is physical investment the key to china's growth miracle?

IS PHYSICAL INVESTMENT THE KEY TO CHINA’S GROWTH MIRACLE?

DIEGO ROMERO-AVILA∗

This paper applies Jones’ test for the empirical validity of AK-type models tothe Chinese economy over the period 1952–2006 (Jones C. I., Quarterly Journal ofEconomics, 110, 1995a, 495–525). We aim to establish whether large permanent move-ments in the physical investment rate cause permanent movements in output growth.The evidence indicates that the one-sector AK model cannot be rejected. We also findthat augmenting the model to allow for transitional dynamics—via imbalances in factorendowments, structural transformation, and R&D-based technology transfer—does abetter job in describing China’s growth dynamics than the basic AK model. (JEL C22,O41, O47)

I. INTRODUCTION

Unprecedented high rates of economic growthin China over the past decades have stimulateda wide interest among researchers and poli-cymakers in establishing whether rapid phys-ical capital accumulation is the main driv-ing force behind such outstanding growth per-formance and whether it suffices to sustainChina’s rapid economic growth in the nextdecades. By exploiting the different impli-cations that the relation between investmentand growth has according to the neoclassi-cal paradigm—pioneered by Solow (1956) andCass (1965)—and endogenous growth modelsof the AK-type, this paper will attempt to testthe empirical validity of the AK model for the

*The author would like to thank Chong-En Bai, Chang-Tai Hsieh, and Jessy Zhenjie Qian for making availabletheir data on physical capital stocks, Carsten Holz forsharing his data on effective investment and fixed assetsvalue, and James Ang and Jakob Madsen for sharingtheir data on research intensity and technological distance.Many thanks also go to Jesus Crespo-Cuaresma, MarkusEberhardt, Octavio Fernandez-Amador, Miguel A. Leon-Ledesma, Daniel Oto-Peralıas, and Simon Wren-Lewis forhelpful discussions. The author is particularly indebted toGian Luca Clementi (Editor) and two anonymous refer-ees of this journal for valuable comments and sugges-tions that led to a substantial improvement of the originalmanuscript. The author acknowledges financial support fromthe Spanish Ministry of Science and Technology throughgrant ECO2009-13357 and from the Andalusian Council ofInnovation and Science under Excellence Project SEJ-4546.

Romero-Avila: Associate Professor, Departamento deEconomıa, Universidad Pablo de Olavide, MetodosCuantitativos e Historia Economica, Carretera de Utrera,Km. 1, 41013 Sevilla, Spain. Phone +34 954348381,Fax +34 954349339, E-mail [email protected]

Chinese economy. The main difference betweenthe AK model and the Solow model is thatthe former allows for the existence of constantreturns to a sufficiently broad definition of cap-ital, which in turn enables investment to havea long-run impact on growth. In contrast, underthe neoclassical paradigm, diminishing returnsto reproducible capital leads inevitably to onlytemporary growth effects along the transitionalpath, and long-run growth depends on exoge-nous technological progress.

Constant returns to reproducible capital char-acterizing linear accumulation-driven modelsmay stem from different sources like (1) positiveexternalities as in the models of Frankel (1962),Lucas (1988), and Romer (1986), (2) boundingthe marginal product of reproducible factorsaway from zero (Jones and Manuelli 1990),

ABBREVIATIONS

ADF: Augmented Dickey FullerADL: Autoregressive Distributed LagFDI: Foreign Direct InvestmentGCF: Gross Capital FormationGDP: Gross Domestic ProductGFCF: Gross Fixed Capital FormationMAIC: Modified Akaike Information CriterionNBS: National Bureau of StatisticsOECD: Organization for Economic Co-operation and

DevelopmentOLS: Ordinary Least SquaresR&D: Research and DevelopmentSOUs: State-Owned UnitsTIFA: Total Investment in Fixed AssetsTFP: Total Factor Productivity

1948

Economic Inquiry(ISSN 0095-2583)Vol. 51, No. 4, October 2013, 1948–1971

doi:10.1111/ecin.12005Online Early publication March 24, 2013© 2013 Western Economic Association International

ROMERO-AVILA: INVESTMENT AND GROWTH IN CHINA 1949

or (3) the existence of constant returns toall accumulable factors characterizing the dif-ferent sectors of the economy (Barro 1990;Rebelo 1991). According to standard linearaccumulation-driven models, steady-state outputgrowth directly depends on the total investmentrate in physical capital. Hence, the dynamics ofoutput growth should behave similarly to thedynamics of the investment rate. As noted byJones (1995a), a hallmark of the endogenousgrowth literature implies that any large perma-nent change in total investment should translateinto a permanent change in long-run growth inthe same direction.

Jones (1995a) constitutes the first attemptat testing the AK-type growth models usinga time series framework. His sample includes15 Organization for Economic Co-operation andDevelopment (OECD) countries with data span-ning over the period 1950–1988. He showsthat the investment share in producer durables(and to a lesser extent the total investment rate)presents an upward deterministic trend and isgenerally found nonstationary when augmentedDickey-Fuller ([ADF], 1979) tests are applied.In contrast, rates of economic growth remainfairly stable. Jones then claims that the factthat output growth rates exhibit no large perma-nent movements “imposes a strong and testablerestriction on endogenous growth models: if anendogenous growth model predicts that perma-nent movements in some variable X have per-manent effects on growth, then either:

a) X must exhibit no large persistent move-ments, or

b) Some other variable (or variables) mustalso have persistent effects on growth that offsetthe movements of X in a way that is determinedby the endogenous growth model” (Jones 1995a,502).

Despite the considerable effort, using thisframework, to try to test the empirical validity ofAK-type growth models for the OECD countries(Jones 1995a; Li 2002; Romero-Avila 2006), nowork has been conducted along these lines forthe Chinese economy. This is surprising becausethis issue should be particularly important for acountry like China which is expected to becomethe leading economy in the coming decades,with a population more than 1.3 billion (whichis more than four times the population of theUnited States). This paper tries to fill this gapin the literature by applying Jones’ test to theChinese economy over the past five decades.

The Chinese economy has shown spectacu-lar growth performance with an average annualgrowth rate of real per capita gross domes-tic product (GDP) equal to 6.60% over theperiod 1953–2006, and particularly, over thereform period (1978–2006) growing at 8.60%per year. Along similar lines, the total invest-ment rate in physical capital measured as realgross fixed capital formation (GFCF) as a shareof real GDP has greatly increased from about10% in the early 1950s to 40% in 2006 (seeFigures 1 and 2). In the context of these sub-stantial increases in both real per capita outputgrowth rates and physical investment rates, it iscrucial for policymakers to determine whetherinvestment in physical capital accounts for thelarge rise in output growth; and if that is thecase, whether these growth effects are perma-nent (obeying the predictions of endogenousgrowth theory), or alternatively, they are causedby a series of level effects brought about bya sequence of transitory shocks as suggestedby neoclassical growth theory. Unlike previousstudies that have only focused on cross-nationaldatasets of industrialized countries without pay-ing any attention to an emerging economy likeChina which has become a main actor in theglobal economy, this case-study analysis enablesus to gain a better understanding of the con-ditions, dynamics, and outcomes governing theChinese growth miracle.

Toward this end, and following the logic ofJones’ test, we first investigate the deterministicand stochastic developments of Chinese outputgrowth and the total investment rate through(1) heteroskedasticity and autocorrelation con-sistent t-statistics and (2) the unit root tests withgood size and power of Perron and Rodrıguez(2003) that allow for an unknown change in theintercept and slope of the series. This allowsus to control for the occurrence of structuralchange over the past decades due to large shockslike those associated with the Great Leap For-ward campaign (1958–1961), the Cultural Rev-olution (1966–1976), the subsequent reformsafter 1978, or the reform intensification afterDeng Xiaoping’s southern tour of China in thespring of 1992. Overall, the analysis of deter-ministic and stochastic trends in output growthand investment rates cannot reject the empiricalvalidity of AK models, as both variables appearstationary.

Second, as a further test of the empiricalvalidity of AK models for the Chinese econ-omy we estimate autoregressive distributed lag

1950 ECONOMIC INQUIRY

FIGURE 1Real GDP Growth Series: 1952–2006

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Source: Data from China Compendium of Statistics 1949–2008 (National Bureau of Statistics 2011).

FIGURE 2Investment Rates Series: 1952–2006

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Real effective investment/Real GDP (2) Real TIFA/Real GDP

Sources: Data from Hsueh and Li (1999), Holz (2006), and China Compendium of Statistics 1949–2008 (National Bureauof Statistics 2011).


(ADL) growth models, augmented with leads todeal with the possible endogeneity of invest-ment. This enables us to shed some light onwhether the growth effect of the total invest-ment rate constitutes a genuine long-run effector a transitory one. Overall, these results providesupport for the existence of a positive growtheffect from physical investment in the long run,as predicted by the one-sector AK model.

Third, as China is unlikely to have reachedthe steady state yet, we augment the basic spec-ification with transitional dynamics caused by(1) imbalances in the physical to human capitalratio as in the two-sector AK model of Lucas(1988) and Uzawa (1965), (2) structural trans-formation caused by sectoral shifts from agri-culture to manufacturing as in the models ofEchevarrıa (1997) and Kongsamut, Rebelo, andXie (2001), and (3) different channels of tech-nology diffusion such as trade and foreign directinvestment (FDI) (Acemoglu and Ventura 2002;Keller 2004; Ventura 1999) and investment inresearch and development (R&D) (Aghion andHowitt 1998; Ang and Madsen 2011; Howitt2000; Jones 1995b). The evidence indicates theexistence of a long-run growth effect from thephysical investment rate even after allowing fortransitional dynamics of very different nature.We further show that augmenting the model toallow for transitional dynamics does a better jobin describing China’s growth dynamics than thebasic AK model over the past five decades.

The remainder of the paper is organized asfollows. Section II briefly reviews the existingevidence on the relationship between physicalinvestment and growth as well as describes thedataset. Section III investigates the deterministicand stochastic developments of the total invest-ment rate and output growth series. Section IVtries to determine the growth impact of thetotal investment rate by estimating ADL growthmodels. Section V extends the basic model byallowing for transitional dynamics of differentsource (imbalances in the endowment of physi-cal and human capital, sectoral shifts and differ-ent channels of technology diffusion). Finally,Section VI summarizes the main findings andconcludes.

II. LITERATURE REVIEW AND DATA ISSUES

A. Brief Literature Review

From a time-series perspective, Jones (1995a)rejects the one-sector AK model for 15 OECD

countries on the basis of the deterministic andstochastic developments of output growth andthe investment rate. Estimation of ADL growthmodels only rendered short-lived growth effectsfrom producer durables and total investment.Given the important policy implications forlong-run growth, these results have not goneunchallenged. A more recent study by Li (2002)has extended Jones’ analysis by widening thesample to 24 OECD countries from 1950 to1992. Li argues that the relevant investment ratefor testing the AK models is the total phys-ical investment share, and finds that the caseagainst AK-type models is weakened when ana-lyzing the deterministic trends of total invest-ment shares and economic growth. He alsoestimates distributed lag growth models, find-ing a positive long-run effect of total invest-ment on growth for most countries. The dif-fering results found by Jones (1995a) and Li(2002) may derive from the different defini-tions of investment used and the fact that Li’sgrowth regressions fail to include autoregressiveterms that help control for adjustment dynamicscharacteristic of Solow-type models. In addition,Li (2002) does not test for the nonstationarityproperties of the data and his conclusions arebased solely on the presence or absence of deter-ministic trends in economic growth and the totalinvestment rate.1 More recently, Romero-Avila(2006) corroborates Jones’ findings by applyingtime series methods with good size and powerproperties to a sample of 26 OECD countriesover the period 1950–1992. The rejection of theAK model holds irrespective of the investmentrate employed (either the total investment rateor the investment rates in producer durables andtotal structures, separately).

Regarding the link between physical invest-ment and growth for the Chinese economy,Chow (1993), Chow and Lin (2002), and Chowand Li (2002) estimate standard productionfunctions for the whole of China over the past50 years. They find capital accumulation to bethe most important factor for increasing output,with estimates of the income elasticity of capital

1. McGrattan (1998) did not estimate ADL growthmodels in order to establish the existence of a positive long-run link between growth and physical investment. Rather,she just presented some descriptive evidence through ascatter plot showing a positive relationship between averageinvestment rates and average growth rates for a large cross-section of countries. This has the shortcoming that it failsto control for the likely endogeneity of investment rates andfor the dynamics in the investment-growth nexus.


of up to 0.7.2 We also find a bunch of stud-ies that investigate the investment-growth linkusing data at the regional and/or provincial level.Using the neoclassical framework of Mankiw,Romer, and Weil (1992), Chen and Fleisher(1996), Gundlach (1997), and Li, Liu, andRebelo (1998) provide evidence of a positivegrowth impact from the total investment rate inphysical capital for the Chinese provinces overthe reform period. Similar findings are obtainedfrom the estimation of production functions andparametric and nonparametric growth account-ing exercises (see, among others, Badunenkoand Tochkov 2010; Bai, Hsieh, and Qian 2006;Henderson, Tochkov, and Badunenko 2007; Liuand Li 2006; Unel and Zebregs 2009).

From this brief review of the literature, wecan observe that—to the best of our knowl-edge—our study constitutes the first attemptat testing the empirical validity of AK-typegrowth models for the Chinese economy fol-lowing Jones’ logic. As the engine of growthfor AK models is the investment rate in physi-cal capital, our analysis will help us contributeto the debate over whether it is the accumula-tion of physical capital or productivity improve-ments responsible for the spectacular growthperformance exhibited by the Chinese economyover the past 50 years, and particularly overthe post-reform period. The evidence providedby Krugman (1994), Young (1995), Collins andBosworth (1996), and Henderson and Russell(2005) for other East Asian countries like Singa-pore, South Korea, and Taiwan assigns to capitalaccumulation the prevailing role in accountingfor these growth miracles.3

B. Data Description and Measurement Issues

A problem with Chinese national accountsdata is the lack of investment deflator until1991, when the National Bureau of Statis-tics (NBS) of the People’s Republic of Chinabegan constructing a fixed asset investment priceindex. However, Hsueh and Li (1999) providean implicit investment deflator over the period1952–1995, which is based on data from theAnnual Report of Statistics on Investment in

2. Similar evidence is provided by Yusuf (1994) andWang and Yao (2003).

3. Using a nonparametric production frontier approach,Badunenko, Henderson, and Zelenyuk (2008) find evidencethat physical capital accumulation is the primary contributorto labor productivity growth in China over the 1990s, whilethe contribution of TFP growth appears very small.

Fixed Assets. Prices of investment are deter-mined by taking the weighted average of pricesof machinery and equipment and prices of con-struction and installation.4

Except for the investment deflator over theperiod 1952–1990 which is not available in offi-cial statistics, for the main other variables ofinterest (GDP, population, employment, implicitGDP deflator, GFCF)5 we employ the offi-cial data source “China Compendium of Statis-tics 1949–2008” (National Bureau of Statis-tics 2011). Therefore, the subsequent officialrevisions made to GDP data and some of thecomponents of aggregate expenditure over thepast decade are incorporated into the figurescovered by the official statistics employed inthe analysis.6

Let us now explain why GFCF is used as themain investment variable instead of other alter-natives like gross capital formation (GCF) orinvestment in fixed assets. Arguably, we employGFCF rather than GCF as the latter includesinventories. As stressed by Young (2003), inthe context of China, unsold inventories of stateenterprises cannot be thought of as representingproductive capital, and are thus excluded fromour measure of physical investment. In addi-tion, as pointed out by Bai, Hsieh, and Qian(2006), GFCF may be superior to the serieslabeled “investment in fixed assets,” as thereare two reasons why the latter may not providean accurate measure of the change in China’sreproducible capital stock. First, the NBS con-siders the value of purchased land and expen-diture on old equipment and old structures asbeing part of investment in fixed assets (despitethe fact they do not imply a rise in China’scapital stock). Second, investment in fixed assetsis based on survey data on large-scale invest-ment projects only (implying expenditures over50,000 Yuan and since 1997 the threshold was

4. Since the dataset of Hsueh and Li (1999) is compiledwith the support of the NBS, this may explain why theHsueh-Li dataset and the official data are very close inthe reform era (Wang and Yao 2003). Following commonpractice, we link the official investment deflator availableover the period 1991–2006 with that of Hsueh and Li (1999)for the period 1952–1990.

5. These are required to compute the growth rate ofreal GDP in per capita and per worker terms and the totalinvestment rate (i.e., the real GFCF share of real GDP). Theseries in real terms are measured in 2006 constant Yuanprices.

6. Table A1 shown in the Appendix provides a detailedaccount of the definitions and sources for all the variablesemployed in the empirical exercise.


raised to 500,000 Yuan), thus neglecting small-scale investment projects. This clearly leadsto understate aggregate investment. In contrast,official GFCF is computed by substracting thevalue of land sales and expenditure on usedmachinery and pre-existing structures from totalinvestment in fixed assets, and then adding theresources invested in low-scale projects.7

Even though the official GFCF series is usedas the main investment measure, for robustnesspurposes we will also employ total investment infixed assets (TIFA) in addition to three series ofeffective investment provided by Holz (2006).The effective investment series try to correctfor the fact that “only approximately three-quarters of all investment expenditures translateinto effective investment” (Holz 2006, 154).8

Holz provides an effective GFCF series whichis obtained by applying an estimated transferrate to GFCF. The series on effective investmentare based on the sum of effective investmentof state-owned units (SOUs) and that for non-SOUs. Since non-SOUs effective investment isnot available before 1981, Holz (2006) estimatesthis series using five different methods. The pre-ferred ones are: (1) the method that extends the1986 value of non-SOU effective investmentback in time to 1949 using the real growthof non-SOU industrial gross output value, and(2) the method that calculates non-SOU invest-ment for the years prior to 1986 as the differencebetween total GFCF and SOU investment, whichis then transformed into effective investment byemploying an estimated non-SOU transfer rate.The two effective investment series are labeledas (1) and (2) in our analysis below.

III. DETERMINISTIC AND STOCHASTICDEVELOPMENTS OF OUTPUT GROWTH

AND INVESTMENT RATES

A. Deterministic Trends Analysis

As a preliminary test of the AK model,we begin the analysis by investigating whether

7. Even though GFCF subtracts the value of land sales,it includes fixed assets created in the improvement of land(unless already included in total investment in fixed assets).In addition, even though the purchase of old structures,old equipment, and land are likely to be of minor sizeparticularly before the 1990s, an appropriate investmentmeasure should net out these purchases at all times—asoccurs with GFCF.

8. The effective investment series try to measure thevalue of the increase in fixed assets rather than the mereexpenditure on investment projects in a given year, whichbefore completion may not produce.

the real output growth and investment ratesseries display deterministic trends in the samedirection. For that purpose, we employ threetrend t-statistics which allow us to make correctinferences about the deterministic developmentsof the series. The linear specification estimatedtakes the form:

xt = α + βt + ut(1)

where t = 1, 2, . . . , T ; xt stands for the obser-vations of the real output growth (in per capitaand per worker terms) and total investment ratesseries; the parameter β measures the averagechange in xt per year. In addition to present-ing the results for the heteroskedasticity consis-tent t-statistic (tHCSE) of White (1980) and theJacknife heteroskedasticity consistent t-statistic(tJHCSE) of MacKinnon and White (1985), wealso report the t-statistic robust to heteroskedas-ticity and autocorrelation (tHACSE) proposed byNewey and West (1987).

Table 1 presents the results of the trend testsfor the growth rate of real per capita output,the growth rate of real per worker output, andthe five measures of the total investment rate inphysical capital. Remarkably, the trend coeffi-cients for the rate of real GDP growth both in percapita and per worker terms appear always pos-itive and statistically significant at the 5% level,reflecting the general tendency for output growthto increase over the past decades. As far as thetotal investment rate is concerned, the evidencesupporting the existence of a positive lineartrend in the series is even more apparent than inoutput growth. A statistically significant upwardtrend is found for the five investment rate serieswith the three t-statistics at the 1% level.9

On the basis of these results, we findwidespread evidence of positive trends in bothoutput growth and investment rate series, as pre-dicted by the AK growth model. Figures (1) and(2) depict the evolution of the output growthand investment rates series, respectively. All the

9. As noted by Jones (1995a, 506), the positive trendin the total investment rate could be an artifact resultingfrom the shift away from structures and toward producerdurables, as observed in many OECD countries over thepostwar era. As the depreciation rate of producer durablesis much higher than that of structures, the increase in totalinvestment could reflect the rise in investment to replaceworn-out producer durables capital. Regrettably, there arenot available data on producer durables investment for theChinese economy, so we are unable to investigate the timeseries properties of investment in producer durables. Still,the use of five different definitions of physical investmentfor the Chinese economy should suffice to characterize thetime series properties of investment rates.


TABLE 1Deterministic Trends in Economic Growth and the Physical Investment Rate

β tHCSE tJHCSE tHACSE

Growth MeasuresReal per capita GDP growth 0.116 1.732∗∗ 1.698∗∗ 1.673∗∗

Real per worker GDP growth 0.116 1.840∗∗ 1.807∗∗ 1.796∗∗

Investment Rate MeasuresReal GFCF share of real GDP 0.361 10.734∗∗∗ 10.430∗∗∗ 7.193∗∗∗

Real effective GFCF share of real GDP 0.245 7.905∗∗∗ 7.638∗∗∗ 6.056∗∗∗

Real effective investment share of real GDP (1) 0.352 11.953∗∗∗ 11.649∗∗∗ 7.809∗∗∗

Real effective investment share of real GDP (2) 0.248 8.378∗∗∗ 8.083∗∗∗ 6.497∗∗∗

Real TIFA share of real GDP 0.571 10.229∗∗∗ 9.907∗∗∗ 6.344∗∗∗

Notes: The time trend coefficient is obtained from a regression on a constant and a linear trend. The growth rate ofreal per capita and per worker output is multiplied by a hundred throughout the analysis. All the statistics are one-tailedtests. The one-sided 1%, 5%, and 10% critical values are 2.33, 1.645, and 1.28, respectively. *** and ** imply rejection ofthe null hypothesis of a statistically insignificant coefficient with the one-tailed test at the 1% and 5% levels, respectively.The tHCSE statistic stands for the heteroskedasticity consistent t-statistic (White 1980). The tJHCSE represents the Jacknifeheteroskedasticity consistent t-statistic (MacKinnon and White 1985). The tHACSE is the heteroskedasticity and autocorrelationconsistent t-statistic (Newey and West 1987).

Sources: Data from Hsueh and Li (1999), Holz (2006), and China Compendium of Statistics 1949–2008. (National Bureauof Statistics 2011).

investment rates exhibit clear upward trendingbehavior over the period under scrutiny, and out-put growth is characterized by a positive, butless marked linear trend.

B. Stochastic Trends Analysis

In order to establish whether large perma-nent movements in the investment rates haveaffected permanently the rate of output growthin the Chinese economy over the past fivedecades—as predicted by the AK model—weemploy the MGLS-class of tests with good sizeand power proposed by Perron and Ng (1996)and Ng and Perron (2001) as well as the ADFGLS

test and the feasible point optimal statistic(P GLS

T ) developed by Elliot, Rothenberg, andStock (1996). The MGLS-class of tests includesMZGLS

α and MZGLSt that are modified versions

of the Zα and Zt Phillips and Perron (1988) testsas well as MSBGLS that modifies the Sargan andBhargava (1983) test.10

As the failure to control for the likely occur-rence of structural shifts in the series can leadto spuriously accept the null hypothesis of a

10. For these tests to exhibit good size properties, it iscrucial to select the appropriate lag truncation (k) of theADF specification. For that purpose, we follow Perron andQu (2007) by selecting the optimal autoregressive order kusing the modified Akaike information criterion (MAIC)constructed with ordinary least squares (OLS) detrendeddata. In our application, we take a maximum lag truncationequal to kmax = int(12(T /100)1/4), where int() denotes theinteger part.

unit root in the series (Perron 1989; Zivot andAndrews 1992), we employ the extended unitroot tests with good size and power of Perronand Rodrıguez (2003), which are robust to thepresence of structural change. All these statisticstest the null hypothesis of a unit root against thealternative of stationarity with structural change.The break date is selected when the sum ofsquared residuals under the alternative hypoth-esis in the computation of the feasible pointoptimal test is minimized. A trimming value of0.10 is used.

Table 2 reports the results from the applica-tion of the five unit root tests to the two outputgrowth series and the five investment rate mea-sures. Regarding real per capita and per workeroutput growth, there is strong evidence of sta-tionarity with a break located in 1961, as westrongly reject the unit root null hypothesis withthe five statistics. Likewise, there appears to bewidespread evidence of stationarity with a breaklocated in 1960 for the investment rate series. Onthe one hand, we are able to reject the unit rootnull at the 5% level or better with four tests (allbut the ADFGLSt-statistic) for the first of the realeffective investment rate series and the invest-ment rate based on TIFA. On the other, the unitroot null is rejected at the 1% level with threetests (the modified Phillips-Perron statistics andthe modified Sargan and Bhargava statistic) forthe investment rates based on GFCF and effec-tive GFCF and the second of the real effectiveinvestment rate series.


TABLE 2Stochastic Trends in Economic Growth and the Physical Investment Rate

kaMAIC MZGLS

α MSBGLS MZGLSt ADFGLS P GLS

T T B

Growth MeasuresReal per capita GDP growth 1 −57.381∗∗∗ 0.093∗∗∗ −5.356∗∗∗ −6.840∗∗∗ 6.746∗∗∗ 1961Real per worker GDP growth 1 −70.019∗∗∗ 0.085∗∗∗ −5.915∗∗∗ −7.503∗∗∗ 4.760∗∗∗ 1961Investment Rate MeasuresReal GFCF share of real GDP 4 −56.867∗∗∗ 0.093∗∗∗ −5.270∗∗∗ −3.240 37.872 1960Real effective GFCF share of real GDP 4 −47.658∗∗∗ 0.102∗∗∗ −4.864∗∗∗ −3.052 15.553 1960Real effective investment share of real GDP (1) 4 −148.485∗∗∗ 0.058∗∗∗ −8.604∗∗∗ −3.420 1.784∗∗∗ 1960Real effective investment share of real GDP (2) 4 −48.215∗∗∗ 0.102∗∗∗ −4.896∗∗∗ −3.020 15.072 1960Real TIFA share of real GDP 2 −64.793∗∗∗ 0.086∗∗∗ −5.559∗∗∗ −3.669 10.608∗∗ 1960Critical Values1% −31.3 0.125 −3.93 −4.74 9.945% −25.9 0.137 −3.56 −4.14 12.0410% −23.0 0.146 −3.36 −3.84 13.83

Notes: aLag length k is determined according to MAIC with the finite-sample modification by Perron and Qu (2007),taking a maximum lag truncation equal to kmax = int(12(T /100)1/4), where int() denotes the integer part. A trimming valueof 0.10 is employed. The 1%, 5% and 10% critical values for the statistics relate to the specification with a break in theintercept and trend. *** and ** imply rejection of the null hypothesis of a unit root at the 1% and 5% significance levels,respectively.

Sources: See Table 1.

It is remarkable that output growth andinvestment rates exhibit shifts in their trendfunction almost in the same year around theperiod of the Great Leap Forward (1958–1961).The years of the launch of the Great LeapForward campaign (1958–1960) characterizedby widespread agricultural collectivization andrapid industrialization were followed by the eco-nomic crash that occurred in 1961 due to disas-trous economic policy surrounding the growthstrategy of the Great Leap Forward. As notedby Demurger et al. (2002), the resulting eco-nomic collapse caused by agricultural failureled to a nationwide famine (with estimated30–40 million casualties) that brought the coun-try to subsistence levels.

In sum, this preliminary evidence cannotreject the empirical validity of the AK modelfor the Chinese economy as both rates of eco-nomic growth and the total investment rate arestationary around a segmented trend that shiftedin the early 1960s at the time of the Great LeapForward campaign.

IV. AUTOREGRESSIVE DISTRIBUTED LAG MODELSOF PHYSICAL INVESTMENT AND GROWTH

Having failed to reject the empirical validityof the one-sector AK model on the basis ofthe deterministic and stochastic developmentsof output growth and investment rates, wenow jointly consider the time series behavior

of both series in order to test the long-runrestriction implied by the AK model. For thatpurpose, let us first consider in the expositionthe linear physical-capital driven AK modelof Frankel (1962) and Romer (1986). Eachfirm j ∈ {1, 2, . . . , N} is characterized by thefollowing production technology

yj = Akαj(2)

where kj stands for the level of physicalcapital employed per firm, and A representsaggregate productivity. In modeling the positive“learning-by-doing” externality stemming fromthe accumulation of physical capital, aggregateproductivity depends on the total amount of cap-ital accumulated by all firms:

A = A0

⎛⎝

N∑j=1

kj

⎞⎠

1−α

(3)

where the aggregate levels of physical capitaland production are given by K = ∑N

j=1 kj andY = ∑N

j=1 yj , respectively. We further assumethat all firms face the same technology and fac-tor prices, so that kj = K/N for all N andA = A0K

1−α. In a nutshell, the accumulationof physical capital at the firm level leads toa rise in aggregate total factor productivity(TFP), which generates constant returns to phys-ical capital. Hence, firm-level production equalsyj = A0K

1−α(K/N)α and aggregate production


equals Y = NA0K1−α(K/N)α = AK , where

A = A0N1−α. Assuming a standard capital accu-

mulation equation: K = ikY − δK, where ikstands for the total investment rate in physi-cal capital and δ denotes the depreciation rateof capital, it can be shown that the steady-stategrowth rate of output (and capital) is given by:

gY = gK = Aik − δ(4)

The restriction given by equation 4 impliesthat any large permanent change in the totalinvestment rate should translate into a perma-nent change in long-run growth in the samedirection. As argued in Jones (1995a), if oneallows for the possibility that not all the effectsof investment on growth occur contemporane-ously, estimating ADL growth models is a validmodeling strategy to obtain the long-run impactof physical investment on growth. The inclu-sion of autoregressive output growth terms in thespecification enables us to control for adjustmentdynamics typical of Solow-type models11:

�yt = α +q∑

j=1

βyj�yt−j +p∑

j=0

βkj ikt−j+ εt

(5)

where y stands for the natural log of real percapita output and ik for the total investmentrate in physical capital. As in Jones (1995a), therestriction in Equation (4) within this dynamicempirical framework can be interpreted as therequirement that

p∑j=0

βkj > 0(6)

as the lag order grows sufficiently large. There-fore, if endogenous growth predictions are valid,the sum of coefficients in the polynomial oninvestment must be significantly different fromzero and positive for a sufficiently large lagorder. In that case, a permanent shock to invest-ment rates would exert a permanent growth-promoting effect. Equation (5) can be rewrittenusing the lag operator as follows:

�yt = α + A(L)�yt−1 + B(L)ikt + εt(7)

where A(L) is a lag polynomial of order q − 1and B(L) is a lag polynomial of order p with

11. This also allows us to control for the transi-tional dynamics that some AK-type models like Jones andManuelli (1990) exhibit. See further discussion on this inSection V, where we augment the basic specification toallow for transitional dynamics of different source.

roots outside the unit circle. Equation 7 can bereparameterized so as to distinguish short-rundynamics from long-run effects

�yt = α + A(L)�yt−1 + B(1)ikt(8)

+ C(L)�ikt + εt

where C(L) is a (p− 1 )th-order lag-polynomialsuch that:

cs = −p∑

j=s+1

bj

where s = 1, . . . , p − 1. In sum, the estimatedcoefficient B(1) should pick up the permanentlong-run growth effect of a permanent changein the total investment rate, while the first-difference terms capture short-run interactionsbetween physical investment and growth. Asnoted by Li (2002), using ordinary least squares(OLS) to estimate Equation (8) is problem-atic because current as well as lagged physi-cal investment may be correlated with the errorterm. This could derive from cyclical fluctua-tions in the economy, which leads investmentrates and output growth to deviate from theirlong-run values, thereby introducing a correla-tion between the disturbance term εt and the cur-rent and lagged values of investment. Li (2002)deals with the issue of business cycle effectsand possible feedbacks from growth to invest-ment by including leads in the distributed lagmodels, as occurs in the dynamic OLS estimatorof Stock and Watson (1993).12 As the inclu-sion of leads imposes a further constraint on theavailable degrees of freedom in the estimationof the ADL models, we first estimate specifi-cations with no leads and then, for robustnesspurposes, in Tables 4 and 5 we present modelsincorporating leads.

Table 3 presents the results for a lag length ofthe polynomial on the total investment rate equalto five, which should be large enough to capturethe permanent effect of physical investment ongrowth. Following the study of Jones (1995a),we also include a lag polynomial on outputgrowth of order five. Five models are estimated,one for each of the measures of investment con-sidered in the analysis.13 Remarkably, the sum

12. A detailed explanation of the method of incorporat-ing leads can be found in an unreported appendix availablefrom the author upon request.

13. In the bottom part of the tables, we provide fourspecification tests. These include the Lagrange-multiplierstatistic for 1st and 2nd order autocorrelation testing thenull hypothesis of no serial correlation; the ARCH test of


of the estimated coefficients in the polynomial,B(1), which captures the permanent long-rungrowth effect of a permanent change in the totalinvestment rate, appears to be statistically signif-icant and positive. This coefficient implies thata 1-percentage-point permanent increase in theinvestment rate (based on real GFCF) bringsabout a cumulative increase in the long-rungrowth rate of real per capita output of about0.26 percentage points.14 The cumulative impactis considerably higher when effective invest-ment measures are employed, ranging from 0.61to 0.79.

Table 4 estimates the same specification asin Table 3, but incorporating a polynomial withfive leads of the first-differenced investmentrates as a way to control for the possible endo-geneity in the investment-growth relationship.The finding of a statistically significant and pos-itive effect of physical investment on long-termoutput growth is reinforced, since now the coef-ficient appears significant at the 1% level for allthe investment measures considered. In Table 5,we estimate the relevant specification but withlag and lead polynomials of different order.15 Itis remarkable that our main finding is robust tothe choice of lag and lead order of the polynomi-als on the investment rate and output growth.16

The evidence lends clear support to the existenceof a highly significant positive effect of invest-ment in physical capital on long-run growth, aspredicted by the AK model.

V. INTRODUCING TRANSITIONAL DYNAMICS

At this point, it is important to note thatthe condition B(1) > 0 is expected to hold

Engle (1982) testing the null of autoregressive conditionalhomoskedasticity; the normality test of Doornik and Hansen(1994) testing the null of normal errors after applying asmall-sample correction; and the heteroskedasticity test ofWhite (1980) testing the null of unconditional homoskedas-ticity.

14. It is important to mention that the inclusion in thespecification of a mean shift in 1961 does not change themain result regarding the statistically significant effect of theinvestment rate on long-run growth.

15. For consistency purposes, we modify the order ofthe lag polynomial on output growth to equal the order ofthe polynomial on investment.

16. The results in Table 5 are obtained with real GFCFas a share of real GDP. Fairly similar results wouldfollow with the other investment rate measures. This is notsurprising given that the ratio of real GFCF to real GDPcorrelates with a coefficient greater than 0.91 with the fourother investment rate definitions. Likewise, the correlationcoefficient between real per capita output growth and realper worker output growth equals 0.94.

only around the steady state. To the extent thata country like China may still have a longdistance to cover before it eventually reachesthe vicinity of the steady state (defined forinstance by the U.S. growth experience), B(1) >0 may also describe the neoclassical growthparadigm characterized by positive transitionalgrowth until convergence is completed. Havingacknowledged this caveat and the fact that thetime span of the data series available is rel-atively short, to know with certainty whetherthe growth impact estimated is the result ofa continuum of level shifts along the transi-tional path or represents a genuine growth effectmay be unachievable even for a country thatmay have already converged. Though admittingthese difficulties, in this section we augmentthe basic specification with transitional dynam-ics caused by three main mechanisms that arethought to have been important in China overthe past decades: (1) imbalances in the ratio ofphysical to human capital as in the models ofLucas (1988) and Uzawa (1965), (2) structuralchange due to sectoral shifts from agriculture tomanufacturing as in the models of Echevarrıa(1997) and Kongsamut, Rebelo, and Xie (2001),and (3) different channels of technology diffu-sion from the leading-edge economy such astrade and FDI (Acemoglu and Ventura 2002;Keller 2004; Ventura 1999) and investment inR&D (Aghion and Howitt 1998; Howitt 2000;Jones 1995b). In sum, the main purpose ofthis section is to conduct robustness exercisesto check whether the long-run effect of phys-ical investment on growth is sensitive to theincorporation of transitional dynamics into thebaseline model given by Equation (5). A caveatassociated with this empirical exercise is that,unlike the ADL model with transitional dynam-ics from factor imbalances, in some cases theADL specifications augmented with transitionaldynamics are not strictly derived from theo-retical growth models of the AK-type. This isparticularly the case for the augmentation withtransitional dynamics from structural transfor-mation. Still, even in such cases, the theoreticalpredictions regarding the effect of those fac-tors on growth are clear, and can thus help usshed some light on the mechanisms governingChina’s transitional dynamics until the conver-gence process is completed.

A. Transitional Dynamics from K/H Imbalances

Unlike the one-sector AK model that doesnot explicitly consider the role of human capital


TABLE 3Autoregressive Distributed Lag Growth Regressions

Real GFCF/Real GDP

Real EffectiveGFCF/Real GDP

Real EffectiveInvestment/Real GDP (1)

Real EffectiveInvestment/Real GDP (2)

Real TIFA/Real GDP

ikt 0.262∗ 0.609∗∗∗ 0.617∗∗∗ 0.789∗∗∗ 0.403∗∗∗(1.853) (3.128) (5.856) (2.970) (3.331)

�ikt 1.779∗∗∗ 2.111∗∗∗ 2.194∗∗∗ 2.173∗∗ 1.226∗∗(5.770) (2.812) (3.276) (2.282) (2.149)

�ikt−1 −0.752∗ −0.686 −0.535 −0.618 0.150(−1.901) (−1.236) (−0.919) (−0.956) (0.405)

�ikt−2 −0.697 −0.349 −0.001 −0.466 0.126(−1.441) (−0.769) (−0.002) (−0.752) (0.394)

�ikt−3 −0.221 −1.120∗∗∗ −0.905* −1.140∗∗∗ −0.269(−0.456) (−3.479) (−1.946) (−3.020) (−0.914)

�ikt−4 −0.095 −0.886 −0.562 −1.148 −0.135(−0.322) (−1.488) (−1.247) (−1.564) (−0.695)

�ikt−5 −0.653∗ −0.902 −0.950 −0.962 −0.186(−1.908) (−1.606) (−1.432) (−1.480) (−0.659)

growtht−1 0.355∗∗ 0.160 −0.022 0.131 0.027(2.216) (1.534) (−0.129) (1.288) (0.165)

growtht−2 −0.064 −0.193 −0.191 −0.190 −0.283∗(−0.307) (−1.608) (−1.578) (−1.497) (−1.910)

growtht−3 −0.171 0.006 −0.060 −0.043 −0.214∗(−0.860) (0.067) (−0.455) (−0.411) (−1.659)

growtht−4 0.032 0.054 −0.046 0.076 −0.035(0.265) (0.344) (−0.245) (0.462) (−0.305)

growtht−5 0.067 −0.059 0.045 −0.100 −0.051(0.676) (−0.433) (0.268) (−0.637) (−0.345)

R2 0.789 0.769 0.775 0.726 0.717Specification TestsAR test 0.826 1.284 2.669∗ 2.009 1.374

[0.446] [0.290] [0.086] [0.152] [0.269]ARCH test 1.114 0.273 0.069 0.417 2.487

[0.297] [0.604] [0.794] [0.522] [0.122]Normality test 2.278 4.550 2.850 12.413∗∗ 3.739

[0.320] [0.103] [0.241] [0.002] [0.154]Heterosk. test 1.374 2.655∗∗∗ 1.666 2.218∗∗ 3.373∗∗∗

[0.218] [0.009] [0.125] [0.037] [0.004]

Notes: The dependent variable is the growth rate of real per capita output. ***, **, and * imply the significance ofthe respective coefficients at the 1%, 5%, and 10% levels, respectively. The coefficients reported are estimated in a modelsuch as �yt = α + A(L)�yt−1 + B(1)ikt + C(L)�ikt + εt . The coefficient on the constant term is not reported for spaceconsiderations. Heteroskedasticity and autocorrelation consistent t-statistics (Newey and West 1987) are given in parentheses.Figures in brackets for the specification tests represent p-values associated with the null hypothesis. ***, ** and * in thebottom panel imply rejection of the null hypothesis associated with each respective specification test at the 1%, 5%, and 10%levels, respectively.


in growth dynamics, the AK model of Lucas(1988) and Uzawa (1965) considers two sectors:the final goods and education sectors character-ized by a different technology. Production offinal goods requires both physical and humancapital, while human capital is the only inputin the education sector. While in the one-sectorAK model the ratio of physical to human cap-ital remains constant, initial imbalances in theK/H ratio relative to the steady-state ratio gen-erate transitional dynamics in the two-sector AKmodel. Hence, economic growth rates rise with

the magnitude of the imbalance between phys-ical and human capital if the latter is relativelyabundant, while doing the opposite if humancapital is relatively scarce.17

As shown by Li (2002), the steady-staterelation between growth and investment inthe two-sector AK model is the same as inthe one-sector model. However, both the total

17. Heckman (2005) has documented the existence ofa high imbalance between the stocks of physical andhuman capital due to the relative scarcity of human capital,particularly in some central and western provinces.


TABLE 4Autoregressive Distributed Lag Growth Regressions with Leads

Real GFCF/Real GDP


Real EffectiveInvestment/Real

GDP (1)


GDP (2)Real TIFA/Real GDP

ikt 0.591∗∗∗ 0.851∗∗∗ 0.592∗∗∗ 0.850∗∗∗ 0.451∗∗∗

(3.428) (3.715) (4.047) (3.181) (3.632)R2 0.862 0.814 0.812 0.779 0.773Specification TestsAR test 2.828∗ 0.659 0.914 0.916 0.315

[0.078] [0.526] [0.414] [0.413] [0.733]ARCH test 0.467 1.221 0.913 1.247 0.065

[0.498] [0.275] [0.345] [0.270] [0.801]Normality test 0.142 1.919 0.995 2.200 0.908

[0.932] [0.383] [0.608] [0.333] [0.635]Heterosk. test 1.186 1.247 1.530 1.307 2.122

[0.408] [0.372] [0.242] [0.340] [0.103]

Notes: The coefficients on the constant term, on the polynomial on real per capita output growth of length equal to five,and on the polynomial comprising the five lag and lead terms on the first-difference of the investment rate are not reported forspace considerations. ∗∗∗ implies the significance of the respective coefficients at the 1% level. ∗ in the bottom panel impliesrejection of the null hypothesis associated with each respective specification test at the 10% level. See Table 3 for the rest.


TABLE 5ADL Growth Regressions: Different Lag and Lead Lengths

6 Lags 6 Lags and Leads 7 Lags 7 Lags and Leads 8 Lags 8 Lags and Leads

ikt (Real GFCF/Real GDP) 0.289∗∗ 0.557∗∗∗ 0.745∗∗∗ 0.823∗∗∗ 0.489∗∗∗ 0.651∗∗

(2.158) (2.691) (4.210) (3.366) (2.984) (2.289)R2 0.802 0.864 0.838 0.916 0.887 0.965Specification TestsAR test 1.290 2.933∗ 1.368 0.871 0.615 2.039

[0.289] [0.076] [0.276] [0.439] [0.548] [0.181]ARCH test 2.620 0.192 0.102 0.213 3.889∗ 0.007

[0.112] [0.663] [0.752] [0.647] [0.055] [0.936]Normality test 1.364 0.003 3.367 0.839 0.676 3.114

[0.505] [0.999] [0.186] [0.657] [0.713] [0.211]Heterosk. testa 1.152 1.512 1.297

[0.377] [0.279] [0.345]

Notes: The coefficients on the constant term, on the polynomial on real per capita output growth of lengths equal to 6,7, and 8 (depending on the specification), and on the polynomial comprising the six, seven and eight lag and lead terms onthe first-difference of the investment rate are not reported for space considerations. *** and ** imply the significance of therespective coefficients at the 1% and 5% levels, respectively. * in the bottom panel implies rejection of the null hypothesisassociated with each respective specification test at the 10% level.

aFor the specifications with a lead polynomial, there were not enough observations to compute the heteroskedasticity test.See Table 3 for the rest.


investment rate and the K/H ratio deter-mine growth along the transitional path in thetwo-sector AK model. As this model featuringwell-defined transitional dynamics is very appeal-ing to China’s economy that presents well-knownfactor imbalances, we check the plausibility ofthe extension of the basic specification with tran-sitional dynamics caused by K/H imbalances.

Following the study of Li (2002), the relevantspecification takes the form:

�yt = α + A(L)�yt−1 + B(1)ikt + C(L)�ikt

(9)

+ D(1) ln(K/H)t + E(L)� ln(K/H)t + εt

where D(1) is expected not to be significantlydifferent from zero, since K/H imbalances only


affect transitional dynamics (as captured bythe polynomial E(L)) rather than steady-stategrowth.

In order to conduct this analysis we need toemploy data on stocks of physical and humancapital. For that purpose, we use the stockof physical capital constructed via the perpet-ual inventory method by Bai, Hsieh, and Qian(2006) on the basis of data on real GFCF. Indoing so, they account for the composition ofinvestment in producer durables and structures,thereby assuming that the depreciation rate is8% for structures and 24% for machinery. Forthe two specifications including effective invest-ment, we use the fixed assets value series pro-posed by Holz (2006).18 Regarding the stock ofhuman capital, we employ the estimate of theaverage years of schooling in the working-agepopulation (15–64 years) obtained by Wang andYao (2003) and further extended by Ang andMadsen (2011). They used the perpetual inven-tory method on the basis of annual data on grad-uates at five schooling levels of different length.

As reported in Table 6, the physical invest-ment rate has a significantly positive effect onlong-run growth, while K/H imbalances donot exert any statistically significant impact onsteady-state growth, as predicted by the two-sector AK model. Regarding the short-run effectfrom K/H imbalances, there is a contempora-neous positive effect on growth, which turnsnegative with a different lag depending on thespecification. For the specifications with effec-tive GFCF and the second effective investmentrate it takes only 1 year, while for the TIFA-based and first effective investment rates thenegative effect appears with a lag of 2 and3 years, respectively. The F -statistics consis-tently reject the hypothesis that the short-runeffects from K/H imbalances are jointly equalto zero. This suggests that the dynamics ofthe K/H ratio should be essential for prop-erly characterizing growth dynamics in China.Overall, the two-sector AK growth model allow-ing for transitional dynamics through imbal-ances in the relative endowment of physical andhuman capital provides a better description ofthe actual China’s growth process than the basicAK model.

18. Holz (2006) constructs this capital stock series onthe basis of effective investment, where non-SOU effectiveinvestment prior to 1986 is estimated using the real growthof industrial non-SOU gross output value, and real scrapvalues are derived as scrap values divided by a k-periodlagged deflator.

B. Transitional Dynamics from StructuralTransformation

As shown in Figure 3, China has experi-enced a marked process of structural economictransformation leading to substantial sectoralshifts from agriculture to manufacturing over thepast five decades. As lower shares of agricul-ture are normally associated with higher pro-ductivity growth (Hobsbawm 1962), these sec-toral shifts are likely to influence transitionalgrowth until convergence to a steady-state posi-tion is attained. This prediction also accordswith the multisector exogenous growth modelof Echevarrıa (1997) where shifts from agri-culture toward manufacturing (characterized bythe highest rate of technical change and laborintensity) should lead to growth accelerationalong the transitional path.19 Along similar lines,Kongsamut, Rebelo, and Xie (2001) stress theimportance of changes in sectoral compositionin affecting growth along the transition, argu-ing that structural change from agriculture toindustry occurs because of the higher incomeelasticities of manufactured relative to agricul-tural products.20

To account for the effect of changes in sec-toral composition along the transitional path,we augment the basic specification with a lagpolynomial of order five on the growth rate ofthe agricultural share of labor.21 On theoreticalgrounds, increases in the share of labor in agri-culture are expected to exert a negative effecton growth along the transitional path. Indeed,

19. Echevarrıa’s model predicts that the investment ratedoes not increase monotonically over time, but instead it dis-plays an inverted U shape, also exhibited by output growth.This occurs because as countries become richer, they investmore and shift resources from agriculture to manufacturing(which has the highest rate of technical change), thus bring-ing about higher output growth. Eventually, the investmentrate decreases, so does growth, and resources are shiftedto the less productive service sector, thus reinforcing thefall in growth. However, it is apparent from our Figures 1and 2 that neither output growth nor the physical invest-ment rates are hump-shaped as predicted by Echevarrıa’smultisector model. Still, it could be argued that China’seconomy has been situated on the upward-sloped portionof the hump-shaped curve over the past five decades, whenresources mostly shifted from agriculture to manufacturingand to a lesser extent to services. In any case, it is beyondthe scope of the paper to discriminate between an AK-typegrowth model and the multisector exogenous growth modelof Echevarrıa (1997).

20. See the discussion in Madsen, Ang, and Banerjee(2010, 283).

21. The same results follow when we use the agricul-tural share of output. These unreported results are availablefrom the author upon request.


TABLE 6ADL Growth Regressions with Transitional Dynamics from K/H Imbalances

Real GFCF/Real GDP



GDP (1)



ikt 0.610∗∗ 0.758∗∗∗ 0.617∗∗∗ 0.761∗∗∗ 0.537∗(2.111) (4.591) (2.877) (3.284) (1.878)

�ikt −0.176 0.728∗∗ 1.573∗∗∗ 1.519∗∗∗ −0.241(−0.331) (2.327) (3.349) (3.921) (−0.701)

�ikt−1 −0.324 −0.458 −0.826 −0.874∗ −0.316(−0.905) (−1.551) (−1.491) (−1.652) (−1.277)

�ikt−2 −0.569 −0.443∗ 0.107 −0.070 −0.160(−1.096) (−1.915) (0.196) (−0.165) (−0.699)

�ikt−3 −0.819∗∗ −0.319 −0.904∗∗ −1.034∗∗ −0.264(−2.242) (−1.598) (−2.399) (−2.543) (−1.173)

�ikt−4 −0.285 −0.914∗∗∗ −0.340 −0.399 −0.251(−0.632) (−2.743) (−0.821) (−0.844) (−0.968)

�ikt−5 −0.130 −0.578∗∗ −0.530 −1.029∗∗∗ −0.083(−0.471) (−2.234) (−1.615) (−2.761) (−0.464)

Ln(K/H)t −0.875 −0.643 −0.701 0.054 −2.638(−0.437) (−0.857) (−0.465) (0.042) (−0.886)

�Ln(K/H)t 1.120∗∗∗ 1.079∗∗∗ 0.751∗∗∗ 1.090∗∗∗ 1.167∗∗∗(4.547) (6.760) (2.613) (3.923) (3.938)

�Ln(K/H)t−1 −0.578 −0.529∗∗ −0.416 −0.761∗∗∗ −0.558(−1.636) (−2.315) (−1.211) (−2.816) (−1.559)

�Ln(K/H)t−2 −0.259 −0.171 −0.308 −0.148 −0.465∗∗(−1.363) (−0.930) (−1.145) (−0.466) (−2.430)

�Ln(K/H)t−3 0.588∗∗ 0.148 −0.281∗∗ −0.103 0.283(2.066) (0.998) (−2.241) (−0.704) (1.014)

�Ln(K/H)t−4 0.051 −0.021 0.613∗∗∗ 0.546∗∗∗ 0.095(0.139) (−0.136) (4.179) (4.966) (0.303)

�Ln(K/H)t−5 −0.495 −0.193 −0.400∗∗∗ −0.421∗∗∗ −0.457(−1.415) (−1.299) (−4.097) (−4.041) (−1.430)

F -statistic 5.858∗∗∗ 14.618∗∗∗ 5.636∗∗∗ 7.331∗∗∗ 10.914∗∗∗[0.000] [0.000] [0.001] [0.000] [0.000]

R2 0.903 0.942 0.887 0.887 0.898Specification TestsAR test 1.134 3.648∗∗ 0.061 1.760 2.804∗

[0.336] [0.039] [0.941] [0.191] [0.078]ARCH test 0.005 3.744∗ 5.091∗∗ 0.254 0.160

[0.946] [0.059] [0.029] [0.617] [0.691]Normality test 2.429 0.709 0.466 2.600 6.389∗∗

[0.297] [0.702] [0.792] [0.273] [0.041]Heterosk. test 0.932 1.884 1.610 1.103 0.674

[0.593] [0.130] [0.201] [0.457] [0.822]

Notes: The dependent variable is the growth rate of real per capita output. ***, **, and * imply the signif-icance of the respective coefficients on the equation �yt = α + A(L)�yt−1 + B(1)ikt + C(L)�ikt + D(1)ln(K/H)t +E(L)�ln(K/H)t + εt at the 1%, 5%, and 10% levels. All the models include a polynomial on real per capita outputgrowth of length equal to five. The coefficients on the constant term and on the polynomial on real per capita output growthare not reported for space considerations. F -statistic tests for the joint significance of the coefficients on the lag polynomialE(L). See Table 3 for the rest.

Sources: Data from Hsueh and Li (1999), Bai, Hsieh, and Qian (2006), Holz (2006), Ang and Madsen (2011), and ChinaCompendium of Statistics 1949–2008 (National Bureau of Statistics 2011).

Table 7 provides evidence for a contemporane-ous negative effect of growth in the agriculturallabor share on output growth along the transi-tional path, irrespective of the investment rateemployed. The F -statistics firmly reject the nullhypothesis that the growth effects from all theterms on the growth rate of the agricultural labor

share are jointly insignificant, thus suggestingthe importance of structural transformation inChina’s growth dynamics. Even though sectoralshifts affect transitional growth, the physicalinvestment rate continues to exert a positiveinfluence on long-run growth, as predicted byendogenous growth models of the AK-type.


FIGURE 3Primary Sector Shares: 1952–2006

0

20

40

60

80

100

120

140

1952

1954

1956

1958

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

Year

Pri

mar

y Se

ctor

Sha

re (

Per

cent

age)

Primary sector employment/ Total employment Primary sector real GDP/Total real GDP

Source: Data from China Compendium of Statistics 1949–2008 (National Bureau of Statistics 2011).

C. Transitional Dynamics from DifferentChannels of Technology Diffusion

Trade and FDI Channels. An example of anopen-economy AK model with transitionaldynamics caused by trade is that of Ventura(1999). The model is characterized by a tech-nology that exhibits diminishing returns at theglobal level measured by world averages. How-ever, countries’ ability to trade and eliminateprice differentials allows them to beat dimin-ishing returns. This is because of the fact thatfactor prices are not altered by changes in fac-tor quantities and there is productivity-adjustedfactor price equalization across countries. Also,factor prices depend only on world averages andthose are generally unaffected by domestic con-ditions.22 As pointed out by Ventura, the growthmiracles of the Asian tigers capture well themain features of this model. In fact, increases in

22. In this setting, one cannot take conditional conver-gence as evidence for diminishing returns to capital. This isbecause, as in Jones and Manuelli (1990), this model predictsthat conditional convergence is associated with aggregatetechnologies with a high elasticity of substitution betweencapital and labor. This ensures that factor prices do notchange too fast, thus generating transitional dynamics.

the capital-labor ratio lead to an incipient excessdemand for labor and excess supply of capitalwhich are not eliminated through factor pricechanges but through changes in the productionstructure from labor to capital-intensive, whoseexcess production is then exported.

As the Asian tigers as well as China havegrown above average, they have shifted frombeing net importers to net exporters ofmanufactures as their relative income levelincreases. This poses a problem since once inter-national markets become saturated, one mayexpect the prices of Chinese exports to fall.Indeed, the more sophisticated open-economyAK model of Acemoglu and Ventura (2002) pre-dicts that the fall in the relative price of exports,when a country keeps growing faster than therest of the world, lowers down its growth rateuntil it converges to the growth rate of the rest ofthe world. This fact again introduces transitionaldynamics into the AK framework.23 According

23. As found by Brandt, Rawski, and Sutton (2008),there has been quality upgrading over the reform era asmeasured by the rise in the R&D and capital intensityof China’s exports, which has counteracted the downwardpressure on price due to international markets saturation.


TABLE 7ADL Growth Regressions with Transitional Dynamics from Structural Transformation

Real GFCF/Real GDP



GDP (1)



ikt 0.304∗∗∗ 0.560∗∗∗ 0.389∗∗∗ 0.577∗∗∗ 0.215∗

(3.442) (3.030) (2.328) (3.218) (1.705)�ikt 0.555 0.631 0.900 0.449 0.170

(1.269) (1.275) (1.536) (1.040) (0.417)�ikt−1 −0.117∗ −0.152 −0.173 −0.171 −0.214

(−1.754) (−0.330) (−0.387) (−0.359) (−0.876)�ikt−2 −1.175∗ −0.941∗∗∗ −0.442 −1.035∗∗∗ −0.157

(−1.872) (−2.703) (−0.678) (−2.662) (−0.728)�ikt−3 −0.285 −1.225∗∗∗ −0.986 −1.291∗∗ −0.226

(−0.360) (−2.671) (−1.549) (−2.501) (−1.156)�ikt−4 −0.865 −1.098∗∗∗ −0.615∗ −1.186∗∗ −0.332∗

(−0.581) (−2.792) (−1.650) (−2.512) (−1.695)�ikt−5 −0.515 −0.264 −0.193 −0.183 −0.289

(−1.335) (−0.483) (−0.356) (−0.313) (−1.165)�Ln(Agr.Lshare)t −0.577∗∗∗ −0.520∗∗∗ −0.477∗∗∗ −0.570∗∗∗ −0.524∗∗∗

(−5.757) (−6.171) (−6.499) (−7.287) (−3.469)�Ln(Agr.Lshare)t−1 −0.086 −0.209∗ −0.173∗ −0.250∗ −0.180

(−0.717) (−2.238) (−1.811) (−2.220) (−1.264)�Ln(Agr.Lshare)t−2 −0.321∗∗ −0.156 −0.161 −0.213 −0.202

(−1.963) (−1.108) (−1.247) (−1.558) (−0.904)�Ln(Agr.Lshare)t−3 0.191 0.270∗∗ 0.259 0.285∗∗ 0.347∗

(1.195) (2.268) (1.554) (2.327) (1.738)�Ln(Agr.Lshare)t−4 −0.248 −0.152 −0.148 −0.140 −0.077

(−1.487) (−1.239) (−0.644) (−1.166) (−0.390)�Ln(Agr.Lshare)t−5 0.021 −0.073 0.021 −0.078 0.068

(0.140) (−0.504) (0.108) (−0.519) (0.399)F-statistic 2.578∗∗ 6.879∗∗∗ 4.898∗∗∗ 8.868∗∗∗ 3.503∗∗∗

[0.038] [0.000] [0.001] [0.000] [0.009]R2 0.860 0.901 0.875 0.896 0.804Specification TestsAR test 0.023 0.903 1.919 1.170 1.804

[0.977] [0.417] [0.165] [0.325] [0.183]ARCH test 0.019 0.069 0.306 0.293 0.250

[0.891] [0.795] [0.583] [0.591] [0.619]Normality test 7.863∗∗ 4.115 5.553∗ 3.024 6.379∗∗

[0.020] [0.128] [0.062] [0.220] [0.041]Heterosk. test 1.872 0.296 0.394 0.405 0.432

[0.113] [0.998] [0.986] [0.984] [0.976]

Notes: The dependent variable is the growth rate of real per capita output. ***, **, and * imply the significance ofthe respective coefficients on the equation �yt = α + A(L)�yt−1 + B(1)ikt + C(L)�ikt + D(L)�ln(Agr.Lshare)t + εt atthe 1%, 5%, and 10% levels. All the models include a polynomial on real per capita output growth of length equal tofive. The coefficients on the constant term and on the polynomial on real per capita output growth are not reported for spaceconsiderations. F-statistic tests for the joint significance of the coefficients on the lag polynomialD(L). See Table 3 for the rest.


to these models, we expect the growth rate oftrade to slow down output growth along the tran-sitional path, until convergence to the rest of theworld is completed.

In addition to the direct effect of tradeon growth along the transition, trade couldalso affect transitory growth by promoting the

assimilation of foreign technologies. Arguably,countries that are more open to internationaltrade and FDI inflows are more prepared to takeadvantage of foreign technology, thus enablingthem to catch up more quickly to the frontier(Keller 2004). Madsen (2009) suggests that tradeopenness is conducive to growth by reducing


trade barriers (tariff and nontariff), increasingfair competition as well as acquiring knowledgeembodied in imports. As noted by Keller (2004),there is some evidence of the existence oflearning-by-exporting externalities from interac-tions with foreign customers, which leads toenhanced product quality standards. Hallward-Driemeier, Iarossi, and Sokoloff (2002) provideevidence for Southeast Asia that suggests thatbecoming a net exporter leads to improved prod-uct quality and higher productivity.

Along similar lines, FDI inflows are expectedto facilitate technological transfer from the best-practice frontier as they carry know-how, man-agerial skills and new knowledge embodiedin foreign investment (Keller 2004; Savvidesand Zachariadis 2005). Xu (2000) and Kellerand Yeaple (2009) find evidence that FDI actsas an important channel for technology diffu-sion. According to these models, the interactionterm between trade or FDI with distance to thebest practice frontier (known as the catch-upterm) is expected to have a significantly posi-tive effect on transitory growth, thus reflectingthe catching-up toward the best-practice frontier.Technological distance is measured as the ratioof TFP in the United States relative to China’sTFP levels.

Models 1–3 of Table 8 augment the basicspecification with transitional dynamics stem-ming directly from the growth rate of tradeand FDI inflows as well as from their inter-action with distance to the frontier. As proxiesfor trade, we employ the sum of exports plusimports over GDP and the ratio of tariff rev-enues over total imports. Model 1 provides evi-dence that trade growth (lagged one and twoperiods) has lowered output growth along thetransition, thus leading to convergence to therest of the world, as in the model of Ace-moglu and Ventura (2002). In addition, trade-based absorptive capacity appears to enhancegrowth by facilitating the transfer of foreigntechnologies, as given by the significant andpositive coefficient on the catch-up term. Whentariffs replace trade openness as a proxy fortrade, the direct and indirect effects of tradegrowth along the transition become insignifi-cant. Model 3 only provides marginal evidenceof the positive direct effect of the growth inFDI inflows on output growth with a lag of 4years, though this may be caused by the shorttime span of the FDI data available only from1982 onwards. For both trade and FDI, the F-statistics fail to reject the null hypothesis that

the growth effects from all the terms on trade orFDI growth are jointly insignificant. This resultholds even after incorporating the catch-up terminto the test. Thus, transitional dynamics causedby the growth rate of trade and FDI inflowsjointly with their interaction with distance tothe technological frontier do not appear to mat-ter much for China’s growth miracle. Despiteincorporating transitional dynamics from tradeand FDI, the main result that the physicalinvestment rate affects long-run growth remainsunchanged.

R&D Channel. In the semiendogenous growthmodel developed by Jones (1995b), under theassumption of diminishing returns to knowl-edge, a rise in the R&D labor share does nothave permanent effects on long-run growth, butcan still affect output growth along the tran-sitional path.24 In addition to affecting growthdirectly along the transition through enhanc-ing a country’s ability to innovate domesti-cally, R&D investment plays a central role inthe assimilation of foreign technologies, whichfacilitates convergence and a more rapid catch-up to the best-practice frontier. In other words,laggards benefit from their backwardness pro-vided they invest sufficiently in domestic R&D,which is necessary to build up an R&D-basedabsorptive capability (Abramovitz 1986; Cohenand Levinthal 1989; Fagerberg 1994). Indeed,recent Schumpeterian growth models allow fortransitional dynamics in an attempt to explainincome convergence. In Aghion and Howitt(1998) and Howitt (2000),25 the size of aquality-upgrading innovation hinges on a firm’sdistance to the best-practice frontier (see alsoGriffith, Redding, and Van Reenen 2003).26 Thecatch-up effect is captured through the inter-action between growth in R&D intensity anddistance to the frontier.

Following the study of Griffith, Redding, andVan Reenen (2003), Madsen (2008), and Angand Madsen (2011), among others, we employ

24. See also the models of Kortum (1997) andSegerstrom (1998).

25. According to Howitt (2000) and Aghion and Howitt(2006), ideas can be transferred effectively across countriesindependently of trade, provided countries have investedsufficiently in domestic R&D to adopt foreign technologies.

26. It is beyond the scope of this paper to discriminateacross the different knowledge-driven endogenous growthmodels for the case of China. For an attempt to do sofor six Southeast Asian countries, including China, refer toAng and Madsen (2011). Their evidence appears to favorSchumpeterian over semiendogenous growth theory.


TABLE 8ADL Growth Regressions with Transitional Dynamics from Different Channels of Technology

Diffusion

Trade Openness(X+M/GDP) (1) Tariffs (2) FDI/GDP (3)

R&DExpenditures

/GDP (4)

R&DEmployment/TotalEmployment (5)

ikt 0.348∗ 0.259∗ 0.343∗∗ 0.275∗ 0.317∗

(1.856) (1.958) (2.286) (1.960) (1.685)�ikt 1.728∗∗∗ 1.913∗∗∗ 1.876∗∗∗ 1.176∗∗∗ 1.700∗∗∗

(6.654) (6.134) (5.217) (2.870) (4.114)�ikt−1 −0.164 −0.822∗ −0.486 −0.824 −0.556

(−0.329) (−1.953) (−1.029) (−1.645) (−1.244)�ikt−2 −0.777 −1.013∗ −0.516 −0.670 −0.520

(−1.263) (−1.900) (−0.931) (−1.222) (−0.832)�ikt−3 −0.599 0.108 −0.032 −0.258 −0.185

(−1.166) (0.190) (−0.055) (−0.587) (−0.373)�ikt−4 −0.306 0.259 −0.083 −0.402 −0.120

(−0.855) (0.778) (−0.252) (−0.874) (−0.441)�ikt−5 −0.750 −0.882∗ −0.484 −0.579∗ −0.485

(−1.598) (−1.801) (−1.118) (−1.936) (−1.191)�Ln(F)t −0.763∗∗ 0.137 −0.213 −0.691∗∗ −0.554

(−2.286) (0.830) (−1.438) (−2.340) (−1.208)�Ln(F)t−1 −0.066∗ 0.013 0.019 0.035 −0.022

(−1.713) (0.446) (0.567) (0.662) (−0.462)�Ln(F)t−2 −0.047 −0.043∗ 0.022 −0.016 0.009

(−0.725) (−1.650) (0.706) (−0.547) (0.184)�Ln(F)t−3 0.047 −0.025 0.020 −0.031 0.088

(1.011) (−0.970) (0.557) (−0.799) (0.868)�Ln(F)t−4 0.052 0.034 0.048∗ 0.080∗∗ −0.121∗∗

(1.002) (1.073) (1.760) (1.968) (−2.201)�Ln(F)t−5 0.015 0.000 0.031 −0.116∗∗∗ −0.078

(0.297) (−0.016) (0.818) (−3.149) (−1.390)�ln(Ft )·ln(AUSA

t /ACHINAt ) 0.333∗∗ −0.047 0.108 0.318∗∗∗ 0.241

(2.390) (−0.716) (1.515) (2.582) (1.132)F-statistic (1) 1.318 1.161 0.958 3.132∗∗ 1.116

[0.280] [0.354] [0.470] [0.017] [0.377]F-statistic (2) 1.139 1.240 0.915 2.712∗∗ 0.975

[0.366] [0.314] [0.508] [0.027] [0.467]R2 0.834 0.827 0.834 0.871 0.828Specification TestsAR test 0.306 1.116 0.310 1.064 0.691

[0.739] [0.342] [0.736] [0.359] [0.509]ARCH test 0.000 0.109 0.801 0.015 0.291

[0.992] [0.743] [0.375] [0.903] [0.592]Normality test 5.150∗ 6.618∗∗ 1.292 3.581 4.926∗

[0.076] [0.037] [0.524] [0.167] [0.085]Heterosk. test 0.737 0.670 0.656 0.738 0.638

[0.767] [0.826] [0.837] [0.766] [0.851]

Notes: ikt relates to real GFCF/ real GDP. The dependent variable is the growth rate of real per capita output.***, **, and * imply the significance at the 1%, 5%, and 10% levels of the respective coefficients on the equation:�yt = α + A(L)�yt−1 + B(1)ikt + C(L)�ikt + D(L)� ln(Ft ) + δ·� ln(Ft )· ln(AUSA

t /ACHINAt ) + εt , where Ft is a vector of

variables including trade openness, tariffs, FDI over GDP, R&D expenditures over GDP, and R&D labor over total labor.All the models include a polynomial on real per capita output growth of length equal to five. The coefficients on the constantterm and on the polynomial on real per capita output growth are not reported for space considerations. F -statistic (1) testsfor the joint significance of the coefficients on the lag polynomial D(L). F-statistic (2) tests for the joint significance of thecoefficients on the lag polynomialD(L) and δ. See Table 3 for the rest.

Sources: Data from Hsueh and Li (1999), Holz (2006), Ang and Madsen (2011), and China Compendium of Statistics1949-2008 (National Bureau of Statistics 2011).


the ratio of domestic R&D expenditures overGDP and the ratio of R&D labor (scientistsand engineers) to the total labor force as mea-sures of domestic research intensity. Table 8presents two models based on each of these twomeasures. It is interesting the fact that growthin R&D expenditures over GDP reduces out-put growth contemporaneously and with a lagof 5 years, while the catch-up term is posi-tive and highly significant. This result may bepartly explained by the Howitt (2000) open-economy extension with technology transfer ofthe Schumpeterian endogenous growth model.According to this model, a rise in R&D leadsto a temporary increase in productivity growth,but as the technology gap with the global best-practice technology narrows down, innovationsincrease average productivity growth by less andless, thus slowing down a country’s growth untilconvergence to the total global growth rate isachieved. Regarding the joint statistical signif-icance of all the terms on the growth rate ofresearch intensity and its interaction with dis-tance to the frontier, the evidence is mixed. TheF-statistic firmly rejects the null hypothesis thatthere are no growth effects when we use R&Dexpenditures over GDP, whereas the null cannotbe rejected for the R&D labor share. Again, thebasic prediction by the AK model that perma-nent rises in the physical investment rate lead topermanent increases in output growth still holds.

D. Discussing the Results

Overall, the evidence from the augmentationswith transitional dynamics of different naturedoes not appear to contradict our main findingthat physical capital accumulation has exerteda positive permanent effect on output growthover the period 1952–2006. In addition, theseextensions of the basic model with transitionaldynamics appear to provide a better descrip-tion of China’s growth dynamics than the basicAK model, since China is unlikely to have con-verged yet to a steady-state position. In fact,we have provided some preliminary evidencethat the two-sector AK model of Lucas (1988)and Uzawa (1965), an AK model extended toaccount for sectoral shifts from agriculture tomanufacturing, or an AK model incorporatingthe role of R&D-based technological transferin similar spirit to the open-economy Schum-peterian growth model of Howitt (2000),27

27. A closed-economy Schumpeterian growth modelwith no technology transfer close to Howitt (2000) is

all exhibiting transitional dynamics, may begood candidates to explain growth dynamics inChina.

Before concluding, it is important to men-tion that our results from the application ofJones’ test to the Chinese case differ substan-tially from those obtained by Jones (1995a) andRomero-Avila (2006) for industrialized coun-tries, who—unlike McGrattan (1998) and Li(2002)—provided strong evidence against theAK model. More specifically, McGrattan (1998)challenged Jones’ findings on the grounds thathis analysis failed to capture long-run trends.Instead, she argued that Jones (1995a) may becapturing short-run patterns in the investmentand growth data, which were not generally coin-cident over the postwar period. She claimedthat such short-lived deviations from long-runtrends are consistent with AK models slightlymore general than the one considered by Jones(1995a) by assuming that government policiescan not only affect investment/output ratios butalso capital/output ratios and labor/leisure deci-sions. In addition, Li (2002) also provided muchweaker evidence against the AK model thanJones by extending the sample to 24 OECDcountries from 1950 to 1992 as well as by focus-ing on the total investment rate, which is thoughtto be more relevant to the AK model than invest-ment in producer durables.

Nevertheless, as argued by Romero-Avila(2006), neither McGrattan (1998) nor Li (2002)investigated the stochastic properties of out-put growth and investment rates series. Rather,they based their conclusions solely on the deter-ministic developments of economic growth andinvestment rates, which Romero-Avila (2006)showed not to be consistent with the keypredictions of AK-type models. In addition,the regressions estimated by Li (2002) were

that of Howitt and Aghion (1998). The latter makes therate of technological progress depend on capital intensity,thereby incorporating physical capital as an input intoR&D. In both models a rise in capital intensity inducesmore R&D by raising the flow of profits accruing to asuccessful innovator, and reducing the interest rate used todiscount the future stream of profits. These models highlightthe complementarity between physical capital accumulationand innovation as factors behind long-run growth. Thisis clearly stated in Howitt and Aghion (1998, 112) asfollows. In the same way that “capital accumulation cannotbe sustained indefinitely without technological progress tooffset diminishing returns, so too technological progresscannot be sustained indefinitely without the accumulationof capital to be used in the R&D process that createsinnovations and in the production process that implementsthem.”


flawed by the failure to include autoregressiveterms that help control for adjustment dynam-ics. Indeed, Romero-Avila (2006) investigatedthe time series properties of output growth andinvestment rates in total physical capital as wellas in producer durables and total structures foran extended sample of 26 OECD countries overthe period 1950–1992. The empirical analy-sis hardly provided any evidence supportingthe existence of large persistent movements inthe growth rates of output (which, if anything,fell over the postwar era), despite large persis-tent movements in physical investment in thegrowth-increasing direction. Moreover, by usingtests with good size and power properties, whichare also robust to structural change, Romero-Avila (2006) confirmed Jones’ findings, therebyensuring that failures to reject the unit root nullwere not caused by the low power of conven-tional unit root tests or by not allowing forstructural change.

All in all, unlike the results for China, theexisting evidence for OECD countries mostlyfavors the rejection of the empirical validity ofthe AK model. This is not surprising since sev-eral studies have found innovation-driven TFPgrowth to be the main determinant of growthin industrialized countries (Badunenko, Hender-son, and Zelenyuk 2008; Jones 2002; Madsen2008), whereas work on China and other EastAsian countries finds capital accumulation to bethe main growth engine (Badunenko, Hender-son, and Zelenyuk 2008; Collins and Bosworth1996; Henderson and Russell 2005; Krugman1994; Young 1995). Indeed, Jones (2002) mod-els the U.S. growth experience as a semien-dogenous growth model in which the world-wide discovery of ideas drives long-run growth.This model features transitional dynamics asso-ciated with growth in research intensity and edu-cational attainment, which are able to explain50% and 30% of growth in labor productiv-ity, respectively. For the case of China, whichis expected to be further away from the steadystate than the United States, our evidence favorsan investment-driven AK-type growth modelaugmented with transitional dynamics stemmingfrom factor imbalances,28 structural change, andR&D-based technological transfer.

28. It can be noted that Li (2002) also provides paneldata evidence for OECD countries supporting the extendedtwo-sector AK model of Lucas (1988) and Uzawa (1965)with transitional dynamics caused by factor imbalances.

VI. CONCLUSIONS

The observation of large persistent increasesin the total investment rate and the growth rateof real per capita output in China over the pastfive decades has constituted the main motivationfor this study. In this context, it is crucialfor policymakers to establish whether investingmore resources in physical capital can raisegrowth rates permanently, that is, affecting trendgrowth, or whether output can at best rise in theshort and medium run but then level off again.

For that purpose, we have first investigatedthe deterministic and stochastic developmentsof the output growth and total investment rateseries of the Chinese economy over the period1952–2006, which do not appear to reject theempirical validity of AK models. Secondly, theestimation of ADL growth models has enabledus to provide support for the existence of a posi-tive long-run growth effect from physical invest-ment, as predicted by the AK model. Whenthis specification was augmented with transi-tional dynamics generated by (1) initial imbal-ances in the K/H ratio, (2) structural transfor-mation caused by sectoral shifts from agricul-ture to manufacturing, and (3) different channelsof technology diffusion such as trade, FDI anddomestic investment in R&D, we still founda long-run growth effect from the physicalinvestment rate in addition to some evidenceof short-run effects from some of the mecha-nisms responsible for the transitional dynamics.Overall, the leading role of the physical capitalaccumulation rate in driving Chinese growth hasbeen confirmed under several alternative speci-fications of the growth process.

Taken as a whole, our results appear to besupportive of the key prediction of the firststream of endogenous growth models, that is,AK-type models, which, by assuming constantreturns to reproducible capital, support the exis-tence of a positive growth effect from physicalinvestment in the long run. Notwithstanding,we have shown that augmenting the AK modelwith transitional dynamics does a better job indescribing China’s growth dynamics than thebasic AK model. These models include the two-sector AK model of Lucas (1988) and Uzawa(1965), an AK model extended to account forsectoral shifts from agriculture to manufacturingas in Echevarrıa (1997) and Kongsamut, Rebelo,and Xie (2001), or an AK model incorporatingthe role of R&D-based technological transfer in


similar spirit to the open-economy Schumpete-rian growth model of Howitt (2000) that jointlyconsiders the role of investment in both physicalcapital and R&D.

In future work, it would be also interest-ing to further investigate which channel may beresponsible for these high social returns to phys-ical capital leading to positive long-run growtheffects. Some of these channels imply the exis-

tence of learning by doing processes in the accu-mulation of physical capital as in Arrow (1962),the presence of positive externalities to the accu-mulation of productive capital (Romer 1986),the process of technological transfer throughtrade of capital goods across countries (Eatonand Kortum 2001), and the embodiment of tech-nological progress in new vintages of capital(Greenwood, Hercowitz, and Krusell 1997).

APPENDIX

TABLE A1Data Definitions and Sources

Variable Definition Source

Real per capita GDP growth Growth rate of per capita GDP expressed in2006 prices

China Compendium of Statistics 1949–2008(National Bureau of Statistics 2011)

Real per worker GDP growth Growth rate of per worker GDP expressedin 2006 prices


Real GFCF share of realGDP

GFCF in 2006 (investment) prices as ashare of GDP in 2006 prices

China Compendium of Statistics 1949–2008(National Bureau of Statistics 2011) andHsueh and Li (1999)

Real effective GFCF shareof real GDP

Effective GFCF in 2006 (investment) pricesas a share of GDP in 2006 prices

China Compendium of Statistics 1949–2008(National Bureau of Statistics 2011), Hsuehand Li (1999), and Holz (2006)

Real effective investmentshare of real GDP (1)

The series on effective investment is basedon the sum of effective investment ofSOUs and that for non-SOUs. Themethod used to extend the 1986 value ofnon-SOU effective investment back intime to 1949 employs the real growth ofnon-SOU industrial gross output value


Real effective investmentshare of real GDP (2)

The series on effective investment is basedon the sum of effective investment ofSOUs and that for non-SOUs. Themethod used is the one that calculatesnon-SOU investment for the years priorto 1986 as the difference between totalGFCF and SOU investment, which isthen transformed into effectiveinvestment by employing an estimatednon-SOU transfer rate


Real TIFA share of real GDP Total investment in fixed assets in 2006(investment) prices as a share of GDP in2006 prices

China Compendium of Statistics 1949–2008(National Bureau of Statistics 2011) andHsueh and Li (1999)

Average years of schooling Average years of schooling in theworking-age population

Wang and Yao (2003) and Ang and Madsen(2011)

Stock of fixed capital Stock of physical capital (in real terms)based on GFCF data

Bai, Hsieh, and Qian (2006)

Fixed assets value Capital stock series based on effectiveinvestment, where non-SOU effectiveinvestment prior to 1986 is estimatedusing the real growth of industrialnon-SOU gross output value, and realscrap values are derived as scrap valuesdivided by a k-period lagged deflator

Holz (2006)

Agr.Lshare Employment in the primary sector as ashare of total employment


Agr.Yshare GDP in the primary sector as a share oftotal GDP


Trade openness Exports plus imports as a share of nominalGDP


Tariffs Total revenues from tariffs as a share oftotal imports



TABLE A1Continued

Variable Definition Source

FDI/GDP Foreign direct investment net inflows as ashare of nominal GDP (this series is onlyavailable from 1982 onwards)

World Development Indicators (World Bank2011)

R&D expenditures/GDP Real R&D expenditures as a share of realGDP

Ang and Madsen (2011)

R&D employment/Totalemployment

Scientists and engineers as a share of totalemployment


Distance to the frontier(AUSA

t /ACHINAt )

Ratio of TFP in the United States to TFP inChina


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