Department of Economics,

University of Portsmouth, UK Aying Liu and Shuffle Yao

I.Introduction

One of the most striking features in China´s economic reform is an increasitig polarisation between the prosperous eastern coastal provinces and the still backward western parts of the country. Regional income inequality has been widening considerably under economic reforms (Yao, I 997a). This has been accompanied by dramatic changes in employment and investment structures at both the national and regional levels.

First, there is an increasing surplus of labour released from agriculture due to increased labour productivity and shortage of land. Second, an increasing number of workers and staff employed by state-owned enterprises (SOEs) have become um- or underemployed due to market liberalization and fierce competition from the private sectors. Third, and most impressively, employment and investments have been shifted or diversified away from agriculture to manufacturing and services, and from heavy and defence-based industries to light industries (Zhao, 1996).

Structural changes in employment and investments have followed a clear regional pattern. The most prosperous provinces and metropolitan cities, especially Shanghai, Beijing, Tianjin, Guangdong and Jiangsu have succeeded in making their economies similar to those of the newly industrialized countries (NICs) in southeast Asia, with per capita gross domestic product (GDP) similar to that of a middle income country (S 1000-2000), a large and increasing proportion of the service sectors and a decreasing proportion of agriculture and manufacturing in the regional economies. On the other hand, the western provinces still resemble the low income developing economies, with per capita GDP less than $400 and a predominant role of agriculture in the local economies. The rest of the country has made a significant progress in the process of industrialization with rapid growth in both manufacturing and services, but economic development has been constrained by the old industrial structure and planning system which need to be fundamentally re-organized as economic reforms reach the stronghold of SOEs.

The diversity and complexity of the Chinese economy in its industrialization drive have important theoretical and empirical implications in economic development. From a theoretical point of view, one should not look at a large economy like China´s as a simple and homogenous nation. A new approach of research has to be adopted in order to fully understand the whole process of industrialization and economic development. This new approach is to examine the country via a multi-tier (different groups of regions) and multi-stage (different stages of industrialization) development perspective. First, one should not overlook the importance of some dynamic and powerful regions in a large country that is regarded as a poor economy as a whole from outside. Deng's development philosophy to set up four special economic zones (SEA) and to open up fourteen coastal cities in the early 1980s was precisely aiming at creating a few fresh growing centres in a country largely dogged by socialist doctrines and by a mono-structure of state ownership. Second, one needs to study whether rapid industrialization in these growing centres can eventually lead the rest of the country to change and develop in a similar way.

Several studies on multi-tier and multi-stage development in China can be found in the literature although theoretically such a strategy has not been formalized. Yao (1997a) studies the negative aspects of such a development strategy through analysing regional income inequality in rural China; Zhao (1996) focuses on a time-series comparison of regional disparities between the development policies of Mao and Deng. There are also other studies (Knight and Song, 1993; Griffin and Zhao, 1993; Hussain et al., 1994), but it appears that all of them examine disparity in terms of income distribution and none of them has studied the causes of regional inequality. This is the primary objective of this study. We examine the structural changes in employment and investments between industries at both the national and provincial levels by use of a shift-share method. It explains not only the structural change of the whole country by industry but also how comparable changes at provincial level have been driven by a national trend in general and a national trend by industry in particular. The residual change by industry for a particular province after excluding the national trend and national industrial trend will reflect the extent to which the province under concern outperforms or is left behind by other regions. By studying the structural changes for all the regions, we are able to identify the growing centres and to see whether the rest of the country are catching up.

In brief, China experienced substantial structural changes in the national economy. The relative importance of agriculture declined, whilst the non-agricultural sectors, especially the service industries sustained continuous and rapid growth. At the provincial level, three distinctive development models emerged; the growing-centre model, the catching-up model, and the backward model. The growing-centre model was exemplified by Guangdong (including Beijing, Shanghai, Jiangsu and other rich and fast growing regions). This model was characterized by the followingfeatures: high and rapidly growing per capita GDP which was above 51,000 in 1996, a small and declining agriculture's share and a large and growing services sector's share in the regional economy. massive foreign direct investments and active participation in international trade. On the other extreme, the backward model was exemplified by Gansu (Including most western parts of the country). This model was characterized by low per capita GDP (less than $400 in 1996), poor infrastructure, poor technologies and lack of skilled labour, lack of investments, and low participation in international trade. In between the growing centre and the western provinces was the catching-up model (including most provinces in central, northeaster and northern China) where the regional economies were dominated by SOEs and heavy industries but significant changes hadtaken place in a drive to diversify their economies and to break away from the former socialist doctrines and rigidity of central planning.

The rest of this paper is organised as follows. Section 11 gives a brief introduction of the shift-share method and the data used in the study. Section 111 presents the results of shift-share analyses on GDP growth, employment and investment changes for all provinces by three aggregate industrial sectors. The results lead to some important conclusions and policy implications in Section IV.

II.Shift-Share Analysis

Shift-share is a standardised and useful analytical tool for spatial analysis. since its introduction in 1960, it has been widely used for analyses of regional employment, structural change affecting different industries, industrial location, migration and economic growth. The method (together with its merits and limitations) is extensively discussed in Stillwell (1970), Fothergill and Gudgin (1979), Stevens and Moore (1980), Casler (1989), Armstrong and Taylor (1993), and Hoppes (1994). Some examples of its use in examing employment change, growth and productivity are found in Moore and Rhodes (1973); Randall (1973); Healey and Clark (1984); and Ishikawa (1992) among others. A recent discussion and application of the method can be found in liu et al (1996).

Over the years, the basic technique has been extended and refined. A number of alternative models have appeared in the literature, such as the Esteban-Marquilas (1972) and Arcelus (1984). Some of those models have enriched or improved the original method by linking shift-share with other techniques such as statistics (Berzeg, 1978; Kurre and Weller, 1989), information theory (Theil and Gosh, 1980), and stochastic regression functions (Arcelus, 1984; Knudson and Barff 1991; Selting and Loveridge, 1994). The classic formulation, however, remains the most dominant model in empirical work.

This study adopts the most basic version of the model discussed in buns (1960) and Armstrong and Taylor (1993). LetDE_ijdenote the change of employment (or investments) for the ith industry in region j from the base period to the end period. By definition, it is the product of total employment of the ith sector in region j in the base period, denoted by E_ij0, multiplied by the growth rate of employment of the ith sector in region j over the whole period, denoted by R_ij. The relationship is expressed in equation (1).

DE_ij= E_ij0R_ij(1)

Further, let R_k and R_ik denote the employment growth rates for the country and for the ith sector of the country respectively. Equation (1) can then be decomposed into three components as expressed in equation (2).

DE_ij= E_ij0R_k + E_ij0(R_ik - R_k) + E_ij0(R_ij - R_ik)(2)

The first term on the right hand side of (2) is usually called the national coniponent of growth. It shows ceteris paribus, the effect on the sector of the local economy, if sector 1 in location j exactly matched the national trend ( R_k ).

The second term in equation (2) is the structural component. It calculates the change in the ith sector that can be attributed to the region's industry mix. If the region has a 'favourable' mix, comprising more fast growing industries, it will , ceteris paribus, experience faster employment growth than the rest of the country.

However, having a favourable industrial structure in the base period is neither a necessary, nor a sufficient condition for faster growth. Hence the third term in equation (2) is the differential (or residual) component. It is that part of regional change not ‘xplained’by the national and structural components. It is a catch-all for measuring the extent to which an industry in the region grew faster (shown by a positive value) or slower (shown by a negative value) than would have occurred had the local industry experienced the national growth rate. Therefore, the sign and size of the structural and differential components have very important economic implications in terms of structural adjustment and improvement in local competitiveness.

Following Randall (1973), equation (2) can be rearranged as equation (3).

DE_ij- E _ij0R_k = E_ij0(R_ik - R_k) + E_ij0(R_ij -R_ik)(3)

The left hand side is called the net relative change (NRC), meaning the difference between the actual change and the national component. It shows whether growth of the ith sector in region j is faster or slower than the national average growth irrespective of industrial structure. NRC can therefore be regarded as an index of relative performance, which the right hand side seeks to explain.

The data used in this study are drawn from China's Statistical Yearbook (SSB, 1985-95). Three sets of regional and sectoral data are employed in both absolute and growth rate terms: (1) gross domestic product (GDP), (2) employment, and (3) investments in basic construction.¹While GDP is used as an indicator of economic development, employment and investments are used for analysing some of the reasons behind the disparities in economic growth.

Ill Results of spatial disparity analysis

III.AAn overall view of changes

Over the data period 1985-94, the Chinese economy grew rapidly. Real GDP more than doubled (about 8.3 per cent per year). Investments on basic construction rose by 134.3, and employment by 23.3 per cent (Table I)- Such growth was, however, by no means even across sectors and regions.

 

Notes: 

Growth rates are measured in 1994 constant prices. NRC = sectoral actual growth rate minus national growth rate. Total GDPs in 1985 are national incomes (NI) in that year multiplied by the average GDP/NI ratios of 1988, 1989 and 1990 for each province. Agricultural and indu strial GDPs in 1985 are estimated by multiplying the respective sectoral NIs with sectoral GDP/N 1 ratios in 1991 for each province. Price indices are RPIs for each province. Industry includes extraction, utilities, manufacturing and construction -- this definition applies in all the other tables hereafter. Hence, agriculture, industry and services in this paper correspond respectively to the first, second and third industries defined by the SSB of China.

Sources: 

SSB (1985-95), various issues.

There was a dramatic shift between three aggregated sectors. Real investments in agriculture declined by 45.4 per cent. Employment rose by only 7.1 per cent. On the other hand, the services sector experienced the most rapid growth. GDP increased by 160.4 per cent, investments 142.5 per cent and employment 70.8 per cent. In relative terms (NRC), both industry and services experienced higher than national average growth in GDP, investments and employment. In agriculture, NRC was
 

1Investments in basic construction include investments that can create new capital assets for production but excludes investments that are used for renovations, or for replacement of old capital assets. In the official statistics, we can only obtain data on basic construction investments of the state-owned sectors by province. However, such data are sufficient to reflect the extent of regional disparities in investments.

negative in all the three indicators.

Given the national and structural influence on economic growth, however, the actual changes varied from one region to another. The difference between the actual and relative changes can be apportioned to the regional industry-mix component and the differential component , and it is to this that we now turn at a more spatially disaggregated level.

III.BShift-share analysis of GDP growth by province

The results of shift-share analysis on GDP growth by province and by sector are presented in Table 2. In terms of total GDP, all provinces that outperformed the national average were located along the eastern coasts except Hebei, Henan, Yunnan and Xinjiang (positive NRC in total GDP). Regions that experienced significantly slower growth than the national trend were either the three metropolitan cities (Beijing, Shanghai and Tianjin), or the backward provinces in the west (Shanxi, (Guizhou, Tibet, Shaarixi, Gansu, and Qinghai The central provices and those in the northeast also lagged behind the national average. Slow growth of the three metropolitan cities were due to the fact that they were already much more industrialized than any other part of the country and the fact that their economies were dominated by SOEs and strict state planning, but poor performance in the western regions was due to their lack of competitiveness.

It is easy to note that the growing centres are mostly located in the east, especially Jiangsu, Zhejiang, FUjian, Shandong, Guangdong, and Hainan. Positive NRC in Guangxi, Hebei, Henan, Yunnan and Xinjiang implied that these regions were catching up with the growing frontier. Indeed, as shown below, Guangxi, Rebei and Xinjiang should be included in the growing centre model by the end of the data period. The fact that most central regions had large negative NRCs was disappointing because they were generally expected to catch up with the growing frontier in terms of growth.

There are some interesting results at the sectoral level. Although the actual growth in agriculture was positive for all provinces, in terms of NRC, all provinces except Saurian had a lower agricultural growth than national GDR In addition, much of the negative NCRs was explained by the structural component, implying a clear national shift against agriculture. On the other extreme, the services sector was the largest gainer in most provinces. Apart from the three metropolitan cities, Tibet, Shaanxi, Gansu and Qinghai, all the other provinces had higher growth of the service industries than that of the national economy. The best performers in services were again located in the east, including Jiangsu, Zhejiang Fujian. Shandong, Guangdong, Guangxi and Hainan. In industry, the picture was less clear but the fast growing provinces were still among the best performers.

The results in Table 2 make it possible to classify all provinces and cities into three different development models. The classification and the basic characteristics of these models are described below.

(1) The growing centre model. This model included three groups of regions. The first group covered Jiangsu, ZheJiang, Fujian, Shandong, Guangdong, Hainan, and Guangxi. All these provinces are located along the eastern coast with per capita GDPs (except Guangxi) and CDP growth much higher than national averages. Although Guangxi's per capita CDP was low in the data period, its growth was one of the highest. The second group included the three metropolitan cities and Liaoning. They had the highest per capita CDP and were the most industrialized regional economics. Although their growth was much below national average, they were still the growing centres as any growth in these regions would have had a significant impact on the surrounding areas. The third group consisted of Hebei and Xinjiang. These two regions had a per capita CDP and growth much higher than the national average. They were different from those in the first group because of their inland location and lack of openness in the first stage of economic reforms in the early 1980s. However, they became two successful examples of the catching up regions that had developed themselves into a growing centre (high income and high growth).

(2) The catching up model. This included most of the central regions (Henan, Hubei, Hunan, Jiangxi, Shanxi, Anhui, Inner Mongolia), the northeastern regions (Jilin and Heilongjiang), and some provinces in the west (Sichuan and Yunnan). Per capita CDP was lower than national average. CDP growth was high but not necessarily higher than national average. Most provinces experienced rapid structural changes with fast growth in industry and services at the expense of agriculture. These provinces comprised over 60 per cent of the country's population and their performance was a crucial factor of the overall success of Deng's multi-tier and multi-stage development strategy.

(3) The backward model. Guizhou Tibet, Shaanxi, Gansu, Qinghai and Ningxia belonged to this model. One basic feature was that they were much poorer than the rest of the country and had much slower growth than the national trend. These regions constituted a small proportion of the national population but they were dominated by minority nationalities. They were remote and mountainous, and hence, had the least prospect of becoming industrialized and prosperous in the foreseeable fliture. In the data period, they all had a negative NRC in CDP growth, implying that the gap between these provinces and the rest of the country was widening, rather than narrowing.

III.C Changes in employment by province

Structural changes between three aggregate sectors by province in both actual and relative terms are presented in Table 3. The NRCs are decomposed into the structural and residual components according to Equation (3) in the previous section.

Some important points of employment changes over the period can be observed. First, agriculture experienced a relative decline (negative NRC) in most provinces, except Jilin, Guizhou and Yunnan. Some provinces even had an absolute decline (negative actual changes). The decline was mainly influenced by the structural component. This was particularly true in the metropolitan cities, the southern coastal provinces (e.g., Jiangsu, Zhejiang and Guangdong), and the provinces with a large share of industry already (e.g., Liaoning).

Second, the general trend of employment in the industrial sector was a large increase in the fastgrowing (e.g., Guangdong, Fujian, Guangxi, and Shandong) or catching-up provinces (e.g., Henan and Anhui), and a slow increase in either the most industrialised cities and provinces (Tianjin, Shanghai, Beijing and Liaoning or the most agrarian or backward regions (e.g., Qinghai, Tibet, Gansu, Guizhou, and Shaanxi). The structural component played a positive role to the sector's increase in employment in all provinces, but the differential component played a positive role only in the growing-centre or catching-up provinces, and a negative role in the rest.

Third, the increase of employment in services was largely due to the structural component. The differential component made a positive contribution in some provinces (e.g., Guangxi, Hunan, 'in).

Guangdong and a negative contribution in others (e.g., Gansu, Tianjin 

Fourth, the shift of employment from agriculture to 'industries and services largely reflected the process of industrialisation. The main feature of employment changes in the metropolitan cities and Liaoning was not expansionary, but shifting between sectors. Their shares of employment in national total were declining as a result of low growth high employment growth occurred due to both structural in employment. For some new developing provinces, changes and the expansion of their economic scales. It is interesting to note that there are three different models of employment changes corresponding to the GDP growth models.

(1) The glowing centre model which was characterised by shifting employment from agriculture to industry and services, whilst overall employment increased rapidly. The causality of increase was shared by both industry mix and location. In the most industrialized cities and province (Liaoning), there was a continuing structural shift out of agriculture towards industry and services although overall employment growth was low.

(2) The catching model which enlarged its employment share 'm industrial sector considerably with a moderate reduction in agriculture and a proportional growth in services. However, there were some critical problems faced by those economies that largely depended on state-owned heavy andmilitary-based industries. NRC in employment in those provinces performed poorly in all sectors. There was a serious un? or underemployment problem in the local economy under economic reforms and rapid structural transformation in an effort to reform SOEs and break. away from traditional state planning.

(3) The backward model which included the backward or the most agrarian provinces where the natural condition was poor and agriculture played a dominant role. The model was featured by a continuous Increase in agricultural employment and the lack of investments in that sector (see below for more detail), an average growth in industry and a below national average growth in services. Employment changes (high growth in agriculture and low growth 'm services) were largely explained by the differential component, or poor competitiveness in the non-agricultural sectors.

III.D Changes in investments by province

During Mao's period, China's development strategy was extremely interior-oriented (geographically) towards the central and the western regions and military-oriented (sectorally) towards the so-called ,Third-Front" industries (San Xian Gong Yue), industries that were to be hidden 'm the most inland and mountainous areas for security purposes. As Zhao (1996) points out more than 50 per cent of national investments went to a few interior provinces, Liaoningg alone gaining 11.6 per cent of national investments in 1953?62, Sichuan 13.6 per cent in 1966?70. That investments generally received least economic returns. Since economic reforms from 1978, China has radically changed the national investment strategy from the interior provinces to the coastal provinces in the east. A package of economic policies was designed and implemented on investment, foreign trade, taxation, land -rent, 'm favour of the SEZs and the open cities along the eastern coast. Since then, investments were heavily concentrated in a few large and medium size cities and a number of provinces in southeast China, among which Guangdong (and later on Hainan), Fujian, Jiangsu, Zhejiang, Shanghai, and Shandong were the largest beneficiaries of Deng's development strategy.

Before economic reforms, China was dogged by Mao's rigid socialist doctrines which did not allow any form of ownership but the state's, and by the inefficiency and lack of competition of SOEs (Yao, 1997b; Liu and Liu 1997; Groves, et. al., 1994). Deng's economic thought as we have formalized at the beginning of this paper as a multi-tier and multi-stage development strategy had two main objectives. First, it was to create a few fast growing centres that would resemble the NICs in southeast Asia (e.g., South Korea, Malaysia, Singapore, China's Hong Kong and Taiwan) in order to prove that if other countries (economies) could make it, so could China mainland itself Second, it was to create a few capitalist enclaves that did not need to follow traditional socialist doctrines without direct confrontation with the old communist guards of Mao's generation. Such fast rig models. once proved successful, could then be applied to the rest of the country. Thus, the SEZs and open cities were the cornerstone of the multi-tier and multi-stage development strategy for industrialization. They helped break up old traditions and established themselves as the indusial power houses of the largest developing economy in the world.

Massive investments into the coastal provinces have greatly improved their economic position. For instance, from 1985 to 1994, Guangdong jumped from eighth to fifth place in terms of per capita GDP (only below Beijing, Tianjin, Shanghai and Liaoning) and from seventh to third in the growth rate rankings (after Fujian and Hainan;).

Changes in investments were related not only to national growth and structural changes, but also to local performance. Table 4 provides the results of shift-share analysis for all provinces of China. With overall national investments increased at 134.3 per cent in 1985?94, a significant shift of investments between sectors took place. Real investments in agriculture shrank by 45.4 per cent, or 180 below the national average, but rose by 140 per cent in industry and 142.5 in services. Given this national trend, investment changes by province reveal some clear patterns. In agriculture, all provinces, except Jilin, Jiangxi, Hainan and Tibet, suffered a real reduction. By contrast, all provinces (except Qinghai) had a real growth in both industry and services.

In relative terms as shown by the values of NRCs, provinces that had higher than national average growth included Hebei, Jilin, Shanghai, Zhejiang FuJian, Shandong, Henan, Hubei, Hunan, Guangdong, Guangxi, Hainan and Xinjiang. Those had lower growth rates were concentrated in the western parts of the country, especially Shanxi, Guizhou, Tibet, Shaanxi, Gansu, Qinghai and Ningma. Jiangsu appears to have a low growth rate in Table 4, but if the investments by the non-state economy were included the growth rate would be 70 per cent higher than the national average. For other provinces, including the non-state sectors' investments did not affect the rankings of provinces because the growth rates of state investments were consistent with the growth rates of total investments in all the other regions. However, because total investments by province cannot be broken down by sector, only the growth rates of state investments are presented.

At the sectoral level, we can make a few general observations. Firstly, investment shortage in agriculture spread all over the country, most provinces experienced an absolute decline. This was closely associated with the structural component and could be used to explain the instability in agricultural production over the data period. Although lack of agricultural investments may have been an inevitable result of long term structural adjustment, an absolute decline in so many regions may have also reflected the myopic view of local governments on the importance of agriculture on which more than 60 per cent of the population depended (Yao, 1996). Secondly, regional variations of investments in industry and services were mainly related to the differential components rather than the structural components, implying that the local factor played a predominant role in attracting investments. The most successful regions were again concentrated along the eastern coast. The poorest western regions had the lowest growth rates not only in industry and services but also in agriculture.

Similar to GDP and employment growth, the analysis on investments also reveals three different growth models.

(1) The glowing centre model which leant its investment strategy greatly upon industry and services. Consequently, the local economy attracted large investments, mostly accounted for by the differential component.

(2) The catching up model which also had massive investments in industry and services. In many of those regions, the growth was much higher than national average. Such regions included Jilin, Henan, Hubei and Hunan.

(3) The backward model with its least competitiveness, had negative NRC of investments in all sectors. Low investment growth could be explained by the sectoral trend in agriculture, but for other sectors, it could only be explained by its poor competitiveness and lack of funds Such regions performed poorly not only in industry and services but also in agriculture.

IV  Conclusions

This paper analyses economic growth of the Chinese regions. As there are three distinct models of economic growth, similar shift-share analyses are conducted on employment and investments to reveal how closely these important indicators are related to economic performance.

The disparity of economic growth between the fast growing centres (e.g., Guangdong) and the backward provinces (e.g., Gansu) was closely related to different competitiveness in attracting production factors. It is explained mainly by local industrial mix in the base year and individual performance during the data period. Of course some external factors, such as biased government policies, geographical location and external shocks, also played a role in the process.

Rapid economic growth caused significant changes in the national economic structure which was closely related to structural changes in employment and investments Structural changes in employment and investments had some important policy implications. Firstly, the shift of employment and investments from agriculture to other sectors may be necessary in an industrialisation process and inevitable as the result of increased agricultural productivity. However, a dramatic decline of agricultural investments mostly affected the poor in the northwester parts of the country, including 60 million rural population who lived in poverty. Thus, if the current trend cannot be changed, it will create more serious social, economic and even political problems in the future. Lack of agricultural investments has undermined sustainable development in the sector which is responsible for feeding the huge and ever increasing population. Secondly, the relative decline of industry that occurred in both the most and the least industrialised provinces had different implications: for the former it meant a forward adjustment towards a more diversified modem economy with an increasing share of services; for the latter, it meant a backward adjustment towards a more agriculture-dominated economy. Thirdly, the relatively high increase in investments with a relatively low increase in employment in most south-coastal provinces resulted in a much higher capital-labour ratio than that in the northwester provinces. This will generate greater inequality in future growth.

By classifying the provinces into three growth models, it is possible to understand the complexity and diversity of the Chinese economy in its rapid industrialization drive. Some high income and the most industrialized regions (e.g., the three metropolitan cities and Liaoning) suffered from their industrial mix inherited from the planned system. Although they were considered to be among the growing centres, their economic growth was much below national average. This was explained by the relatively high level of industrialization at the beginning of the data period. The fact that they lacked the dynamism of the coastal regions suggested that they were struggling to break away from the former state planning system and to solve the SOE inefficiency and other related problems. The eastern provinces of Guangdong, Jiangsu, Zhejiang, Shandong, Hainan, FuJian and Guangxi performed much better than the rest of the country in every aspect. Hence, they can be regarded as the growing frontier of China. The successful performance of Hebei and Xinjiang suggests that it is possible for the inland areas to catch up with the growing frontier. By contrast, the performance of most catching up regions and backward provinces was rather disappointing.

Deng's multi-tier and multi-stage development strategy was to create a few fast growing centres m a relatively short period of time. It was also hoped that these growing centres would become the leading regions for the rest of the country to follow. Successful development in Guangdong and other fast growing provinces along the eastern coast over the last two decades suggests that Deng's strategy was a great success in achieving its first objective. In addition, most parts of China managed to follow Guangdong. As a result, the whole national economy grew rapidly for a rather long period of time. The momentum of growth is still high and it is not difficult to predict that many of the Chinese provinces will be able to achieve a per capita GDP similar to that of the middle income economies in the next 10 to 20 years.

However, the negative effects of Deng's development strategy were also clear. There were more income inequality and political tension between the prosperous cities and provinces in the east and the poor and backward regions in the west. The analysis in this paper suggests that the poor regions were unable to reap the whole benefits of economic reforms. Lack of investments, lack of skilled labour who may have migrated to the more prosperous regions, and lack of policy incentives and government support were the major constraints on economic development. Therefore, the second objective of Deng's strategy, i.e., for the poor regions to catch up with the rich, remained largely unrealized in the data period. On the contrary, the ever widening gaps between the rich and the poor regions were growing and will continue to grow in the foreseeable future.

References:

Arcelus, F. J. (1984)'An extension of shift-share analysis', Growth and Change, 15, 3?8,

Armstrong, H. and Taylor, J. (1993) Regional economics and policy (2nd ed.), Harvester Wheatheaf, UK.

Berzeg, K. (1978) 'The empirical content of shift-share analysis', Journal of Regional Science, 18, 463?469

Casler, S.D, (1989)'A theoretical context for shift and share analysis', Regional Studies, 23, 43?48.

Dupn, E S Jr (1960) 'A statistical and analytical technique for regional analysis', Papers of the Regional Science Association, 6, 97?112.

Esteban-Marquilas, J. M. (1972) 'A reinterpretation of shift-share analysis', Regional and Urban Economics, 2 (3), 246?255.

Fothergill, S. and Gudgin, G. (1979) 'In defence of shift-share analysis', Regional and Urban Economics, 12, 249?255.

Griffin, K. and Zhao, R. (1993) The distribution of income in China, St, Martin Press (USA).

Groves, Theodore; Hong, Yongmiao; MeMillan, John; and Naughton, Barry (1994)'Autonomy and incentives in Chinese state enterprises', Quarterly Journal of Economics, 109, 1:183?209.

Healey, M. and Clark, D, (1984) 'Industrial decline and government response in the West Midlands: the case of Coventry' Regional Studies, 18, 303?318.

Hoppes, R.. B. (1994) 'Rejoinder: industry-level shift-share analysis', Economic Development Quarterly, 8, 214?217.

Hussain, A.; Lanjouw, P.; and Stem, N. (1994) 'Income inequalities in China: evidence from household survey data´, World Development, 22(12), 77?96.

Ishikawa, Y. (1992) 'The 1970s migration turnaround in Japan revisited: a shift-share approach',

Paper in Regional Science: the Journal of RSAI, 71, 2:153?73 3

Knight, J. and Song, L. (1993) 'The spatial contribution to income inequality in rural China´, Cambridge Journal of Economics, 103, 122-127.

Kurre, J.A. and Weler, BR. (1989) 'Forecasting the local economy, using time-series and shift­

share techniques', Environment and Planning A, 21, 753 )?70.

Knudson, D. and Barff, R. (1991) 'Shift-share analysis as a linear model', Environment and Planning A, 23,421?43 1.