by

Federico Frattini, Francesco Nicolli, Giorgio Prodi

Technological capabilities and growth: A study of economic convergence among Chinese prefectures

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SEEDS Working Paper 29/2014 December 2014 by Federico Frattini, Francesco Nicolli, Giorgio Prodi.

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Technological capabilities and growth: A study of

economic convergence among Chinese prefectures.

Federico Frattinia

Francesco Nicollia,b

Giorgio Prodia,c ,*

Abstract

The paper focuses on the role of technological capabilities in pushing regional catch-up in China. A

leading force behind its fast economic growth has been the government action in reforming rules

and institutions and supporting structural change in the long run. The local clustering process of

technological capabilities represents an important piece in this strategy. The regional endowment of

technological capabilities is approached by the geographical distribution of innovation activities

among prefectures. The analysis aims to verify if there is convergence among the prefectural in-

come levels and technological capabilities positively affect the intra-national catching-up process.

Accordingly, this contribution presents a growth convergence estimation model that includes four

indexes for innovation systems already adopted in literature. Indicators refer to the information

about Chinese patent applications at EPO in the period 1996–2010 (OECD REGPAT and Citations

databases, January 2014). In order to fit the research questions, patent data have been restricted, re-

organized and originally regionalized by the authors running a semantic search of prefectures’

names in the “address” field associated to each Chinese inventor. Main results show that an abso-

lute convergence process already started, although disparities decline slowly, and the accumulation

of technological capabilities can foster this dynamic.

JEL Classification: O30, O47, O53, R19

Keywords: China, growth, patent, region, catch-up

a University of Ferrara, Department of Economics and Management

b IRCrES – CNR Milan

c Visiting research fellow at CCWE – Tsinghua University, Beijing

* Corresponding author: giorgio.prodi@unife.it

1

1. Introduction

The paper focuses on the connections between the Chinese long-run development and technolog-

ical capabilities as a factor pushing the economic catch-up. Often, the growth of Chinese GDP is

considered the main consequence of the increase in exportations, but this explanation is not enough.

Several contributions in literature already showed that improvements in factors productivity and

technological sophistication cannot be overlooked among the determinants of economic develop-

ment in China (Brandt, Rawski, & Sutton, 2007; Brandt & Thun, 2010; S. Chen, Jefferson, &

Zhang, 2011; Naughton, 2007; Rodrik, 2006). The leading force in this process mainly was the

governmental action in reforming rules and institutions, the pillar of a process of structural change

enabling China to seize the opportunities provided by the progressive integration of the internation-

al markets (Brandt et al., 2007; Naughton, 2007).

Nonetheless, a tumultuous growth has the side effect to stress the intra-national economic dispar-

ities, at least in the first stages. Actually, an important piece of Chinese development has been rep-

resented by the attraction of foreign technologies, science parks and the spin-offs creation (K. Chen

& Kenney, 2007; Hu, 2007; Kroll & Liefner, 2008; Li, 2009) that at beginning clustered in Region-

al Innovation Systems (RIS) de facto and later has been incorporated in a more comprehensive

strategy (Motohashi & Yun, 2007). This transition from the attraction to the creation of technolo-

gies, i.e. making the technological capabilities building process endogenous, probably represents an

essential improvement in the Chinese development process leading to a new wave of growth and, at

least in part, the reduction of the initial internal disparities. Accordingly, the aim of the paper is to

show that, despite the fastness of growth, in China (H1) there is a intra-national convergence of the

income growth rates and (H2) this convergence is positively affected by the local development of

technological capabilities.

The Chinese development strategy and its main steps since 1978 are briefly depicted in Section

2, stressing the role of capabilities building and clustering as tools for a more balanced development

(Frattini & Prodi, 2013b) and especially of technological capabilities in pushing the catching-up

process (Lee, 2013). Then, Section 3 formally states the research questions and the model specifica-

tion. The analysis essentially relies on indexes of innovation systems based on patent data and al-

ready adopted in literature to measure technological catch-up in middle-income countries (Lee &

Yoon, 2010; Lee, 2013; Park & Lee, 2006). Indicators refer to the information about Chinese patent

applications at the European Patent Office (EPO) in the period 1996–2010 that cover the most part

of the innovation activities in China. Data are available in the OECD REGPAT and Citations data-

bases, but originally regionalized by the authors at prefectural level running a semantic search of

prefectures’ names in the “address” field associated to each registered Chinese inventor. Moreover,

an international benchmarking of regions according the World Bank income classification is intro-

duced in the estimations to partially disentangle the puzzled development scenario in China. Final-

ly, the regression results are presented and discussed in Section 4, while the most relevant implica-

tions of the empirical evidence are deepened in Section 5.

2

2. Economic growth and technological capabilities in China

Economic growth undoubtedly is a pillar of the human progress. The disparities in growth capa-

bilities are a measure of leadership, at least since the first Industrial Revolution in the XVIII centu-

ry. Then, the history of the world development has essentially been the account of new growth fron-

tiers, on a hand, and the challenges of catching-up followers, on the other. The first extraordinary

feature in this long narration is the cumulativeness that not only shifts the frontier ever further, but

also reshapes it continuously providing development with more and more meanings. The second

aspect is the interrelation among all the participants to an unavoidable challenge, which potentially

can turn stark economic growth in a better future for all.

Cumulativeness and interrelation of the economic development obviously influence the own

mechanism of catch-up. The fundamental hypothesis in the economic theory is that the productivity

growth in the long run is inversely related to the productivity initial levels (Abramovitz, 1986).

Nonetheless, it cannot be excluded that this relation would be affected by the purpose of production

(whom to produce for) and its constraints (how to produce). In other words, to narrow their gap fol-

lowers have to engage the same challenges as the leaders or even to aim for overtaking them.

Hence, measuring how fast the growth could be is not enough: developing economies also have to

be aware about how far they can go and improve the fact of development by novel approaches.

Although it is usual to look at growth convergence comparing the productivity or the income

growth rate across countries (Barro & Sala-i-Martin, 1992, 1997; Caselli, Esquivel, & Lefort,

1996), economic catch-up within countries has also assumed increasing relevance (Barro, Sala-i-

Martin, Blanchard, & Hall, 1991; Quah, 1996). Reducing income disparities among regions is im-

portant in particular for those countries experiencing internal structural gaps or facing a tumultuous

growth process. Actually, the risk is mainly to generate several losers. A fast growth makes eco-

nomic development an extremely puzzled phenomenon at national level, because the factors push-

ing regions catch-up vary not only in time, but also in space according to the distribution and accu-

mulation of imbalances.

China has experienced a formidable growth during the last three decades, a process mainly driv-

en by government initiatives aimed to seize the opportunities in the increasing integration of inter-

national markets. Nevertheless, China is not exempt from the dangers hidden behind this phenome-

non that can be overcome by a long-run strategy supporting the whole economic system in both the

creation of new capabilities to grow and the structural adaptation to growth.

2.1. A long-run development strategy

Sometimes the Chinese economic model is subject to external criticisms about the stickiness of

intra-national disparities and the retardation of competitive factors in comparison to the advanced

economies. Similarly, growth in China is frequently considered the main consequence of the in-

crease in exportations, but literature has shown that export is only one of the forces driving the Chi-

3

nese development (He & Zhang, 2010). Actually, this process has been fostered by a general en-

hancement of industrial capabilities (Brandt et al., 2007; Felipe, Kumar, Usui, & Abdon, 2013) im-

proving factors productivity and technological sophistication (S. Chen et al., 2011; Rodrik, 2006)

that can be exploited in a progressively closer interaction with international markets (Naughton,

2007). But the nature of growth does not reduce the risk of strong imbalances anyway (J. Chen &

Fleisher, 1996; Fleisher & Chen, 1997).

What can be useful in compensating the side effects of fast growth is a long-run development

strategy that is fulfilled in a time perspective apt to make the efforts in convergence appreciable

(Andersson, Edgerton, & Opper, 2013). Such a strategy in China has been formulated since 1978,

when the government started a long-lasting process of structural reforms aimed to a progressive and

experimental transition toward the integration in the international economy (Frattini & Prodi,

2013a; Naughton, 2007). The crucial moment along this way certainly is the access to the World

Trade Organization (WTO) in 2001, but this is only the beginning of a recent phase. Before that, the

policy maker addressed the most part of its attention to the creation of the industrial capabilities

needed to face the international competition and to seize the opportunities it provides (Brandt &

Rawski, 2007). As frequent in developing countries, the accumulation of these capabilities has been

initially boosted by the attraction of foreign investments in Special Economic Zones (SEZ), striving

for capturing their economic and technological spillovers (Zeng, 2010a).

2.2. Building endogenous technological capabilities

Foreign Direct Investments (FDI) represent an important piece of the Chinese development strat-

egy concerning a wider set of policy actions that specifically aimed to improve technological capa-

bilities, as attracting foreign technologies (C. Chen, Chang, & Zhang, 1995; Cheung & Lin, 2004;

Fu, 2008; Girma, Gong, & Gorg, 2008), establishing science parks (Hu, 2007; Zeng, 2010a), and

supporting the spin-offs creation (Y.-C. Chen, 2006; Kroll & Liefner, 2008). According to the dra-

matic increase in patenting (Figure 1), in China innovation activities started significantly spreading

during the Nineties, when FDIs and the activities of MNEs were one of the most favorite tools in

promoting development (Brandt et al., 2007; Frattini & Prodi, 2013b; Naughton, 2007). Actually,

these tools represented a gaping doorway to faster growth (Zeng, 2010b) and technological upgrade

(C. Chen et al., 1995; X. Chen & Sun, 2000; Y.-C. Chen, 2007). As a consequence the seeds of new

technological capabilities in China have been in most cases exogenous and unbalanced at the be-

ginning (Lin, Liu, & Zhang, 2009; Sun, 2002).

Nonetheless, regional innovation activities are essential in technological catch-up (Fagerberg,

Srholec, & Verspagen, 2010; Fagerberg & Srholec, 2008; Fu, Pietrobelli, & Soete, 2011; Guan,

Mok, Yam, Chin, & Pun, 2006), because the absorption of the imported technologies is a funda-

mental step in generating local capabilities, but it could slow down the process of industrial upgrad-

ing without strong complementarities to some indigenous efforts (Fu et al., 2011; Sun, 2002). Co-

herently, the success of the Chinese development model has always been closely related to those

4

processes of endogenous capabilities building needed to seize the opportunities provided by the in-

creasing integration of the international markets (Brandt et al., 2007; Naughton, 2007). Of course,

this happened for the technological capabilities too, as shown by the declining relation between for-

eign investments and innovation activities (Figure 2).

Figure 1. Patent applications at EPO per million of inhabitants: prefectures, inventors’ location, 1996-2010.

Source: elaboration on All China Data Center and OECD REGPAT database, January 2014.

Figure 2. Patent applications at EPO and inward FDI: Pearson’s correlation index,

prefectures, inventors’ location, 1996-2010.

Source: elaboration on All China Data Center and OECD REGPAT database, January 2014.

Moreover, the rise of the average number of patents per prefecture in fostered by both the in-

crease of innovation activities within the best performing regions and their diffusion among a larger

numbers of regions (Figure 1 again), even though the newcomers will probably take some time be-

fore assuming the features of RIS, i.e. regions where innovation activities are systemic and support-

ed by well-structured processes of governing, financing and learning (Cooke, Gomez Uranga, &

0

20

40

60

80

100

120

0.01

0.1

1

10

100

1000

10000

1996 1998 2000 2002 2004 2006 2008 2010

pre

fect

ure

s

pa

ten

t a

pp

lica

tio

ns

year

prefectural value average value prefectures count

-0.2

0

0.2

0.4

0.6

0.8

1

1996 1998 2000 2002 2004 2006 2008 2010

Pea

rson

's

corr

elati

on

coef

fici

ent

year

patents / inward FDI correlation mobile average (3 periods)

5

Etxebarria, 1997). The local structuration of the technological processes undoubtedly brings to

completion the shift from exogenous to endogenous capabilities building and the achievement of a

more gainful competitiveness. Nonetheless, this is consequent to understanding how the diffusion

of innovation activities can prompt the economic catch-up without to induce other imbalances.

3. Research settings

3.1. Research questions

The first hypothesis in our contribution is (H1) an intra-national catch-up process in China.

Growth economists originally introduced the idea of convergence as an implication of the tradition-

al neoclassical growth models. They usually considered two types of convergence. The first one, al-

so known as conditional convergence, states that the economic growth rate decreases as the econo-

my gets close to its own steady state. Conversely, the second type concerns the idea of absolute

convergence occurring when poor countries exhibit growth rates that are higher than the richer

countries’ ones, i.e. developing countries are “catching up” the frontier. It should be noticed that the

first concept does not necessarily imply the second one and that this occurs only when both the

leaders and the followers are converging to the same identical steady state (Barro & Sala-i-Martin,

2003).

The tools to test these hypotheses are well established and represent a solid part of the growth

theory debate where the notion of convergence usually assumes two different dimensions. One set

of tests usually performed, and known in literature as β-convergence, refers to the absolute conver-

gence and aims to check whether the latecomers are catching up the leaders. The second idea of

convergence, instead, is also known as σ-convergence and is analyzed testing the cross-sectional

dispersion of the growth rates over the time period and looking at its increase or decrease. Conver-

gence of the first type tends to generate convergence of the second one, but this is not always the

case. In fact β-convergence is a necessary but not sufficient condition for σ-convergence, because

other factors could intervene in the growth process.

Accordingly, the model specification we present aims to verify if there is β-convergence of the

income growth rates across the Chinese prefectures. In addition, we introduce among the explanato-

ry variables some indicators for the innovations systems in order to verify if (H2) technological ca-

pabilities positively affect the catch-up process, as one of the factors intervening on the overall

growth dynamics.

3.2. Model specification

Equation (1) describes the econometric specification we adopt to test β-convergence:

6

(1) log β

1log

–1 β

–1 β

–1 β

–1 β

–1

where Δ log ( gdppci,t ) is the annual GDP per capita growth rate in the prefecture i and year t and β

is the first parameter of interest, because associated to the previous levels of GPD per capita (

gdppci,t–1 ). A negative value of β implies that those regions that are farer from the steady state in

the past (t–1) grow on average faster than the regions closer to the steady state between t and t–1.

This result is what basically implies the effect of β-convergence. Moreover, the model is extended

with four indexes for innovation systems, namely cyti,t–1 , orgi,t–1 , cnci,t–1 , lczi,t–1 , that should cap-

ture the endowment of technological capabilities. Finally, ’t is a set of controls including variables

traditionally included in the convergence studies, t is the year-specific dummy for region-invariant

shock effects (e.g. national policies, inflation, etc.) and i,t is the residual.

3.3. Data description

3.3.1. Income data and international benchmarking

As already mentioned, a very fast growth generally makes the intra-national development sce-

nario much more puzzled. For this reason, we refer to the World Bank income classification (Table

1) that allows disentangling the Chinese picture benchmarking the prefectures’ income to different

levels around the world (Figure 3). Looking at the GDP per capita in 2010, the most part of the pre-

fectures belong to the upper-middle- and lower-middle-income groups (Table 1). Income data are

available for 285 prefectures only over the 345 total, but it is important to remark that they cover

the 92.6% of the national population anyway.

3.3.2. Measuring technological capabilities

Technology can be measured in several ways (Keller, 2004). One solution is to look at the input

of the technological processes, as the R&D expenditure or personnel, or their effects, as the changes

in Total Factor Productivity. As frequent in literature, our choice is to use a measure of input, i.e.

patents, to quantify the technology and, more precisely, technological capabilities. Of course, this is

a very complex notion and the one-dimensional empiric approach has its own cons, but patent data

are very flexible and can be combined in order to create specific technological indicators.

Table 1. Income classification (US$ 2010).

Income group Income per capita

Minimum Maximum Prefectures

High-income 12,276 15

Upper-middle-income 3,976 12,275 144

Lower-middle-income 1,006 3,975 125

Low-income 1,005 1

Source: World Bank Atlas.

7

Figure 3. Income classes distribution: prefectures, 2010.

Source: elaboration on All China Data Center.

The analysis essentially relies on four indexes of innovation systems already adopted in literature

to depict the catch-up in middle-income countries (Lee, 2013). The cycle time of technologies ( cytit

, Equation 2) is a measure of the time span between the predecessor and the successor technology

(Jaffe & Trajtenberg, 2005), which in our data set is given by the lag in years between the publica-

tion of citing and cited patent. This indicator is expected to have a negative impact on catch-up, be-

cause longer cycles presume a higher stock of knowledge to be cumulated in order to enter the

technological field (Lee, 2013; Park & Lee, 2006). Conversely, the originality of inventions ( orgit ,

Equation 3) is calculated as inverse concentration index of the cited technological fields (Hall,

Jaffe, & Trajtenberg, 2001; Trajtenberg, Henderson, & Jaffe, 1997) and it should positively affect

growth, due to a less narrowed knowledge-base supporting the economic processes. The third indi-

cator is a measure of the (granted) patent concentration across the inventors ( cncit , Equation 4) de-

fined as a usual Herfindahl-Hirschman concentration index that is generally higher in more devel-

oped areas, where innovation activities have a greater social diffusion because they are more com-

mon practices (Lee, 2013). Lastly, the localization of knowledge creation and diffusion ( lczit ,

Equation 5) focuses on the probability a patent citation localization to match the cited patent locali-

zation, compared to the geographically unconditional matching (Jaffe, Trajtenberg, & Henderson,

1993; Lee & Yoon, 2010). Indicators are formally defined as follows:

(2)

(3) 1 –

8

(4)

(5) –

where P is the set of patents p, C the set of citations c, J the set of inventors j, K the set of techno-

logical fields k, l the citation time lapse, i the Chinese prefecture the inventor is located in, o the rest

of world, w all the world, and t the patent or citing patent priority year depending on the index. In

addition, the regressions include two dummy variables dpatit and dcitnit accounting for the manipu-

lation of data respectively that has been necessary to balance the data set for patents and citations

respectively.

The indicators refer to information about patent applications at EPO by Chinese inventors be-

tween 1996 and 2010 included in the OECD REGPAT and Citations database, January 2014. None-

theless, data have been restricted and reorganized in order to better fit the research question. At

first, Hong Kong, Macau and Taiwan regions are excluded in order to avoid extra-territorial distor-

tions and, at second and more important, patents have been originally regionalized at the prefectural

level (Figure 4) that is not available among the information provided in the OECD REGPAT data-

base. The procedure adopted is based the semantic search of prefectures’ names in the “address”

field associated to each Chinese inventor and, as a control, the regional attributions obtained have

been compared to the in-database provincial attribution.

Figure 4. Patent applications at the EPO per million of inhabitants:

prefectures, inventors’ location, sample average 1996-2010.

Source: elaboration on OECD REGPAT database, January 2014.

9

Table 2. Descriptive statistics.

Code Variable description N Mean St Dev Min Max Source

gdppc Gross Domestic

Product per capita

(Yuan)

4275 16005.23 18274.27 1106.01 175125.2 All China Data

Center, City

Statistics

cyt Technology cycle

time

4275 1.99 5.84 –0.12 80.33 Elaboration on

OECD RAGPAT

and Citations

databases, January

2014

org Originality of

inventions

4275 0.073 0.18 0 0.91 Elaboration on

OECD RAGPAT

and Citations

databases, January

2014

cnc Concentration of

inventive activities

4275 0.94 0.21 0 1 Elaboration on

OECD RAGPAT

and Citations

databases, January

2014

lcz Local diffusion of

knowledge

4275 0.11 1.31 –.003 40 Elaboration on

OECD RAGPAT

and Citations

databases, January

2014

pdens Person per sq.km

(log in the analysis)

4005 431.62 388.61 4.7 11564 All China Data

Center, City

Statistics

fork Actually utilized

foreign capital per

capita (USD, log in

the analysis)

3662 77.52 193.12 0 2396.01 All China Data

Center, City

Statistics

hedu Student enrolment

in higher education

per 10000 persons

(log in the analysis)

3864 0.009 0.015 0.001 0.12 Elaboration on All

China Data Center,

City Statistics

emp Employees per

10000 persons (log

in the analysis)

4023 0.16 0.18 0 0.66 Elaboration on All

China Data Center,

City Statistics

indva Secondary industry

value added (share

on total)

4145 0.461 0.11 0.08 0.91 All China Data

Center, City

Statistics

3.3.3. Control variables

Our set of baseline controls includes the logarithm of population density ( log pdensit ), which

account for the different demographic structures across the prefectures. We do not have a priori ex-

pectations on this variable and either a positive effect, linked to possible scale economies generated

by the urban agglomeration, or a negative one, due to the decrease in the living standards caused by

10

an eventual congestion, may emerge. Secondly and coherently to the relevant role of the inward

Foreign Direct Investments (FDI) in the Chinese development, we introduce in the model equation

the actualized level of foreign capital ( log forkit ), which is expected to be positively correlated

with economic growth. Thirdly, a measure of human capital cannot be omitted in explaining devel-

opment. For this reason, the estimations include a measure of both the student enrolment in high

education ( log heduit ) and the level of employment ( log empit ) as factors positively affecting the

growth rate. Finally, the model specification also controls for the composition of the prefectural

economies and, more precisely, the relevance of the manufacturing sector ( indvait ). Again, more

industrialized provinces are expected to have experienced higher growth rates.

Despite the patents data, the information related to income, population and the control variables

refer to the City Statistics in the All China Data Center database. Descriptive statistics of all the var-

iables included in the regressions are reported in Table 2, while a correlation matrix is presented in

Table 3.

Table 3. Correlation matrix.

1 2 3 4 5 6 7 8 9

lagged cyt 1

lagged org 0.6745 1

lagged cnc –0.4055 –0.5639 1

lagged lcz 0.0965 0.1525 –0.2161 1

log pdens 0.2060 0.2564 –0.2575 0.0766 1

log fork 0.3413 0.3959 –0.3719 0.1177 0.4676 1

log hedu 0.3519 0.4360 –0.4390 0.1535 0.3536 0.5731 1

log emp 0.2264 0.3141 –0.3668 0.1188 0.3685 0.4177 0.3649 1

indva 0.1057 0.1068 –0.0603 0.0075 0.1109 0.2253 0.1471 0.0456 1

4. Results and discussion

Econometric results concerning our test of β-convergence for the log gdppc are shown in Table

4: Model I represents the baseline specification, where the convergence hypothesis has been tested

alone; the patent-based indicators to account for the characteristics of the innovation systems and

the endowment of technological capabilities are introduced in Model II; Model III and IV replicate

the last specification in the sub-samples of high-income and upper-middle-income (III) and lower-

middle-income and low-income prefectures (IV); finally, Model V presents an augmented specifi-

cation testing the robustness of Model II after variables usually adopted in the growth empirics en-

ter the regression.

11

Table 4. Estimation results.

I II III IV V

lagged log

gdppc

–0.1032*** –0.1328*** –0.0942*** –0.3395*** –0.3101***

(0.0155) (0.0178) (0.0308) (0.0329) (0.0441)

lagged cyc –0.0001 –0.0003 –0.0009 –0.0005

(0.0005) (0.0004) (0.0017) (0.0006)

lagged org 0.1079*** 0.0259** 0.2055** 0.0706**

(0.0345) (0.0131) (0.0977) (0.0293)

lagged cnc –0.0479*** –0.0316* 0.0230 –0.0829***

(0.0180) (0.0168) (0.0287) (0.0229)

lagged lcz –0.0010 –0.0006 –0.0600 –0.0003

(0.0014) (0.0012) (0.0925) (0.0013)

log pdens –0.0276***

(0.0078)

log fork 0.0473***

(0.0085)

log hedu 0.0172**

(0.0072)

log emp 0.0942***

(0.0200)

indva 0.7041***

(0.1081)

Intercept 0.6199*** 1.0086*** 0.9264*** 2.2851*** 2.8380***

(0.1185) (0.1522) (0.2769) (0.2715) (0.3840)

Sample Full Full High- and

upper-middle-

income

Lower-middle-

and low-income

Full

dpat Yes Yes Yes Yes

dcitn Yes Yes Yes Yes

Year FE Yes Yes Yes Yes Yes

N 3990 3990 2058 1932 3156

Standard errors clustered by prefecture (in parentheses). Significance levels: * 10%, ** 5%, *** 1%.

All the models display a significant pattern of β-convergence in the period 1996-2010 (I-V), cor-

roborating the hypothesis that the laggard prefectures experienced higher growth rates with respect

to richer ones. Moving to the technology indicators (II), it can be noticed that two of these variables

org and cnc exert respectively a positive and negative effect on the income per capita growth that

are coherent with our theoretical expectations. Main results are confirmed in the two different in-

come sub-samples and, in particular, the speed of convergence is higher among the low-income and

the lower-middle-income prefectures as suggested by theory (IV). The org variable is significant in

both the regressions (III-IV), but cnc is significant in the high-income and upper-middle-income

group only (IV). Lastly, the augmented specification (V) confirms the core model results about both

12

the convergence hypothesis and the effect of the patent-based indexes, while the control covariates

are all statistically significant and show the expected impact on the income growth rate. Moreover,

the mean variance inflation factor is always below 3.5 suggesting that multicollinearity is not an is-

sue with these estimates.

The technological capabilities indicators deserve some more considerations. As already men-

tioned, they have been identified in literature as the variables discriminating at the best between the

innovation activities in the high-income and the middle-income countries (Lee, 2013). Neverthe-

less, our analysis refers to a partially different income scenario. Probably, this is the first reason

why two among the four indexes introduced in the regressions display non-significant coefficients.

Then, a second explanation concerns the source of patent data. Looking only at the patent applica-

tions at the EPO basically means to observe only a small portion of the invention activities per-

formed in the developing countries, even though at very high level of legal and procedural homoge-

neity (OECD, 2009). Actually, filling patents at the EPO is more costly than the national patent of-

fices and should be moved by a clear motivation in protecting property rights outside the country

borders. Accordingly, using EPO data, and the US or the Japanese as well, works de facto as an in-

vention quality threshold (Johnstone, Haščič, & Popp, 009; Popp, 00 ). The side effect of this

feature also implies a third explanation for the non-significance of coefficients, i.e. some prefec-

tures miss more data than others and, at the extreme, show no data at all. Nevertheless, if these three

aspects reduce the evidences found in the regression, at the same time we may say that what has

been found is statistically robust.

Figure 5. σ-convergence in log gdppc.

Finally, Figure 5 illustrates the degree of σ-convergence for the log gdppc, i.e. the reduction in

the variability of the income levels across prefectures. The disparities rose sharply until the end of

the Nineties’ and turn to steadily decline in 1997 going to stabilize after 2001. As a result, in 2010

the coefficient of variation is slightly below the initial value, suggesting as the process of β-

convergence observed in the previous estimation has started a process of absolute convergence, but

0.04

0.06

0.08

0.10

0.12

0.14

1996 1998 2000 2002 2004 2006 2008 2010

coef

fici

ent

of

vari

ati

on

(sd

/mea

n)

year

13

that this process is, however, still not concluded. Therefore, the results state that H1 is anyway fully

verified and there is an economic catch-up going among the Chinese prefecture, while H2 is only

partially verified, because some target variables are not significant, but technological capabilities

appear as a factor positively affecting the economic growth in China.

5. Conclusions

The analysis presented moves mainly from the hypothesis that technological capabilities have an

important role in the developing countries catching-up process. The problem of persistent economic

disparities is relevant not only across countries, but also within those nations that experienced a fast

growth. China undoubtedly is one of them and the paper aimed to understand if and how the income

levels are converging across the Chinese prefectures. Accordingly, in explaining the income growth

we extended a traditional convergence model introducing four indicators as proxies for the endow-

ment of technological capabilities.

The first result is the evidence about a β-convergence, i.e. the laggard prefectures are growing

faster than the richer ones. In addition, we found the rate of convergence to be higher in the group

of lower-middle-income and low-income regions, according to the theoretical predictions. Nonethe-

less, the second result concerns the intensity of σ-convergence that has gone stabilizing and sketch-

es an absolute convergence process that is slow and still not complete. In this picture, technological

capabilities exhibit a positive effect on the income growth rate and, as a consequence, we can say

that they can foster the economic catch-up among the Chinese prefectures.

What the estimation model probably misses to capture is a structural change in the technological

capabilities building process. As discussed in Section 2, foreign investments in China represented a

powerful tool to push economic growth, industrial development and technological processes during

the Nineties’, but the relation between innovation activities and inward FDI seems to be weakening

in time. This does not mean at all that foreign capitals are not still important, even though the phe-

nomenon could be interpreted as a progressive shift from exogenous to endogenous processes of

capabilities building. Such a change would be definitely consistent with the overall Chinese devel-

opment strategy, but it needs further empirical investigations to be tested.

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