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Regional integration in China: a statistical model
Economics Letters 79 (2003) 35–42 www.elsevier.com / locate / econbase
Regional integration in China: a statistical model Xinpeng Xu a , *, J.P. Voon b a
Department of Business Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong b Lingnan University, Kowloon, Hong Kong Received 9 January 2002; received in revised form 15 July 2002; accepted 17 September 2002
Abstract We illustrate that a statistical model can be used to gauge the degree of market integration. The model is applied to the case of China, which is undergoing transition from a central planning to a market economy and there is a growing interest as to the evolution of its market integration. 2002 Elsevier Science B.V. All rights reserved. Keywords: Economic growth; Regional integration; China; Error components model JEL classification: F15; O53
1. Introduction Many economic models have been used to measure the degree of integration across countries and industries (for example, the gravity model of trade as exemplified by Krugman, 1991). Models have also been developed for measuring integration across provinces within a country. It is of interest that substantial effort has been devoted in recent years to the study of China’s provincial integration (for recent reviews, see Young, 2000; Naughton, 1999). The extent of economic integration across Chinese provinces was often reflected by a simple correlation measure (World Bank, 1994), which sometimes can be very misleading since a lot of factors have not been controlled for in the correlation. The degree of integration was also implied inter alia by the magnitude of inter-regional trade (Naughton, 1999), as indicated by the ratio of provincial outflows and inflows to provincial GDP. An increasing trend of the indicator, as interpreted by Naughton (1999), signals that regional markets are growing more integrated. However, this trade flow to GDP ratio is well-known to be a problematic measure of the degree of market integration as a larger volume of trade between two regions may be the result of economies of scale even though trade barriers remain intact along the border (Schmitt and Yu, 2001). * Corresponding author. Tel.: 1852-2766-7139; fax: 1852-2765-0611. E-mail address: [email protected] (X. Xu). 0165-1765 / 02 / $ – see front matter 2002 Elsevier Science B.V. All rights reserved. doi:10.1016/S0165-1765(02)00285-9
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The evolving trend of the similarity of provincial production structure has also been used to gauge the degree of market integration (Young, 2000). However, as pointed out by Naughton (1999), there is the lack of a theoretical yardstick with which to evaluate changes. The ongoing rapid industrialization experienced by Chinese provinces may by itself lead to a kind of measured ‘convergence’, but hardly one that should be considered ominous. Moreover, changes in production structure in China during the reform era sometimes reflect movement away from the inappropriate patterns of regional specialization imposed under the planned economy. Reversal of inefficient patterns of specialization may be efficiency-enhancing, but it looks like convergence. In contrast to the previous economic frameworks, this paper uses a statistical model for measuring economic integration across regions or provinces. We illustrate that a modified version of the statistical approach used by Costello (1993) and Stockman (1988) can be used to gauge the degree of market integration.1 Several advantages may be alluded to the model. Firstly, using this model, integration is defined as the co-movements of price variables across provinces. Co-movement is synonymous with the notion of integration because a price variable tends to move together (or in the same direction) across provinces if province-specific effects are of minor importance. Regions are defined as being integrated if province-specific factors (for example, protectionism in a particular region) are less important than sector-specific factors (such as a common technology shock or demand shock that affect a particular sector in all provinces) in accounting for the economic fluctuations. A rationale for this is that if province-specific effects were important, provinces would appear to be ‘separated’ and co-movements of economic variables across provinces could not be facilitated. Secondly, unlike a simple correlation technique, a myriad of other factors affecting the co-movement across provinces can be controlled for in the model.2 Provinces cannot be defined as being integrated if a rise in price for one province is due to a technology shock and that a rise in price for another to the introduction of a preferential policy. Only when price variation attributable to the same factor is synchronized after controlling for other factors can provinces be viewed as being integrated. Studies of regional integration provide implications for efficient resource allocation and regional economic development, as well as guidance for regional policy decisions.3 Previous studies, however, have not reached a consensus on the degree of economic integration across Chinese provinces. For example, Young (2000) argues that China has devolved into a fragmented domestic market controlled by local officials, and that the markets are segmented by local protectionism in spite of the improvement in logistics infrastructure. Naughton (1999) seems predisposed to the view that Chinese provinces are reasonably integrated: specifically, significant inter-industry and intra-industry trades
1 The statistical model has not explicitly been used for measuring integration across regions and industries. Stockman (1988) used this approach to examine the sources of fluctuations in the growth rate of manufacturing production across eight developed countries. Costello (1993) used the model to determine what fraction of the variation in productivity growth can be attributed to industry-specific shocks and what fraction can be attributed to country-specific shocks. 2 There are a large number of factors that account for the variations in price. Using the statistical model, all the variables affecting price variations can be grouped into four major categories: common national effects, sector-specific effects, province-specific effects and the interaction effects between these three factors. This reflects the generality of the model. 3 The implication of a less-integrated economy, from resource allocation’s perspective, is that a series of static and dynamic gains accruable from production according to comparative advantage, economies-of-scale, diffusion of technical knowledge and increasing competition, are not exploited. The degree of regional integration also has important macroeconomic implications, especially for monetary policy.
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across provinces were reported.4 Using our definition of integration, this paper shows that Chinese provinces were reasonably integrated, both in the long run and in the short run, with province-specific factors accounting for less than one fifth of the variance of price variables.
2. The model The model originally specified by Stockman (1988) and Costello (1993) is: y(i, j, t) 5 g(i, j) 1 f(i, t) 1 m( j, t) 1 u(i, j, t) i 5 1 . . . I; j 5 1 . . . J; t 5 1 . . . T
(1)
where y(i, j, t) represents the rate of price changes of sector i in province j at time t; g(i, j) denotes factors specific to sector i in province j; 5 f(i, t) is a vector specific to industry i and to time t but common to all provinces (the interaction between a fixed industry and a time effect); 6 m( j, t) is a vector specific to province j at each time period but is common across sectors within each province (the interaction between a fixed province and a time effect); 7 and u(i, j, t) is an idiosyncratic disturbance to industry i in province j at time t, assumed to be an independently and identically distributed, normal random variable with zero mean. To allow us to gauge both short-run and long-run integration across provinces, Eq. (1) is rewritten as: y(i, j, t) 5 h(i) 1 k( j) 1 g(i, j) 1 b(t) 1 f(i, t) 1 m( j, t) 1 u(i, j, t).
(2)
Three additional terms are specified: h(i) is a time-invariant vector specific to sector i but common to all provinces; 8 k( j) is a vector specific to province j but common to all sectors and time,9 and b(t) is a 10 pure time effect. If provinces were integrated with one another, one would expect a sector-specific shock common to all provinces to play a dominant role in explaining price growth. On the contrary, province-specific factors are more important in explaining the variations of price variables than sector-specific shock if provinces are less integrated to each other. Eq. (2) shows that, in the long run,
4 Some recent studies have pointed to the evidence of integration across Chinese provinces (e.g. Tang, 1994) while others (e.g. Mody and Fang, 1997; World Bank, 1994) have argued otherwise. 5 An example of an exogenous shock specific to both sectors i and province j is: a union strike in Guangdong’s chemical industry. 6 More precisely, f(i, t) represents sector-specific effects which cause temporary deviations from the price variation pattern in sector i (in all provinces) during the year t. It says for instance how much in 1994 the price variation in the agricultural sector was above its trend. 7 This term represents provincial transitory deviations of price changes with respect to the national business cycle. 8 An example is a demand shock that affects a manufacturing industry in all provinces. h(i) denotes the sectoral trends of price growth. It is the unweighted mean over the yearly average price growth in sector i. 9 An example is a preferential tax treatment applicable to Guangdong province only. This policy affects all the different industries in Guangdong. Note that the k( j) term has not been specified in previous models. However, it is crucial to our analysis of provincial integration. 10 b(t) represents national policy or national business cycle effects that affect price growth across all sectors and province at a particular year t. The national business cycle effects are assumed to be zero on average over time.
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provincial integration is reflected by the importance of the h(i) term relative to the k( j) and g(i, j) terms. Short-run integration, on the other hand, depends on the extent to which f(i, t) plus b(t) components 11 are bigger than the m( j, t) term. The model may be estimated using a dummy variable method. However, direct estimation of Eq. (2) is impossible because some combinations of the dummy variables are perfectly collinear. Two approaches have been used to deal with the problem. One is to choose a sector or a province as well as a time period as reference point so that a combination of the parameters can be identified through the normalization procedure (see Stockman, 1988; Costello, 1993). This approach has two major setbacks. Firstly, the results arising from the variance decomposition are reference-point dependent. Secondly, it is difficult to disentangle the co-variation of provincial and sectoral effects since they are correlated: one can only account for the orthogonal components of each effect. An alternative approach, developed by Marimon and Zilibotti (1998), is to take as a reference point not a particular province, sector or year but their respective sample means. This restriction assumes all components to be orthogonal and therefore overcomes the limitations of the first approach.12 The alternative approach is used in this study. However, the restrictions of Marimon and Zilibotti (1998) are modified in order to reflect our model specification. The restrictions are now expressed as:
O g(i, j) 5 0, i 5 1, . . . ,I; O g(i, j) 5 0, j 5 1, . . . ,J; O f(i, t) 5 0, t 5 1, . . . ,T; O f(i, t) 5 0, i 5 1, . . . ,I; O m( j, t) 5 0, t 5 1, . . . ,T O m( j, t) 5 0, j 5 1, . . . ,J; O h(i) 5 0 O k( j) 5 0; O b(t) 5 0. (3) N
I
j51
i51
i51
N
T
I
j51
t51
i 51
T
t51 J
T
j51
t 51
I
The dummy-variable regression with the above restrictions (Eq. (3)) now gives exact identification to Eq. (2). With this set of restrictions, the estimated vectors must be interpreted as deviations from sample means. For example, b(t) was previously used to capture the short run business cycle effects, but since o Tt51 b(t) 5 0, the vector now has to be interpreted as common temporary deviations from long-run trends.
3. The data and statistical results The disaggregate retail price data across 28 Chinese provinces were drawn from two sources: (1) the data over the 1985–1993 period from The Collection of Price and Other Macroeconomic 11
The b(t) term can be interpreted as a non-province-specific factor, which canvasses, inter alia, co-movement of price variation across provinces. 12 As pointed out by Marimon and Zilibotti (1998), in the absence of a theoretical model which explains a correlation structure, the assumption that all effects are orthogonal may be viewed as a natural benchmark, as is the case with a standard identifying assumption in structural VAR models. Marimon and Zilibotti (1998) showed that this corresponds to taking the respective sample means instead of a country, industry or year, as a reference point.
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Indicators since the establishment of the P.R. China published by the China Price Information Center and (2) the data over 1994–1998 from China Statistical Yearbook (CSY 1995–1999) published by the National Bureau of Statistics (NBS). All these data were collected through the surveys conducted by the Urban and Rural Social-Economic Survey Division of the NBS. There was a significant change in the range of the commodity canvassed by the published price indexes over the two different time periods. Over the 1985–1993 period, the retail price index includes grain; side food; fresh vegetable; poultry; aquatic products; other food; fresh fruit; clothing; articles for daily use; stationary and recreational goods; newspapers and magazines; medicines; and construction materials (13 sectors). The retail price index released by NBS over the period 1994–1998 represents a more detailed coverage, ranging from grain; oil and fat; meat, poultry and eggs; aquatic products; vegetables; fresh fruits; dried fruits; beverages, tobacco and liquor; clothing shoes and hats; textiles; traditional Chinese and western medicines; cosmetics; newspapers and magazines; sporting goods; articles for daily use; household appliances; jewelry; fuels; building material; mechanical and electrical products; telecommunication; postage; transportation; bathing and haircuts; recreation; tuition and child care; repair and other services; to medical and health care (28 sectors). To gauge the degree of market integration and how it evolves over time, we calculate the price variations for China using the data for the two time periods, 1985–1993 and 1994–1998. Our panel-data regressions on prices were first performed using the 13 sectors’ disaggregate price indexes in 28 provinces for the period 1985–1993.13 In addition, we run regressions on our disaggregate price data over two sub-periods: 1985–1989 and 1990–1993. The first sub-period witnessed the implementation of the dual-track price system reform with a significant portion of the commodity prices still under state control while in the second period, up to 75% of the prices of the retailed goods were determined by the markets. The period after 1994 was subject to a more liberal price reform. Before interpreting the results, it is important to point out that price integration in China could emerge from either the Central Government price control (hence resulting in price co-movement across provinces) or the free-market forces (which also gives rise to price co-movement). Government price control and free market forces, both being captured by the sector-specific component of the model,14 contribute significantly to a price-integrated market. Therefore, it would be more meaningful for us to compare the results for the period 1994–1998 with that for 1990–1993, since these two periods are least subject to the government price control. That is, by comparing these two periods, we have controlled to some extent for the price co-movement arising from the government price regulation. Our objective in this paper is to examine the degree of price co-movement due to the free market forces only, which is the correct definition of price integration. Table 1 shows that the model performs reasonably well for all the regressions, with the R-square consistently higher than 0.85 and highly significant F statistics (all at 0.0001 level), and there is no significant evidence of serial correlation of the residuals. All the regressions over the different periods 13 An anonymous referee has pointed out to us the importance of an intertemporal comparison of our empirical results. The data over the period 1985–1993 were omitted from our previous analysis. By including these data points, we have to drop three provinces (namely, Tibet, Hainan and Chongqing) from our regression due to the paucity of the data. Hainan was included, however, for the period 1994–1998. 14 Government price control may not be province-specific because the price for a particular sector could be controlled by the Central Government (rather than by the Provincial Government). This is in contrast to a province-specific effect exemplified by the different policies or trade barriers imposed by the different State Governments.
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Table 1 Analysis of variation of intertemporal disaggregated price data: short run versus long run 1985–1993
1985–1989
1990–1993
1994–1998
Long run Sector-specific effects Province-specific effects
100 79 21
100 81 19
100 75 25
100 85 15
Short run Sector-specific effects Province-specific effects
100 94 6
100 95 5
100 85 15
100 98 2
Total sum of squares Model sum of squares R-square F-statistics a Marginal significance level Total no. of observations a
41.45 35.21 0.85 21.54 0.0001 3276
24.95 21.87 0.88 17.78 0.0001 1820
13.18 11.31 0.86 12.30 0.0001 1456
68.00 59.00 0.87 18.90 0.0001 4205
Prob.F 50.0001. Dependent variable is ln of the price level. Source: authors’ calculations.
showed that, in the short run,15 only between 2 and 15% of the price variations were explained by province-specific factors whilst the remaining variations by sector-specific factors that are specific to the particular sector but common across all provinces. In the long run, province-specific factors account for 15 to 25% of the price variations whilst sector-specific factors account for the bulk of the variations. Our results in the short run are consistent with, or reinforce, those in the long run: in all cases the price variations are dominated by sector-specific effects. These imply that Chinese provinces were relatively integrated. However, the provincial integration (price co-movement) could be the outcome of the rampant price control by the Central Government over the period prior to the 1990s. The fact that price co-movement in China could have been manipulated by the Central Government was shown by the large sector-specific effects (up to 95%) over 1985–1993 when commodity price control was imposed across provinces. In this case, the Central Government price intervention became a sector-specific effect since prices for a sector were controlled quite evenly across all Chinese provinces during the earlier period. Hence, the results showing the large sector-specific effects (e.g. column 2 of Table 1) may not reasonably be used to measure provincial market integration in China. To net out the synchronized price control effect, we compare the results for the period 1990–1993 to those for 1994–1998. We found that China was indeed quite integrated considering the results reported for the periods 1990–1993 as well as 1994–1998. It is of interest that the level of provincial ‘free market’ integration increased substantially over time. Province-specific effects account for 15% in 1990–1993 but merely 2% in 1994–1998. The progressive removal of trade distortions as well as price regulations by the Central Government since the early 1990s coupled with the usually high price 15
The short-run analysis is characterized by three components, namely, sector-specific effects, province-specific effects, and national business cycle effects. Since the national business cycle effects cannot reasonably be used to gauge the level of price integration, we therefore provide a normalization of the results by ensuring that the sector-specific effects and province-specific effects add up to 100%.
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transmission effects (as compared to output growth) would have contributed to the low level of segmentation.16 From 1985–1989 to 1990–1993, we found that China has become less integrated despite the dwindling Central Government price control effect over time. This could be explained by the fact that price co-movement was indeed overshadowed by the price control synchronized by the Government across provinces during the earlier period. The effect that had given rise to provincial segmentation due to the removal of the price synchronization was shown to be larger than the effect from the removal of provincial policy distortions or barriers that had given rise to provincial integration over the same period. Hence, as the Government price control / synchronization was removed over time, the market became more segmented despite the gradual abolitions of the other potential forms of provincial barriers or distortions. It may be of interest herein to cite the levels of price integration in the US and Europe in order to check the informativeness of the statistics reported in this paper. Wynne and Koo (2000)17 recently showed, using the correlation between the cyclical component of prices, that both the districts in the US and the countries in EU are relatively integrated. Their paper reinforces the previous results that have pointed to a higher degree of market integration both in the US and in EU (see for example Rogers et al., 2001; Christodoulakis et al., 1995; Hess and Shin, 1998). This paper used a different measure to gauge the degree of integration for China. Hence, it is not possible to deduce if China is more or less integrated than the US and EU. We have merely shown that Chinese provinces are relatively integrated. This is in contrast to the previous popular beliefs.
4. Summary and conclusions Regional economic integration has significant implications not only for the efficient resource allocation and regional economic development, but also for national policy formulations. In this study, we have investigated this important issue using a statistical model that decomposes sectoral price variation in each province into national effects, sector-specific effects and province-specific effects. We found significant co-movements across provinces, both in the short and long run. To control for the ubiquitous state price interventions in China that could give rise to price co-movement and hence a wrong perception of market integration, we have reported the results for the recent years when the price controls in different sectors across provinces have been significantly removed. The results showed that China is relatively integrated across provinces, and the level of integration has increased over time. The conclusions arising from the analyses are robust, regardless of the time scale (short or long run), period under investigation (1990–1993 or 1994–1998) and the degree of data aggregation (aggregate versus disaggregate data). In this paper, an expanded version of the error components model has been developed for the purpose of gauging the degree of integration across provinces. The 16
We have run a separate regression for the period 1994–1998 using the aggregate price data (as opposed to the disaggregate data) in order to test the robustness of our findings. The results (available on request) were similar in magnitude to those using the disaggregate data. 17 The authors examined the business cycle similarities and differences among the 12 Federal Reserve districts in the US and the 15 countries that make up the EU.
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model, together with the necessary modifications of the restriction requirement, has all the explanatory terms crucial to the assessment of the level of provincial integration. This is the major innovation of the paper.
Acknowledgements We are grateful to an anonymous referee for helpful comments. Xinpeng Xu thanks the Hong Kong Polytechnic University for financial support through the University Research Grants (ICRG project no. A-PD22).
References China Price Information Center, 1996. The Collection of Price and Other Macroeconomic Indicators for the Period Since the Establishment of the P.R. China. China Price Information Center, Beijing, China. Christodoulakis, N., Dimelis, S.P., Kollintzas, T., 1995. Comparisons of business cycles in the EC: idiosyncrasies and regularities. Economica 62, 1–27. Costello, D.M., 1993. A cross-country, cross-industry comparison of productivity growth. Journal of Political Economy 101 (2), 207–222. Hess, G.D., Shin, K., 1998. Intra-national business cycles in the United States. Journal of International Economics 44, 289–313. Krugman, P., 1991. Geography and Trade. MIT Press, Cambridge, MA. Marimon, R., Zilibotti, F., 1998. Actual versus virtual employment in Europe: is Spain different? European Economic Review 42, 123–153. Mody, A., Fang, Y.W., 1997. Explaining industrial growth in coastal China: economic reforms and what else? World Bank Economic Review 11 (2), 293–325. Naughton, B., 1999. How much can regional integration do to unify China’s markets? In: Paper for Conference on Policy Reform in China. Center for Research on Economic Development and Policy Research, Stanford University, November 18–20. Rogers, J.H., Hufbauer, G.C., Wada, E., 2001. Price Convergence and Inflation in Europe. Institute for International Economics, Washington, DC, Working paper. Schmitt, N., Yu, Z., 2001. Economies of scale and the volume of intra-industry trade. Economics Letters 74, 127–132. Stockman, A.C., 1988. Sectoral and national aggregate disturbances to industrial output in seven European countries. Journal of Monetary Economics 21, 387–409. Tang, K.K., 1994. Economic integration of the Chinese provinces: a business cycle approach. Journal of Economic Integration 13 (4), 549–570. World Bank, 1994. China: Internal Market Development and Regulation. World Bank, Washington, DC. Wynne, M., Koo, J., 2000. Business cycles under monetary union: a comparison of the EU and US. Economica 67, 347–374. Young, A., 2000. The razor’s edge: distortions and incremental reform in the People’s Republic of China. The Quarterly Journal of Economics CXV (4), November.
Regional integration in China: a statistical model Xinpeng Xu a , *, J.P. Voon b a
Department of Business Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong b Lingnan University, Kowloon, Hong Kong Received 9 January 2002; received in revised form 15 July 2002; accepted 17 September 2002
Abstract We illustrate that a statistical model can be used to gauge the degree of market integration. The model is applied to the case of China, which is undergoing transition from a central planning to a market economy and there is a growing interest as to the evolution of its market integration. 2002 Elsevier Science B.V. All rights reserved. Keywords: Economic growth; Regional integration; China; Error components model JEL classification: F15; O53
1. Introduction Many economic models have been used to measure the degree of integration across countries and industries (for example, the gravity model of trade as exemplified by Krugman, 1991). Models have also been developed for measuring integration across provinces within a country. It is of interest that substantial effort has been devoted in recent years to the study of China’s provincial integration (for recent reviews, see Young, 2000; Naughton, 1999). The extent of economic integration across Chinese provinces was often reflected by a simple correlation measure (World Bank, 1994), which sometimes can be very misleading since a lot of factors have not been controlled for in the correlation. The degree of integration was also implied inter alia by the magnitude of inter-regional trade (Naughton, 1999), as indicated by the ratio of provincial outflows and inflows to provincial GDP. An increasing trend of the indicator, as interpreted by Naughton (1999), signals that regional markets are growing more integrated. However, this trade flow to GDP ratio is well-known to be a problematic measure of the degree of market integration as a larger volume of trade between two regions may be the result of economies of scale even though trade barriers remain intact along the border (Schmitt and Yu, 2001). * Corresponding author. Tel.: 1852-2766-7139; fax: 1852-2765-0611. E-mail address: [email protected] (X. Xu). 0165-1765 / 02 / $ – see front matter 2002 Elsevier Science B.V. All rights reserved. doi:10.1016/S0165-1765(02)00285-9
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The evolving trend of the similarity of provincial production structure has also been used to gauge the degree of market integration (Young, 2000). However, as pointed out by Naughton (1999), there is the lack of a theoretical yardstick with which to evaluate changes. The ongoing rapid industrialization experienced by Chinese provinces may by itself lead to a kind of measured ‘convergence’, but hardly one that should be considered ominous. Moreover, changes in production structure in China during the reform era sometimes reflect movement away from the inappropriate patterns of regional specialization imposed under the planned economy. Reversal of inefficient patterns of specialization may be efficiency-enhancing, but it looks like convergence. In contrast to the previous economic frameworks, this paper uses a statistical model for measuring economic integration across regions or provinces. We illustrate that a modified version of the statistical approach used by Costello (1993) and Stockman (1988) can be used to gauge the degree of market integration.1 Several advantages may be alluded to the model. Firstly, using this model, integration is defined as the co-movements of price variables across provinces. Co-movement is synonymous with the notion of integration because a price variable tends to move together (or in the same direction) across provinces if province-specific effects are of minor importance. Regions are defined as being integrated if province-specific factors (for example, protectionism in a particular region) are less important than sector-specific factors (such as a common technology shock or demand shock that affect a particular sector in all provinces) in accounting for the economic fluctuations. A rationale for this is that if province-specific effects were important, provinces would appear to be ‘separated’ and co-movements of economic variables across provinces could not be facilitated. Secondly, unlike a simple correlation technique, a myriad of other factors affecting the co-movement across provinces can be controlled for in the model.2 Provinces cannot be defined as being integrated if a rise in price for one province is due to a technology shock and that a rise in price for another to the introduction of a preferential policy. Only when price variation attributable to the same factor is synchronized after controlling for other factors can provinces be viewed as being integrated. Studies of regional integration provide implications for efficient resource allocation and regional economic development, as well as guidance for regional policy decisions.3 Previous studies, however, have not reached a consensus on the degree of economic integration across Chinese provinces. For example, Young (2000) argues that China has devolved into a fragmented domestic market controlled by local officials, and that the markets are segmented by local protectionism in spite of the improvement in logistics infrastructure. Naughton (1999) seems predisposed to the view that Chinese provinces are reasonably integrated: specifically, significant inter-industry and intra-industry trades
1 The statistical model has not explicitly been used for measuring integration across regions and industries. Stockman (1988) used this approach to examine the sources of fluctuations in the growth rate of manufacturing production across eight developed countries. Costello (1993) used the model to determine what fraction of the variation in productivity growth can be attributed to industry-specific shocks and what fraction can be attributed to country-specific shocks. 2 There are a large number of factors that account for the variations in price. Using the statistical model, all the variables affecting price variations can be grouped into four major categories: common national effects, sector-specific effects, province-specific effects and the interaction effects between these three factors. This reflects the generality of the model. 3 The implication of a less-integrated economy, from resource allocation’s perspective, is that a series of static and dynamic gains accruable from production according to comparative advantage, economies-of-scale, diffusion of technical knowledge and increasing competition, are not exploited. The degree of regional integration also has important macroeconomic implications, especially for monetary policy.
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across provinces were reported.4 Using our definition of integration, this paper shows that Chinese provinces were reasonably integrated, both in the long run and in the short run, with province-specific factors accounting for less than one fifth of the variance of price variables.
2. The model The model originally specified by Stockman (1988) and Costello (1993) is: y(i, j, t) 5 g(i, j) 1 f(i, t) 1 m( j, t) 1 u(i, j, t) i 5 1 . . . I; j 5 1 . . . J; t 5 1 . . . T
(1)
where y(i, j, t) represents the rate of price changes of sector i in province j at time t; g(i, j) denotes factors specific to sector i in province j; 5 f(i, t) is a vector specific to industry i and to time t but common to all provinces (the interaction between a fixed industry and a time effect); 6 m( j, t) is a vector specific to province j at each time period but is common across sectors within each province (the interaction between a fixed province and a time effect); 7 and u(i, j, t) is an idiosyncratic disturbance to industry i in province j at time t, assumed to be an independently and identically distributed, normal random variable with zero mean. To allow us to gauge both short-run and long-run integration across provinces, Eq. (1) is rewritten as: y(i, j, t) 5 h(i) 1 k( j) 1 g(i, j) 1 b(t) 1 f(i, t) 1 m( j, t) 1 u(i, j, t).
(2)
Three additional terms are specified: h(i) is a time-invariant vector specific to sector i but common to all provinces; 8 k( j) is a vector specific to province j but common to all sectors and time,9 and b(t) is a 10 pure time effect. If provinces were integrated with one another, one would expect a sector-specific shock common to all provinces to play a dominant role in explaining price growth. On the contrary, province-specific factors are more important in explaining the variations of price variables than sector-specific shock if provinces are less integrated to each other. Eq. (2) shows that, in the long run,
4 Some recent studies have pointed to the evidence of integration across Chinese provinces (e.g. Tang, 1994) while others (e.g. Mody and Fang, 1997; World Bank, 1994) have argued otherwise. 5 An example of an exogenous shock specific to both sectors i and province j is: a union strike in Guangdong’s chemical industry. 6 More precisely, f(i, t) represents sector-specific effects which cause temporary deviations from the price variation pattern in sector i (in all provinces) during the year t. It says for instance how much in 1994 the price variation in the agricultural sector was above its trend. 7 This term represents provincial transitory deviations of price changes with respect to the national business cycle. 8 An example is a demand shock that affects a manufacturing industry in all provinces. h(i) denotes the sectoral trends of price growth. It is the unweighted mean over the yearly average price growth in sector i. 9 An example is a preferential tax treatment applicable to Guangdong province only. This policy affects all the different industries in Guangdong. Note that the k( j) term has not been specified in previous models. However, it is crucial to our analysis of provincial integration. 10 b(t) represents national policy or national business cycle effects that affect price growth across all sectors and province at a particular year t. The national business cycle effects are assumed to be zero on average over time.
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provincial integration is reflected by the importance of the h(i) term relative to the k( j) and g(i, j) terms. Short-run integration, on the other hand, depends on the extent to which f(i, t) plus b(t) components 11 are bigger than the m( j, t) term. The model may be estimated using a dummy variable method. However, direct estimation of Eq. (2) is impossible because some combinations of the dummy variables are perfectly collinear. Two approaches have been used to deal with the problem. One is to choose a sector or a province as well as a time period as reference point so that a combination of the parameters can be identified through the normalization procedure (see Stockman, 1988; Costello, 1993). This approach has two major setbacks. Firstly, the results arising from the variance decomposition are reference-point dependent. Secondly, it is difficult to disentangle the co-variation of provincial and sectoral effects since they are correlated: one can only account for the orthogonal components of each effect. An alternative approach, developed by Marimon and Zilibotti (1998), is to take as a reference point not a particular province, sector or year but their respective sample means. This restriction assumes all components to be orthogonal and therefore overcomes the limitations of the first approach.12 The alternative approach is used in this study. However, the restrictions of Marimon and Zilibotti (1998) are modified in order to reflect our model specification. The restrictions are now expressed as:
O g(i, j) 5 0, i 5 1, . . . ,I; O g(i, j) 5 0, j 5 1, . . . ,J; O f(i, t) 5 0, t 5 1, . . . ,T; O f(i, t) 5 0, i 5 1, . . . ,I; O m( j, t) 5 0, t 5 1, . . . ,T O m( j, t) 5 0, j 5 1, . . . ,J; O h(i) 5 0 O k( j) 5 0; O b(t) 5 0. (3) N
I
j51
i51
i51
N
T
I
j51
t51
i 51
T
t51 J
T
j51
t 51
I
The dummy-variable regression with the above restrictions (Eq. (3)) now gives exact identification to Eq. (2). With this set of restrictions, the estimated vectors must be interpreted as deviations from sample means. For example, b(t) was previously used to capture the short run business cycle effects, but since o Tt51 b(t) 5 0, the vector now has to be interpreted as common temporary deviations from long-run trends.
3. The data and statistical results The disaggregate retail price data across 28 Chinese provinces were drawn from two sources: (1) the data over the 1985–1993 period from The Collection of Price and Other Macroeconomic 11
The b(t) term can be interpreted as a non-province-specific factor, which canvasses, inter alia, co-movement of price variation across provinces. 12 As pointed out by Marimon and Zilibotti (1998), in the absence of a theoretical model which explains a correlation structure, the assumption that all effects are orthogonal may be viewed as a natural benchmark, as is the case with a standard identifying assumption in structural VAR models. Marimon and Zilibotti (1998) showed that this corresponds to taking the respective sample means instead of a country, industry or year, as a reference point.
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Indicators since the establishment of the P.R. China published by the China Price Information Center and (2) the data over 1994–1998 from China Statistical Yearbook (CSY 1995–1999) published by the National Bureau of Statistics (NBS). All these data were collected through the surveys conducted by the Urban and Rural Social-Economic Survey Division of the NBS. There was a significant change in the range of the commodity canvassed by the published price indexes over the two different time periods. Over the 1985–1993 period, the retail price index includes grain; side food; fresh vegetable; poultry; aquatic products; other food; fresh fruit; clothing; articles for daily use; stationary and recreational goods; newspapers and magazines; medicines; and construction materials (13 sectors). The retail price index released by NBS over the period 1994–1998 represents a more detailed coverage, ranging from grain; oil and fat; meat, poultry and eggs; aquatic products; vegetables; fresh fruits; dried fruits; beverages, tobacco and liquor; clothing shoes and hats; textiles; traditional Chinese and western medicines; cosmetics; newspapers and magazines; sporting goods; articles for daily use; household appliances; jewelry; fuels; building material; mechanical and electrical products; telecommunication; postage; transportation; bathing and haircuts; recreation; tuition and child care; repair and other services; to medical and health care (28 sectors). To gauge the degree of market integration and how it evolves over time, we calculate the price variations for China using the data for the two time periods, 1985–1993 and 1994–1998. Our panel-data regressions on prices were first performed using the 13 sectors’ disaggregate price indexes in 28 provinces for the period 1985–1993.13 In addition, we run regressions on our disaggregate price data over two sub-periods: 1985–1989 and 1990–1993. The first sub-period witnessed the implementation of the dual-track price system reform with a significant portion of the commodity prices still under state control while in the second period, up to 75% of the prices of the retailed goods were determined by the markets. The period after 1994 was subject to a more liberal price reform. Before interpreting the results, it is important to point out that price integration in China could emerge from either the Central Government price control (hence resulting in price co-movement across provinces) or the free-market forces (which also gives rise to price co-movement). Government price control and free market forces, both being captured by the sector-specific component of the model,14 contribute significantly to a price-integrated market. Therefore, it would be more meaningful for us to compare the results for the period 1994–1998 with that for 1990–1993, since these two periods are least subject to the government price control. That is, by comparing these two periods, we have controlled to some extent for the price co-movement arising from the government price regulation. Our objective in this paper is to examine the degree of price co-movement due to the free market forces only, which is the correct definition of price integration. Table 1 shows that the model performs reasonably well for all the regressions, with the R-square consistently higher than 0.85 and highly significant F statistics (all at 0.0001 level), and there is no significant evidence of serial correlation of the residuals. All the regressions over the different periods 13 An anonymous referee has pointed out to us the importance of an intertemporal comparison of our empirical results. The data over the period 1985–1993 were omitted from our previous analysis. By including these data points, we have to drop three provinces (namely, Tibet, Hainan and Chongqing) from our regression due to the paucity of the data. Hainan was included, however, for the period 1994–1998. 14 Government price control may not be province-specific because the price for a particular sector could be controlled by the Central Government (rather than by the Provincial Government). This is in contrast to a province-specific effect exemplified by the different policies or trade barriers imposed by the different State Governments.
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Table 1 Analysis of variation of intertemporal disaggregated price data: short run versus long run 1985–1993
1985–1989
1990–1993
1994–1998
Long run Sector-specific effects Province-specific effects
100 79 21
100 81 19
100 75 25
100 85 15
Short run Sector-specific effects Province-specific effects
100 94 6
100 95 5
100 85 15
100 98 2
Total sum of squares Model sum of squares R-square F-statistics a Marginal significance level Total no. of observations a
41.45 35.21 0.85 21.54 0.0001 3276
24.95 21.87 0.88 17.78 0.0001 1820
13.18 11.31 0.86 12.30 0.0001 1456
68.00 59.00 0.87 18.90 0.0001 4205
Prob.F 50.0001. Dependent variable is ln of the price level. Source: authors’ calculations.
showed that, in the short run,15 only between 2 and 15% of the price variations were explained by province-specific factors whilst the remaining variations by sector-specific factors that are specific to the particular sector but common across all provinces. In the long run, province-specific factors account for 15 to 25% of the price variations whilst sector-specific factors account for the bulk of the variations. Our results in the short run are consistent with, or reinforce, those in the long run: in all cases the price variations are dominated by sector-specific effects. These imply that Chinese provinces were relatively integrated. However, the provincial integration (price co-movement) could be the outcome of the rampant price control by the Central Government over the period prior to the 1990s. The fact that price co-movement in China could have been manipulated by the Central Government was shown by the large sector-specific effects (up to 95%) over 1985–1993 when commodity price control was imposed across provinces. In this case, the Central Government price intervention became a sector-specific effect since prices for a sector were controlled quite evenly across all Chinese provinces during the earlier period. Hence, the results showing the large sector-specific effects (e.g. column 2 of Table 1) may not reasonably be used to measure provincial market integration in China. To net out the synchronized price control effect, we compare the results for the period 1990–1993 to those for 1994–1998. We found that China was indeed quite integrated considering the results reported for the periods 1990–1993 as well as 1994–1998. It is of interest that the level of provincial ‘free market’ integration increased substantially over time. Province-specific effects account for 15% in 1990–1993 but merely 2% in 1994–1998. The progressive removal of trade distortions as well as price regulations by the Central Government since the early 1990s coupled with the usually high price 15
The short-run analysis is characterized by three components, namely, sector-specific effects, province-specific effects, and national business cycle effects. Since the national business cycle effects cannot reasonably be used to gauge the level of price integration, we therefore provide a normalization of the results by ensuring that the sector-specific effects and province-specific effects add up to 100%.
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transmission effects (as compared to output growth) would have contributed to the low level of segmentation.16 From 1985–1989 to 1990–1993, we found that China has become less integrated despite the dwindling Central Government price control effect over time. This could be explained by the fact that price co-movement was indeed overshadowed by the price control synchronized by the Government across provinces during the earlier period. The effect that had given rise to provincial segmentation due to the removal of the price synchronization was shown to be larger than the effect from the removal of provincial policy distortions or barriers that had given rise to provincial integration over the same period. Hence, as the Government price control / synchronization was removed over time, the market became more segmented despite the gradual abolitions of the other potential forms of provincial barriers or distortions. It may be of interest herein to cite the levels of price integration in the US and Europe in order to check the informativeness of the statistics reported in this paper. Wynne and Koo (2000)17 recently showed, using the correlation between the cyclical component of prices, that both the districts in the US and the countries in EU are relatively integrated. Their paper reinforces the previous results that have pointed to a higher degree of market integration both in the US and in EU (see for example Rogers et al., 2001; Christodoulakis et al., 1995; Hess and Shin, 1998). This paper used a different measure to gauge the degree of integration for China. Hence, it is not possible to deduce if China is more or less integrated than the US and EU. We have merely shown that Chinese provinces are relatively integrated. This is in contrast to the previous popular beliefs.
4. Summary and conclusions Regional economic integration has significant implications not only for the efficient resource allocation and regional economic development, but also for national policy formulations. In this study, we have investigated this important issue using a statistical model that decomposes sectoral price variation in each province into national effects, sector-specific effects and province-specific effects. We found significant co-movements across provinces, both in the short and long run. To control for the ubiquitous state price interventions in China that could give rise to price co-movement and hence a wrong perception of market integration, we have reported the results for the recent years when the price controls in different sectors across provinces have been significantly removed. The results showed that China is relatively integrated across provinces, and the level of integration has increased over time. The conclusions arising from the analyses are robust, regardless of the time scale (short or long run), period under investigation (1990–1993 or 1994–1998) and the degree of data aggregation (aggregate versus disaggregate data). In this paper, an expanded version of the error components model has been developed for the purpose of gauging the degree of integration across provinces. The 16
We have run a separate regression for the period 1994–1998 using the aggregate price data (as opposed to the disaggregate data) in order to test the robustness of our findings. The results (available on request) were similar in magnitude to those using the disaggregate data. 17 The authors examined the business cycle similarities and differences among the 12 Federal Reserve districts in the US and the 15 countries that make up the EU.
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model, together with the necessary modifications of the restriction requirement, has all the explanatory terms crucial to the assessment of the level of provincial integration. This is the major innovation of the paper.
Acknowledgements We are grateful to an anonymous referee for helpful comments. Xinpeng Xu thanks the Hong Kong Polytechnic University for financial support through the University Research Grants (ICRG project no. A-PD22).
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