Is China an optimum currency area?

Journal of Asian Economics 16 (2005) 612–634

Is China an optimum currency area? Hans N.E. Bystro¨m *, Karin Olofsdotter 1, Lars So¨derstro¨m Department of Economics, Lund University, PO Box 7082, S-220 07 Lund, Sweden Received 31 January 2005; received in revised form 12 May 2005; accepted 31 May 2005

Abstract This paper analyzes regional differences across Chinese regions, employing an optimum currency area framework. Empirically, we consider the cross-sectional correlation measure of Solnik and Roulet [Solnik, B., & Roulet, J. (2000). Dispersion as cross-sectional correlation. Financial Analysts Journal, 56, 54–61.] when examining data on GDP, trade, inflation and regional budget between 1991 and 2001. Our preliminary results suggest that China probably is more of an optimum currency area than first expected. It is debatable, though, whether Hong Kong and Macao are appropriate as candidates. The results also indicate that there might be other constellations of regions that could be closer to an optimum currency area than the current Yuan area. # 2005 Elsevier Inc. All rights reserved. JEL classification: C32; F33; O53 Keywords: China; Optimum currency area; Regional developments; Cross-sectional correlation

1. Introduction The last two decades, China has been one of the fastest growing economies in the world. As average annual growth rates have reached above 10%, the country has taken important steps towards economic transition. Still, the economic development that China is currently experiencing is not unproblematic. For one thing, regional inequalities seem to increase. There are a number of studies dealing with Chinese regional development and, although * Corresponding author. Tel.: +46 46 2229478; fax: +46 46 2224118. E-mail addresses: [email protected] (Hans N.E. Bystro¨m), [email protected] (K. Olofsdotter). 1 Tel.: +46 46 2228654, fax: +46 46 2224613. 1049-0078/$ – see front matter # 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.asieco.2005.05.002

H.N.E. Bystro¨m et al. / Journal of Asian Economics 16 (2005) 612–634

613

they provide mixed results for the 1980s, most of them suggest growing economic divergence in the 1990s. Another problem is the level of unemployment. The actual figure on the real unemployment rate is highly uncertain, but some analysts put the figure at around 8–0% in urban areas. In addition, there are hidden numbers in the countryside where maybe as much as around 150 million people have little to do and in the coming years may move to urban areas. These people will add to urban employment pressures (The Economist, 2004). Since the regions of mainland China constitute a single country, they also have the same currency. The literature on optimum currency areas (OCA) emphasizes that if regions or countries sharing the same currency (or with fixed exchange rates) experience economic divergence, it is of crucial importance that there are available adjustment mechanisms. One of the most important adjustment mechanisms, as suggested in this research area, is flexible factor markets characterized by high factor mobility and adjustable factor prices. Alternatively, the area requires a well-functioning redistribution system that can equalize across the regions/countries. If these mechanisms are not present, the currency area faces the risk of growing divergence and persistent high unemployment rates in some of the regions. Thus, the combination in China of growing regional disparities and high unemployment rates should be of major concern. In addition, the Asian crisis has made redistribution policies difficult in many East Asian countries in general and, in China, the share of central government transfers of the total budget is declining (Hill, 2002). In this paper we use the literature on optimum currency areas as a guideline for analyzing regional differences across the Chinese regions and we ask three questions.1 First, is mainland China an optimum currency area? This, we believe, is an interesting question in itself. Moreover, we find the OCA perspective useful as it may help us to draw attention to the most problematic areas when it comes to regional economic differences. Second, we look at the special administrative regions Hong Kong and Macao, which maintained their currencies after the hand over of power to Beijing, and ask how these regions would fit to the Yuan currency area. The inclusion of Hong Kong and Macao to the analysis is also relevant for the third question, namely, whether there are sub-regions that are more suitable as currency areas than the current Yuan area. Admittedly, this is a highly theoretical question. Nevertheless, it accentuates the problem induced by having economic convergence among some regions but economic divergence among others. This paper relates to as well as differs from previous studies on optimum currency areas and regional development in China. As far as we know, previous studies of optimum currency areas in East Asia do not consider sub-regions below national levels. In general, these studies suggest that China should not be a part of an East Asian currency area (e.g. Goto, 2003; Liang, 1999; Yuen, 2000). By taking a regional perspective, we are able to investigate if this is due to large regional disparities in China, and if some of the Chinese regions may be more proper candidates for a currency union than other regions. In addition, the paper differs from other studies of regional performance in China – that mainly have 1 Our approach emerged from a discussion between Jonathon Moses and Lars So¨derstro¨m at a seminar in Shanghai.

614

H.N.E. Bystro¨m et al. / Journal of Asian Economics 16 (2005) 612–634

focused on one aspect such as inequality, income per capita or GDP – by taking into account a number of relevant factors. In order to analyze these questions we rely on available regional data between 1991 and 2001 for 10 Chinese regions defined as branches of the People’s Bank of China and, when brought into the analysis, similar data for Hong Kong and Macao. Due to the relatively short time period we base our empirical investigation on the cross-sectional correlation measure of Solnik and Roulet (2000) in addition to the ordinary sample correlation. The cross-sectional correlation coefficient not only has the advantage that it focuses on the correlation among a whole group of variables but it also has the advantage of being instantaneous. Our preliminary results suggest that China probably is more of an optimum currency area than first expected. At least, our findings suggest that China was more suitable as a currency area at the end of the observed period than in the beginning. Hong Kong and Macao, however, seem less appropriate as candidates. Finally, the results indicate that there might be other constellations of regions that could be closer to optimum currency areas than the current Yuan area. The paper is organized as follows: Section 2 discusses the literature on optimum currency areas and Section 3 presents the variables and data. Section 4 describes the econometric method and in Section 5 the empirical results are provided. Section 6 concludes the paper.

2. Optimum currency areas The theory of optimum currency areas, originating from the article by Mundell (1961), tries to determine the desirability for a group of regions/countries to adopt the same currency.2 As this line of research has emphasized, it is of great importance that the members of the currency area are similar in their economic structure since this reduces the risk of so called asymmetrical shocks, i.e. macroeconomic disturbances that have different effects on different members. These disturbances should be avoided in a currency area as individual regions/countries will not be able to use monetary policy or floating exchange rates to stabilize their economy. If they do occur, however, growing discrepancies among members could be avoided if factor markets are well-functioning. Although there is no exact theory and definite set of variables, the OCA literature has suggested a number of preferable characteristics of a currency area that either reflect a low risk of asymmetrical shocks or indicate that proper adjustment mechanisms are present.3 To begin with, members should exhibit co-variation in economic activity implying positive correlation of output disturbances. Closely related is similarity in production structure as this will reduce the risk of asymmetrical sector-specific disturbances. At the individual region or country level, a varied production structure is desirable since regions with a 2 Early additional contributions to the theory were made by McKinnon (1963) and Kenen (1969). For an overview of the OCA literature, see Tavlas (1993). 3 In the literature the term currency area/union is used synonymously with monetary union, although strictly speaking, the currency union does not require a common central bank.

H.N.E. Bystro¨m et al. / Journal of Asian Economics 16 (2005) 612–634

615

higher degree of product diversification will be less vulnerable to macroeconomic instabilities than regions with lower diversification. Also, a high degree of openness, including both the openness of a specific member towards the rest of the world as well as the degree of trade intensity among members, has been proposed. When it comes to adjustment mechanisms, a high degree of factor mobility or, alternatively, flexible price and wage determination have been identified as important. In particular it has been suggested that labor markets should respond quickly to differences in productivity through high interregional mobility and/or that real wages should be downward flexible. The possibility to make fiscal transfers across members in the case of economic instabilities could also act as an adjustment mechanism. There are several other, more or less specific, characteristics that have been identified in the literature. It should, however, be emphasized that the relevance of all the factors is to some extent affected by whether the currency area is a single country or consists of several countries. Thus, in the latter case, factors such as political intensions, cultural similarities and geographical closeness may become crucial for a successful currency area.4 What, then, can the theory of optimum currency areas tell us with regard to China? Clearly, China is already a currency area and a single nation and, therefore, does not have to deal with some of the problems facing a group of countries trying to decide whether to form a currency union or not. There are, however, some issues of concern. First, studies of regional development in China confirm large regional disparities across Chinese regions. This may not be surprising as China is a developing country. More important, though, is the indications of growing discrepancies in the 1990s, specifically between the coast and the inland (e.g. Bhalla, Yao, & Zhang, 2003; Wei & Liu, 2004). One major explanation is the Chinese reform policy in the end of the 1980s that focused on economic development in the east by opening up coastal cities and establishing development and economic zones. While this led to an inflow of domestic and foreign capital to the coastal provinces, central and western regions experienced both a disadvantage in the allocation of physical capital and an outflow of human capital to the growth centres in the east (Wei & Liu, 2004). Second, labor appears to be less mobile than one would expect in a country with large income differences (Johnson, 2003). Partly, this could be attributed to the system of household registration (hukou) that despite recent reforms poses limitations to labor mobility. It is true that China has a large ‘‘floating population’’, mainly consisting of rural workers moving between urban and rural areas without changing registration status. On the other hand, there are studies suggesting low rates of inter-urban mobility (Johnson, 2003) and the presence of major distortions in the labor markets are further supported by China’s high unemployment rate in general and the considerable variations in unemployment across regions. Third, redistribution policies appear to be limited. One explanation is the political preferences based on growth viz. equity arguments. Although some recent distribution programs focus on poor regions, Hill (2002) argues that less attention is now paid to regional equalization and that the share of transfers from the central government has declined. 4 The original theory of OCAs has been criticized as being too static, disregarding dynamic effects. Hence, even if a currency union is not initially ‘‘optimal’’ it may become one as economic integration among members increase over time.

616

H.N.E. Bystro¨m et al. / Journal of Asian Economics 16 (2005) 612–634

Other explanations could be poor administrative capacity as well as limited resources where, for instance, the central budget constitutes only around 15% of GDP. As stressed by the OCA literature, a currency area with increasing economic divergence across regions in combination with insufficient labor mobility and low ability to stabilize the economy through fiscal transfers faces the risk of large fluctuations in output and structural mass-unemployment. Thus, considering the already high unemployment rates in China, this possible scenario is worrying. Hence, we investigate the desirability for the Chinese regions to have the same currency, the Yuan, and, furthermore, try to see if there are sub-regions more suitable for a currency area than the country as a whole. Doing this, we also consider if some of the mainland regions are more correlated with the special administrative regions Hong Kong and Macao than with the other regions within the Yuan area.

3. Variables and data In order to evaluate to what degree China is an optimum currency area we rely on several economic factors based on the OCA framework and analyze how they are correlated across 10 Chinese regions. In 1998 the Peoples Bank of China (PBoC) established nine transprovincial regional branches and financial supervisory agencies in the provincial capital cities without branches:












Beijing Tianjin Branch Shenyang Branch Shanghai Branch Nanjing Branch Jinan Branch Wuhan Branch Guangzhou Branch Chengdu Branch Xian Branch Chongqing

Excluding Chongqing due to lack of data prior to 1997, these branches define the 10 mainland China regions that we focus on in this study. In addition to these 10 regions, Hong Kong and Macao became special administrative regions (SARs) of China in 1997 and 1999, respectively. While Hong Kong and Macao have their own currencies, they also have close cultural, social and economic ties with the rest of China. Furthermore, mainland China, Hong Kong and Macao have all had their currencies, the Yuan, the Hong Kong Dollar and the Pataca, pegged to the US dollar for much of the period (from 1994 in the case of the Yuan and from 1983 in the case of the Hong Kong Dollar and the Pataca (via the Hong Kong Dollar)).5 5 It would of course also be interesting to consider Taiwan in this analysis. However, Taiwan is excluded due to lack of data.

H.N.E. Bystro¨m et al. / Journal of Asian Economics 16 (2005) 612–634

617

The choice of variables that we consider in the empirical analysis is highly restricted by data availability. Thus, the variables where we have found sufficient data are:









degree of product variation; economic size; degree of openness; similarity in production structure; co-variation in output growth; similar inflation rates; co-variation in external trade (openness); co-variation in regional budget.

While the first three economic factors are region specific, the other five are measured relative to the whole currency area. Starting with the region specific factors, a region with a high degree of product diversification and a high degree of openness is expected to be a good candidate for joining a currency area. Also, the larger the size of the economy the better since a large economy is probably more diversified than a small. All the other factors are intended to more or less illustrate the degree of economic similarity across regions. While the similarity in production structure and co-variation in output growth as well as external trade are directly linked to the OCA literature, the correlation in inflation rates and regional budget surplus/deficits should be treated as alternative measures.6 The exact definition of the variables and how they are measured are explained in Section 5. Yearly data on GDP, consumer prices, imports, exports, regional fiscal incomes and expenditures are collected from China Statistical Yearbook (National Bureau of Statistics, 2002) and covers the time period 1991–2001.7 When we incorporate the special administrative regions Hong Kong and Macau in the analysis, corresponding data are from IMF’s International Financial Statistics (output and external trade) and Heston, Summers and Aten PWT 6.1 (inflation).

4. Econometric method The covariation of economic disturbances is often studied using vector autoregressive (VAR) methods (Lafrance & St-Amant, 1999). However, we have too few time series observations in our sample of economic factors to apply vector autoregressive methods. Instead, we quantify factor co-movements by calculating traditional (Pearson) sample correlation coefficients as well as cross-sectional correlations. When we estimate pair-wise sample correlations between the 10 mainland China regions and Hong Kong and Macao we use the whole time series sample. The problem with this is that we rely on data from different points in time and therefore do not get instantaneous estimates of the correlation between the different regions. An alternative way of studying time varying correlations has therefore been suggested by Solnik and Roulet 6 7

These variables could of course also reflect differences in monetary and fiscal policies. Except for inflation figures we have no data for 1997.

618

H.N.E. Bystro¨m et al. / Journal of Asian Economics 16 (2005) 612–634

(2000). They look at stock market returns and instead of taking a time series approach they suggest a cross-sectional approach based on the dispersion of stock market movements at one particular point in time. This approach is therefore suitable for studies of how correlations change over time. In addition, while other approaches normally focus on pairwise correlations, the idea of the cross-sectional correlation coefficient is to produce a global measure of correlation that captures several markets’ co-movements in one single coefficient.8 In order to calculate the cross-sectional correlation, Solnik and Roulet (2000) starts by defining a world return, the average return of all markets, around which all national market returns are distributed. If the markets are highly correlated then they will usually demonstrate similar returns at a particular point in time, and if they are weakly correlated the returns will be spread out around the world return. This tendency for national market returns to move (or not to move) together is summarized in the quantity called dispersion; the dispersion is calculated as the standard deviation of the returns around the world return and the more spread out the returns are the higher is the dispersion. Furthermore, since one relies on cross-sectional data one can easily calculate the dispersion at each point in time, thereby producing a time varying measure without the dependency problems of the traditional time-series based sample correlation measure. Solnik and Roulet (2000) proposes a simple model of how the cross-sectional dispersion measure se(t) can be transformed into a cross-sectional correlation coefficient riw(t) that can be compared to traditional correlations: 1 riw ðtÞ ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 ð1 þ s e ðtÞ=s 2w ðtÞÞ

(1)

where sw(t) is the volatility of the world return. We follow Solnik and Roulet by implicitly assuming se(t) and sw(t) to be constant over time and therefore compute se(t) and sw(t) as the sample standard deviations over the entire time period. However, while Solnik and Roulet derive their correlation measure using national stock market returns we instead focus on regional percentage changes in economic factors like GDP, prices and trade. The world return volatility, sw(t), in (1) is therefore replaced by the volatility of the (unweighted) average percentage change in GDP, price, trade, etc. among all regions, and the dispersion is a measure of how spread out the percentage changes are around the average. The cross-sectional correlation coefficient riw(t) that we compute in this way can then be compared to the traditional sample correlation.

5. Empirical results 5.1. Does mainland China constitute an optimum currency area? In order to address this question we start by looking at the region-specific variables that are available. 8

An alternative approach could be to use principal component analysis (Goto, 2003).

H.N.E. Bystro¨m et al. / Journal of Asian Economics 16 (2005) 612–634

619

5.1.1. Degree of product variation To illustrate production variation, we consider different sectors’ share of total production by calculating9

product variation ¼ 100 

n X

fi2

(2)

i¼1

where f i is the share of sector i in total production in a region. Due to lack of detailed sectoral data on output, we use data on urban employment in 16 sectors in 2001 to create this index that ranges from 0 to 100 and the lower the value the higher the degree of product variation.10 The value for the 10 mainland China regions are presented in Table 1, and according to the index, the most diversified region is Xian, followed by Beijing and Chengdu. The absolute values must be considered fairly low, although it is difficult to compare these values with those from other studies that use different sectors, and the relatively high degree of product variation constitutes an argument in favour of these regions forming an optimum currency area. 5.1.2. Economic size Our measure of product variation suffers from the problem of sectoral bias towards service instead of manufacturing. As an alternative we suggest using the regions’ economic size and Table 2 shows the economic size of the regions measured by regional GDP in 2001. Thus, the largest regions are the coastal regions Shanghai, Jinan, Guangzhou and Nanjing whose shares of national GDP all are larger than 10%. At the other end, Beijing is economically the smallest region which may not be too surprising since the region only consists of the capital city. Besides Beijing, the regional GDP for the western regions Chengdu and Xian are both less than 10% of national GDP. 5.1.3. Degree of openness We measure openness by calculating each region’s external total trade (sum of exports plus imports) as a share of its GDP and the figures in Table 3 are averages for the period 1994–2001. Most striking is the very high degree of openness for the Beijing region where total trade is 1.68 times its GDP. Guangzhou, with a value of 1.18, is also very open, which is probably explained by the geographical proximity to Hong Kong. With total trade amounting to over 50% of GDP, Shanghai is also relatively open followed by Nanjing (0.27) and Shenyang (0.21). At the other end, the less open regions, where the share of external trade is 10% or lower, are the central region Wuhan together with the western regions Chengdu and Xian. Next, we turn to the area specific characteristics. 9

This measure is for example used by Jonung and Sjo¨holm (1998). Obviously, it would be better to use data on total employment by sector but, unfortunately, we do not have access to this data. We have also calculated the degree of product variation by using (less disaggregated) output data. This, however, yields very similar results to the urban employment data which suggests that the results are reliable. 10

H.N.E. Bystro¨m et al. / Journal of Asian Economics 16 (2005) 612–634

620

Table 1 Degree of product variationa Region Xian Beijing Chengdu Shenyang Tianjin Wuhan Chongqing Guangzhou Jinan Nanjing Shanghai a

11.4 12.1 12.1 13.0 13.1 13.1 13.6 14.6 15.1 16.4 17.4

Uses data on urban employment 2001 by region for 16 sectors where: Sectors i.

1: Farming, Forestry, Animal Husbandry and Fishery 2: Mining and Quarrying 3: Manufacturing 4: Production and Supply of Electricity, Gas and Water 5: Construction 6: Geological Prospecting and Water Conservancy 7: Transport, Storage, Post and Telecommunications 8: Wholesale & Retail Trade and Catering Services 9: Finance and Insurance 10: Real Estate Trade 11: Social Services 12: Health Care, Sporting and Social Welfare 13: Education, Culture & Arts, Radio, Film and Television 14: Scientific Research and Polytechnical Services 15: Government Agencies, Party Agencies and Social Organizations 16: Others

Table 2 Economic size—regional GDP 2001 Region

Yuan (100 millions)

Share of national GDP

Shanghai Jinan Guangzhou Nanjing Wuhan Tianjin Shenyang Chengdu Xian Beijing

15953 15078 13425 12802 10821 10744 10626 7721 5001 2846

0.15 0.14 0.13 0.12 0.10 0.10 0.10 0.07 0.05 0.03

H.N.E. Bystro¨m et al. / Journal of Asian Economics 16 (2005) 612–634

621

Table 3 Openness Region Beijing Guangzhou Shanghai Nanjing Shenyang Tianjin Jinan Xian Chengdu Wuhan

1.68 1.18 0.52 0.27 0.21 0.20 0.16 0.10 0.08 0.07

5.1.4. Similarity in production structure When examining production structures we use the same data on regional urban employment as before since these data are the least aggregated. Again, we consider sectors’ shares and calculate the absolute difference in the production structure between regions j and k by 1

n  X Empi; j Empi;k  Pn  Pn  Emp Emp i¼1

i¼1

i; j

i¼1

i;k

    0:5 

(3)

where Empi,j is the number of employees in sector i in region j. The similarity between two regions, j and k, ranges from 0 to 1 and the higher the value the more similar are the two regions. The calculations for all regions are showed in Table 4. It should be noted that the values are relative and to some extent depend on the number of sectors studied. Furthermore, the use of urban employment data does not allow us to take into account the rural sector. However, groups of regions that are more similar to each other can be traced out. The figures in Table 4 indicate similar production structures in most mainland China regions, supporting the argument that China is an optimum currency area. Once more, however, Beijing stands out as an exception. Beijing is most similar to Shanghai and Guangzhou, but compared to the overall picture Beijing is definitely the most dissimilar Table 4 Similarity in production structure Tianjin

Shenyang

Shanghai

Nanjing

Jinan

Wuhan

Guangzhou

Chengdu

Xian

0.736

0.740 0.902

0.794 0.837 0.844

0.738 0.894 0.885 0.903

0.726 0.939 0.849 0.866 0.902

0.737 0.928 0.904 0.841 0.895 0.895

0.770 0.894 0.890 0.887 0.928 0.861 0.908

0.769 0.903 0.841 0.813 0.854 0.888 0.918 0.859

0.744 0.906 0.884 0.810 0.852 0.868 0.924 0.865 0.915

Beijing Tianjin Shenyang Shanghai Nanjing Jinan Wuhan Guangzhou Chengdu

622

H.N.E. Bystro¨m et al. / Journal of Asian Economics 16 (2005) 612–634

Table 5 GDP growth rate correlations, all industries Tianjin Shenyang Shanghai Nanjing Jinan 0.622 0.433 0.027

0.459 0.098 0.436

Wuhan Guangzhou Chengdu Xian

0.190 0.254 0.331 0.058 0.151 0.335 0.795 0.253 0.283 0.741 0.249 0.245 0.936 0.093 0.039 0.854 0.264 0.119 0.935 0.680 0.210 0.090

0.247 0.259 0.242 0.127 0.253 0.468 0.359 0.267

Hong Kong

Macao

0.800 0.509 0.226 Beijing 0.635 0.157 0.091 Tianjin 0.106 0.018 0.064 Shenyang 0.494 0.553 0.759 Shanghai 0.236 0.595 0.876 Nanjing 0.030 0.262 0.499 Jinan 0.356 0.097 0.147 Wuhan 0.256 0.558 0.819 Guangzhou 0.193 0.797 0.618 Chengdu 0.491 0.306 Xian 0.857 Hong Kong

Note: Bold numbers indicate statistically significant correlations a the 5% level.

region. Looking at the sector specific data, one explanation is the large share of social services which constitutes 15% of total urban employment in Beijing but on average only 6% in the rest of the country. 5.1.5. Co-variation in output growth In this subsection we look at real GDP growth from 1991 to 2001.11 The growth data is divided into total industry growth and industry specific growth (primary industry, secondary industry and tertiary industry). The average annual growth rate in China over the time period has been as high as 11% but the rate has slowly decreased from around 14% in the beginning of the 90s to less than 10% around the turn of the millenium. It is clear from looking at the data12 that there are regional differences in behavior. The Beijing region stands out in the post Asian crisis period with an average annual growth rate as high as 13%, and in the early 1990s the coastal regions Shanghai, Nanjing and Guangzhou were the fastest growing regions with growth rates exceeding 20%. The only region that has had a negative real growth in any of the years in the sample is Chengdu which saw an average annual growth rate of 0.7% over the Asian crisis years. No other region seems to have been affected by the Asian crisis when it comes to real GDP growth, however. In Table 5 we present pair-wise sample correlations between regional growth rates for the total industry. The higher the correlations the more suitable the regions are to form a currency area. The question is where to draw the line, and we have decided to call two regions sufficiently correlated if the correlation coefficient is statistically significant at the 5% level. Sometimes we strengthen our conclusions by drawing on Bayoumi and Eichengreen (1994) who suggests 0.5 to be a critical minimum correlation level (between demand or supply shocks) for forming a currency area13. At the moment we concentrate on 11 As regional GDP deflators are not available, real GDP growth is computed by deflating the nominal GDP figures with the national GDP deflator provided by IMF’s International Financial Statistics. This may not cause any major concern, however, as inflation rates are highly correlated across regions. 12 Actual figures are available from the authors upon request. 13 Using the threshold 0.5 instead of the threshold of statistical significance does not change the qualitative conclusions drawn in the paper.

H.N.E. Bystro¨m et al. / Journal of Asian Economics 16 (2005) 612–634

623

Fig. 1. GDP growth cross sectional correlations (mainland China including Hong Kong and Macao).

the mainland China regions leaving out Hong Kong and Macao until Section 5.2. Only few correlations are significant and the regions with the highest correlations are the coastal regions Shanghai, Nanjing and Guangzhou. This is probably because these regions are neighboring regions and because all of them early on harbored special economic zones. In addition to the ordinary sample correlations we also present time series of crosssectional correlations in Fig. 1. The upper curve represents cross-sectional correlations among the 10 mainland China regions. Figures like this make it possible to track intertemporal changes in correlations across the 10 regions. First of all, the GDP growth crosssectional correlation among the 10 Chinese regions is only significant after the Asian Crisis, and only from 1998 onwards is the correlation an indication of co-moving regions. From 1991 to 1998, on the other hand, there is no clear link between the regions. Furthermore, a clear upward trend in the cross-sectional correlation is evident, which means that the regions progressively become closer linked to each other with the passage of time. Table 6 GDP growth rate correlations, primary industry Tianjin

Shenyang

Shanghai

Nanjing

Jinan

Wuhan

Guangzhou

Chengdu

Xian

0.567

0.388 0.848

0.606 0.880 0.889

0.215 0.615 0.648 0.444

0.200 0.743 0.407 0.535 0.565

0.607 0.985 0.869 0.894 0.574 0.633

0.002 0.643 0.707 0.548 0.930 0.519 0.600

0.415 0.814 0.562 0.777 0.570 0.816 0.775 0.611

0.409 0.919 0.706 0.661 0.581 0.768 0.887 0.502 0.636

Note: see Table 5.

Beijing Tianjin Shenyang Shanghai Nanjing Jinan Wuhan Guangzhou Chengdu

H.N.E. Bystro¨m et al. / Journal of Asian Economics 16 (2005) 612–634

624

Table 7 GDP growth rate correlations, secondary industry Tianjin

Shenyang

Shanghai

Nanjing

Jinan

Wuhan

Guangzhou

Chengdu

Xian

0.206

0.240 0.591

0.401 0.555 0.232

0.201 0.435 0.009 0.940

0.096 0.447 0.252 0.764 0.887

0.009 0.895 0.721 0.532 0.373 0.346

0.126 0.714 0.341 0.924 0.913 0.770 0.611

0.359 0.628 0.484 0.807 0.731 0.575 0.600 0.880

0.742 0.625 0.283 0.070 0.151 0.184 0.357 0.388 0.138

Beijing Tianjin Shenyang Shanghai Nanjing Jinan Wuhan Guangzhou Chengdu

Note: see Table 5.

In Tables 6–8 and in Fig. 2 we present sample correlations and cross-sectional correlations for various sub-industries. The primary industry GDP growth rates, i.e. agriculture (including farming, forestry, animal husbandry and fishery), are slightly more correlated than the total industry growth rates. More or less all regions, with the noticeable exception of Beijing, have positively correlated growth rates but the correlations that are statistically significant do not form a clear pattern. The only region that is singled out is Beijing, whose growth pattern is different from the other regions. This is most likely caused by the industry in Beijing being mostly tertiary. When it comes to the secondary industry GDP growth rates, i.e. industry (including mining and quarrying, manufacturing, production and supply of electricity, water and gas) and construction, the correlations are overall somewhat lower. Again, Beijing is uncorrelated with the other regions but other than that it is difficult to find any clear pattern except the atypical growth pattern of the Shenyang region. Finally, the tertiary industry GDP growth rate, i.e. all other industries not included in primary or secondary industry (transportation, postal and telecommunications, banking, insurance and other services), is the most strongly correlated industry sector. All regions except Beijing are significantly correlated. All the sub-industry growth rates are highly correlated (see Fig. 2) also when the cross-sectional measure is used. The highest cross-sectional correlation is again found for the tertiary industry. And just as for the total industry, the individual industry cross-sectional correlations increase over time. Table 8 GDP growth rate correlations, tertiary industry Tianjin

Shenyang

Shanghai

Nanjing

Jinan

Wuhan

Guangzhou

Chengdu

Xian

0.264

0.504 0.865

0.515 0.893 0.939

0.510 0.897 0.957 0.998

0.537 0.910 0.949 0.975 0.983

0.361 0.926 0.979 0.915 0.936 0.941

0.341 0.877 0.819 0.933 0.917 0.870 0.813

0.510 0.949 0.922 0.913 0.917 0.925 0.930 0.864

0.258 0.849 0.880 0.903 0.900 0.826 0.880 0.865 0.839

Note: see Table 5.

Beijing Tianjin Shenyang Shanghai Nanjing Jinan Wuhan Guangzhou Chengdu

H.N.E. Bystro¨m et al. / Journal of Asian Economics 16 (2005) 612–634

625

Fig. 2. GDP growth cross sectional correlations, sub-industries (mainland China).

5.1.6. Similar inflation rates We now turn to inflation rates (CPI) and the correlations are displayed in Table 9. Over the sample period, 1995–2001, the overall inflation level in China decreased from more than 15% in 1995 to less than 1% towards 2000 and 2001. Directly after the Asian crisis years China as a whole even experienced deflation for two years and Beijing is the only region not to experience deflation in that period. Over the entire post-crisis period Beijing also demonstrates significantly higher inflation rates than the other regions. The inflation rates have been very similar across the regions (including Beijing), and correlation estimates are strikingly high. In Table 9 all sample correlations are highly significant at 0.97 or higher. We also present time series of cross-sectional correlations between regional inflation rates in Fig. 3. The upper curve represents the cross-sectional correlation among the 10 mainland China regions and it is clear that the correlation is very high over the entire sample. One can also observe a slight increase in correlation over time. Table 9 Inflation correlations Tianjin Shenyang Shanghai Nanjing Jinan Wuhan Guangzhou Chengdu Xian Hong Kong Macao 0.988 0.983 0.988

Note: see Table 5.

0.979 0.992 0.975

0.996 0.981 0.975 0.978

0.994 0.990 0.988 0.975 0.992

0.985 0.993 0.987 0.998 0.983 0.983

0.977 0.994 0.972 0.992 0.974 0.977 0.989

0.977 0.989 0.996 0.986 0.974 0.983 0.994 0.979

0.986 0.987 0.996 0.973 0.983 0.994 0.984 0.975 0.992

0.809 0.771 0.855 0.755 0.790 0.802 0.792 0.715 0.830 0.834

0.569 0.588 0.641 0.508 0.502 0.585 0.533 0.518 0.585 0.605 0.719

Beijing Tianjin Shenyang Shanghai Nanjing Jinan Wuhan Guangzhou Chengdu Xian Hong Kong

H.N.E. Bystro¨m et al. / Journal of Asian Economics 16 (2005) 612–634

626

Fig. 3. Inflation cross sectional correlations (mainland China including Hong Kong and Macao).

5.1.7. Co-variation in external trade (openness) Again, as a measure of openness, we consider the total trade parameter (exports plus imports as a share of regional GDP). We look at the time period 1994–2001, and over this time period the openness for China as a whole has fluctuated widely. For instance, from 1995 to 1996 the openness was reduced by 13% and from 1999 to 2000 the openness increased by 18%. When we calculate sample correlations, however, it is clear that the regions’ changes in external trade (openness) have been positively correlated over the time period (see Table 10). No two regions are negatively correlated and many correlations are statistically significant. We also consider regional co-variation in exports since it could be argued that changes in exports better correspond to economic fluctuations than total trade. Hence, sample correlations of annual changes in exports as a share of GDP are showed in Table 11, and again many correlations are statistically significant. The pattern is similar to that for the total trade parameter. Table 10 Openness correlations Tianjin Shenyang Shanghai Nanjing Jinan Wuhan Guangzhou Chengdu Xian Hong Macao Kong 0.920

0.902 0.941

Note: see Table 5.

0.923 0.914 0.973

0.773 0.882 0.890 0.908

0.854 0.860 0.944 0.872 0.779

0.896 0.713 0.821 0.891 0.645 0.757

0.548 0.448 0.527 0.702 0.643 0.303 0.721

0.628 0.494 0.652 0.675 0.334 0.564 0.824 0.511

0.885 0.740 0.665 0.720 0.646 0.679 0.738 0.458 0.279

0.795 0.877 0.776 0.678 0.688 0.840 0.478 0.031 0.207 0.743

0.707 0.702 0.653 0.790 0.766 0.400 0.697 0.897 0.474 0.585 0.324

Beijing Tianjin Shenyang Shanghai Nanjing Jinan Wuhan Guangzhou Chengdu Xian Hong Kong

H.N.E. Bystro¨m et al. / Journal of Asian Economics 16 (2005) 612–634

627

Table 11 Exports correlations Tianjin Shenyang Shanghai Nanjing Jinan Wuhan Guangzhou Chengdu Xian Hong Macao Kong 0.725

0.596 0.974

0.797 0.916 0.913

0.733 0.961 0.948 0.930

0.681 0.930 0.951 0.956 0.879

0.683 0.675 0.716 0.865 0.791 0.776

0.593 0.302 0.322 0.603 0.488 0.425 0.883

0.879 0.824 0.742 0.866 0.729 0.864 0.609 0.369

0.772 0.641 0.504 0.626 0.685 0.448 0.377 0.306 0.627

0.651 0.942 0.881 0.770 0.906 0.760 0.466 0.109 0.696 0.753

0.539 0.523 0.510 0.610 0.731 0.393 0.698 0.692 0.267 0.694 0.552

Beijing Tianjin Shenyang Shanghai Nanjing Jinan Wuhan Guangzhou Chengdu Xian Hong Kong

Note: see Table 5.

In Fig. 4 we present cross-sectional correlation among the various regions for both the total trade measure and the export measure. The solid curve represents the cross-sectional correlation among the 10 mainland China regions and it is significant over the entire sample. As before, we also observe a slight increase in this correlation over time. The correlation among exports, on the other hand, does not reveal as significant a trend for the last years. 5.1.8. Co-variation in regional budget Finally, we compare changes in the regional fiscal budget across regions from 1994 to 2001. The net fiscal budgets vary in magnitude over the time period and across the regions, but they do not seem to have been affected by the Asian crisis. If anything, one can detect a general shift from contracting to expanding budgets around the crisis. After the crisis there are almost no examples of regions where the fiscal budget is contracting.

Fig. 4. Openness cross sectional correlations (mainland China including Hong Kong and Macao).

H.N.E. Bystro¨m et al. / Journal of Asian Economics 16 (2005) 612–634

628

Fig. 5. Regional fiscal budget cross sectional correlations (mainland China).

With the notable exception of Shanghai and Nanjing, all sample correlations are positive even though only few are statistically significant. The negative correlations with Shanghai are partly caused by that region’s extreme expansion (46%) from 1999 to 2000 followed by the equally extreme contraction (42%) the following year. In Fig. 5 the cross-sectional correlation among the mainland China regions is presented and there is a clear break in the trend around the Asian crisis. Since then the correlation has steadily decreased from the very high level around the crisis, and towards the end of the sample the cross-sectional correlation is as low as 0.4. This pattern (which very much is caused by the extreme behavior of Shanghai) differs from what we have seen for the other variables. If the variable is mainly treated as reflecting economic similarity, this clearly weakens the arguments for China currently constituting an optimum currency area. However, we should remember that the regions already operate within a currency union. The growing divergence of regional budgets may therefore be a sign of differences in regional fiscal policies that aims to facilitate regional adjustment. Thus, the development could equally be interpreted in Table 12 Regional fiscal budget correlations Tianjin

Shenyang

Shanghai

Nanjing

Jinan

Wuhan

Guangzhou

Chengdu

Xian

0.702

0.686 0.637

0.376 0.074 0.110

0.347 0.380 0.128 0.717

0.833 0.862 0.808 0.148 0.119

0.614 0.830 0.651 0.462 0.427 0.924

0.479 0.520 0.481 0.463 0.230 0.628 0.770

0.688 0.942 0.671 0.215 0.365 0.851 0.877 0.763

0.605 0.952 0.575 0.088 0.411 0.727 0.735 0.589 0.958

Note: see Table 5.

Beijing Tianjin Shenyang Shanghai Nanjing Jinan Wuhan Guangzhou Chengdu

H.N.E. Bystro¨m et al. / Journal of Asian Economics 16 (2005) 612–634

629

favor of an OCA and that the low co-variation in this variable contributes to the high covariation in other variables (see Table 12). 5.1.9. Summary Based on the various findings above it is difficult to draw a clear-cut conclusion. Some findings support the hypothesis that China does make up an optimum currency area; such as a high degree of product variation, the similarity in production structure, the high subindustry output growth correlations, the similarity in regional inflation rates, the high trade correlations, and the apparent ability to use sub-national fiscal policy for adjustment, while others suggest the opposite; such as a low degree of openness for many regions and low total industry output growth correlations. Eventually, however, weighting in the relative importance of the various economic factors we draw the conclusion that the arguments in favor of China constituting an optimum currency area are stronger than those against. While it is debatable whether the Yuan area was optimal in the early 1990s we find a trend towards closer knitted regions, and a corresponding trend towards China becoming an optimum currency area, over time. 5.2. Does mainland China together with Hong Kong and Macao Constitute an optimum currency area? Hong Kong and Macao have kept their own currencies, the Hong Kong dollar and the Macao Pataca, after the hand over of power to Beijing. In other words, these two Chinese regions have certain monetary policy freedoms, whether it is optimal or not, and it is reasonable to ask what the effect would be of including them in the Yuan currency area.14 While doing that one should not forget that Hong Kong and Macao are at a very different stage of economic development compared to the rest of China; in 2004, GDP per capita (purchasing power parity adjusted) in Hong Kong and Macao was approx. $29,000 and $19,000, respectively, while it was around $5000 in China. 5.2.1. Economic size In economic terms, Hong Kong is a large and Macao is a small region compared to the regions of mainland China. In 2001, the GDP of Hong Kong was 1360 billion Yuan and the GDP of Macao was 51,380 million Yuan. This would make Hong Kong the third largest region in the proposed Yuan currency area and Macao, by far, the smallest region. Based on size, therefore, Hong Kong is more likely to have a more diversified production structure. 5.2.2. Degree of openness Again, we measure openness by calculating the external total trade (sum of exports plus imports) as a share of GDP. For Hong Kong, an average of this indicator for the period 1994–2001 is 2.36 and for Macao the same average is 0.65. This is an indication of the outward looking nature of these two regions, particularly Hong Kong. Hong Kong would be, by far, the most open region in the proposed Yuan currency area and Macao would be the fourth most open. 14

We have neither fiscal budget data nor product variation data for Hong Kong and Macao.

630

H.N.E. Bystro¨m et al. / Journal of Asian Economics 16 (2005) 612–634

5.2.3. Co-variation in output growth As we can see in Table 5, typical correlations in real GDP growth were neither higher nor lower when Hong Kong and Macao were included. Except for the significant correlation between the two regions themselves, the only regions to be significantly linked to Hong Kong and Macao are the coastal regions Shanghai, Nanjing, Guangzhou as well as the inland region Chengdu. The high correlations between Chengdu and Hong Kong and Macao are due to Chengdu being the only Chinese region negatively affected by the Asian crisis. The lower curve in Fig. 1 shows the cross-sectional correlation among the 10 mainland China regions and Hong Kong and Macao, and the correlation is clearly reduced when Hong Kong and Macao are included. These two regions have different real GDP growth rates than the other regions, something that most likely is caused by the emphasis on tertiary production (services) in these two regions. The correlation is trending upwards, however, and there are signs of the two regions’ growth rate patterns converging with the other regions’ over time. 5.2.4. Similar inflation rates When we compare inflation rates in Hong Kong and Macao with those in mainland China we, not surprisingly, find large differences. First of all, the inflation rates in 1995 and 1996 were much lower in Hong Kong and Macao. Second, the period of deflation after the Asian crisis were much more severe and long lasting in these two regions than in the rest of China. While the correlations between Hong Kong and Macao and the other regions are high, as can be seen in Table 9, they are nonetheless much lower than those among the mainland China regions. Macao’s inflation rate is not significantly correlated with any other region’s except Hong Kong’s and all correlations are closer to 0.5 than to 1. These findings are confirmed by the time series behavior of the cross-sectional correlation. The lower curve in Fig. 3 shows the cross-sectional correlation among the mainland China regions together with Hong Kong and Macao, and the correlation is clearly lower when Hong Kong and Macao are included. The difference is decreasing, however, and the effect of including Hong Kong and Macao is smaller after the Asian crisis. Our previous conclusion, based on GDP growth patterns, that Hong Kong and Macao are different from the rest of China is further strengthened by the inflation patterns. The correlation between the various regions’ inflation rates is lower when Hong Kong and Macao are included but their inflation rates have become more correlated with mainland China’s over time. 5.2.5. Co-variation in external trade (openness) When we compare the yearly changes in external trade in Hong Kong and Macao with the changes in mainland China we find no significant difference in patterns. This can also be observed in Table 6 where the correlations involving Hong Kong and Macao are of the same size as the other correlations. This is confirmed by the time series behavior of the cross-sectional correlation in Fig. 4 where the difference between the two curves barely is detectable. Towards the end of the sample the difference is getting even smaller. Our previous conclusion, from investigating growth and inflation patterns, that Hong Kong and Macao are different from the rest of China is therefore not confirmed when we look at changes in the degree of openness.

H.N.E. Bystro¨m et al. / Journal of Asian Economics 16 (2005) 612–634

631

5.2.6. Summary The Asian financial crisis 1997–1998 affected Hong Kong and Macao much more than it affected China. The growth rates in China were not much lower over the crisis years than before or after the crisis while in Hong Kong and, particularly, in Macao the previous strong positive growth rates were reversed to significant negative growth rates. The crisis can be seen as an external shock to the system and it is evident that the response (in terms of real GDP changes) goes from being symmetric to highly asymmetrical when Hong Kong and Macao is added. Even though the shock came before the two regions were handed over to China it nevertheless lends some support to the opinion that these two regions, as of 2001, do not make up an optimum currency area together with China. This opinion is further strengthened by the observation that including Hong Kong and Macao weakens the overall correlation among the regions’ growth rates as well as their inflation rates. From our point of view, though, the evidence against these two regions being good candidates for an optimum currency area with the rest of China are not overwhelming. Our results can be compared to those of other authors. Liang (1999), for instance, claims that, in 1999, Hong Kong and China do not satisfy the necessary conditions for forming an optimum currency area by themselves. Instead, a larger currency area including United States and Japan seems more promising. A weakness of our approach is the fact that the 10 regions of mainland China already share the same monetary policy. This might in itself bias our results since these regions might be more strongly correlated simply because they already form a monetary union (Shioji, 2000). This somewhat weakens our argument that Hong Kong and Macao should not form a currency area with the rest of China. 5.3. Are there other candidates for making up an optimum currency area? Are there other sub-regions of China that might constitute an optimum currency area? Perhaps China could be divided into two or more currency areas? In order to investigate this we suggest two possible sub-groupings of regions and in order to find an answer to whether these areas are optimum currency areas we focus on sample correlations and crosssectional correlations in GDP growth.15 The close relationship between Hong Kong, Macao and the coastal regions raises the question of whether, perhaps, the entire coastal region could be more of an optimum currency area than the current Yuan currency area. We define the coastal region as Jinan, Nanjing, Shanghai, Guangzhou together with Hong Kong and Macao. As we can see in Table 5 these six regions are generally highly correlated with each other when we look at the entire sample. This supports our hypothesis of the coastal area being suitable for forming a currency area. However, when we compute cross-sectional correlations among the coastal regions as well as among the remaining inland regions we do not find large differences (see Fig. 6). Furthermore, the cross-correlations among the coastal regions are not significantly higher than those among the regions forming the current Yuan area. A 15 The GDP growth is the most important economic factor to consider when investigating whether a region is suitable for being a currency area.

632

H.N.E. Bystro¨m et al. / Journal of Asian Economics 16 (2005) 612–634

Fig. 6. GDP growth cross-sectional correlations (mainland China including Hong Kong and Macao).

more careful study is therefore needed before one can determine for sure whether the coastal region is a more suitable currency area than the current Yuan area. Another possibility is to exclude regions that for one reason or another seem to be different than the other. This can of course be done in several ways but one example based on our previous results is to look at China without Beijing, Xian, Hong Kong and Macao. These four regions stand out as either having atypical production structures, or being the smallest, or being the most diversified or as being special administrative regions. Other than that, we admit that this is not a very natural choice of a currency area and the exercise is obviously of mere academic interest. At the same time, however, it exemplifies the

Fig. 7. GDP growth cross sectional correlations (mainland China including Hong Kong and Macao).

H.N.E. Bystro¨m et al. / Journal of Asian Economics 16 (2005) 612–634

633

possibility of forming alternative Chinese currency areas. We focus on GDP growth and while ordinary correlations in Table 5, using the entire sample, are neither higher nor lower for the remaining regions the cross-sectional correlations indicate a significant convergence towards the end of the sample (see Fig. 7). In 2001 the correlation among the remaining eight regions is as high as 0.95.16 Based on the two examples above, focusing simply on GDP growth correlations, we come to the conclusion that, using more elaborate statistical techniques and a wider set of economic factors, it might very well be possible to find groups of regions that, at least in theory, could be closer to optimum currency areas than the current Yuan currency area. Whether these areas are feasible candidates also in practice, considering political, geographical and other economic reasons is of course a different question.

6. Conclusions In this paper we have tried to assess to what degree China is an optimum currency area. In order to do that we have focused on various economic factors based on the OCA framework and analyzed how they are correlated across 10 Chinese regions as well as Hong Kong and Macao. Some economic factors are region specific, while others can be attributed to the whole currency area. Based on the variation of the various region specific factors and on the sample correlations and time-series of cross-sectional correlations among them we draw the conclusion that the arguments in favor of China constituting an optimum currency area, in 2001, are stronger than those against. While it is debatable whether China was an optimum currency area in the early 1990s we find a trend pointing in the direction of China getting closer to being an optimum currency area towards the turn of the millennium. This trend is also in line with the general notion that the possibility that regions adopting the same currency constitute an OCA increase over time when participating regions become more integrated. Furthermore, the asymmetrical effect of the Asian financial crisis 1997–1998 and the observation that including Hong Kong and Macao in a possible greater Yuan currency area weakens the overall correlation among the regions’ growth rates and inflation rates supports the argument that Hong Kong and Macao are different from the rest of China, in the context of currency areas, and that none of these two special administrative regions seems ready to be included, as of 2001, in the current Yuan currency area. This conclusion is weakened, however, by the fact that the mainland China regions very well might be more strongly correlated simply because they already form a monetary union. Finally, we try to assess whether there might exist other constellations of regions that might form optimum currency areas, and weather China possibly could be divided into several currency areas. We suggest two such sub-groupings of regions. One contains the entire coastal region including Hong Kong and Macao and the other (admittedly theoretical) group includes all regions except Beijing, Xian, Hong Kong and Macao. Based 16 For comparative reasons we also include another group of eight regions where the somewhat atypical regions Shanghai, Nanjing, Hong Kong and Macao are excluded.

634

H.N.E. Bystro¨m et al. / Journal of Asian Economics 16 (2005) 612–634

on these two examples, we come to the conclusion that, at least in theory, there might very well exist groups of regions that could be closer to optimum currency areas than the current Yuan currency area. Whether these areas also are feasible candidates in practice, considering political, geographical and other economic reasons is an entirely different question.

Acknowledgements The authors are grateful to Zhou Lin for her help with data collection and to participants at the conference on ‘‘Regional Monetary Cooperation and Cooperation: The Experience in Europe and Feasibilities in Asia’’ at Fudan University in Shanghai, China on October 21, 2004 for helpful comments. Financial support from Stiftelsen Bankforskningsinstitutet is gratefully acknowledged.

References Bayoumi, T., & Eichengreen, B. (1994). One Money or Many? Analyzing the Prospects for Monetary Unification in Various Parts of the World. Princeton Studies in International Finance (No. 76). Princeton University Press, Princeton. Bhalla, A., Yao, S., & Zhang, S. (2003). Regional performance in China. Economics of Transition, 11, 25–39. Goto, J. (2003). Financial Cooperation in East Asia and Japan’s Role. Mimeo: Kobe University. Heston, A., Summers, R., & Aten, B. (2002, October). Penn World Table Version 6.1. Center for International Comparisons at the University of Pennsylvania (CICUP). Hill, H. (2002). Spatial disparities in developing East Asia: A survey. Asian-Pacific Economic Literature, 16(1), 10–35. Johnson, D. G. (2003). Provincial migration in China in the 1990s. China Economic Review, 14, 22–31. Jonung, L., & Sjo¨holm, F. (1998). Should Finland and Sweden form a monetary union. SSE/EFI Working Paper Series in Economics and Finance (No. 224). Kenen, P. (1969). The theory of optimum currency areas: An eclectic view. In R. Mundell, & A. Svoboda (Eds.), Monetary Problems of the International Economy. Chicago: University of Chicago Press. Lafrance, R., & St-Amant, P. (1999). Optimal Currency Areas: A Review of the Recent Literature (Working Paper 99-16). Bank of Canada. Liang, H. (1999). Do Hong Kong SAR and China Constitute An Optimum Currency Area? An Empirical Test of the Generalized Purchasing Power Parity Hypothesis (IMF Working Paper, 99/79). Mckinnon, R. (1963). Optimum currency areas. American Economic Review, 53, 717–725. Mundell, R. A. (1961). A theory of optimum currency areas. American Economic Review, 51, 657–665. National Bureau of Statistics. (2002). China Statistical Yearbook. China Statistics Press. Shioji, E. (2000). Monetary Shocks and Endogeneity of the Optimum Currency Area Criteria: Reconsidering the European Monetary Unification. Discussion Paper, Yokohama National University. Solnik, B., & Roulet, J. (2000). Dispersion as cross-sectional correlation. Financial Analysts Journal, 56, 54–61. Tavlas, G. S. (1993). The ‘New’ theory of optimum currency areas. The World Economy, 16, 663–685. The Economist. (2004, September 9). No right to work. The Economist. Wei, Y., & Liu, X. (2004). Convergence or Divergence? Debate on China’s Regional Development (Lancaster University Management School Working Paper No. 2004/002). Yuen, H. (2000). Is Asia an Optimum Currency Area? Shocking Aspects of Output Fluctuations in East Asia. Working Paper, National University of Singapore.