The Convergence Analysis of the Output per Effective Worker and Effects of FDI Capital Intensity on OECD 10 Countries and China

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1 Middle Eastern Finance and Economics ISSN: Issue 8 (2010) EuroJournals Publishing, Inc The Convergence Analysis of the Output per Effective Worker and Effects of F Capital Intensity on OECD 10 Countries and China Ringo Kwok Keung Yau East China Normal University Abstract Foreign Direct Investment has been playing an important role in the capital movement of the world. In this paper, capital stocks are divided into three types, namely Domestic Investment capital stock, Non-service F capital stock and Service F capital stock. This study extends the models developed by Solow (1956) [1], Solow (1957) [2] and Mankiw, Romer, Weil (1992) [3] (abbreviated MRW) analyzing the convergence status of output per effective worker and elasticities of different capital stocks. Keywords: Service F capital intensity [note 1] Non-service F capital intensity EG 3 steps cointegrating regression FM-OS stationary time-series regression The phenomena of economy globalization is the integration of merging, acquisition and investment. The total F stock has increased from 8% in 1990 to 24% in This is not just a change in quantity but a change in quality. Although most of the F flows are happening within OECD countries, F is playing an important role in external capital source for developing countries, such as China and India. F is bringing in not just capital but also technology advancement. OECD countries under study in this paper are Australia, Austria, Finland, France, Germany, Iceland, Italy, Norway, UK and USA. The common point of these 10 OECD countries are their keen promotion for attracting F. Other than these 10 countries, China is included in our comparative analysis because it is the largest developing country. Granger 3 steps cointegrating regression which was developed by Engle, Granger and Yoo [4] [5] and FM-OS [6] will be used. However, in order to have a comparative analysis, OS/GS regression will be conducted for 1 st difference of the cointegrating time series [note 2]. The theme of empirical study is to quantify the contribution of different capital intensities and Total Factor Productivity on the output per worker (productivity) and investigate the convergence status of the output per effective worker. [note 1] capital intensity is defined as capital stock per worker. Capital intensity = capital stock/ working population. [note 2] If the residual is auto-correlated, then Generalized least square regression should be performed. I. Related Research The poor economies are growing rapidly all because that they are learning from rich countries. Sala-i- Martin (1996) [7] developed theory on classical convergence which is significantly different from endogenous growth theory. Classical convergence is based on Solow-Swan model and the path of

2 Middle Eastern Finance and Economics - Issue 8 (2010) 124 convergence is pointing to the steady state value. The growth rate of poor economies is faster as they are still at a distance from the steady state. Conditional convergence which has been frequently investigated is one of the most essential empirical tools. The representative study was done by MRW in Following that, Knight et al. (1993) [8], Sala-i-Martin (1994) [9], Cohen (1995) [10], Bernard and Jones (1996) [11], Sala-i-Martin (1996) [12], ee et al. (1995, 1997) [13], Quah (1996) and Galor (1996) [14] have also conducted similar research and study. In order to understand Classical convergence, we must fully understand Solow-Swan model. Its inherit assumption is the 1:1 conversion from saving to investment. The marginal return is diminishing when the investment increases. Adopting the new Cobb-Douglas function, long-run growth comes from technology advancement. Dorwick and Roger (2002) [15] used panel data for empirical study based on Solow model. They found that 51 countries output per worker fell into conditional convergence. However, absolute divergence was found for the data between 1970 to With reference to the study conducted by MRW andthe analysis performed by Barro and the Sala-i-Martin (1995) [17] and Islam (1995) [18], the non-linear ordinary differential equation (ODE) will be simplified by log linear approximation (refer to the study on behavioral convergence by Jochonia S Mathunjwa Jonathan R.W. Temple (2007) [19]. TFP in the MRW s paper is equal to ln A(t) in this paper. Whereas is the labor income share in national income as defined in Solow Model. After log linear approximation, the non-linear ODE will reduce to first order linear ODE which is d lnν = ( δ + a + n)( α 1) ln υ lnυ dt [1] Y Whereas υ =, υ is the steady state value of the output per effective worker, δ is A depreciation rate and n is the population growth rate, 1 α is equal to. The farther the current value away from the steady state value is, the higher the growth rate of the output per effective worker will be. Assuming the output per effective worker is lnυ T τ, after time duration τ, it will be lnυ T, the solution for equation [1] is as follows:- τ τ lnυ = e lnυ + 1 e lnυ [2] T T τ ( ) Whereas the convergence speed = ( δ + a + n)( α 1) YT YT τ et υ T =, υt τ = and ln AT = ln AT τ + aτ AT T AT τ T τ According to equation [2], MRW developed the model for output per worker, τ τ ln Y ln Y aτ 1 e ln A 1 e ln Y + ξ [3] T T τ = + ( ) T τ ( ) T τ Whereas ξ = ( 1 e τ τ α ( δ + a + n) ) lnυ = ( e ) ( ) 1 [ ]ln[ ] α 1 s ln AT τ is the initial technology level at time T τ MRW made the following assumptions:- i.) The estimated values of n, a, s and δ n is the population growth rate which can be obtained from yearly working population data. a isthe growth rate of technology level. MRW (1992) and Islam (1995) used 1.7 % to be the growth rate for US from 1960 to 1985 and other middle size economies to be 2%. [3.1]

3 125 Middle Eastern Finance and Economics - Issue 8 (2010) Problem 1: a to be 2% means that the economy has already been at steady state. However, this may not be true. Some economies may not be at steady state. Problem 2: the growth rate of technology level is the same for all countries. However, all countries may differ a lot in technology level. S: is saving rate. Investment over GDP can be used as a proxy of saving rate. Problem 3: saving rate which was assumed to be constant can change with time. Problem 4: saving rates for all countries which were assumed to be the same should be different. Problem 5: Investment over GDP ratios and saving rates may vary a lot among all countries. δ : is depreciation rate. MRW assumed it to be 3%. Problem 6: the deprecation rate may change with time. Problem 7: the depreciation rates which were assumed to be the same should be different. Problem 8: the depreciation rates may have different estimated values. ii.) Assuming the steady state values of output per effective worker to be the same. Problem 9: the steady state values of output per effective worker are different. 1 e τ ln A was included in the constant of the regression equation. iii.) ( ) T τ Problem 10: the initial technology levels of all countries should be different. The assumptions made by MRW are so many that the maximum heterogeneity cannot be obtained. II. Model and Variables In order to avoid making too many unnecessary assumptions, an empirical model should be built for satisfying the following conditions. Condition 1: The model should extend Solow accounting model and is able to analyze the effects of various capital stocks on the output per worker. Condition 2: The model should avoid the multi-collinearity resulted from correlation among explanatory variables. Otherwise, the accuracy for the estimation of parameters will be doubtful. Condition 3: The model not only considers the effects from classical convergence but also takes into account the technology advancement. Condition 4: The model should outline a comparative analysis on the growth effect of economies for different capitals of different countries. Condition 5: The model should be a linear regression model as the number of data may be very limited. Condition 6: The model should not contain any unnecessary assumptions and therefore it is more reliable, accurate and reasonable. Condition 7: Based on the model, some practical applications can be developed. In accordance with the aforementioned conditions, we are going to extend the model by Solow [1957] and MRW [1992]. 1. The extension of Solow and MRW models The model developed by MRW introduced some assumptions which may not allow us to dig out the heterogeneity of different economies. a. Solow Model In 1957, Solow introduced improved Cobb Douglas production function α Y = A(t)K [4] In this paper, we use the following aggregate production function which was studied by MRW (1992)

4 Middle Eastern Finance and Economics - Issue 8 (2010) 126 ( ) α Y K [A t ] = [5] Taking into account different kinds of capital stocks which are namely domestic investment capital stock S, non-service F capital stock S and service F capital stock S. From equation [5], we get Y = [ S ] [ S ] [ S ] [A t ] [6] ( ) Whereas, and are the factor income elasticities of three kinds of capital stocks respectively. After re-arrangement of terms, taking natural logarithm for all items We have Y ( t ) ln ( t ) = S D I ( t ) S N F ( t ) S F S ( t ) D I ln + N F ln + F S ln + ln A ( t ) [7] ( t ) ( t ) ( t ) In discrete time domain, we have the econometric model Y ( j + 1) = S D I ( j + 1) S N F ln ( j + 1) ( j + 1) S F S ( j + 1) D I ln + N F ln + F S ln + ln A ( j + 1) + u ( j + 1) ( j + 1) ( j + 1) ( j + 1) et Y ( j + 1) ( ) be the output per worker = y j + 1 ( j + 1) S D I ( j + 1) = s (j+ 1) be the capital intensity of domestic investment ( j + 1) S N F ( j + 1) = s (j+ 1) be non-service F capital intensity ( j + 1) S F S ( j + 1) = s ( j+ 1) be service F capital intensity ( j + 1) Substitute the capital intensities into equation [8], we can obtain the function of technology advancement ln y ( j + 1) D I ln s D I ( j + 1) F S ln s F S ( j + 1) N F ln s N F ( j + 1) u ( j + 1) ln A ( j + 1) = [9] This equation has already taken into account the statistical residual or disturbance u(j+ 1). b. MRW model Firstly, we have to obtain the income elasticities for all types of capital stocks. Secondly, based on the income elasticities, we can use equation [8] to obtain the function of technology level. According to the conditions of convergence for the output per effective worker ( ) [8] ŷ t, the log linearization approximation (as stated in MRW) is d ln( yˆ ( t)) = λ ln( yˆ ( t) ln( yˆ ( t) dt [10] [note3] Whereasλ = ( n + g + d) [10.1]; n is the population growth rate, g is the growth rate of technology level and d is the depreciation rate. ŷ (t) is the actual output per effective worker at time t, y ˆ (t) is the steady state value. The above equation can be rewritten as d l n ( y ( t ) / A ( t ) ) y y ( t ) [11] = λ l n ( ) l n ( ) d t A A ( t ) Whereas y(t) is the output per worker. Steady state value is defined to be the value within steady state which is estimated by the data of the observable time period. We have y y ( t ) = lim A t + A ( t ) The solution of the ordinary differential equation [11] is

5 127 Middle Eastern Finance and Economics - Issue 8 (2010) λ t y λ t y ( t 0 ) ln ( y ( t ) / A ( t )) = ( 1 e ) ln ( ) + e ln ( ) [12] A A ( t 0 ) In discrete time domain, we have λ ( j) y λ ( j) y (1) ln( y ( j + 1) / A( j + 1)) = ( 1 e ) ln( ) + e ln ( ) [13] A A(1) Its econometric model is λ ( j) y λ ( j) y ( 1) ln ( 1) ( 1 ) ) [14] y j + = e ln ( ) + e ln ln A ( j 1) v ( j A A ( 1) Considering the output per effective worker, we get the following econometric model λ ( j ) y λ ( j ) y ( 1) ) [15] ln yˆ ( t ) = ( 1 e ) ln ( ) + e ln v ( j A A ( 1 ) [note 3] with reference to Mankiw, Romer and Weil (1992) p.422. Barro, Sala-i-Martin (1995) p. 37 and Islam (1995) p [note 4] the steady state value will vary if the parameters of equation [3.1] change. c. Extension of Solow and MRW model y et C 1 = ln( ) be the steady state value at equilibrium A y ( 1) ln be the initial value of observable period under study A ( 1) Assuming that A(j+1) has already contained all unexplained exogenous variables, such as human capital. Substitute equation [9] into [14], we get λj 1 i e.lns i ( 1) lns i ( j 1) λj) λj 1 + [16] u ( j + 1) lny ( j + 1 ) =.(C ( 1 e ) + e 1 ln y ( 1 ) + ) + + v ( j + 1) Whereas index i means i-th type capital stock and i is the corresponding income share. In this paper, there are only three types of capital stocks under study, namely domestic investment capital stock, non-service F capital stock and service F capital stock. The advantage of the model is to consider the economy driving force from capital accumulation, convergence from output per effective worker and the growth of the technology level. et w(j+1) = [17] u ( j + 1 ) + v ( j + 1 ) 1 As the dependent variables and the independent variables of equation [8] and [14] are the same, 2 assuming the R to be the same. Therefore, we will have u(j+1) = v(j+1). From equation [17], we get W(j+1) = 2 [18] v ( j + 1) Minimizing v (j+1) in equation [14] is equivalent tominimizing is a constant in equation [18] 2 w (j+1) in equation [16] as III. Selection of Data and Empirical Study We may refer to the appendix 2 for data source. In this section, we would like to introduce the procedures of empirical analysis. The focal point will be equation [16]. In equation [16], ifλ is a constant, then the regression model will be linear. Ifλ is required to be determined, then the regression is nonlinear. As the number of observable data is very limited, we d better have an estimated value ofλ. Therefore, we develop a procedure for obtaining. Estimated value of the speed of convergenceλ

6 Middle Eastern Finance and Economics - Issue 8 (2010) 128 λ = ( n + g + d) ; nis population growth rate, g is the growth rate of technology level, d is the depreciation rate. Population growth rate n: the arithmetic average of the yearly population growth rates during the period. Depreciation rate d: (i) Proxy by the ratio of fixed capital consumption and Gross Net Investment. (ii) Estimate the value from the relationship between net capital stock, flow and deprecation [note 5] g (Growth rate of technology level) Estimated value for growth rate of technology level The initial value can be estimated by growth rate of ln (y) The accurate value of g can be approximated by the iterative procedures as follows. 1. Based on the initial value of g estimated by the growth rate of ln(y), λ can be obtained and equation [16] becomes a linear regression equation., and can be obtained. The steady state value of the output per effective worker is treated as a parameter to be estimated. 2. Substitute, and into equation [9] to obtain lna(j+1) and obtain the new value of g by first differencing lna(j+1). 3. Repeat step 1 and 2 until the convergence of g is achieved. [note 5] Net capital stock data for OECD countries can be found in OECD database. Using the flow and stock data sourced from statistics of China to obtain the relevant data for regression. [note 6] at steady state of the output per effective worker, we can assume y A I. Cointegrating regression Process the following steps for equation [16] a. NG-Perron test To verify the first difference of concerned time series is stationary. b. Johansen [20,21,22] cointegration test c. EG 3 steps Rearranging terms in equation [16], we get the convergence model λ j i e ln S i ( 1) ln S i ( j 1) λ j) λ j y ( j ) ( ) + + ( ) 1 1 u ( j 1) ln + 1 =.(C 1 1 e + e 1 ln y 1 ) v ( j 1) Perform Granger 3 steps on equation [8] [appendix 1] d. Unit root test y = A Table 1: Ng-Perron Unit Root Test NP Unit Root Test d(y) d(sdi) d(snf) d(sfs) Australia Austria China (KPSS) Finland France Germany Iceland Italy Norway UK USA

7 129 Middle Eastern Finance and Economics - Issue 8 (2010) From the unit root test, first differences of concerned variables for China, Germany and USA are stationary. Therefore, the model will be only applied to these three countries. e. FM-OS Performing FM-OS on equation [14] to obtain the income elasticities for all capital stocks. Table 2: Granger 3 step correction and FM-OS ADF China Germany US : 1% significant : 5% significant : 10% significant Johansen 1 cointegrating eqn: cointegrating eqn: cointegrating eqn: Granger 1 st step Estimated Coefficient ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Granger 3 rd step Correction ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) FM-OS ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) IV. Regression for Stationary Time Series In order to make sure the estimation of parameters is accurate; we take the first difference of equation [8] and conduct the following tests f. NG-Perron Unit Root Test Prove that the time series of equation [8] after taking 1 st difference is stationary. g. Perform OS for the following equation ( + ) ( ) = ( ) ( + ) ( ) + ( + ) ( ) + ( + ) ( ) + ( + ) ( ) lny j 1 lny j 1 lns j 1 lns j lns j 1 lns j lns j 1 lns j lna j 1 lna j DW test for residual h. JB test for residual i. White test for residual j. M test for residual Table 3: east squares on first difference of equation [8] ADF (null: Not stationary) China Constrained OS least square regression ( ) ( ) ( ) D.W. Test (null: Autocorrelated ) JB Test (null: normal) White Test (null: No heteroskedast icity) M Test (null: No auto correlation)

8 Middle Eastern Finance and Economics - Issue 8 (2010) 130 Germany US : 1% significant : 5% significant : 10% significant ( ) ( ) ( ) ( ) ( ) ( ) Table 4: Comparison on the three capital income elasticities(granger 3 steps FM-OS and least squares on first difference of equation [8]) Granger3 steps China Steady state ŷ ( ) ( ) ( ) ( ) Germany Steady state ŷ ( ) ( ) ( ) ( ) US Steady state ŷ ( ) ( ) ( ) ( ) east squares on first difference ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) FM-OS (0.1045) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) [note 7] The productivity in appendix 3 refers to the productivity per worker IV. Conclusion How do the population growth rate, the growth rate of technology level, depreciation rate d, saving rate s and income share of labor affect the economic growth of a country?

9 131 Middle Eastern Finance and Economics - Issue 8 (2010) λj The value of y(j+1) will be reduced and e will be reduced by increasing n and this was discussed in the MRW paper. Increase of n not also shortens the half time to convergence but also reduces the steady state value y ln ( ) A. In equation [3.1], g increases and the convergence speed increases. However, as the value of depreciation rate and income share of labor have dominant effect on the convergence speed and steady state value, the effect from g is not very significant. In equation [14], that g increases can speed up the growth rate of A(j+1) and therefore the output per effective worker can have significant growth. In equation [3.1], if d increases, then the convergence speed will increase. The convergence speed will significantly increase but the steady state value will decrease. In equation [3.1] and [3], with s increase, the convergence speed will remain unchanged but the growth rate of productivity will increase due to growing capital accumulation. In equation [15], the convergence path of the output per effective worker will much depend on the steady state value y ln A, the initial value y (1 ) ) and the convergence speedλ. The effects of l n ( A (1 ) the function of technology level A are : the initial value A(1) determines the initial value of productivity and the growth rate of A affects the convergence speed in equation [10.1] and steady state value in equation [3.1]. For small data samples, in order to make sure the parameters to be estimated are accurate enough, we take the first difference of equation [8] and conduct OS or GS regression. It was found that the parameters were quite consistent for different methods. The empirical study also showed that the outputs per effective worker of different countries were converging to different steady state value with different convergence speed (refer to table 5 for details). The growth rate of China was the highest among all because of longer distance from initial value to its steady state value. Except for China, Germany and USA, the concerned variables of Australia, Austria, France, Iceland, Italy and Norway and UK are not of I(1). Thus, the same model cannot be applied to these countries. The convergence path for the output per effective worker and the productivity (for unit worker) for all countries are shown in appendix 3. Using EG 3 steps, it was found that the goodness of fit was very perfect especially when ECM was taken into account. The cointegrating relationship was counterverified by the negative coefficients of ECM in appendix 4. The accuracy of model was also supported by the statistical significance of parameters in ECM as shown in appendix 4. The ADF test of residuals (ECM) for Granger Step also implies cointegration among explanatory variables. Table 5: Initial value, steady state value, convergence speed and half time to convergence for all countries Countries Initial value y (1) ln ( ) A (1) Steady state value C 1= y l n ( ) A Convergence speed λ Half time to convergence [note 8] China Germany US [Note 8] half time to convergence T is determined by e λt = 0.5

10 Middle Eastern Finance and Economics - Issue 8 (2010) 132 Reference [1] Solow RM (1956) A Contribution to the Theory of Economic Growth. Quarterly Journal of Economics 70 [2] Solow RM (1957) Technical Change and the Aggregate Production Function. Review of Economics and Statistics 39 [3] Mankiw NG, Romer D, Weil DN (1992) A Contribution to the Empirics of Economic Growth. The Quarterly Journal of Economics 107 [4] ENGE, R.F. and C.W.J. GRANGER (1987) Cointegration and Error Correction: Representation, Estimation and Testing, Econometrica, 55, [5] ENGE, R.F. and B.S. YOO (1991) Cointegrated Economic Time Series: An Overview with New Results in R.F. Engle and C.W.J. Granger (eds.), ong-run Economic Relationships: Readings in Cointegration, Oxford University Press, New York. [6] PHIIPS, P.C.B. and B.E. HANSEN (1990) Statistical Inference in Instrumental Variables Regression with I(1) Processes, Review of Economic Studies, 57, [7] Sala-i-Martin X (1996) The Classical Approach to Convergence Analysis Economic Journal 106 [8] Knight M. oayza N and Villanueva D (1993) Testing the Neoclassical Theory of Economic Growth: A Panel Data Approach. International Monetary Fund Staff Papers 40(3), [9] Sala-i-Martin X (1994) Regional Cohension: Evidence and Theories of Regional Growth and Convergence CEPR Discussion Paper 1075 [10] Cohen D. (1995) Tests of the Convergence Hypothesis: Some Further Results. CEPR Working Paper [11] Bernard A and Durlauf S (1996) Comparing Apples to Oranges: Productivity Convergence and Measurement Across Industries and Countries American Economic Review 86(5) [12] Sala-i-Martin X. (1996) The Classical Approach to Convergence. Econmomic Journal, 106 (437), [13] ee K, Pesaran M and Smith R (1997) Growth and Convergence in Multi-Country Empirical Stochastic Solow Model Journal of Applied Econometrics 12(4) [14] Galor O (1996) Convergence? Inferences from Theoretical Models Economic Journal 106 (437) [15] Dowrick S and Rogers M (2002) Classical and Technological Convergence: beyond the Solow- Swan Model Oxford Economic Review 79(5) [16] Barro R. and Sala-i-Martin X. (1995) Economic Growth. New York McGraw-Hill. [17] Barro R. and Sala-i-Martin X. (1995) Technology Diffusion, Convergence and Growth. Centre for Economic Policy Research, Working Paper [18] Islam N (1995) Growth Empirics: A Panel Data Approach Quarterly Journal of Economics, 110(4), [19] Jochonia S Mathunjwa and Jonathan R W Temple (2007), Department of Economics, University of Bristol, Discussion Paper [20] JOHANSEN, S. (1988) Statistical Analysis of Cointegration Vectors, Journal of Economic Dynamics and Control, 12, [21] JOHANSEN, S. (1991) Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models, Econometrica, 55, [22] JOHANSEN, S. and K. JUSEIUS (1990) Maximum ikelihood Estimation and Inference on Cointegration: with Application to the Demand for Money, Oxford Bulletin of Economics and Statistics, 52,

11 133 Middle Eastern Finance and Economics - Issue 8 (2010) Appendix 1 Granger first step:- where X(i) = G.1 Y ( i ) = X ( i ) + u ( i ) T [ X 1(i) X 2 (i).. X N (i)] = [ ] 1 N Granger second step:- ( ) ( ) ( ) ( ) Granger third step:- Y i = α X i + α Y i 1 X i 1 + ε 1 2 i ( ) ε i = η α 2 X i + v ( i ) where η = [ η. η ] 1 N c o r r = + η G.2 G. 3 Appendix 2 Data Source i) OECD Countries (Except China and US) Data Source Year Australia Austria Finland France Germany Iceland Italy Norway UK Nominal GDP OECD Real GDP OECD PPP Real GDP/Nominal GDP Nominal nonservice F stock Real nonservice F stock Nominal service F stock OECD Nominal non-service F stockppp OECD Real service Nominal service F F stock stockppp abor force OECD Growth rate of labor abor force force during years of growth rate study abor income OECD, Average during share years of study Capital stock OECD F stock OECD Domestic investment stock Depreciation rate ii) US Capital stock F stock W, average of fixed capital consumption rate during Data Source Year US Nominal GDP OECD Real GDP OECD PPP Real GDP/ Nominal GDP Nominal non-service F stock U.S. Bureau of Economic Analysis Real non-service F stock Nominal non-service F stock PPP Nominal service F stock U.S. Bureau of Economic Analysis Real service F stock Nominal service F stock PPP abor force OECD 80-06

12 Middle Eastern Finance and Economics - Issue 8 (2010) 134 abor force growth rate Growth rate of labor force during years of study abor income share OECD, Average during years of study Capital stock OECD F stock U.S. Bureau of Economic Analysis Domestic investment stock Capital stock F stock Depreciation rate W, average of fixed capital consumption rate during iii) China Data Source Year China Nominal GDP China statistical yearbook Real GDP China statistical yearbook PPP Real GDP/ Nominal GDP Nominal non-service F flow Yearbook of China's foreign economic relations and trade Real non-service F flow Nominal non-service F flow PPP Real non-service F stock Flow of last year(1-depreciation rate)+flow of current year Nominal service F flow Yearbook of China's foreign economic relations and trade Real service F flow Nominal service F flow PPP Real service F stock Flow of last year(1-depreciation rate)+flow of current year abor force China statistical yearbook abor force growth rate Growth rate of labor force during years of study Appropriation China statistical yearbook Domestic oans China statistical yearbook Self-Raised Funds and Others China statistical yearbook Foreign Investment China statistical yearbook Domestic investment stock Appropriation + Domestic oans + Self-Raised Funds and Others +0.7 Foreign Investment F stock Yearbook of China's foreign economic relations and trade Depreciation rate Calculated from China statistical yearbook Appendix 3 Diagrams of convergence of output per effective worker of all countries China:

13 135 Middle Eastern Finance and Economics - Issue 8 (2010) Germany:

14 Middle Eastern Finance and Economics - Issue 8 (2010) 136 USA: Appendix 4 ECM Error correction Countries ECM D(S) D(S) D(S) D(A) and adjusted ECM ADF China Germany US (0.2056) (0.2638) (0.1583) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) (0.0002) (0.0023) (0.0471)