The leading role of manufacture in regional economic growth in China: A spatial econometric view of Kaldor s law

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1 The leading role of manufacture in regional economic growth in China: A spatial econometric view of Kaldor s law Dong GUO Laboratoire d Economie et de Gestion UMR5188 CNRS Universite de Bourgoge, Pole d Economie et de Gestion, BP 26611m Dijon, France Abstract Following the theoretical arguments from the new economic geography and the endogenous growth theory, taking spatial effects into account in the economic growth process has been associated to the existence of externalities that cross the regional borders. While China as a whole has experienced a fast industrialization and longlasting economic growth since the end of 1970 s by implementing the strategy of developing manufacture with priority, which fits the hypothesis of Kaldor s engine of growth, seldom is there concern of manufacturing growth on regional economic growth in China empirically. Therefore, this paper proposes to detect the spillover effects of manufacture contributed to the regional economic growth by testing the Kaldor s law using the regional data of the growth process from 1978 to 2004 in China. Submit to Workshop on Agglomeration and growth in knowledge-based societies Kiel, Germany, April 20-21, 2007 (First draft, please do not quote)

2 1. Introduction China s development experience since the country s open-up in 1978 has been caught attentions due to the long-lasting growth rate of the county with the average yearly growth rate of GDP being 9.39% 1 during the period of 1978 to In retrospect to China development strategy, the preference of developing manufacturing sector has been implemented as a key development strategy throughout the history of modern China since 1949 (Lin et al. 2003). Especially during the reform period, taking advantage of intensive labor force, the strategy of development manufacturing is taken as the form of absorbing foreign direct investment (FDI) and encouraging exports. In fact, the average annual growth rate of industry in China from 1978 to 2004 is 11.46% 2, which is more than 2 percentage points higher than GDP growth rate. China s development experiences fit Kaldor s laws (1966), which proposed the idea of economic growth is caused by manufacturing sector from the perspective of demand-driven. In short, Kaldor s law includes three different propositions, namely, the first law of manufacturing industry is the engine of economic growth; the second law is that manufacturing growth induces productivity growth in manufacturing through the dynamic and static returns to scales (also known as Verdoorn s law ); and the third law states manufacturing growth induces productivity growth outside manufacturing, by absorbing idle or low productivity resources in other sectors (Thirlwall 1991: 34). The empirical testing of Kaldor s law has been conducted either at country level or at regional level. At country level, some are conducted as international comparisons (Parikii 1978, McCombie 1983, Thirwall 1983, and McCombie and Thirwall 1994, Necmi 1999) while some are conducted at country level individually, the UK (Stoneman 1979), Australia (Whiteman 1987), Greece (Drakopoulos and Theodossiou 1991), the US (Wulwick 1991, and Atesoglu 1993), Turky (Bairam At regional level, McCombie and De Ridder (1983), Bernat (1996) tested the case of the US. Casetti and Tanaka (1992) evaluated the validity with regard to Japan. Pons-Novell and Viladecans (1999) and Fingleton (2004) tested Kaldor s law relevance to the economic experience of European regions (NUTS I and NUTS II 1 Calculated from China s Statistical Yearly Book 2005, table Calculated from China s Statistical Yearly Book 2005, table

3 respectively), Wells and Thirlwall (2003) tested Kaldor s law across African countries. Dasgupta and Singh (2006) uses a Kaldorian framework to examine the evidence of deindustrialization in developing countries at a low levels of income, the jobless growth in these economies and the fast expansion of the informal sector. Note that considering the neighboring effects on regional economic growth as the existence of externalities that cross the regional borders, Bernat (1996), Pons-Novell and Viladecans (1999), and Fingleton (2004) applied spatial econometric techniques to their empirical testing of Kaldor s law at regional level. The empirical testing of Kaldor s law on China s case has been conducted by Hansen and Zhang (1996) and Jeon (2006). Hansen and Zhang (1996) employed pooled regional dataset of 28 regions covering the period of 1985 to 1991, while Jeon (2006) applied panel dataset of 24 regions covering a much longer period from Both researches have confirmed the validity of the Kaldor s law in China. However, neither of them has taken into account the possible spillover effect across the regional borders in the process of economic growth. The problem of possible biased results and hence misleading conclusions of ignoring the influence of spatial locations in the process of economic have been addressed in the filed of economic geography and regional science, suggesting accompanying spatial heterogeneity and dependence in regional growth specification (Amstrong 1995, Rey and Montouri 1999, López-Bazoe et al. 1999, Fingleton and López-Bazo 2006, among others). The purpose of this paper is to detect the leading role of manufacturing in the process of economic development in China by using the data from 31 regions from 1978 to 2004, especially, to test weather the externalities exist across the regional borders in the economic growth process in China. The rest of the paper is structured as follows. The next section will give a brief review of the Kaldor s law, followed by a description of China s regional economic development. After data descriptions in section 4, section 5 will provide the empirical analysis results and corresponding discussions. The last section concludes. 2. Reviews of Kaldor s law and spatial effects Kaldor s law was put up by Kaldor in 1966 and 1967, stating that manufacturing sector is the engine of economic growth by examining the dynamic cross-country 2

4 econometric analysis of the 12 OECD countries using growth rate, productivity growth rate and employment growth rate in the 1950s and the early 1960s to provide recommendations for promoting the growth of manufacturing sector in the Britain economy (Kaldor 1966, 1967). In fact, Kaldor s ideas were influenced by Young (1928), who emphasized the overall macroeconomic spillover effect of the extension of manufacturing industry. Kaldor also ascribes the concept of dynamic economies of scale to Arrow (1962) s notion of learning by doing with the idea of the faster growth rate of manufacturing, the faster growth of productivity of manufacturing because Kaldor believes that dynamic economies of scale can only occur in the sector of manufacturing not in agriculture nor in service. Unlike the total factor productivity in neoclassical economics, which is entirely based on supply side, Kaldor s law considers the productivity in both supply and demand side. On the demand side, Kaldor suggested that the income elasticity of demand for manufacturing good is much higher than that for agriculture, or less or similar to service good, while on the supply side, manufacturing was regarded to have greater potential for productivity growth. Therefore, based on the stylize tendencies concerning the demand and supply conditions in agriculture, manufacturing and service, Kaldor s law was derived concerning the relationship between the growth of output, employment and productivity in different sector of the economy (Daspupta and Singh 2006). The first law states that the growth rate of GDP is positively related to the growth rate of manufacturing sector: the faster growth of the manufacturing growth rate in a region or a country, the faster the GDP growth rate correspondingly (equation 1). Note that in this setting, the causality comes from the expansion of manufacturing sector to GDP growth. As such, the first law is used to be called the engine of growth hypothesis. (1) q GDP = a 1 + a 2 q m,a 2 > 0 where q GDP is the growth rate of GDP, growth. q m is the growth rate of manufacturing output The second law, also called Verdoorn s law, observed by Dutch economist in 1949 as well, is that manufacturing productivity growth rate is positively related to the manufacturing output growth rate. Traditionally, Verdoorn s law has been 3

5 estimated as a linear relationship between the exponential growth rate of labor productivity (p) and of output (q). (2) p m = b 1 + b 2 q m,b 2 > 0 where p m is the growth rate of labor productivity in manufacturing. is often called Verdoorn s coefficient, which has been found around 0.5 empirically. It implies that one percentage of growth in output will induce 0.5 percentage increase in productivity growth. Verdoorn s law indicates an existence of increasing return to scale in the sense that technical change is endogenous induced by the output growth (Fingelton and McCombie 1998). Note that as Kaldor noticed a minor problem emerging from definitional identity from the labor productivity p m = q m e m, which implies, in an econometric sense, a strong correlation between dependent and independent variables. To handle this problem, another specification is suggested as in equation (3). b 2 (3) e m = c 1 + c 2 q m where e m is the growth rate of labor employment in manufacturing sector, and c 1 = b 1 and c 2 =1 b 2. The third law states that growth in manufacturing output leads to growth in overall productivity growth in an economy, which is observed as the positive relationship between the labor productivity growth rate of all productive sectors and manufacturing output growth rate (equation 4). (4) p = d + d q d 0 m 1 2 m, 2 > where p m is the productivity growth for all productive sectors. An alternative way to express the third law is: (5) q = d + d e d 0 GDP 0 1 m, 1 > where e m is the growth rate of manufacturing employment. The existence of dualist feature of an economy especially in a development country explains the third law. An economy is a dual economy if there are wage differentials between the high productivity sectors (usually are the manufacturing sectors) and the low productivity sectors (usually they are agriculture sector). The transferring of labor from the lowproductivity (agricultural sector) sector to higher-productivity sector (manufacturing sector) will not decrease the output of low-productivity sector, but will increase the 4

6 productivity of manufacturing due to the much increase output of manufacturing. On the other hand, the productivity outside the manufacturing sector increases because of the absorbed labor by manufacturing sector (Cripps and Tarling 1973, Drakopoulos and Theodossiou 1991, Kaldor 1968, Thirlwall 1983). However, Thirlwall (2003) criticized that it is hard to test the direct relationship between the labor transfer and the growth of productivity of the economy due to the difficulties of measuring the productivity growth in many other activities outside manufacturing. Therefore, Cripps and Tarling (1973), Thirlwall (1993), and Atesoglu (1993) suggested to regress the growth of GDP on the growth of employment in manufacturing and non-manufacturing as sectors for testing the third law as shown in equation (6). (6) q = d + d e d e d, d 0 GDP 0 2 m 3 nm, 2 3 > where e is the growth rate of manufacturing employment, e is the growth rate of m non-manufacturing employment. While the Kaldor s law has been proved to be valid empirically either though individual country s case or international comparisons, there exist some debates on Kaldor s law, mainly lying in the aspects of the direction of causality (Rowtorn 1975), the omission of capital factor in the process of growth (Fingleton and McCombie 1998), and the estimation method (Harris and Lau 1998). We will not take further discussions on these aspects in this study, but would take into account the possible spillover effects among regions during the growth process which has been ignored in most of the empirical testing on Kaldor s law on regional database except for Bernat (1996), Fingleton (1998), Pons-Novell and Viladecans-Marsal (1999), and Fingleton and López-Bazo (2006). As mentioned earlier, to take into account of the influence of spatial location on the growth process is to accommodate spatial heterogeneity and spatial dependence in regional growth process. In general, spatial heterogeneity is the lack of uniformity of the effects of space, with the spatial units being far from homogenous (Anselin 1988: 13). Spatial dependence can be considered as the existence of the relationship between what happens in one position in space and what happens elsewhere (Cliff and Ord 1973). This is embodied in Tobler s First Law of Geography (Tobler 1979), where everything is related to everything else, but near nm 5

7 things are more related than distant things. This simply implies that it should be expected that stronger relationships within and among variables that are sampled at places that are near to one another rather than for those that are far removed from one another. Empirically, two possible conditions may lead to the existence of spatial dependence. First, there is the measurement error for observations in contiguous spatial units. The second factor that may cause spatial dependence is more fundamental (Anselin 1988: 11-12). Spatial interaction theories, diffusion processes, and spatial hierarchies yield formal frameworks to structure the dependence between phenomena at different locations in space. In most of the cases, the diffusion processes get involved more in the form of spillover effects taken effect as interactions among regions through interregional trade or migration. Usually, the spatial dependence is integrated in regression model as the form of spatial lag model (7) or spatial error model (8). (7) y = ρ Wy + Xβ + ε (8) y = Xβ + ε, ε = λw ε + ξ where, y is the dependent vector, X is the exogenous variables, β is the vector of coefficients, ρ and λ are the spatial autoregressive coefficients. Note that W is the contiguity matrix describing the spatial arrangement of the observations taken into account. It is called spatial weight matrix formally, with elements w ij, where ij index corresponds to each observation pair. The nonzero elements of the weight matrix reflect the potential spatial interaction between two observations, which may be expressed in different ways, such as simple contiguity (having a common border), distance contiguity (having centroids within a critical distance band), or in function of inverse distance or squared inverse distance. Zero-valued elements indicate the lack of spatial interaction between two observations. Note that by convention, the diagonal elements of the weights matrix are set to zeros (Anselin 1992). Considering the importance of implication of Kaldor s law for regional economic policy, Bernat (1996) was among the first to specify the growth process including spatial effects by testing the simplest version of Kaldor s law for the states in the US. With the results, he concluded that spatial error modes are more appropriate model specification for testing the first law and the second law, while spatial lag model for the third law testing. Bernat (1996) interpreted the spatial lag 6

8 model as a region s growth is directly affected by growth in neighboring regions, and this effect is independent of the effect of the exogenous variables, while spatial error model as a region s growth is affected by growth in neighboring regions only to the extent that neighboring regions have above or below normal growth. Here the so-called normal growth is defined by equation (6). Even though it is undisputable that spatial specification based on empirical cases depend on the set of regions, time period, specifications, Fingleton and Lopez-Bazo (2006) would take the specification as spatial lag model by assuming that externalities in a long-run growth rate is mostly a substantive phenomenon caused by technological diffusion and pecuniary externalities. Therefore, in this paper, we hypothesis that the spatial specification on Kaldor s law would be spatial lag model. Before we move on to the econometric view of Kaldor s law testing in China s regions, we would provide a brief introduction on China s regional economic development. 3 Brief introduction of China s regional economic development Following other developing countries, China gave priority to the development of manufacturing industry as an unchanged development strategy throughout the development phase since the country was founded in Apparently, the effects of this strategy in China has not been the same due to the striking long-lasting economic growth rate in the post-reform time since the country was opened up in Lin et al. (2003) explained this differences as the two different strategies applied in prereform and post-reform time, which are comparative advantage-defying (CAD) strategy and comparative advantage following (CAF) strategy respectively in prereform time and post-reform time. According to Lin (2003), a country s development strategy can be broadly divided into two mutually exclusive groups: (i) a comparative advantage-defying (CAD) strategy, which attempts to encourage the firms to deviate from the economy s existing comparative advantages in their entry into an industry or choice of technology; and (ii) the comparative advantage following (CAF) strategy, which attempts to facilitate the firms entry into an industry or choice of technology according to the economy s existing comparative advantages. In the pre-reform time, the strategy of comparative advantage-defying (CAD) was designed to develop heavy-industry for the sake of political and strategical security consideration for the newly-found China even though China was scarcity in capital which was necessary for the heavy industry. As a result, the misleading 7

9 strategy resulted in low economic growth rate, distorted economic structure, low efficiency. The average growth of GDP rate from 1952 to 1978 was 6.1%, with in which, the average growth rate of heavy industry is 1.47 time as the light industry from 1952 to Even though the CAD strategy was successful in the sense that the share of manufacturing sector increased from 12.6% in 1949 to 46.8% in 1978 (Lin et al. 2003: 78-80), the total factor productivity grew at only 0.5% annually (from 1952 to 1981) based on the favorable estimation, comprising only one fourth of the average of the 19 developing countries (World Bank 1985). Note that CAD strategy was not regionally balanced in that interior provinces, such as Sichuan, Hubei, Gansu, Shaanxi, Henan, and Guizhou were received much of the state investment for massive construction of military industrial complexes since 1964 due to the consideration of national security (or so-called Three Front Construction ). Furthermore, many companies in Shanghai and other coastal cities were relocated to the mountains in Guizhou, Sichuan, and Hubei, where highways and railroads were deficient or nonexistent, water and electricity were in short supply, and the sources of raw materials were far away. A significant proportion of the relocated factories could not produce anything for many years, and the equipment rusted into junk (Démurger et al. 2002). Yang (1997) evaluated that about one-third of the investment were wasted, with a significant proportion of the relocated factories could not produce anything for many years, and the equipment rusted into junk. In the post-reform time since 1979, after the successful reform in agricultural sector, China started to implement comparative advantage following strategy (CAF). There are two aspects in shifting the CAD to CAF in the post-reform time. First, the transformation of central planning system to market system provides a macroeconomic environment for enterprises to perform according to the principles of market economies. For example, enterprises will be operated on labor-intensive industries, most of which are light industries rather heavy industries. Secondly, taking into account of comparative advantage of regions to implement the open door policy aimed to absorb foreign direct investment in the coastal regions. Geography is regarded as one factor of production in the sense that local-specific factor would influence foreign investment (Dunning 1985). The eastern part of China has much better economic development conditions and investment environment, besides its 8

10 geographically being close to ocean in that geography is regarded as one of the importance factor to economic performance (Gallup et al. 1999). The reform started with the household responsibility system in 1978 in the agricultural sector in that it is designed to increase farmers autonomies to simulate the incentives. It was successful from 1978 to 1984, the Chinese agricultural production increased at annual rate of 7.7%, almost half of which were due to the increasing incentives (Fan 1997, Huang and Rozelle 1994, Wen 1993, Lin 1992, McMillan et al. 1987). The success of the agricultural reform provided the good example for this gradual reform to move on to manufacturing sectors. A series reforms to improve the performance of state-owned enterprises including decentralization of fiscal system, deregulation of the prices, and strategy on the international front accumulated to the process of transmitting the central planning system to market system. Along with the market system being transformed gradually, the open-door policy was implemented as opening up four cities in south provinces of Guangdong and Fujian to provide favorable policies to foreign investors in For example, foreign-invested enterprises (FIEs) are entitled to preferential tax treatment (a twoyear tax holiday, and a three-year tax deduction of 50 percent). In 1984, the open areas enlarged to 14 coastal cities following the similar strategy of implementing favorable policies to FIEs. In the late 80s and the early 90s, Hainan and Shanghai were included in the open regions. After the southern tour of Deng Xiaoping in 1992, the open-up trend has been spread to all the regions in China. During this process, the open-up brought the pour of foreign direct investment in China, with more than 90 percent concentrated in the southern and coastal regions. This resulted in the fast growth of manufacturing in those areas due to the fact labor-intensive and exportoriented FDI represents a principal type of foreign investment in China although, more and more FDI started to aim at the domestic market of China. From 1978 to 2004, the annual average growth rate of manufacturing is more than 2 percentage points higher than the overall growth rate of GDP. In the coastal regions, the difference between manufacturing growth rate is much higher than the inner land regions (figure 1). <Insert figure 1 here> 9

11 4. Data All the data used for this study come from NBS (2004), which could be said as the most comprehensive data source for regional economies in China so far with most of the regions covering the period of 1949 to Complete regions at provincial level in China include Hong Kong, Macao and Taiwan as shown in figure 2, however, due to the incomparable datasets, we do not includes these three regions in this study. Nevertheless, Hannan Province and Chongqing Municipality, founded by separating from Guangdong Province (in 1985) and Sichuan Province (in 1999) respectively, will be treated as separated regions, which was not the case in the earlier studies on China s regional economy. As the period of reform period since 1978 is the time for China s economy to speed up, the data in this study will cover the period from 1978 to <Insert figure 2> We follow the three classification of industry in NBS (2004) in the China s economy, which are the primary industry, the secondary industry, and tertiary industry. The primary industry refers to agriculture, forestry, animal husbandry and fishery. The secondary industry includes manufacturing and construction. Secondary industry refers to mining and quarrying, manufacturing, production and supply of electricity, water and gas, and construction. Tertiary industry refers to all other economic activities not included in primary or secondary industry. Note that usually in the statistics on regional growth by sector, the secondary industry is grouped into two categories: manufacturing and construction. However, as regional employment in NBS (2004) is not categorized into manufacturing and construction, but as secondary industry, therefore we would take secondary industry as manufacturing industry in this study. 5. Empirical analysis 5.1 OLS estimations The results 3 of OLS estimation for Kaldor s law are shown in table 1. The first law testing is based on equation (1). The coefficient of can be interpreted as 10 percentage of growth in manufacturing will induce more than 5.4 percent growth in 3 All the growth rates calculated in this study are logarithmic values. 10

12 GDP. The result is supportive to the first law of Kaldor that manufacturing sector is the engine of the economy, which, on the other hand, reflects the effective effects of economic development strategy in Chin of putting forward to develop manufacturing with preference. The second law testing (or Verdoorn s law testing) is based on equation (3). In this case, the Verdoorn s coefficient is 0.42, which is the similar to what have been observed as about one half in the earlier literature. In this case, it can be interpreted as 10 percentage growth in output of manufacturing will induce 4.2 percentage growth in productivity 4. This result indicates there exists increasing return to scale in the case of China s regional economy: If the output growth rate of an industry is strongly correlated to the investment including foreign direct investment (FDI), the fast growth of manufacturing in China mainly would be the result of the large share of foreign direct investment in China. Since 1978 of open up till the end of 1998, almost 60 percent (59.08%) of the cumulative FDI go to manufacturing sector (Li et al. 2001: 308). While the spillover effect of technology progress happened among different enterprises especially between the state-owned ones and FDI enterprises, which could be one factor of the fast growth of output in manufacturing, the result of testing Verdoorn s law also provide another example of the increasing return to scale existing in manufacturing sectors in China. The estimation result on the third law based on equation (6) can be interpreted as the 10 percent growth rate in GDP would be due to the 2.7 growth in employment growth rate in manufacturing sector and 5.4 percent decreasing in non-manufacturing employment. Therefore, with such expected positive coefficients, which are significant as well, the third law is verified. The leading fast growth of manufacturing sector plays an important role in improving the overall productivity in China s economy. So far, all the estimations on Kaldor s law are valid to indicate the very positive role of manufacturing sector in China s regional economic during the period of post-reform time since 1978 until As mentioned earlier, this also supports the previous studies on Kaldor s law in China (Hanson and Zhang 1996, Jeon 2006). <Insert table 1> 4 Note that the Verdoorn s coefficient in this study are both smaller than what has been estimated in Hansen and Zhang (1996) and Jeon (2006) due to the difference in growth rate calculation and estimation method. 11

13 5.2 Testing spatial dependence However, as shown the maps of regional economic growth earlier (figure 1), it seems display a spatial pattern as the coastal regions showing much higher growth rates than the inner lands regions implying a possible strong spatial autocorrelations among regions. Usually, in spatial analysis, the testing of spatial autocorrelation can be conducted by Moran s I (Anselin 1988, Anselin and Florax 1995, Anselin et al. 2004). (9) I N N N i= 1 j= 1 = N S 0 i= 1 w z z z ij 2 i i j, where S w, i j, N is the number of 0 geographical units, z is the values. W, is the spatial weight matrix as mentioned earlier. Note that building an appropriate weight matrix is the crucial part in spatial analysis. Usually, there are kinds of methods to construct weight matrices as mentioned earlier (see Anselin 1988, Rey and Florax 1995 for details). Empirically, for example, Dall erba (2005) built a spatial weight matrix by considering traveling time between geographical units, while Nazara (2003) considered the interregional economic relationship as those based on observations from an interregional inputoutput table of Indonesia as another form of spatial weight matrix. In China s case, approaches other than those based on geographical contiguity would include the work of Ying (2000) who built a weight matrix based on geographical distance to analyze the spillover effect between core-periphery regions the distance band was set up for different scales until the spatial effect are found. Setting up different distance bands to build different spatial weight matrices, he found that defining a neighbor at the distance of 800 kilometers allows maximizing the effect of spatial dependence. In addition to geographical contiguity pattern or distance to construct spatial weight matrix, spatial weight matrices can be constructed on economic connections, such as immigration, trade relation, and social networks. In this study, not only we apply the distance measure for creating the weight matrices as in Ying (2000), we will also try to construct the weight matrix expressed by geographical contiguity and nearest neighbors ( see table 2). The spatial weight matrix Queen by geographical contiguity is defined by the Queen contiguity, which means to the largest extent to include neighbor whenever they are connected. The K4 N N = i= 1 j= 1 ij 12

14 is the constructed by choosing the four nearest neighbors, while Km5 is the spatial weight matrix based on the arc distance, which is set up 500km as threshold to define neighbors. Note that by the distance contiguity, we calculated the distance between provinces based on the location of the capital cities of provinces rather than on the geographical centroid of each province. The reason for doing so is that empirically the interaction among provinces are more likely happened between capital cities through highways rather among the centroid, which might not have any transportation routes. <Insert table 2 here> As shown in table 3, all the values of Moran s I for the three variables regional GDP growth rate, manufacturing growth rate, and employment growth rate in manufacturing are positive and highly significant for all the spatial weight matrices. This indicates the existence of positive spatial autocorrelation in regional growth in China from 1978 to This means, in other words, similar values of three variables, either the high or the low, are more spatial clustered than could be caused purely by chance. All of these demonstrates that the pattern of regional economic growth in China during this period is not random but with spatial pattern of similar regions clustered together that is, the higher growth rate regions is clustered in the coastal regions, while the lower growth rate regions in the inner land areas as it is shown in figure 1. <Insert table 3 here> Now that there exists the spatial dependence among regions in the growth process in China, we would not ignore it when modeling the regional growth. In fact, the presence of spatial dependence has important consequences for some of the inference obtained from the classical econometric methodology, and may indeed invalidate them. In the presence of spatial dependence, the OLS estimation for the parameters will be non-biased, but inefficient. On the other hand, economically for the long run, there exists the regional spillover effects among regions, which might be captured in the spatial econometric models (Fingleton and López-Bazo 2006). Therefore, it is necessary to incorporate spatial effects in this study. As it is mentioned earlier, spatial dependence can adopt two spatial models: spatial lag model (equation 7) and spatial error model (equation 8). The testing of the 13

15 specification of the model can be conducted through the testing of error items in OLS with spatial weight matrix. The three testing of spatial autocorrelations are shown in the table 4: Moran s I, Largrange multiplier test for spatial lag model, and Lagrange multiplier test for spatial error model (Anselin 1988). In fact, Moran s I can provide a very general test on spatial autocorrelation, but cannot provide alternatives on which type spatial autocorrelations it would be. Therefore, further testing is provided to differentiate the spatial lag (LM lag ) and spatial error (LM error ) with specifications provided by Anselin and Rey (1992). Empirically, different spatial autocorrelation would results in interpretation of spatial process for the region growth, which would provide different policy implication further. Note that the testing of spatial dependence is based on the assumption of normality of error terms of OLS. In this study, all the testing for normal error distributions of the three laws estimation by OLS are not significant indicated by Jarque-Bera tests shown in table 1. Further testing on heteroskedasticity of the error terms from OLS by tests of Breusch-Pagan also show non-significance. This stresses the validity of the presence of spatial dependence in the models. Apparently different spatial weight matrix generate different testing results, however there exists the strong evidence of the presence of spatial dependence with spatial matrix of Queen and K4. The first and the third law displays very strong spatial autocorrelations with geographical contiguity weight matrix (Queen), while the second law with nearest neighbors (K4), while for the distance weight matrix (Km5), all the models of the three law do not show any strong spatial autocorrelations effects existed. Therefore, for the specification of spatial models, the spatial error model specification for the first law (with Queen weight matrix) and the second law (with K4 weight matrix) is superior to spatial lag model specification, while for the third law (with Queen weight matrix), the spatial lag model is more superior to spatial error model. The specification of spatial model in this study is very similar to the results by Bernat (1996) in which both the first law and the second law are specified as spatial error models and the third law as spatial lag model even though in that case, the one spatial weight matrix by geographical contiguity fits for all the models. <Insert table 4 here> 5.3 Estimation for spatial models 14

16 The estimation for spatial models for Kaldow s law will be conducted based on the testing results shown in table 4, that is, apply spatial error model to the first law using spatial weight matrix of Queen, the spatial lag model to the third law shows using Queen spatial weight matrix, and spatial error model to the second law using K4. The results are displayed in table 5. Apparently, the fits of all the models are improved with correcting the existed spatial dependence as shown in the improved values of the log-likelihood (LIK) and the Akaike information criterion (AIC) 5. For example, all the likelihood values (LIK) from the spatial models are increased compared to OLS, while the AICs are decreased, which is the proof of the improved fits of models. On the other hand, the coefficients of variables displays much differences compared to those in OLS. As if there were no significant spatial dependence in the model, there should not be much changes in the coefficients between OLS and spatial models. <Insert table 5 here> The coefficient of manufacturing growth rate in the first law with spatial error model is increased to from in OLS. This indicates that manufacturing growth rate can generate more positive growth rate to GDP with the presence of spatial dependence. As Bernat (1996) interpreted spatial error model as a region s growth is affected by growth in neighboring regions only to the extent that neighboring regions have above or below normal growth, in which, the normal growth is defined by equation (8). High growth in one region would not affect neighboring states as long as the growth is consistent with the underlying relationship between GDP growth and manufacturing growth. Neighboring regions could not be affected whenever GDP growth rate deviated from the expected value, as indicated by a large residuals in equation (8) (Bernat 1996). In this case, the neighboring regions have positive effects on regional growth of GDP. Since the spatial error coefficient in the testing for the Verdoorn s law is not so significant, the Verdoorn s coefficient in the second law with spatial error model does not change much compared to non-spatial model: in spatial error model, it is 0.41 in spatial error model compared to 0.42 in OLS estimation. However, the positive coefficient of lamda, which is 0.33, in the spatial error model indicates the 5 The presence of spatial dependence makes the R-square an unreliable measure of the goodness of fit, which is not reported here (see Anselin 1988). 15

17 positive spatial spillover effects from the neighboring regions especially when neighboring regions has above normal growth rate (Bernat 1996). Therefore, the existing increasing return to scale in manufacturing sector can also come from the spillover effect of the neighboring regions. As the neighbors here are defined due to the four-nearest neighbors, which is more related to the distance among regions rather than geographical contiguity, this results can imply the importance of the transportations. The estimation of the third law, the coefficient of the spatial lag coefficient is not so significant, therefore the improvement of model specification is not so apparent. The positive relationship between GDP growth and employment growth in manufacturing, and negative relationship between GDP growth and nonmanufacturing are still kept in spatial lag model, with decreased the degrees of those relationship. That is, regional 10 percent of GDP growth is associated with 2.4 percent of manufacturing employment growth (about 3 percent less compared to OLS), but 3.3 percent of neighboring regional GDP growth. The result here verifies the third law that the manufacturing output growth will lead to the overall productivity, but also prove the regional spillover would also exert positive effect on the improvement of the overall productivity. 6. Conclusions This study presented another example of the validation of Kaldor s law in the case of China s regional economy during the period of economic reform along with other empirical analysis on this issues. That is, manufacturing is the main driving force of the economy in China, there exists increasing return to scale in manufacturing sector as well, and the manufacturing growth also trigger the overall productivity to grow. As the presence of spatial dependence have been tested in the models, which has been ignored by most of the empirical analysis on this issue, integrating the spatial effects in the model estimation detects the positive regional spillover effects in the regional economic growth process in China during the period of 1978 to

18 Reference Anselin L (1988) Spatial econometrics: methods and models. Kluwer Academic, Dordrecht. Anselin L (1992) SpaceStat tutoral. Anselin L, Florax RMJ (1995) New directions in spatial econometrics. Spinger- Verlag. Berlin. Anselin L, Rey S (1992) Properties of tests for spatial dependence in linear regression models. Geographical Analysis 23: Amstrong H (1995) Convergence among regions of the European Union, , Papers in Regional Science 74: Atesigky HS (1993) Manufacturing and economic growth in the United States. Applied economics 25: Bernat A (1996) Does manufacturing matter? A spatial econometric view of Kaldor s laws. Journal of Regional Science 36: Bairam E (1991) Economic growth and Kaldor s law: the case of Turkey, Applied Economics 23: Cliff A, Ord J (1973) Spatial autocorrelation. London, Pion. Cripps TF, Tarling AP(1973) Growth in advanced capitalist economies, Cambridge, Cambridge University Press Dall erba S (2005) Productivity convergence and spatial dependence among Spanish regions. Journal of Geographical Systems 7: Dasgupta S, Singh A (2006) Manufacturing, services and premature deindustrialization in developing countries. Research Paper No. 2006/49. UNU- WIDER Démurger S, Sachs JD, Woo WT, Bao S, Chang G, Mellinger A (2002) Geography, Economic Policy and Regional Development in China. NBER Working Paper 8897, National Bureau of Economic Research, Cambridge, MA. Drakopoulos SA, Theodossiou I (1991) Kaldor s approach to Greek economic growth. Applied Economics 23: Fan S (1997) Effects of technological change and institutional reform on production growth in Chinese agriculture. American Journal of Agricultural Economics 73: Fingleton B (2004) Regional economic growth and convergence: insights from a sptail econmetric perspective, in Anselin L, Florax R and Rey S (Eds) Advances in Spatial Econometrics,Springer-Verlag, Berlin. Fingleton B, López-Bazo E (2006) Empirical growth models with spatial effects, paper present in International Workshop on: Spatial Econometrics and Statistics, May, Fingleton B, McCombie JSL (1998) Increasing returns and economic growth: some evidence for manufacturing from the European Union regions. Oxford Economic Papers 80:

19 Florax RJ, Rey S (1995) The impact of misspecified spatial structure in linear regression models. In: Anselin L, Florax FJ (eds) New Directions in Spatial Econometrics. New York, Springer-Verlag. Gallup JL, Sachs JD, Mellinger A (1999). Geography and Economic Development. International Regional Science Review 22 (2): Hansen JD, Zhang J (1996) A Kaldorian approach to regional economic growth in China, Applied Economics 28: Harris RID, Lau E (1998) Verdoorn s law and increasing return to scale in the UK regions, : some new estimates based on the cointegration approach. Oxford Economic Papers 50: Huang J, Rozelle S (1994) Technological change: the re-discovery of the engine of productivity growth in China s rural economy. Journal of Development Economics 49: Jeon Y (2006) Manufacturing, increasing returns and economic development in China, : a Kaldorian approach. Work Paper No , Department of Economics, University of Utah. Lin, J. Y. (2003). Development Strategy, Viability, and Economic Convergence. Economic Development and Cultural Change, 51 (3): Lin JY, Cai F, Zhou L (2003) The China miracle: development strategies and economic reform (Revised edition). Chinese University Press, Hong Kong. López-Bazo E, Vayá E, Artis M (2004) Regional externalities and growth: evidence from European regions, Journal of Regional Science 44: Kaldor N (1966) Cause of the slow rate of economic growth of the United Kindom: an inaugural lecture, Cambridge University Press. Kaldor N (1967) Strategic factors in economic development, New York: Ithaca. Kaldor N (1968) Productivity and growth in manufacturing industry:a reply. Economica 35: Li X, Liu X, Parker D (2001) Foreign direct investment and productivity spillovers in the Chinese manufacturing sector, Economic Systems 25: Lin JY (1992) Rual reforms and agricultural growth in China. American Economic Review 82: McCombie J (1983) Kaldor s law in restrospect, Joural of Post-Keynesian Economics 5: McCombie J, Thirwall AP (1994) Economic growth and the Blance-of-payments constraint. St. Martin s Press, London. McCombie J, Pugno M, Soro B (2003) Productivity Growth and Economic Performance: Essays on Verdoorn s Law. London: Palgrave-Macmillan. McMillan J, Whalley J, Zhu L (1989) The impact of China s economic reforms on agricultural productivity growth. Journal of Political Economy 97: National Bureau of Statistics (NBS) (2004) China compendium of statistics , Compiled by Department of Comprehensive Statistics of National Bureau of Statistics, China Statistics Press. 18

20 Necmi S Kaldor s growth analysis revisited. Applied Economics 31: Parika A (1978) Differenc in growth rated and Kaldor s law, Economica 45: Pons-Novell J, Viladecans-Marsal E (1999) Kaldor s laws and spatial dependenc: evidence for the European regions, Regional Studies 33: Rey S, Montouri B (1999) US regional income convergence: a spatial econometric perspective, Regional Studies 33: Rowthorn RE (1975) What remains of Kaldor s law, Economic Journal 85: Stoneman P (1979) Kaldor s law and British economic growth, Applied Economics 11: Thirlwall AP (1983) A plain man s guide to Kaldor s growth laws. Journal of Post- Keynesian Economics 5: Thirlwall AP (1991) Nicholas Kaldor In Edward J Nell (ed) Nicholas Kaldor and mainstream economic: confrontation or convergence? St.Martin s Press, New York. Wells H, Thirlwall AP (2003) Testing Kaldor's Growth Laws across the Countries of Africa, African Development Review 15: Wen G (1993) Total factor productivity change in China s farming sector: Economic Development and Cultural Change 42: Whiteman JL (1987) Productivity and growth in Australian manufacturing industry, Journal of Post Keynesian Economics 9: World Bank (1985) China: long term issues and options. Oxford: Oxford University Press. Young A (1928) Increasing returns and economic progress? The Economic Journal 38:

21 Tables and figures Table 1 OLS estimation results of Kaldow s law The first law q GDP = q (6.167) (9.902) R 2 = Jarque - Bera (0.6009) m Breusch - Pagan (0.6845) The second law e m = q ( ) (4.3764) R 2 = Jarque - Bera , Breusch - Pagan (0.7355) m (0.6845) Breusch-Pagan (0.6845) The third law q = e e, d, d > 0 GDP 0 m nm 2 3 ( ) (2.4919) (1.8633) R 2 = Jarque - Bera , Breusch - Pagan (0.5333) (0.2150) Table 2 The list of spatial weight matrices Spatial weight matrix Queen K4 Km5 Construction rules Geographical contiguity (Queen) Four nearest neighbors Greater than 0 less than 500km Table 3 Moran s I test for spatial autocorrelation (normal approximation) Weight matrix Moran s I p-value GDP7804 Queen SEC7804 Queen EMP7804 Queen GDP7804 KM SEC7804 KM EMP7804 KM GDP7804 K SEC7804 K EMP7804 K Table 4 Testing of spatial models The first law The second law The third law Spatial weight matrix: geographical contiguity (Queen) Value p-value Value p-value Value p-value Moran s I LM lag RLMlag

22 LM error RLMerror Lagrange Multiplier (SARMA Spatial weight matrix: distance less than 500 kilometer (KM5) Value p-value Value p-value Value p-value Moran s I LM lag RLMlag LM error RLMerror Lagrange Multiplier (SARMA Spatial weight matrix: 4 nearest neighbor (K4) Value p-value Value p-value Value p-value Moran s I LM lag RLMlag LM error RLMerror Lagrange Multiplier (SARMA) Table 5 Estimation results of Kaldow s law considering spatial dependence The first law The second law The third law Dependent variables GDP growth rate Employment GDP growth rate growth rate Spatial model Spatial error Spatial error Spatial lag Weight matrix Geographical contiguity (Queen) 4-nearest neighbors (K4) Geographical contiguity (Queen) Constant (5.2647) (-2.433) (3.4511) Manufacturing output growth rate ( ) (4.5573) Manufacturing employment growth rate (2.408) Non-manufacturing employment growth rate (-1.732) lamda (4.3672) (1.4932) rho (1.4745) LIK LIK (in OLS) AIC AIC (in OLS)

23 Figures GDP7804 SEC7804 EMP7804 GDP SEC EMP Figure 1 China s regional economic growth rate from 1978 to 2004 ² Neimenggu Heilongjiang Jilin Xinjiang Xizang Qinghai Liaoning Beijing Gansu HebeiTianjin Ningxia Shanxi Shandong Shaanxi Henan Jiangsu Anhui Hubei Shanghai Sichuan Chongqing Zhejiang Hunan Jiangxi Guizhou Fujian Yunnan Guangxi Guangdong Taiwan Hongkong Hainan Decimal Degrees Figure 2 The administrative map of China 22

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