the probability mass at two relative GDP levels in 2014, one above and one below the group mean. In the HDR plot, the horizontal line at unity mainly crosses the 95 and 99% HDRs. For economies with initial GDP above average, many modes are located around the relative GDP level of 1.07 in 2014. Modes for some economies with initial GDP below average correspond approximately to the relative GDP level of 0.87 in 2014. These patterns corroborate the observations in Fig. 1 and the rejection of the null of level convergence for all economies with the log *t* test. Also of interest is that the diagonal line crosses the 50% HDRs of economies with initial relative GDP between 0.8 and 1.15, which means stability and persistence for these economies over time. In other words, they are susceptible to the so called middle-income trap.

*t*convergence test results on these two clubs are reported in Table 5. Results show that there is significant evidence for growth convergence but not level convergence within each of the clubs. In fact, routine application of the Phillips and Sul (2007) clustering algorithm classifies the economies into one large and one small growth convergence club, displayed in Table 5 as growth clubs 1 and 2, respectively. Growth club 1 encompasses the high-level club members and some fast-growing members drawn from the low-level club. Figure 3 shows the time paths of the cross-sectional means of the relative transition parameter values of the clubs using both ways of classification, by GDP level in the left panel, and by GDP growth in the right panel. In both cases, the relative transition paths indicate that the clubs first diverge and then converge. In fact, the growth clubs can be merged to form the single growth club for the entire sample of economies (Phillips and Sul 2009), albeit falling short of achieving level convergence.

### Consumption

*t*convergence test results for CEXP. According to the reported

*t*-statistic, the null of growth convergence for all economies cannot be rejected at all conventional significance levels. However, the

*t*-statistic for testing level convergence is significantly negative, thus lending no support for the convergence of the economies to equality.

Log *t* convergence test results for CEXP 1970–2014

Group | Coefficient |
| Converging economies |
---|---|---|---|

| |||

All | − 0.033 | − 0.406 | All |

High-level club | 0.197 | 2.130 | AUS, BRN, HKG, JPN, KOR, MAC, MYS, NZL, SGP, TWN |

Low-level club | 0.045 | 0.239 | CHN, IDN, IND, KHM, LAO, MMR, PHL, THA, VNM |

Growth club 1 | 0.237 | 1.827 | AUS, CHN, HKG, JPN, KOR, MAC, MMR, MYS, NZL, SGP, THA, TWN |

Growth club 2 | 0.297 | 1.910 | BRN, IDN, IND, KHM, LAO, PHL, VNM |

| |||

All | − 0.033 | − 25.341*** | – |

High-level club | 0.197 | − 19.493*** | – |

Low-level club | 0.045 | − 10.325*** | – |

Growth club 1 | 0.237 | − 13.576*** | – |

Growth club 2 | 0.297 | − 10.932*** | – |

*t*convergence test above are consistent with the patterns observed in the SCD and HDR plots. The SCD plot in the left panel of Fig. 5 shows that the probability mass, instead of clustering along the diagonal line, is concentrated around the two sides of the CEXP group average in 2014. With reference to the HDR plot in the right panel of Fig. 5, the smallest consumers in 1970 grew to become relatively much bigger spenders in 2014, with their modes lying well above the diagonal line. On the contrary, the biggest consumers in 1970 tended to slow down their consumption growth rates and became relatively smaller consumers in 2014, as illustrated by their modes being located below the diagonal. In addition, the diagonal line crosses some 95 and 99% HDRs, while the horizontal line at unity crosses mainly the 95 and 99% HDRs. The observed intra-distribution mobility provides evidence for growth convergence with respect to CEXP among the economies.

However, level convergence in CEXP is not supported by the patterns exhibited in the plots. In the SCD plot, most of the probability mass is concentrated at either high or low relative CEXP in 2014. Referring to the HDR plot, the horizontal line at unity mainly crosses the 95 and 99% HDRs. The modes characterizing economies with the highest (lowest) initial relative CEXP in 1970 are found to be clustering around the relative CEXP level of 1.1 (0.88) in 2014. It is also noteworthy that the 50% HDRs of economies with initial CEXP between 0.85 and 0.98 are crossed by the diagonal line. Stability and persistence of their relative CEXP over time suggest that these economies lag behind the others in improving and upgrading their CEXP levels and patterns.

*t*clustering algorithm, economies can be classified into two CEXP growth clubs. The first growth club is comprised of all the high-level club members except Brunei with negative growth rate, and low-level club members with high growth rates. The second CEXP growth club includes all members from the second GDP growth club plus Brunei and two additional low GDP and CEXP economies. Thus, catch-up in terms of income tends to be a necessary, but not sufficient, condition for catch-up in terms of consumption. While an economy may be able to achieve spectacular economic growth as measured by income, the ultimate share of income that households can commandeer to fulfill the basic needs and to raise their living standards depends on factors not captured by income alone. Results presented in Table 6 suggest CEXP growth convergence, rather than level convergence, within each club. Examining the transition paths of the cross-sectional means of the relative CEXP transition parameters of the clubs presented in Fig. 6, there is evidence for the two clubs in each classification to converge to each other toward the end of the sample period, despite protracted period of persistence and/or divergence during the transition.

### Government Expenditure

*t*convergence test results for GEXP are set out in Table 7. The null of growth convergence for all economies cannot be rejected at any conventional significance level, according to the reported

*t*-statistic. By contrast, the

*t*-statistic for the null of level convergence is highly significant, which means that no empirical support can be established for convergence of the economies in their GEXP levels. This is consistent with the observations found in Fig. 7 above.

Log *t* convergence test results for GEXP, 1970–2014

Group | Coefficient |
| Converging economies |
---|---|---|---|

| |||

All | 0.048 | 1.042 | All |

High-level club | 0.399 | 5.051 | AUS, BRN, HKG, JPN, KOR, MAC, MYS, NZL, SGP, THA, TWN |

Low-level club | 0.371 | 9.323 | CHN, IDN, IND, KHM, LAO, MMR, PHL, VNM |

Growth club 1 | 0.278 | 3.125 | AUS, BRN, CHN, HKG, JPN, KHM, KOR, MAC, MYS, NZL, SGP, THA, TWN, VNM |

Growth club 2 | 0.572 | 1.241 | IDN, IND, LAO, MMR, PHL |

| |||

All | 0.048 | − 42.741*** | – |

High-level club | 0.399 | − 23.331*** | – |

Low-level club | 0.371 | − 9.323*** | – |

Growth club 1 | 0.278 | − 19.379*** | – |

Growth club 2 | 0.572 | − 3.097*** | – |

*t*convergence analysis.

*t*convergence to GEXP, and economies are divided into two growth clubs. The null of level convergence, on the contrary, is strongly rejected within each club. Notice that the first growth club includes all members from the high-level club as well as some members with high GEXP growth rates from the low-level club. The cross-sectional means of the relative GEXP transition parameters of the converging clubs are plotted in Fig. 9. It can be deduced that growth convergence of GEXP for all economies can be supported. This is because although the two clubs under different classification schemes showed prolonged period of persistence and/or divergence during transition, they eventually exhibited the tendency to converge to each other.

In passing, notice that GEXP spans over a wider range of levels across economies in comparison with GDP and CEXP. This means that it may be a lengthier process for the economies to achieve convergence in GEXP level than in GDP and CEXP levels, which is reasonable given that fiscal convergence is related to a deep form of economic integration in terms of monetary unification.

### Discussion

This subsection discusses some general possibilities that have led to the growth convergent and level divergent behavior of the Asian economies as found above. More in-depth analysis of the various structural and policy factors driving the relative performance of the economies is beyond the scope of this paper and is left for future investigation.

In general, more evident signs of growth convergence among the economies with respect to the three macroeconomic variables can be found from the late 1990s and turning into the 2000s. This finding is synchronous with the developments in regional economic integration. RCEP is envisaged as an ASEAN-centered regional free trade agreement (FTA). Although ASEAN was established in 1967, it was not until 1993 that the ASEAN FTA came into force. The poorest economies only joined ASEAN afterward, with Vietnam in 1995, Laos and Myanmar in 1997, and Cambodia in 1999. Furthermore, the ASEAN+3 (ASEAN with China, Japan and South Korea collectively) cooperation process began only in 1997, while the ASEAN + 1 FTA initiatives (ASEAN with Australia and New Zealand, China, India, as well as Japan separately) became effective in stages as late as between 2005 and 2010.

Economic conditions of economies.

*Sources*: Penn World Table 9.0; 2017 Index of Economic Freedom, World Heritage Foundation; KOF Index of Globalization (Dreher 2006); Global Consumption Database, World Bank

Economy | Capital stock per capita (2014) | Human capital index (2014) | Economic freedom index (2017) | Globalization index (2013) | Share of food and beverages consumption in total, % (2010) | Government integrity index (2017) |
---|---|---|---|---|---|---|

AUS | 162,790 | 3.80 | 81.02 | 81.93 | – | 74.81 |

BRN | 283,096 | 2.74 | 69.77 | 67.38 | – | 40.90 |

CHN | 49,356 | 2.47 | 57.40 | 60.73 | 36.22 | 41.63 |

HKG | 218,900 | 3.20 | 89.82 | – | – | 80.34 |

IDN | 51,839 | 2.36 | 61.94 | 57.75 | 48.55 | 44.73 |

IND | 16,906 | 2.05 | 52.64 | 51.26 | 44.63 | 44.29 |

JPN | 139,808 | 3.54 | 69.58 | 67.86 | – | 86.06 |

KHM | 5940 | 1.82 | 59.52 | 50.32 | 46.20 | 12.85 |

KOR | 132,989 | 3.59 | 74.26 | 65.42 | – | 67.34 |

LAO | 15,293 | 1.87 | 54.01 | 30.38 | 52.34 | 32.60 |

MAC | 233,194 | 2.73 | 70.67 | 41.35 | – | 37.10 |

MMR | 7453 | 1.78 | 52.48 | 34.42 | – | 29.60 |

MYS | 63,346 | 2.97 | 73.78 | 79.14 | – | 51.83 |

NZL | 98,561 | 3.29 | 83.75 | 78.15 | – | 89.87 |

PHL | 20,183 | 2.65 | 65.61 | 57.86 | 47.12 | 38.66 |

SGP | 284,572 | 3.52 | 88.58 | 86.93 | – | 87.91 |

THA | 53,495 | 2.66 | 66.22 | 70.45 | 39.63 | 40.70 |

TWN | 142,288 | 3.20 | 76.51 | – | – | 70.50 |

VNM | 14,894 | 2.62 | 52.44 | 49.91 | 53.24 | 24.65 |

Among the low-level economies, Cambodia, Laos, India, Indonesia and the Philippines often fall into the small growth convergence club. Cambodia and Laos had experienced a prolonged period of war, political instability and regime change. India had protracted period of protectionist policies with widespread state intervention and regulation. Indonesia was one of the HAPEs identified by the World Bank (1993), but was hardest hit by the 1997–1998 Asian Financial Crisis. Slow economic progress was brought about by political and economic instability, social unrest, corruption and terrorism. The economy of the Philippines stagnated under the rule of dictatorship, with economic mismanagement and political volatility. Myanmar and Vietnam belong to the small growth convergence club with respect to GEXP and CEXP only. Myanmar suffered from protracted period of stagnation, mismanagement and isolation, but economic development has benefited substantially from its membership into ASEAN. In recent years, Vietnam has undergone high rate of industrialization and has been receiving large amount of foreign direct investment flows. China and Thailand have always been associated with the high-level economies in the large growth convergence clubs. While Thailand was among the HPAEs according to the World Bank (1993), China’s economic growth has been spectacular upon its open door policy since four decades ago.

## Conclusion

In the new wave of regionalism in Asia, ASEAN + 6 economies have recently embarked on negotiations for the formation of RCEP, the world’s largest economic bloc. This paper studies whether the economic transitional and growth dynamic experiences of these economies during the sample period have been conducive or detrimental to fostering regional economic cooperation and integration. To this end, converging (or diverging) patterns of not only income, but also consumption and government expenditure, are examined. Robust empirical results are obtained when employing a nonlinear time-varying factor convergence approach in conjunction with nonparametric distribution dynamics techniques.

Asian economies are found to evolve along heterogeneous transition dynamic growth paths in all the three macroeconomic variables under investigation. For each of these variables, there is evidence in favor of growth convergence among the economies. In other words, lower-level economies tend to catch up with higher-level ones. The slow pace of growth convergence among the prospective RCEP members during the sample period is expected to be speeded up, with the adoption of the ASEAN Economic Community Blueprint 2025 in 2015, and the acceleration of negotiations within RCEP upon the US withdrawal from TPP in 2017. Thus, there is optimism toward the successful pursuit of RCEP in fostering greater economic cooperation and integration in the region.

Notwithstanding, level convergence among the economies in terms of all three macroeconomic variables is not supported. Substantial disparities remain across the board of economies. Middle-income economies are susceptible to the middle-income trap. Income convergence is a necessary, but not sufficient, condition for consumption convergence. The majority of below average consumers lag behind the others in improving and upgrading their consumption levels and patterns. There is also a long way ahead for the economies to achieve fiscal convergence in level for monetary unification. The prospect of a deepening of economic cooperation and integration in the region thus hinges on the design of regional policies and coordinated measures to effectively increase the economic opportunities of the less developed economies so as to achieve more equitable share of benefits among the regional members.

## Notes

### Acknowledgements

We are grateful to an anonymous referee for suggesting additional conciliation of the methodologies employed, and a discussion on factors driving the convergence (or divergence) behavior of the economies. We are also indebted to Josef Brada, the editor, for his valuable comments. This research was supported by the Research Committee of the University of Macau under research grant no. MYRG2016-00170-FBA.

## References

- Ahn, C.Y., and I. Cheong. 2007. A search for closer economic relations in East Asia.
*Japanese Economic Review*58: 173–190.CrossRefGoogle Scholar - Afxentiou, P.C., and A. Serletis. 1996. Government expenditure in the European Union: Do they converge or follow Wagner’s law?
*International Economic Journal*10: 33–47.CrossRefGoogle Scholar - Aizenman, J., and E. Brooks. 2008. Globalization and taste convergence: The case of wine and beer.
*Review of Development Economics*16: 217–233.Google Scholar - Apergis, N., C. Christou, and C. Hassapis. 2013. Convergence in public expenditures across EU countries: Evidence from club convergence.
*Economics and Finance Research*1: 45–59.CrossRefGoogle Scholar - Aulia, M.K. 2017. The convergence analysis of the economic growth of ASEAN+3 countries and its influencing factors.
*International Journal of Business and Management Review*5: 22–41.Google Scholar - Barro, R.J., and X. Sala-i-Martin. 1992. Convergence.
*Journal of Political Economy*100: 223–251.CrossRefGoogle Scholar - Bashtannyk, D.M., and R.J. Hyndman. 2001. Bandwidth selection for kernel conditional density estimation.
*Computational Statistics and Data Analysis*36: 279–298.CrossRefGoogle Scholar - Bernard, A.B., and S.N. Durlauf. 1996. Interpreting tests of the convergence hypothesis.
*Journal of Econometrics*71: 161–174.CrossRefGoogle Scholar - Blot, C., and F. Serranito. 2006. Convergence of fiscal policies in EMU: A unit-root tests analysis with structural break.
*Applied Economics Letters*13: 211–216.CrossRefGoogle Scholar - Carlino, G.A., and L.O. Mills. 1993. Are US regional incomes converging? A time series analysis.
*Journal of Monetary Economics*32: 335–346.CrossRefGoogle Scholar - Cuffaro, M., F. Cracolici, and P. Nijkamp. 2006.
*Economic Convergence Versus Socio-Economic Convergence in Space*. New York: Mimeo.Google Scholar - D’Agostino, G., J.P. Dunne, and L. Pieroni. 2016. Government spending, corruption and economic growth.
*World Development*84: 190–205.CrossRefGoogle Scholar - Dasgupta, P. 1990. Well-being and the extent of its realisation in poor countries.
*Economic Journal*100: 1–32.CrossRefGoogle Scholar - de Bandt, O., and F.P. Mongelli. 2000. Convergence of fiscal policies in the euro area.
*ECB Working Paper No. 20*. European Central Bank.Google Scholar - de Brouwer, G., A. Ramayandi, and D. Turvey. 2006. Macroeconomic linkages and regional monetary cooperation: Steps ahead.
*Asian Economic Policy Review*1: 284–301.CrossRefGoogle Scholar - Dholakia, U.M., and D. Talukdar. 2004. How social influence affects consumption trends in emerging markets: An empirical investigation of the consumption convergence hypothesis.
*Psychology and Marketing*21: 775–797.CrossRefGoogle Scholar - Dreher, A. 2006. Does globalization affect growth? Empirical evidence from a new index.
*Applied Economics*38: 1091–1110.CrossRefGoogle Scholar - Dudek, H., G. Koszela, and M. Krawiec. 2013. Changes in households expenditures structures in the European Union–is there convergence?
*Roczniki Naukowe Ekonomii Rolnictwa i Rozwoju Obszarów Wiejskich*100: 43–50.Google Scholar - Esteve, V., S. Sosvilla-Rivero, and C. Tamarit. 2000. Convergence in fiscal pressure across EU countries.
*Applied Economics Letters*7: 117–123.CrossRefGoogle Scholar - Estrada, G., Lee, S.-H., and D. Park. 2014. Fiscal policy for inclusive growth: An overview.
*ADB Economics Working Paper Series No. 423.*Asian Development Bank.Google Scholar - Evans, P., and G. Karras. 1996. Convergence revisited.
*Journal of Monetary Economics*37: 249–265.CrossRefGoogle Scholar - Evans, P., and J.U. Kim. 2005. Estimating convergence for Asian economies using dynamic random variable models.
*Economics Letters*86: 159–166.CrossRefGoogle Scholar - Feenstra, R.C., R. Inklaar, and M.P. Timmer. 2015. The next generation of the Penn World Table.
*American Economic Review*105: 3150–3182.CrossRefGoogle Scholar - Felipe, J. 2000. Convergence, catch-up and growth sustainability in Asia: Some pitfalls.
*Oxford Development Studies*28: 51–69.CrossRefGoogle Scholar - Fiaschi, D., and A.M. Lavezzi. 2005.
*Growth and Convergence Across European Regions: An Empirical Investigation*. Mimeo: Department of Economics, University of Pisa.Google Scholar - Goto, J. 2002. Economic preconditions for monetary cooperation and surveillance in East Asia. In
*Report on the Study Group on Strengthening Financial Cooperation and Surveillance*, ed. Institute for International Monetary Affairs. Tokyo.Google Scholar - Heng, T.M., and T.C. Siang. 1999. A neoclassical analysis of the ASEAN and East Asian growth experience.
*ASEAN Economic Bulletin*16: 149–165.CrossRefGoogle Scholar - Hyndman, R.J., D. Bashtannyk, and G. Grunwald. 1996. Estimating and visualizing conditional densities.
*Journal of Computational and Graphical Statistics*5: 315–336.Google Scholar - Ito, T. 2016. Growth convergence and the middle income trap.
*Working Paper Series No. 349*. Center on Japanese Economy and Business, Columbia University in the City of New York.Google Scholar - Jajkowicz, O., and A. Drobiszová. 2015. The effect of corruption on government expenditure allocation in OECD countries.
*Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis*63: 1251–1259.CrossRefGoogle Scholar - Jayanthakumaran, K., and R. Verma. 2008. International trade and regional income convergence.
*ASEAN Economic Bulletin*25: 179–194.CrossRefGoogle Scholar - Jovanovic, M. 1998.
*International Economic Integration: Critical Perspectives on the World Economy*. London: Routledge.Google Scholar - Kawai, M. 2007. Evolving economic architecture in East Asia.
*Kyoto Economic Review*76: 9–52.Google Scholar - Kawai, M., and G. Wignaraja. 2008. EAFTA or CEPEA: Which way forward?
*ASEAN Economic Bulletin*25: 113–139.CrossRefGoogle Scholar - Khan, H. 1991. Measurement and determinants of socioeconomic development: A critical conspectus.
*Social Indicators Research*24: 153–175.CrossRefGoogle Scholar - Kočenda, E., A.M. Kutan, and T.M. Yigit. 2008. Fiscal convergence in the European Union.
*North American Journal of Economics and Finance*19: 319–330.CrossRefGoogle Scholar - Kónya, I., and H. Ohashi. 2007. International consumption patterns among high-income countries: Evidence from the OECD data.
*Review of International Economics*15: 744–757.CrossRefGoogle Scholar - Lei, C.K., and P.S. Tam. 2010. A panel data approach to the income convergence among Mainland China, Hong Kong and Macao.
*Journal of the Asia-Pacific Economy*15: 420–435.CrossRefGoogle Scholar - Lei, C.K., and P.S. Tam. 2013. Stochastic convergence of the Greater China economies: A panel unit root approach. In
*China’s Development and Harmonisation: Toward a Balance with Nature, Society and International Community*, ed. B. Wu, S. Yao, and J. Chen. London: Routledge.Google Scholar - Li, H., and Z. Xu. 2007. Economic convergence in seven Asian economies.
*Review of Development Economics*11: 531–549.CrossRefGoogle Scholar - Lim, L.K., and M. McAleer. 2004. Convergence and catching up in ASEAN: A comparative analysis.
*Applied Economics*36: 137–153.CrossRefGoogle Scholar - Liobikienė, G., and J. Mandravickaitė. 2011. Achievements of Lithuanian sustainable development during the integration process into the European Union.
*Technological and Economic Development of Economy*17: 62–73.CrossRefGoogle Scholar - Liobikienė, G., and J. Mandravickaitė. 2013. Convergence of new members of the EU: Changes in household consumption expenditure structure regarding environmental impact during the prosperous period.
*Environment, Development and Sustainability*15: 407–427.CrossRefGoogle Scholar - Mankiw, G., D. Romer, and D. Weil. 1992. A contribution to the empirics of economic growth.
*Quarterly Journal of Economics*107: 407–437.CrossRefGoogle Scholar - Mauro, P. 1995. Corruption and growth.
*The Quarterly Journal of Economics*110: 681–712.CrossRefGoogle Scholar - Mo, P.H. 2001. Corruption and economic growth.
*Journal of Comparative Economics*29: 66–79.CrossRefGoogle Scholar - Moon, W. 2006. Income convergence across nations and regions in East Asia.
*Journal of International and Area Studies*13: 1–16.Google Scholar - Park, D. 2000a. Intra-Southeast Asian income convergence.
*ASEAN Economic Bulletin*17: 285–292.CrossRefGoogle Scholar - Park, D. 2000b. Is the Asia-Australasia region a convergence club?
*Asian Economic Journal*14: 415–419.CrossRefGoogle Scholar - Pellegrini, L., and R. Gerlagh. 2004. Corruption’s effect on growth and its transmission channels.
*Kyklos*57: 429–456.CrossRefGoogle Scholar - Perović, L.M., S. Golem, and M.M. Kosor. 2016. Convergence in government spending components in EU15: A spatial econometric perspective.
*Amfiteatru Economic*18: 240–254.Google Scholar - Phillips, P.C.B., and D. Sul. 2007. Transition modeling and econometric convergence tests.
*Econometrica*75: 1771–1855.CrossRefGoogle Scholar - Phillips, P.C.B., and D. Sul. 2009. Economic transition and growth.
*Journal of Applied Econometrics*24: 1153–1185.CrossRefGoogle Scholar - Pinkovskiy, M. and X. Sala-i-Martin. 2016. Newer need not be better: Evaluating the Penn World Tables and the World Development Indicators using nighttime lights.
*NBER Working Paper No. 22216*. National Bureau of Economic Research.Google Scholar - Pomfret, R. 2005. Sequencing trade and monetary integration: Issues and applications to Asia.
*Journal of Asian Economics*16: 105–124.CrossRefGoogle Scholar - Quah, D.T. 1993. Empirical cross-section dynamics in economic growth.
*European Economic Review*37: 426–434.CrossRefGoogle Scholar - Quah, D.T. 1996. Convergence empirics across economies with (some) capital mobility.
*Journal of Economic Growth*1: 95–124.CrossRefGoogle Scholar - Quah, D.T. 1997. Empirics for growth and distribution: Stratification, polarization, and convergence clubs.
*Journal of Economic Growth*2: 27–59.CrossRefGoogle Scholar - Robson, P. 1998.
*The Economics of International Integration*. London: Routledge.Google Scholar - Sachs, J.D., and A. Warner. 1995. Economic reform and the process of global integration.
*Brookings Papers on Economic Activity*1995: 1–118.CrossRefGoogle Scholar - Sala-i-Martin, X.X. 1996. Regional cohesion: Evidence and theories of regional growth and convergence.
*European Economic Review*40: 1325–1352.CrossRefGoogle Scholar - Schott, J.J. 1991. Trading blocs and the world trading system.
*World Economy*14: 1–17.CrossRefGoogle Scholar - Sen, A. 1987. The standard of living: Lecture I, concepts and critiques. In
*The Standard of Living*, ed. G. Hawthorn. Cambridge: Cambridge University Press.CrossRefGoogle Scholar - Ševela, M. 2004. Convergence of household expenditures of the EU-member and acceding countries in the years 1995–2002.
*Agriculture Economics—Czech*50: 301–307.Google Scholar - Skidmore, M., H. Toya, and D. Merriman. 2004. Convergence in government spending: Theory and cross-country evidence.
*Kyklos*57: 587–620.CrossRefGoogle Scholar - Solarin, S.A., E.M. Ahmend, and J. Dahalan. 2014. Income convergence dynamics in ASEAN and SAARC blocs.
*New Zealand Economic Papers*48: 285–300.CrossRefGoogle Scholar - Tam, P.S. 2017.
*On intra- and extra-ASEAN income convergence*. Mimeo, University of Macau.Google Scholar - Wan, G.H. 2005. Convergence in food consumption in Rural China: Evidence from household survey data.
*China Economic Review*16: 90–102.CrossRefGoogle Scholar - Wang, M.S. 2012. Income convergence within ASEAN, ASEAN+3: A panel unit root approach.
*Applied Economics Letters*19: 417–423.CrossRefGoogle Scholar - World Bank. 1993.
*The East Asian Miracle: Economic Growth and Public Policy*. Washington, D.C.: World Bank.Google Scholar - Wu, W., and O.A. Davis. 1999. The two freedoms, economic growth and development: An empirical study.
*Public Choice*100: 39–64.CrossRefGoogle Scholar