Boriss Siliverstovs
email: bsiliverstovs@diw.de

Dierk Herzer
email: dierkherzer@gmx.de

C32 -Time-Series Models < , F43 -Economic Growth of Open Economies < , O47 -Measurement of Economic Growth|Aggregate Productivity <

Keywords: Export-led growth, Chile, Cointegration Export-led growth, Chile, cointegration JEL code: O47, F43, C32

This study examines the export-led growth hypothesis using annual time series data from Chile in a production function framework. It addresses the problem of specification bias under which previous studies have suffered and focuses on the impact of manufactured and mining exports on productivity growth. In order to investigate if and how manufactured and mining exports affect economic growth via increases in productivity, the study uses Johansen cointegration technique. The estimation results can be interpreted as evidence of productivity-enhancing effects of manufactured exports and of productivity-limiting effects of mining exports.

Introduction

One of the fundamental economic questions is how countries can achieve economic growth. One of the answers to this question relies on the export-led growth (ELG) hypothesis which postulates that export expansion, especially of manufactured goods, is a key factor in promoting economic growth. There exist a vast literature that explores the link as well as direction of causation between exports and economic growth. However, it seems that overall conclusions are, at best, mixed and contradictory (for an exhaustive review, see Giles and Williams, 2000a,b).

In this study, we attempt to shed an additional light on this important research topic by testing the ELG hypothesis for Chile. Chile is an interesting case study because of its recent economic history 1 . During the last four decades Chile experienced a pattern of high economic growth, which was as accompanied by a significant increase of manufactured exports both in relative and absolute terms. Chilean exports grew particularly rapidly after 1974, when a comprehensive program of economic stabilisation and restructuring was initiated. Particularly, in less than four years (1975)(1976)(1977)(1978)(1979), Chile has abolished practically all quantitative import restrictions and exchange rate controls, as well as it drastically reduced imports tariffs as a part of a trade liberalisation program. [START_REF] Bergoeing | Policy-driven productivity in Chile and Mexico in the 1980s and 1990s[END_REF] argue that these structural reforms not only significantly contributed to the export growth in the late 1970s but also these reforms have laid a sound foundation that helped the domestic economy recover from the severe economic crisis that hit most Latin American countries in 1982.

From the methodological side, our study is motivated by recent advances in testing the export-led growth hypothesis that goes beyond the traditional approach where the ELG hypothesis has been largely addressed by testing for Granger causality in the bivariate systems which included growth rates of output and of exports. Much of this literature that addressed shortcomings of the traditional approach in various countries and/or whole regions has been published in this journal : [START_REF] Ghatak | Exports, export composition and growth: Cointegration and causality evidence for Malaysia[END_REF] and [START_REF] Khalafalla | Export-led growth and structural change: Evidence from Malaysia[END_REF] -for Malaysia, [START_REF] Shan | On the export-led growth hypothesis: The econometric evidence from China[END_REF] -for China, [START_REF] Moosa | Is the export-led growth hypothesis valid for Australia?[END_REF] -for Australia, [START_REF] Balaguer | Structural change in exports and economic growth: Cointegration and causality analysis for Spain (1961-2000)[END_REF] -for Spain, [START_REF] Sharma | Export and economic growth in industrialized countries[END_REF] -for five industrialised countries (Germany, Italy, Japan, United Kingdom, and United States), [START_REF] Sharma | Causal analyses between exports and economic growth in developing countries[END_REF] -for 30 developing countries, and Abu-Quarn and Abu-Bader (2004) -for MENA region; and in the related Applied Economics Letters journal: [START_REF] Jin | Export-led growth and the US economy: Another look[END_REF] and [START_REF] Shan | Export-led growth and the US economy: Some further testing[END_REF] -for United States, [START_REF] Shan | On the export-led growth hypothesis: The econometric evidence from China[END_REF] -for Australia, [START_REF] Biswal | Export-led growth hypothesis: Cointegration and causality analysis for Taiwan[END_REF] and [START_REF] Darrat | Testing export exogeneity in Taiwan: Further evidence[END_REF] -for Taiwan, inter alia. Nevertheless, it is our impression that thorough investigation of whether the export-led growth hypothesis is relevant for Chile is still lacking in the literature.

Among the few studies that have examined the causal relationship between this export performance and the Chilean economic growth, [START_REF] Figueroa | Exportaciones, orientación al comercio y crecimiento: Un enfoque de cointegración[END_REF], Amin Gutiérrez de [START_REF] Gutiérrez De Piñeres | Export diversification and structural dynamics in the growth process: The case of Chile[END_REF], and [START_REF] Agosin | Trade and growth in Chile[END_REF] find evidence of export-led growth. However, these studies suffer from several 1 For a survey on the evolution of growth and exports in Chile, see, for example, [START_REF] Agosin | Trade and growth in Chile[END_REF]. [START_REF] Gutiérrez De Piñeres | Export diversification and structural dynamics in the growth process: The case of Chile[END_REF] can be criticised for using a simple two-variable framework in their causality test. Admittedly, causality tests are extremely sensitive to omitted variables. Even if exports are found (not) to cause growth in bivariate models, this same inference does not necessarily hold in the context of larger economic models that include other relevant variables such as capital and labour [START_REF] Awokuse | Is the export-led growth hypothesis valid for Canada?[END_REF]. Indeed, [START_REF] Figueroa | Exportaciones, orientación al comercio y crecimiento: Un enfoque de cointegración[END_REF][START_REF] Figueroa | Exportaciones, orientación al comercio y crecimiento: Un enfoque de cointegración[END_REF][START_REF] Agosin | Trade and growth in Chile[END_REF] estimate a larger model, but they fail to incorporate imports along with exports in their estimates. According to [START_REF] Riezman | The engine of growth or its handmaiden? A time series assessment of export-led growth[END_REF], omitting the import variable can result in spurious conclusions regarding the ELG hypothesis, because particularly capital goods imports are necessary inputs for enhancement of export and domestic production. Furthermore, export growth may relieve the foreign exchange constraint, allowing capital goods to be imported to boost economic growth. Another problem that is ignored by [START_REF] Figueroa | Exportaciones, orientación al comercio y crecimiento: Un enfoque de cointegración[END_REF], Amin Gutiérrez de [START_REF] Gutiérrez De Piñeres | Export diversification and structural dynamics in the growth process: The case of Chile[END_REF], and [START_REF] Agosin | Trade and growth in Chile[END_REF] is that exports, via the national income accounting identity, are themselves a component of gross domestic product. Accordingly, exports are partly endogenous within an output equation. The outcome of this is a strong bias in favour of a correlation between these two variables, whatever actual causal relationship may exist between them [START_REF] Greenaway | What does liberalisation do for exports and growth?[END_REF]. Finally, it should be pointed out that [START_REF] Figueroa | Exportaciones, orientación al comercio y crecimiento: Un enfoque de cointegración[END_REF], Amin Gutiérrez de [START_REF] Gutiérrez De Piñeres | Export diversification and structural dynamics in the growth process: The case of Chile[END_REF][START_REF] Gutiérrez De Piñeres | Export diversification and structural dynamics in the growth process: The case of Chile[END_REF][START_REF] Agosin | Trade and growth in Chile[END_REF] focus on 'aggregate' exports only. This may mask important differences between different export categories. Even if there is evidence in favour of the ELG hypothesis relating to certain export categories, this may not be reflected at the aggregate level, and spurious conclusions may be drawn when disaggregated exports are not examined [START_REF] Ghatak | Exports, export composition and growth: Cointegration and causality evidence for Malaysia[END_REF].

The objective of this paper is to re-examine the evidence found in previous studies on the Chilean economy by carefully addressing the problematic issues pointed out above. The paper contributes to the existing literature in the following ways: First, in order to tackle the possible specification bias, we go beyond the two-variable causality relationship and estimate an export-augmented neoclassical production function. Second, we test the ELG hypothesis while controlling for capital goods imports in order to capture the role of exports in financing capital goods imports, which in turn are expected to promote growth. Third, we separate the 'economic influence' of exports on output from that incorporated into the 'growth accounting relationship' by defining the output variable net of exports. Fourth, we do not focus on total exports, but we decompose Chile's exports into its main export categories. That is to say, we examine the separate effects of mining and manufacturing exports on Chilean economic growth.

Our main finding is that we find empirical support for the ELG hypothesis in Chile with the unidirectional Granger causality running from the manufactured exports to the output but not vice versa. At the same time we record differentiated impact of the main Chilean export categories (manufactured and mining) on the output. The latter result could be interpreted as the productivity-enhancing effects of manufactured exports and as productivity-limiting effects of mining exports.

The rest of the paper is organised as follows. Section 2 discusses the theoretical background of the

Theoretical Background and Empirical Model

The export-led growth hypothesis postulates that export expansion is a key factor in promoting long-run economic growth. Several arguments can be put forward to justify the ELG hypothesis theoretically.

From a demand-side perspective, it can be argued that sustained demand growth cannot be maintained in small domestic markets, since any economic impulse based on the expansion of domestic demand is bound to be exhausted quickly. Export markets, in contrast, are almost limitless and hence do not involve growth restrictions on the demand side. Thus, exports can be a catalyst for income growth, as a component of aggregate demand [START_REF] Agosin | Trade and growth in Chile[END_REF].

At the same time, from a supply-side perspective, export expansion could promote economic growth through an increase in total factor productivity. First, an expansion in exports may promote specialisation in sectors in which a country has comparative advantages, and lead to a reallocation of resources from the relatively inefficient non-trade sector to the more productive export sector. Second, the growth of exports can increase productivity by offering larger economies of scale [START_REF] Helpman | Market Structure and Foreign Trade[END_REF].

Third, export growth may affect total factor productivity through dynamic spillover effects on the rest of the economy [START_REF] Feder | On exports and economic growth[END_REF]. The possible sources of these knowledge externalities include productivity enhancements resulting from increased competitiveness, more efficient management styles, better forms of organisation, labour training, and knowledge about technology and international markets [START_REF] Chuang | Learning by doing, the technology gap, and growth[END_REF].

In short, knowledge is generated through a systematic learning process initiated by exports and spilling over to the domestic economy. Fourth, export expansion may indirectly affect growth by providing the foreign exchange that allows for increasing levels of capital goods imports [START_REF] Riezman | The engine of growth or its handmaiden? A time series assessment of export-led growth[END_REF].

Increasing capital goods imports in turn stimulate output growth by raising the level of capital formation. Furthermore, recent theoretical work suggests that capital goods imports from technologically advanced countries may increase productivity and thereby growth, since knowledge and technology is embodied in equipment and machinery and therefore transferred through international trade [START_REF] Chuang | Learning by doing, the technology gap, and growth[END_REF].

However, as it has been pointed out in the literature, it is important to distinguish between the manufactured and primary export categories when referring to their impact on the country economic performance. Thus, [START_REF] Lucas | Making a miracle[END_REF] argues that the dynamic technological spillover effects are mainly associated with the manufactured exports rather than with the primary export. Moreover, several authors hypothesise that primary exports, and thus mining exports, could be an obstacle to greater productivity growth. The main arguments advanced in support of this hypothesis are: (i) Primary products offer no sustainable potential for knowledge spillovers, and an increase in primary exports can draw resources away from the externality-generating manufacturing sector [START_REF] Sachs | Natural resource abundance and economic growth[END_REF]. (ii) Primary exports are increasing GDP variability and macroeconomic uncertainty. High instability and uncertainty may, in turn, hamper efforts at economic planning and reduce the quantity as well as efficiency of investments [START_REF] Dawe | A new look at the effects of export instability on investment and growth[END_REF]. As a matter of fact, Chilean economy is extremely vulnerable to fluctuating copper prices as shown in [START_REF] Romaguera | Impacto macroeconomico de la inestabilidad del precio del cobre en la economía Chilena[END_REF]. Against this background, we assume that the effects of exports on Chilean productivity and growth differ significantly between primary and manufactured products and therefore include both variables in our empirical analysis. Thus, at the outset of the trade liberalisation reforms Chilean exports relied heavily on mining products, in particular copper, which accounted for about 80 percent of export income. As the results of undertaken reforms, the relative importance of the mining products has been steadily decreasing over the last decades. At the same time, the export share of manufactured goods rose from 7 percent in 1973 to about 45 percent in 2001. Although the share of mining in goods exports decreased from 90 percent in 1973, mining still accounts for 41 percent of goods exports in 2001.

Given the theoretical considerations on the possible exports role in growth promotion, we place our analysis in the framework of a simple neoclassical production function:

Y t = A t K α t L β t , (1)
where Y t denotes the aggregate production of the economy at time t, and A t , K t , L t are the level of total factor productivity, the capital stock, and the stock of labour, respectively. Because we want to investigate if and how manufactured and mining exports affect economic growth via increases in productivity, we assume that total factor productivity can be expressed as a function of manufactured exports, IX t , mining exports, M X t , capital goods imports, CM t , and other exogenous factors, C t :

A t = f (IX t , M X t , CM t , C t ) = CM δ t IX γ t M X ρ t C t , (2)
Next we combine equation (2) with equation ( 1) and obtain

Y t = C t K α t L β t CM δ t IX γ t M X ρ t , (3)
where α, β, δ, γ, and ρ are the elasticities of output with respect to K t , L t , CM t , IX t , and M X t .

Taking natural logs, ln, of both sides of equation ( 3) results in the following linear function:

ln Y t = c + α ln K t + β ln L t + δ ln CM t + γ ln IX t + ρ ln M X t + e t , (4)
in which all coefficients are constant elasticities, c is a constant parameter, and e t is the usual error term, which reflects the influence of all other factors.

ln N Y t = c + α ln K t + β ln L t + δ ln CM t + γ ln IX t + ρ ln M X t + e t . ( 5
)
This equation is estimated to determine the impact of increasing manufactured exports and mining exports on economic growth via increases in productivity. Hence, the null hypothesis that the manufactured exports does not promote growth in the long-run is given by H 0 : γ = 0. Accordingly, the ELG hypothesis is supported by the data if we find that an estimate of γ is positive and statistically significant. Similarly, the long-run effect of the primary exports on the output is given by the estimate of ρ.

The Econometric Approach

In our modelling we follow the general-to-specific approach advocated in [START_REF] Hendry | Evaluating econometric models by encompassing the VAR[END_REF] and Hendry andJuselius (2000, 2001), inter alia 2 . In particular, we start with an unrestricted VAR(p) model transformed into the error-correction form

∆x t = Πx t-1 + p-1 i=1 Γ i ∆x t-i + µ + ΦD t + ε t , ε t ∼ N n (0, Σ) (6)
where µ and D t denote a constant term and intervention dummies, respectively. Then we proceed as follows. We test for cointegration and subsequently impose the implied reduced rank restrictions on the unrestricted VAR model. Then we test for the long-run exogeneity of the system variables. We use the results of the weak exogeneity tests in order to build a parsimonious time series model for the output that passes all diagnostic tests, displays constant coefficients and possesses remarkable forecasting properties as well as in order to address the issue of causality between the output and exports variables. 1 shows the evolution of the variables in the period under consideration.

Since the Chilean economy has been subject to the several severe shocks during the last half of the past century, we introduce the following intervention dummies D t = (DI71, DI81) in order to control for the large outliers in the empirical model. The former dummy variable accounts for the effects of the Allende government which persuaded inward policy, whereas the latter accounts for the effects of the recession in 1982 accompanied by the overevaluation of peso, rising international interest rates, and falling commodity prices. These intervention dummies DIxx take value of 1 in 19xx and -1 in 19xx + 1 and zero otherwise.

As discussed in Hendry andJuselius (2000, 2001), such form of the intervention dummies ensures that the asymptotic critical values of the cointegration test remains intact.

At the first stage we determine the lag order of the VAR(p) model in equation ( 6) by means of the sequential modified likelihood ratio (LR) test, discussed in [START_REF] Lütkepohl | Introduction to Multiple Time Series Analysis[END_REF]. Given rather large number of explanatory variables n = 6 for a given sample size T = 42, we allow for maximum of three lags in order to allow for sufficient degrees of freedom in our testing procedure. The results of the LR test procedure are reported in the upper panel of Table 1. As seen, the lag length p = 2 is selected.

Table 1 reports the univariate as well as the multivariate misspecification tests of the chosen VAR(2) model. As seen, the VAR(2) model adequately describes the data. Even though the univariate tests report single rejections of the null hypotheses of no residual autocorrelation and normality of the residuals at the 5% significance level, we cannot reject the null hypothesis that the residuals are multivariate Gaussian white noise at the conventional significance levels. In addition, the residuals seem to have no ARCH [START_REF]Nup CHOWs 1% Figure 5: The recursive Chow test statistics for the conditional model (8) scaled by the corresponding 1% critical values 1970 1980 1990 2000 0[END_REF] effects.

Furthermore, against the background of the several economic crises that hit the Chilean economy, it is important to check whether the empirical model displays constant parameters. The structural stability of the estimated system is checked with the three versions of the system Chow tests: 1-step, Break-point, and Forecast Chow test, displayed in Figure 2. The graphs display the recursive test statistics scaled by the respective 1% critical values. We find no evidence of structural instability of our model and proceed further by addressing its cointegration properties.

After having found the adequate unrestricted model, the next step is to proceed imposing restrictions on that model. Hence, we address the cointegration rank of the estimated system. We use the Johansen Full Information Maximum Likelihood (FIML) procedure for this purpose. Table 2 reports the results of the trace and λ-max tests. As seen, both tests indicate the presence of one cointegrating relation in the system. This conclusion is supported by the magnitude of the modulus of the largest 6 eigenvalues of the companion matrix reported for the unrestricted and restricted r = 1 models. As seen, there are 5 eigenvalues that are quite close to the unity in the unrestricted model and the sixth eigenvalue takes the values of 0.700 and of 0.730 in the unrestricted and restricted models, respectively.

Thus we impose the cointegration rank r = 1 on the system (6) and proceed with testing for station- [START_REF] Johansen | Testing structural hypotheses in a multivariate cointegration analysis of the PPP and the UIP for the UK[END_REF]. This is a multivariate version of the Augmented Dickey-Fuller test with the null hypothesis of stationarity rather then non-stationarity.

Since a linear combination of I(1) variables that is I(0), or I(0) variables themselves, could only belong to the cointegration space, it investigates whether any of the variables alone belong to the cointegration space. This test has an asymptotic χ 2 distribution with the (pr) = 5 degrees of freedom.

The test for the long-run exclusion [START_REF] Johansen | Testing structural hypotheses in a multivariate cointegration analysis of the PPP and the UIP for the UK[END_REF] investigates whether any of the variables can be excluded from a cointegrating vector. This test has an asymptotic χ 2 distribution with the r = 1 degrees of freedom. Finally, test for the long-run weak exogeneity investigates whether the dependent variables adjust to the equilibrium errors, represented by a cointegrating relation.

Tables 3 and4 report the results of the tests for (trend-)stationarity and long-run exclusion, performed on the matrix of the long-run coefficients, and the tests for long-run weak exogeneity, performed on the matrix of the adjustment coefficients, respectively. According to the stationarity test, the null hypothesis that each variable is I(0) or I(0) around a linear deterministic trend is decisively rejected. Moreover, the results of the test for long-run exclusion suggest that none of the variables could be excluded from the long-run relation. This is the important result, which implies that all the variables, considered in the present study, are relevant for modelling the long-run relationship, and hence any other model based on a smaller subset of these variables would suffer from the omitted variable bias problem.

According to the univariate long-run weak exogeneity test results (see the upper panel of Observe that all the coefficient estimates have an expected signs and all estimates are significantly different from zero, which conform with the results of the long-run exclusion restriction tests reported in Table 3 above.

Observe that our estimation results provide empirical support for the ELG hypothesis in Chile as the coefficient estimate of γ, that measures the long-run influence of the manufactured exports on the output, is positive and statistically significant, γ = 0.047. At the same time, we record the negative and also statistically significant coefficient estimate of ρ, that measures the long-run impact of the mining exports on the output. This result is in line with the theoretical consideration on the negative role that primary exports could exert on the economic performance of a country as discussed above and it complies with the results reported in [START_REF] Ghatak | Exports, export composition and growth: Cointegration and causality evidence for Malaysia[END_REF] who tested the ELG hypothesis for Malaysia.

As expected, the capital and labour stock as well as the capital goods imports contribute positively to the output. An interesting point is that the estimate of the capital elasticity is rather high. This finding of a relatively high capital elasticity may be in line with economic theory that suggests that opening to trade and the elimination of distortions increase the average quality of capital and improve the allocation of capital towards sectors with higher marginal productivity. In addition observe, that the sum of capital and labour elasticities is greater than one, which might indicate the presence of increasing returns to scale. The existence of increasing returns to scale can theoretically explain the exceptionally high growth rates of the Chilean economy observed in the late seventies and in 1985 -1997.

As shown in [START_REF] Johansen | Testing weak exogeneity and the order of cointegration in UK money demand[END_REF], the status of long-run weak exogeneity of some variables allows us to reformulate the model ( 6) in terms of a conditional model, where we condition on the current and past values of the weakly exogenous variables, and the marginal models for these weakly exogenous variables. The conditional model involves two variables, ln N Y t and ln M X t , and for the rest of the variables ln K t , ln L t , ln IX t , ln CM t we have marginal models, that does not include the error-correction term. Our main focus, however, is on modelling the output ln N Y t variable. Therefore we report the results of the conditional model only for this variable. ∆ ln L t-1 -0.490

(5.22) ec t-1 + 0.405

(5.03) σ = 0.020, R 2 = 0.894, T = 40, F AR(1-2) (2, 31) = 1.94[0.160], F ARCH(1) (1, 31) = 0.50[0.486], χ 2 N orm (2) = 0.91[0.634], F Het (12, 20) = 0.50[0.887], F RESET (1, 32) = 3.17[0.084] (8)
The conditional model ( 8) is parsimonious, it has very good explanatory power, its coefficients are well determined, and the diagnostic tests show no signs of misspecification. Observe that the error-correction term is highly significant and it has the expected sign.

In addition, the recursive Chow test statistics also indicate parameter constancy (see Figure 5). This conclusion is also reenforced by the recursively calculated coefficients shown in Figure 6 along with the one-step ahead residuals. The remarkable stability of the coefficients is also supported by the ability of the model to produce accurate 1-step ahead forecasts of the dependent variable ∆ ln N Y t displayed in Figure 7 Since the existence of cointegration between the variables implies existence of either unidirectional of bidirectional Granger causality, the next step is to address the direction of the causality between the output on the one hand and each of the manufactured and mining exports on the other.

In determining the Granger causality direction we can use information from the long-run weak exogeneity tests reported in Table 4 above. Recall that the long-run weak exogeneity hypothesis was rejected for both variables ln N Y t and ln M X t . This results leads to the following conclusions. First, there is a bidirectional Granger causality between these two variables. Second, the manufactured exports, ln IX t , Granger causes the output variable, ln N Y t , as well.

In order to determine whether there is Granger causality from the output to the manufactured exports, we estimate the following marginal model for the latter variable with absolute t-values reported in The model ( 9) displays no signs of misspecification and no parameter instability as seen in Figures 8 and9. Observe that in order to fulfil the residual normality assumption we have included the impulse dummies Dxx which take value of 1 in 19xx and zero otherwise, which are absent in the conditional model ( 8).

Hence, the conclusion on whether the output variable, ln N Y t Granger causes the manufactured exports can be based on the corresponding t-statistic value of 0.003 which yields the p-value of 0.997. This result suggests that the output variable does not Granger cause the manufactured exports in Chile.

Conclusions

This paper uses Johansen cointegration technique to examine the productivity effects of manufactured and mining exports in the context of the export-led growth hypothesis. We test the ELG hypothesis for Chile using the using the time series data for 1960 -2001. In our analysis we employ a simple framework of an augmented neoclassical production function where total factor productivity is assumed to be a function of mining and manufactured exports, and capital goods imports.

In our study we address several aspects that have been overlooked in the previous literature that tested the ELG hypothesis on the Chilean data. First, we employ the multivariate analysis which goes beyond the two-variable causality relationship between the output and exports in estimating exportaugmented neoclassical production function. Second, we test the ELG hypothesis while controlling for capital goods imports in order to capture the role of exports in financing capital goods imports, which in turn are expected to promote growth. Third, we separate the 'economic influence' of exports on output from that incorporated into the 'growth accounting relationship' by defining the output variable net of exports. Fourth, we do not focus on total exports, but we decompose Chile's exports into its main export categories, i.e. manufactured and mining exports.

Our main finding is that the manufactured exports Granger causes output but not vice versa which supports the export-led growth hypothesis for Chile. At the same time, our results indicate bidirectional Granger causality between the non-export GDP and mining exports. We also record the differentiated impact that mining and manufactured exports exert on the aggregate output. This justifies our decision to split the Chilean exports in its main categories. In connection with the theoretical foundations underpinning our model, this estimation result can be interpreted as evidence of productivity-enhancing effects of manufactured exports and of productivity-limiting effects of mining exports. The latter may be due to the problem of fluctuating commodity export prices and earnings, especially copper prices, which is well known in the Chilean literature. [START_REF] Romaguera | Impacto macroeconomico de la inestabilidad del precio del cobre en la economía Chilena[END_REF], for example, found that copper price volatility had negative effects on Chilean GDP growth. Our results are consistent with the fact that manufactured exports might offer greater potential for knowledge spillovers and other positive externalities than traditional primary exports.

Accordingly, the primary conclusion that emerges from this study is that while mining and manufactured export earnings certainly contributed to the Chilean national income, exports of manufactured products have been especially important for productivity and thus for long-run economic growth. This conclusion has crucial policy implications. It is particularly important to promote exports of manufacturing goods -by avoiding trade-distorting measures that would counteract the comparative advantages, and building new comparative advantages and export opportunities in the Chilean manufacturing sector.

Finally, our findings suggest that there exists a long-run relationship between capital, labour, capital goods imports, manufactured exports, mining exports on the one hand and non-export GDP on the other. We use this cointegrating relationship in order to build an error-correction model for the Chilean net-of-exports GDP that displays constant parameters over the estimated sample and remarkable ex-post forecasting ability. Notes: '0' denotes the zero restriction on the coefficient of the corresponding variable, '•' denotes unrestricted coefficient in the 6 × 1 vector of the adjustment coefficients.

Acknowledgement

The number of degrees of freedom v in the χ 2 tests corresponds to the number of zero restrictions imposed.

  derives the empirical model. In Section 3 the econometric methodology is described and the empirical results are presented. Section 4 summarises our findings.

  price and volume fluctuations. Increasing primary exports may therefore lead to

  -run exclusion, and long-run weak exogeneity of the variables. The test of stationarity of the variables in the model has been suggested in

  for years 1982-2001. The corresponding parameter constancy forecast tests are Forecast χ 2 (20) = 19.54[0.487] and Chow F (20, 13) = 0.79[0.684]. In particular, notice the ability of the model to predict the recession in 1982.

Figure 2 :Figure 3 :

 23 Figure 2: System Chow test statistics scaled by the corresponding 1% critical values

FigureFigure 8 :Figure 9 :

 89 Figure 4: Cointegrating relation, equation (7)

  To remedy this problem, it is necessary to separate the 'economic influence' of exports on output from the influence incorporated into the 'growth accounting relationship'. Following[START_REF] Ghatak | Exports, export composition and growth: Cointegration and causality evidence for Malaysia[END_REF], we deal with this issue by using the aggregate output, net of mining and manufactured exports, N Y t (N Y t = Y t -IX t -M X t ), instead of total output, Y t . By replacing Y t with N Y t , we finally obtain an equation that represents the long-run relationship between the variables of interest:

	5
	effects.
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	It is problematic, however, that exports -via the national accounting identity -are themselves a
	component of output. A positive and statistically significant correlation between manufactured exports,
	mining exports, and aggregate output is therefore almost inevitable, even if there are no productivity

  t is the Chilean capital stock in real terms, which was computed on the basis of accumulated capital expenditure using the perpetual inventory method. The labour variable, L t , represents the total number of people employed each year. The variables CM t , IX t , and M X t represent real imports of capital goods, real exports of manufactured goods, and real exports of mining products, respectively. All variables except L t are measured in Chilean pesos at constant 1996 prices. The annual data span the period from 1960 till 2001. They were gathered from the Indicadores económicos y sociales de Chile 1960-2000 and 2 All computations and graphics has been made with PcGive 10.1 and GiveWin 2.20, seeDoornik and Hendry (2001a,b). Boletínes mensuales published by the Chilean Central Bank. Figure

	6
	the
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The vector x t = (ln N Y t , ln K t , ln L t , ln IX t , ln M X t , ln CM t ) consists of the following variables: The non-export output, N Y t , is measured by real Chilean GDP net of mining and manufactured exports. K

Table 4

 4 , ln L t , ln IX t , ln CM t that are weakly exogenous either at the 5% or 1% significance levels. In order to check, whether this result is robust to the change in the sample size, we report the value of the recursive test statistics of the latter null hypothesis, scaled by the 1% critical value, in Figure3. Observe, that the restriction that the four variables ln K t , ln L t , ln IX t , ln CM t are weakly exogenous with respect to the long-run parameter values is accepted for all sample sizes. Hence, this restriction seems to be reasonable, and in our further analysis we treat these four variables as weakly exogenous with respect to the long-run parameters.Imposing the long-run weak exogeneity restrictions on the ln K t , ln L t , ln IX t , ln CM t variables results in the following cointegrating vector (displayed in Figure4) with absolute t-values reported in parentheses

								8
	below the coefficient estimates
	ln N Y t =	0.695 (11.03)	ln K t + 0.675 (8.07)	ln L t + 0.047 (4.00)	ln IX t -0.333 (5.57)	ln M X t + 0.164 (5.39)	ln CM t	(7)
		F o
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					v i
					e w

), we can accept the null hypothesis that the variable ln IX t , that represents the manufactured exports, is weakly exogenous at any conventional significance level. At the same time, the null hypothesis of long-run weak exogeneity of the output variable ln N Y t is decisively rejected. In addition, observe that according to the univariate test results, we cannot reject the null hypothesis that the variables ln L t and ln CM t are weakly exogenous only at the 1% significance level. Nevertheless, as the joint long-run weak exogeneity test results suggest (reported in the lower panel of Table

4

), we cannot reject the null hypothesis that the three variables ln L t , ln IX t , ln CM t are weakly exogenous at the 10% significance level. Imposition of further zero restrictions on the adjustment coefficients yields the following group of the four variables ln K t

  The estimated conditional model for ln N Y t with absolute t-values reported in parentheses below the

					9
	coefficient estimates is
	∆ ln N Y t = 2.264	∆ ln K t -1.406	∆ ln K t-1 -0.090	∆ ln CM t-1
	(6.96)		(4.30)	(4.83)
	+ 0.656	∆ ln L t + 0.491
	(4.96)	(4.15)
	F o
	r
	P e
			e
			r
			R e
				v i
				e w

Table 1 :

 1 This research was carried out while the second author visited the German Institute for Economic Research (DIW Berlin). Hospitality and stimulating research environment of the receiving institution is gratefully acknowledged. The earlier version of the paper has been presented at the 3rd Nordic Econometrics Meeting in Helsinki, Finland. Standard disclaimer applies. Specification tests

	F o
	r
	P e
	e
	r
	R e
	v i
	e w

Table 4 :

 4 Tests for long-run weak exogeneity

	ln N Y t	ln L t	ln K t	ln M X t	ln IX t	ln CM t	χ 2 (v)
	Long-run weak exogeneity
	0	•	•	•	•	•	16.30	[0.000]**
	•	0	•	•	•	•	4.43	[0.035]*
	•	•	0	•	•	•	7.71	[0.005]**
	•	•	•	0	•	•	9.75	[0.001]**
	•	•	•	•	0	•	0.01	[0.965]
	•	•	•	•	•	0	4.05	[0.044]*
	•	0	•	•	0	0	6.25	[0.100]
	0	0	•	•	0	0	19.54	[0.001]**
	•	0	0	•	0	0	8.240	[0.083]
	•	0	•	0	0	0	13.77	[0.008]**

Notes: '0' denotes the zero restriction on the coefficient of the corresponding variable, '•' denotes unrestricted coefficient in the 6 × 1 cointegration vector when testing for the stationarity and long-run exclusion and 7 × 1 cointegration vector when testing for trend-stationarity of the variables.

The number of degrees of freedom v in the χ 2 tests corresponds to the number of zero restrictions imposed.


Shujie Yao
email: s.yao@mdx.ac.uk

Ph.D Chunxia Jiang

Genfu Feng

senior lecturer, Dirk Willenbockel

On the Efficiency of Chinese Banks And WTO Challenges

Keywords: B23 -Econometrics|Quantitative Studies <, N25 -Asia including Middle Production efficiency, banking, China WTO, Efficiency, Banking, China JEL: C52 G14 G21

and research institutions in France or abroad, or from public or private research centers.

Introduction

China has experienced rapid economic growth for more than a quarter century since economic reforms started in 1978. In the meantime, the banking sector has been subject to a process of fundamental structural change and reform. The key objective of reform was to change the sector from a centralized, state-owned, monopolistic and policy-driven to a decentralized, multi-ownership, competitive and profit-oriented system.

Despite the significant changes and reforms in the past decades, the banking system in China is still renowned for its low efficiency and mounting non-performing loans (NPLs), making further reforms more and more difficult and challenging. As China joined the World Trade Organization (WTO) in December 2001, the domestic banking market will have to be completely open up for competition with foreign banks and other overseas financial institutions. Many foreign banks have now entered China for business activities involving foreign currency transactions. The Hong Kong and Shanghai Bank Corporation (HSBC) is now allowed to do business in Shanghai involving transactions in Renminbi, the Chinese currency. In two years time, all foreign banks will be able to do any business that can be done by domestic banks. By June 2004, there were already 100 foreign banks conducting Renminbi businesses in 13 large cities, and 53 of them were allowed to do such businesses with domestic enterprises (People's Daily, 2004).

The time scale for intense competition is short, but most domestic banks, especially the four large state-owned commercial banks, are still ridden with mounting NPLs and low efficiency. The task to make these banks competitive with international banks such as HSBC is undoubtedly onerous and extremely challenging for the Chinese authorities. In response to the immediate challenges, the central government has decided to speed up the pace of reforms. Apart from bailing out a huge sum of NPLs from the four state-owned banks, the state council has decided to support the Bank of China and China Construction Bank with $45 billion from its total foreign exchange reserves of $403 billion. This is by far the boldest and toughest decision of the government to convert the big state banks into truly commercial institutions. The implication is that these two banks will become joint-stock companies, which will soon be placed in the stock exchanges. If this reform is successful, a similar reform measure will be applied to the other two state banks, the China Industrial and Commercial Bank and the China Agricultural Bank.

One theoretical rationale for the latest banking reform is provided by the agency theory.

In the past, whenever the state banks ran into difficulty, the principal (the state) had to bail them out. The agents (the bank managers), knowing that the principal was the ultimate resort of help, lent relentlessly to whatever clients they considered to be trustworthy, resulting in mounting NPLs that could never be recovered. By turning the state banks into joint stock companies, the incentive structure changes, and it is hoped that the state will never have to bail them out in the future. In the short run, the state has a responsibility to remove all or much of the NPLs to generate a fresh capital structure similar to that of a truly commercial bank so that the banks can compete with foreign entrants on a level playing field. In the long term, the state banks will have to be entirely responsible for their own profits and losses without political or administrative interference.

A second theoretical perspective on the banking reform is related to budgetary constraints. In the past, soft budget constraints meant that state banks were largely capitalized using state funds. In the future, once they are listed in the stock market, they have to rely more and more on raising capital from shareholders, rendering them to be responsible for shareholders' interests rather than state or local government interests.

The competitiveness of these banks will depend on their ability to earn profits and Whether the motivation of reform is based on the principal-agent problem, or on the impact of budgetary constraints, the ultimate goal is to increase efficiency and competitiveness of domestic banks. Whether this goal can be achieved depends on the answers to the following two questions. First, can ownership reform and hard budget constraints help improve efficiency? Second, can China change the ownership structure of its banking system and subject all the state banks to hard budgets in such a short time before foreign banks flush into the country for competition?

In this paper, we aim to answer the first question based on available data and information. We cannot answer the second question, as it is not yet clear how quickly the state council can move to re-capitalize the four large state banks.

To answer the first question, we employ a stochastic frontier production function and use panel data of 22 state-owned and non-state banks for the period 1995-2001. Two hypotheses are tested. First, joint stock or non-state banks are more efficient than the state-owned banks. Second, banks that are subject to a harder budget constraint are more efficient than banks that are subject to a softer budget.

The regression results support both hypotheses and show that the average efficiency level is 63% in the data period, which is relatively low compared with that of the US or European banks. Two factors are found to have a significant impact on the level of efficiency: ownership characteristics and equity/asset ratio. On average, non-state banks outperform state banks by 8-18% depending on whether the output of banking is measured by the amount of loans or by profitability. The equity/asset ratio measures the extent of risk taken by banks. It also reflects the extent to which banks are subject to a hard budget. If a bank is well capitalized by the state, the equity/asset ratio is high, and hence less reluctant to take risk. It is found that banks with a high equity/asset ratio are less efficient because they are better capitalized, less risk-taking, and hence subject to a The empirical results not only support our hypotheses, but also support the government efforts to reform the state banks through changing their ownership structure and subjecting them to a hard budget. Whether the state can successfully transform the state banks, however, remains a challenging issue for the future, but is beyond the scope of this paper.

The rest of this paper is organized as follows. Section 2 provides background information on the Chinese banking system. Section 3 reviews the literature on efficiency studies, paying special attention to the banking sector. Section 4 discusses methodological issues, including model specifications and data. Section 5 presents and evaluates regression results. Section 6 concludes.

Bank reform and WTO challenges

Banking system reform

Throughout the pre-reform period 1949-78, the Chinese banking system was entirely dominated by the People's Bank of China (PBOC), which acted as a central bank and the only commercial bank in the country [START_REF] Dai | Developing China's capital market and speeding up banking reform[END_REF]. Following economic reforms since 1978, Bank of China (ICBC). The banking system was overwhelmingly dominated by the PBOC and the Big Four until the mid-1990s when some non-state banks and joint-stock banks were allowed to run businesses throughout the country [START_REF] Wong | Competition in China's domestic banking industry[END_REF]. The present banking system in China can be illustrated in Figure 1.Figure 1 China's banking system

The Big Four are renowned for their low efficiency, loss-making and mounting NPLs.

Two main factors were responsible for their current plight. One was that each of them provided services mainly to state-owned enterprises (SOEs) within a designated sector of the economy. They were operating as some well-encapsulated monopolistic institutions, with no responsibility and incentives to penetrate and compete across regions and sectors. The ICBC provided services to commercial and industrial activities in the urban areas. The ABC was responsible for rural finance. The BOC mainly focused on foreign exchange dealing and foreign businesses. The CCB provided services to urban large construction projects. Each of the Big Four had provincial and local branches and each branch operated within a designated region under the administrative control and guidance of the respective local authority. As a result, all banks and their local branches had their own servicing niches, ruling out any possibility of free competition. Another factor was that the Big Four acted as governmental agencies in a planned economy. They were effectively arms of government administration to implement production plans projected by the State and Regional Planning Commissions. They extended loans to SOEs on the basis of fulfilling the national and regional production plans, regardless of profitability, resulting in huge volumes of NPLs and losses [START_REF] Wong | Competition in China's domestic banking industry[END_REF]. Since the early 1980s, the banking system has experienced some changes, and the intention of such changes is to move the state banks away from being driven by policy towards being driven by profit and competition. In the process of transition, the government has assumed concrete steps to reform in order to create a more competitive and efficient system. The first step was to remove the limits that a specialized bank had to serve a designated sector in order to create a competitive market-based financial system in 1985. However, the competition was limited because the operations of the Big Four were subject to frequent intervention by the central and local governments.

The local branches were under government control and much of the lending activities were still driven by the needs of policy makers.

The second step was the establishment of three policy banks in order to take over the function of extending policy loans from the Big Four in 1994. Nevertheless, the state commercial banks still play a significant role in policy lending. The serving and lending capacity of the specialized policy banks are unable to meet the need of policy lending previously provided by the Big Four due to the lack of a branch network and capital.

Moreover, the state commercial banks are often subject to pressure from both the central and regional authorities to make loans to their preferred sector and enterprises. Big Four to clean up their balance sheets and to make them more competitive. In addition, a five-classification loan standard scheme has been applied to all domestic banks in order to control the NPL ratio of new loans.

Apart from reforming the Big Four, the state allowed regional banks or even non-state banks to be established and to compete with the Big Four. The establishment of joint-stock commercial banks has injected vigor into the Chinese banking industry by creating a new source of competition. In addition, 90 local banks, known as city commercial banks, were formed by consolidating former local urban cooperatives. City commercial banks all adopted a shareholding ownership structure and were restricted geographically within their own localities. These joint-stock banks operate on a pure commercial basis focusing on profit maximization and market share.

Having assumed effective measures in a step by step manner, much headway has been made in enhancing the competitiveness and efficiency of the banking system. Currently, an open and competitive system has been primarily established, comprising the PBOC as the central bank, along with the Big Four as mainstay, joint-stock commercial banks as growth engines, local commercial banks and foreign banks as complementarities.

However, despite a rapid expansion of non-state banks, the Big Four still overwhelmingly dominate the Chinese banking industry. The four firm concentration ratios of total assets, loans and deposits were respectively 84.93%, 84.26%, and 88.51% in 1998, whilst the four firm concentration ratios of profits was only 55.33% [START_REF] Wong | Competition in China's domestic banking industry[END_REF]. These rough indicators suggest that the Big Four are large but much less profitable than other types of banks, an issue of interest to be investigated in this paper.

Facing WTO challenges

Although much progress has been made on reforming the banking system, the Big Four still face with many internal and external challenges. The internal plight is the huge In all the four main indicators of performance, the Bank of China is greatly outperformed by any of the other banks. The Bank of China's profit/assets ratio is only a small fraction of that of Citibank. Its net income/equity ratio is only one fourth of that of Citibank, and its net income/asset ratio is just one third of that of HSBC. In the past, there were high entry barriers and business restrictions for foreign banks, including geographic restrictions and entry requirements. Under the WTO rules, there is no restriction on foreign currency business upon accession in all parts of the country.

F o r P e e r
For Renminbi (RMB), or local currency business, however, the opening process as shown in Table 2 is

No restriction

Source: WTO data cited in [START_REF] Huang | Is WTO accession an opportunity or challenge for Chinese state commercial banks?[END_REF].

Hence, how to reform domestic banks, especially the Big Four, and how to improve their efficiency has become an urgent and important issue of concern after WTO accession. Indeed, the need for reforming the Big Four is due to the pressure of competition from both outsiders and insiders. With China's entry into WTO, domestic commercial banks will face more and more competition and challenges from foreign banks. The Big Four will compete with their sophisticated foreign rivals on an international competitive market basis. With further reform, the Chinese banking and the merger of Lloyds and TSB. As for scope economies, however, empirical studies reveal small cost inefficiencies [START_REF] Saunders | Financial institutions management[END_REF].

More recently, efficiency research has shifted to production efficiency which consists of two components: technical efficiency and allocative efficiency. Technical efficiency refers to the ability of optimal utilization of available resources either by producing maximum output for a given input bundle or by using minimum inputs to produce a given output. Allocative efficiency refers to the ability to achieve the optimal combination of inputs and outputs for a given level of prices [START_REF] Lovell | Production frontiers and productive efficiency[END_REF] data set, the best-practice frontier or the worst-practice frontier can be estimated. The difference between the best-practice frontier and the practice of a particular firm reflects its x-inefficiency [START_REF] Reifschneider | Systematic departures from the frontier: a framework for the analysis of firm inefficiency[END_REF]Stevenson, 1991, Molyneux et al, 1996).

In the literature, two main controversial issues addressed by researchers are how to define and measure inputs and outputs of banks and how to determine the best-practice frontier to evaluate their performance. Indeed, how to measure banking outputs and inputs is one of the most difficult issues because of the distinct features of banks.

Unlike manufacturing firms producing physical goods, banks not only produce unidentifiable products-intermediary services, but also provide a wide range of products-multi-products. A number of measures on banking output have been employed in early efficiency research, such as the number of deposit and loan accounts and the dollars in each account.

By emphasizing the basic nature of a bank's production process rather than stock variables, a services flow offered to customers can be considered as bank output. There are two main approaches to measure services flow: the production approach and the intermediation approach. The production approach treats banks as firms producing different deposit and loan accounts. The number and type of transactions and documents are considered to be the best measure of bank output. However, such specific data are generally unavailable and therefore, in practice, the number of deposit and loan accounts is usually employed as the measure of bank output. The intermediation approach pioneered by [START_REF] Sealey | Inputs, outputs and a theory of production and cost at depository financial institutions[END_REF] treats banks as financial intermediaries channeling funds between depositors and creditors. In the production process, the value of bank loan and investment is considered to be output, while labour and deposit capital are treated as inputs. This approach is distinguished from the production approach by adding deposits to inputs, with consideration of both operating cost and interest cost [START_REF] Goddard | European banking[END_REF]. Neither the production approach nor the intermediation approach is perfect, they are complementary instead (Berger and 13 can be applied to different levels of efficiency research. The production approach is appropriate for studying the cost efficiency of banks by addressing the operation costs of banking. The intermediation approach is appropriate for studying the economic differentiation of banks by controlling the overall costs of banking [START_REF] Ferrier | Measuring cost efficiency in banking: econometric and linear programming evidence[END_REF]. This approach takes interest expenses into account, which is useful not only for examining bank efficiency but also for frontier analysis.

Different estimation techniques have been applied to bank efficiency research. [START_REF] Berger | Efficiency of financial institutions: international survey and directions for future research[END_REF] provides a valuable survey on 130 financial institution efficiency studies in which five main approaches are identified. These approaches can be classified into two main categories -parametric and non-parametric techniques.

Parametric technique and non-parametric technique were roughly equally adopted.

Overall, their survey shows similar efficiency estimates resulting from parametric and non-parametric techniques. On average, there is about 20% cost inefficiency and about half of profit inefficiency in the US. Studies employing non-parametric techniques obtain lower average efficiency estimates and greater dispersion than studies using parametric techniques. Despite the similarity of average efficiency estimates, disagreement about inefficiency rankings of individual firms exists.

Parametric methods can be subdivided into three main approaches to determine the best-practice frontier. The first approach is the stochastic frontier approach (SFA) developed independently by [START_REF] Aigner | Formulation and estimation of stochastic frontier production models[END_REF] and [START_REF] Meeusen | Efficiency estimation from Cobb-Douglas production functions with composed Error[END_REF]. SFA specifies a functional form for the cost, profit or production function, which allows inefficiencies to be included in the error term. Two distributional assumptions on the error terms to separate the two components are (1) the inefficiencies follow an asymmetric half-normal distribution, based on the logic that inefficiencies only increase costs above frontier levels, and (2) random errors follow a symmetric standard normal distribution because random fluctuations can either increase or reduce costs (Bauer at el.,1993). Relevant literature on applications of SFA to banking can be found in [START_REF] Ferrier | Measuring cost efficiency in banking: econometric and linear programming evidence[END_REF] and Bauer at el. (1993). In China, this approach has been used to investigate grain production and technical efficiency by [START_REF] Yao | Determinants of grain production and technical efficiency in China[END_REF]. However, to the best of our knowledge there is no such study for the Chinese banking sector. On the other hand, there are many empirical studies for the banking sector in other countries or economies, including [START_REF] Shen | Cost efficiency and banking performances in a partial universal banking system: application of the panel smooth threshold model[END_REF] and [START_REF] Huang | Estimating of scale and scope economies in multiproduct banking: evidence from the Fourier flexible functional form with panel data[END_REF] for Taiwanese banks, [START_REF] Elyasiani | The comparative efficiency performance of small and large US commercial banks in the pre-deregulation and post-deregulation eras[END_REF] and [START_REF] Fare | The effect of risk-based capital requirements on profit efficiency in banking[END_REF] for the US banks, and [START_REF] Girardone | Analyzing the determinants of bank efficiency: the case of Italian banks[END_REF] for Italian banks.

The other two parametric approaches are the distribution free approach (DFA) and the thick frontier approach (TFA). DFA assumes that efficiency differences are stable over time. The estimated efficiency of each firm is the difference between its mean residual and industrial mean residual on the frontier. TFA has no restriction of distributional assumption. TFA estimates provide an overall level of efficiency rather than point efficiency estimates for individual firms. This approach has been employed in [START_REF] Berger | Efficiency of financial institutions: international survey and directions for future research[END_REF], [START_REF] Bauer | Efficiency and productive growth in US banking[END_REF] and [START_REF] Drake | X-efficiency and scale economies in policing: a comparative study using the distribution free approach and DEA[END_REF].

Unlike parametric techniques that specify a functional form for the production function, non-parametric techniques impose fewer restrictions on the production frontier.

Non-parametric methods can be divided into two subcategories: data envelopment analysis (DEA) and free disposal hull (FDH). DEA is a linear programming method used for estimating efficiency of decision making units. DEA creates a production frontier directly based on a particular data set of firms rather than a specific functional form. The distance from the best-practice frontier reflects a firm's inefficiency [START_REF] Charnes | Measuring the efficiency of decision making units[END_REF]. Examples of DEA applications in banking are [START_REF] Ferrier | Measuring cost efficiency in banking: econometric and linear programming evidence[END_REF]. In China, one recent application of the DEA technique is found in [START_REF] Zheng | Efficiency, technical progress, and best practice in Chinese state enterprises (1980-94)[END_REF] on state-owned enterprise performance. FDH is a special case of DEA by relaxing the hypothesis of convexity [START_REF] Berger | Efficiency of financial institutions: international survey and directions for future research[END_REF]. DEA has been widely applied in many recent empirical studies, including [START_REF] Hauner | Explaining efficiency differences among large German and Australian banks[END_REF] for German and Austrian banks, [START_REF] Ataullah | Financial liberalization and bank efficiency: a comparative analysis of India and Pakistan[END_REF] for Indian and Pakistani banks, [START_REF] Casu | A comparative study of efficiency in European banking[END_REF] for European banks, and Favero and Pari (1995) for Italian banks. Each approach to determine the efficiency frontier possesses certain advantages and disadvantages. Non-parametric technique allows efficiency to vary over time and does not require prior assumptions about the distribution of inefficiency across observations. However, its important drawback is the assumption of no random errors influencing bank performance. Ignoring the existence of potential errors, the effects of omitted errors may be included into efficiency estimates [START_REF] Berger | Efficiency of financial institutions: international survey and directions for future research[END_REF].

Another drawback of the non-parametric technique is the neglect of price effects on efficiency-allocative efficiency, addressing only technological efficiency. As to the parametric technique, a crucial disadvantage is the pre-specified functional form for efficiency frontier, which may result in an inaccurate efficiency measurement [START_REF] Berger | Efficiency of financial institutions: international survey and directions for future research[END_REF]. Given that we use a panel data of 22 banks covering 6 years, there will be significant variations of efficiencies across different kinds of banks in different time periods. In other words, the effects of random errors could be large. As a result, the parametric approach, especially the stochastic frontier production function approach, is considered to be most suitable for this study.

The stochastic frontier production model

A theoretical model

This study adopts the intermediation approach to measure bank inputs and outputs since its key concern is to identify the main determinants of efficiency. Outputs of banks are defined as the book value of pre-tax profit, and/or the book value of loans. This is because the specific frontier production model does not allow multi-outputs.

The value of loans includes short-term, medium and long-term, and other loans, after deducting loan loss reserves. The value of pre-tax profit is an accounting item. Bank inputs are defined as fixed assets, deposit, equity and labour in both profit and loan models.

Previous studies have adopted a two-stage estimation procedure with shortcoming of ) (

0 it it it t it U V x t Y + + + = , i=1, …, N; t=1,…,T, (1)
where i and t denote firm and time, Y it the logged output variable, it x a vector of logged input variables, it V a random variable assumed to be distributed with mean zero and a constant variance N (0, 2 v ), a vector of unknown parameters to be estimated, it U a non-negative random variable associated with technical inefficiency of production, which is assumed to be independently distributed as truncation at zero of N( it m , 2 u ).

The specification of the technical inefficiency effects, it U , is

it it t it W z t U + + + = 0 , (2)
where it z is a vector of explanatory variables associated with technical inefficiency of production over time, a vector of unknown coefficients to be estimated, it W a random variable defined by the truncation of the normal distribution with zero mean and variance 2 .

The technical efficiency of production for the i-th bank at the t-th time is defined as:

F o r P e e r R e v i e w ) exp( ) exp( it it it it W z U TE = = .
(3)

The time trend variable t included in the stochastic production function (1) accounts for neutral technical progress at a constant rate, while the presence of t in the inefficiency function ( 2) is for capturing temporal changes in inefficiency at a constant rate against the shifting frontier with respect to time. Therefore, productivity changes are decomposed into the shift in the frontier and a movement towards or off the frontier [START_REF] Yao | Determinants of grain production and technical efficiency in China[END_REF].

An empirical model

Because bank output can be measured as profit or the value of loans, the same production function is estimated in two different versions. One uses pre-tax profits as output, the other the value of loans. The profit model is shown in equations ( 4) and ( 5).

The loan model has the same structure and explanatory variables as the profit model. Hence, the E/A ratio can be interpreted in different aspects. A lower E/A may mean that the bank is less capitalized and subject to a harder budget constraint, but it has to take more risk in order to increase loans to its clients. If the government is involved in changing the E/A ratio, different banks will be subject to different budget constraints.

Usually, the state will support the state-owned banks, helping them to have a lower E/A ratio than the non-state banks, ceteris paribus. As a result, if the E/A ratio is negatively associated with efficiency, it implies that soft budget will lead to low efficiency, or vice versa.

Data

The data are obtained from Bankscope for 22 commercial banks over the period 1995-2001. Of the 22 banks, 2 have data for 1996-2001, 7 for 1995-2000, and

Results and interpretations

The profit model

Maximum-likelihood (ML) estimates of parameters in the profit model are obtained using a modification of the computer program, FRONTIER 4.1 [START_REF] Coelli | A Guide to FRONTIER Version 4.1: A computer program for stochastic frontier production and cost function estimation[END_REF]. These ML estimates and the standard errors are reported in Table 4. (2) Negative sign in the inefficiency function indicates that the variable has a positive effect on production efficiency and vice versa.

The signs of the estimated coefficients are as expected and all coefficients are statistically significant at or below the 5% critical level except that for labour. The insignificance of the estimated labour coefficient is not surprising given that most banks may be still overstaffed even after many years of reforms. The estimated coefficients for fixed-asset, deposit and equity are their elasticities with respect to profits. Deposit and equity have roughly the same value of elasticity at 0.3503 and 0.3311, respectively. The elasticity of fixed-asset is relatively small at 0.1611, but it is significant. The presence of the one-sided error component is justified by the LR test, which is highly significant. The estimates of variance ratio ( ) /( and ownership characteristic. The E/A ratio has a positive impact on inefficiencies.

Banks are more efficient with a low E/A ratio which reflects more risk-taking. In other words, well-capitalized banks are less efficient, confirming our hypothesis on budget constraints. If banks are subject to a soft budget, such as the state-owned banks, their capital assets are manly raised from state funds. But if banks are subject to a hard budget, they have to raise capital from shareholders. One consequence is that they will tend to be less capitalized, and hence have to take more risk to make profits [START_REF] Koch | Bank management[END_REF].

Another explanatory variable is ownership characteristic measured by a dummy variable taking a value of 0 for state-owned banks and a value of 1 for joint-stock banks.

As reported in the second part of (2) Negative sign in the inefficiency function indicates that the variable has a positive effect on production efficiency and vice versa.

The estimated average technical efficiency is 63.91%, which is slightly higher than in The Big Four still acted as government's arms in a policy-driven financial system despite many years of reform to reduce intervention and policy lending.

Conclusions

The estimated average efficiency of the sample banks is quite low at 63% in both the profit and loan models, but not fundamentally different from that of previous studies for other countries. In the literature, the average efficiency score is about 80% for the US banks [START_REF] Berger | Efficiency of financial institutions: international survey and directions for future research[END_REF]. The difference of efficiency estimates is consistent with the fact that Chinese banks are more subject to government control and intervention despite many years of reform.

The Big Four have a dominant position in the banking industry. They were given a legal status in 1995, the first year of our data period, as commercial banks with a principal objective of making profits. However, after many years of reforms, the efficiency level was still low and did not improve significantly over time. On the one hand, it suggests that government intervention in lending decision still persists, on the other hand, it reveals that there exists a great potential for efficiency improvement.

In this paper, we hypothesize that ownership reform and change of budgetary constraints should lead to more competition and hence greater efficiency gains. Our data set provides information for constructing a stochastic frontier production function indicating that joint-stock banks outperform state-banks by 18% in profitability and 8% in loans. The empirical results also suggest that banks which are subject to a hard budget, and hence less capitalized, tend to take more risk and become more efficient than those which are subject to a softer budget constraint and hence more capitalized.

The results have important policy implications on bank reforms in China in face of the WTO challenges in the immediate future.

As China has to open up its banking market for international competition, domestic banks have to become more efficient as quickly as possible. Two fundamental reforms are needed. First, the state banks, which have enjoyed a dominant position, have to become truly commercialized, to be entirely free from government control and intervention, to be re-capitalized so that their capital structure will resemble that of a western commercial bank, to have a better corporate government and incentive structure so that internal efficiency can increase as rapidly as possible. The second reform is to allow more and more medium and small banks to enter the market and compete with the state banks. The number of small and medium banks has increased enormously in recent years, but most of them are still under control by regional or local governments. Most of them are not well managed and may lack the economies of scale and scope to compete with foreign banks.

Although the empirical results in this paper confirms that ownership reforms and change of budgetary constraints can force banks to become more efficient, the problems faced by the Chinese banks are so many and so difficult that it may take many more years for them to compete successfully with foreign banks. The most recent decision to re-capitalize BOC and CCB is a significant step towards this direction.

However, whether this reform is successful will depend on how the Big Four respond to the new reform method. We have not paid much attention to the problem of NPLs which is estimated to be as high as 30-40%, although the official figure is only 15-20%.

If banks are still ridden with so much NPLs and if corporate governance is still heavily influenced by politics, the chance of success is very small. Hence, our conclusion is that China will have to face more pains in the near future when foreign large banks enter the domestic market and compete head on with the Big Four. This is where the real fight will begin and more radical reform measures have to be taken not only on re-capitalization but also on appointment of senior management and the way that banks are currently managed.

F

  the banking system has undergone significant changes. The first change was the breaking up of PBOC into two arms, the central bank and the commercial operation. The central bank still retains the name PBOC. The commercial operation was split into four specialized state-owned banks, or the Big Four in the rest of this paper: the Agricultural Bank of China (ABC), the China Construction Bank (CCB), the Bank of China (BOC), and the Industrial and Commercial

  gradual in terms of time and locality. The geographic restriction is phased out in six stages. Upon accession, RMB business by foreign banks is allowed in four large cities, Shanghai, Shenzhen, Tianjin and Dalian. It will then be expanded to Guangzhou, Zhuhai, Qingdao, Nanjing and Wuhan within one year of accession, to Jinan, Fuzhou, Chengdu and Chongqing within two years, to Kunming, Beijing and Xiamen within three years, to Shantou, Ningbo, Shenyang and Xian within four years, and to all parts of the country within five years (i.e., 2006) after accession.

  unprecedented opportunities to enjoy their increasing weight in the international financial system. The prospect of the Chinese economy also motivates the Big Four to reform themselves.3. Measuring banking efficiency: a selective reviewOver the last half a century, much attention has been devoted to banking efficiency study. Regarding the sources of inefficiency in banking, earlier studies tended to focus on economies of scale by examining whether costs per unit can be reduced by increasing output, and economies of scope by examining whether costs per unit can be lowered by joint production. Empirical studies of scale and scope economies show significant scale economies for medium-sized banks of $100 million to $5 billion in assets in the 1980s. However, recent studies indicate that scale economies have increased substantially, existing for large banks of $10 billion to $25 billion in assets in the 1990s. The recent merger and acquisition in the UK banking sector provides a good example of scale economies involving huge commercial banks. Such examples include the merger of the Royal Bank of Scotland with the National Westminster Bank, the Bank of Scotland with Halifax, and the earlier acquisition of Midlands Bank by HSBC

  its assumptions concerning the independence of the inefficiency effects in the two estimation stages. This study adopts a single-stage estimation technique proposed by[START_REF] Battese | A model for technical inefficiency effects in stochastic frontier production function for panel data[END_REF] andCoelli (1992), with an assumption that non-negative technical inefficiency effects are a function of firm-specific variables and time. The distributional assumption is that the inefficiency effects are independently distributed as truncations of normal distributions with constant variance, but with means that are a linear function of observable variables. The model shown below allows the estimation of both technical change in the stochastic frontier and time-varying technical inefficiencies.

  indicates that the inefficiency element it U is stochastic.The average level of technical efficiency over the sample period is 63%, leaving a gap with the maximum possible level of 37%. The average estimated efficiencies of 22 banks are plotted in Figure2. The most efficient bank is China Merchants Bank Co Ltd with an average technical efficiency of 91.23%, whilst the most inefficient bank is ABC with an efficiency of 40.95%. Wide efficiency differentiations across banks observed here indicate that there is a substantial potential for improving the overall efficiency of Chinese banks.

Figure 2

 2 Figure2Average Efficiency of China's banks (profit)(1995- -2001)

Figure

  Figure3Average Efficiency of the Big Four(Profit)(1995--2001)

  the profit model. The average estimated efficiencies for 22 individual banks are graphed in Figure 5. The most efficient bank is China Minsheng Banking Corporation with an average technical efficiency of 89%, while the most inefficient bank is the Bank of China (BOC) with an average technical efficiency of 58.03%.

  Figure5Average efficiency (loans)(1995)(1996)(1997)(1998)(1999)(2000)(2001)

  the profit and loan models can perhaps be attributed to the distinct degree of government intervention on banks. Direct government intervention resulted in a similar efficiency trend and level among the Big Four in the loan model.

Figure 6

 6 Figure6Average efficiency of the Big Four (Loan)(1995)(1996)(1997)(1998)(1999)(2000)(2001)

  volume of NPLs, resulting from policy lending to loss-making SOEs. The external plight is the lack of operational experiences in a market-based financial system, brought about by the specialization of the Big Four to serve the SOE sector. The current competitive position of Chinese banks is insufficient to compete with foreign banks with immense financial muscles and international experience. For example, the second largest bank of the UK, the Royal Bank of Scotland, generated £6.19 billion of pre-tax profit in 2003. Its workforce was only 120,000, implying that each employee generated on average more than £50,000 (or $90,000) of profit. Obviously, none of the Chinese Big Four would be able to compete with the Royal Bank of Scotland if there were no protection.

	Net income/ equity 15.29 12.2 Credit Agricole 7.18 Citibank HSBC Bank of China 4.61	Net income/ R assets e Net interest revenue/ assets 1.27 4.36 v i 0.938 2.07 e 0.42 1.24 w 0.303 1.50	Operating profit / assets 7.16 3.58 2.69 0.44

Table 1 compares the performance between the Bank of China, China's best performing state bank, and three top world-class banking groups, Citibank, HSBC and Credit Agricole in 2002. Table 1 Comparison of profitability level in 2002 (%) Source: Bankscope.

Table 2

 2 Opening schedule for the banking sector after WTO accession

	Year	Business Scope	Geographic Coverage
	2002	No restriction on Foreign	Business RMB business in
		Currency	9 cities
	2003	RMB business to Chinese	13cities
		enterprises
	2004		16 cities
	2005		20 cities
	2006	RMB business to all
		Chinese clients

  Equity includes share or/and own capital, as well as retained profits. Assets include loans, fixed assets and other assets. It can also be defined as total liabilities plus equity, where total liabilities include deposits, borrowing from other institutions and other funds. If a bank is capitalized and supported by the state, equity also includes state capital. If a bank is a joint-stock company, part of its equity will be share capital. As total assets include loans, a bank is subject to higher risk with a lower E/A ratio, as for a given amount of equity, the bank is exposed to more liabilities. If a bank is well capitalized with support from the government, E/A can be increased if total liabilities are fixed. The recent efforts of the state council to inject $45 billion to BOC and CCB are to help them raise the E/A ratio, and hence reduce their risk. An earlier effort by the government to use AMCs to remove some NPLs from the Big Four served the same purpose.

						F o
										r
												P e
																e
																	r
	ln(	profit	it	)	+ =	0	Equity t + 2 1 ln( + 4	it Deposit U v it V Labour it Fixedasset + + + ) ln( ln( ) ln( it ) 5 3 R e i	it	)	(4)
	U it	=	0	+	1	ln(	E	/	A	ratio	)	+	2	Ownership	e w	(5)
	where subscripts i and t respectively denote banks and time; ln denotes natural
	logarithm. In this model, output is measured by profit before tax, while inputs are
	measured by fixed assets, deposit, equity and labour. In the inefficiency function, two
	explanatory variables are bank-specific variables-equity/assets (E/A) ratio and
	ownership characteristic which are expected to have effects on inefficiency. In
																	18

estimation, the ownership variable takes the value of one for non-state banks and zero for state-owned banks.

Table 4

 4 Regression results, dependent variable = ln (profit before tax)

	Variables	ML estimates	T-Value
	A. Production Function
	Intercept	0.8469	5.5502
	Time	-0.0940	-6.6178
	Fixed-asset	0.1611	2.6673
	Deposit	0.3503	4.6373
	Equity	0.3311	2.6200
	Labour	0.1020	0.8928
	B. Inefficiency Function
	Intercept	-10.9032	-6.2360
	Equity/Asset Ratio	4.3702	13.6217
	Ownership	-2.8344	-4.8537
	C. Variance parameters
	Sigma-squared	3.8463	4.3056
	Gamma	0.9951	237.3926
	D. Diagnosis and other information
	LR test	146.4644
	Ln (likelihood)	-95.5927
	Number of observations	154
	Number of years	7
	Number of cross-sections	22
	Average technical efficiency	0.6300

Notes: (1) All the variables are in natural logarithms.

  Table4, ownership has a significant impact on technical efficiency with an estimated coefficient of -2.53. The negative sign indicates that joint-stock banks are more efficient. The impact of ownership characteristic on bank efficiency is depicted in Figure4. Joint-stock banks are found to have outperformed state-owned banks by about 18 percentage points over the data period.

	Table 3 Regression results, dependent variable = ln (loans)
		Figure 4 Average efficiency of the Big Four and joint-stock banks Variables ML estimates T-Value
	A. Production Function	(profit)(1995--2001)
	Intercept Time	1.00	0.1909 -0.0109		1.6207 -0.6404
	0.90 Fixed-asset	0.0841		2.4651
	Deposit	0.80	0.4606		11.5178
	Equity	0.70	0.6232		9.5670
	F o r P e e r B. Inefficiency Function 0.00 0.10 0.30 0.40 0.50 0.60 Labour ዊhዊhዊhዊhዊhዊhዊhዊhዊhዊh Intercept F Time o Equity/Asset Ratio r Ownership 0.20 1994 1995 1996 1997 1998 ዊhዊhዊhዊhዊhዊhዊhዊhዊhዊhዊhዊhዊhዊhዊhዊhዊh ዊhዊhዊhዊhዊhዊhዊhዊhዊhዊhዊhዊhዊhዊhዊhዊhዊhዊh 0.0860 -0.2683 0.0268 0.4525 -0.2756 1999 2000 C. Variance parameters P Sigma-squared 0.0347 e Gamma 0.1141 e r D. Diagnosis and other information	2001	1.2203 -1.9051 1.4709 7.3413 -6.4406 2002 8.6795 0.5730
	The estimated average technical efficiency of state-owned banks is 59.61% and that of joint-stock banks is 77.48%. This result suggests that ownership structure is an R e LR test 92.2811 Ln (likelihood) 40.7622 R e Number of observations 154
	important variable in explaining the variations of overall inefficiency. Moreover, Figure 4 exhibits a similar trend of technical efficiencies between state-owned and non-state banks. The average efficiencies of state-owned and non-state banks increased from v i e w 5.2 The loan model The picture that emerges from the loan model is similar to that of the profit model, as shown in Table 5. The signs of the coefficients are consistent with that of the profit Number of years 7 v i Number of cross-sections 22 e Average technical efficiency 0.6392 w Notes: (1) All the variables are in natural logarithms.
	1995 to reach a peak in 1996 and 1997, respectively. During the Asian Financial Crisis, model and all coefficients are statistically significant except for labour. The estimated
	the efficiency level declined and touched the lowest point in 1998. Thereafter, the coefficients for fixed-asset, deposit and equity are 0.0841, 0.4606 and 0.6232,
	average technical efficiencies of both state and non-state banks increased steadily over respectively. Deposit and equity have stronger impact than in the profit model. The
	the last four years of the data period. These similar trends reflect both the external as negative coefficient of year indicates that the output level tends to decrease by 1.09%
	well as the internal shocks. External shocks were largely triggered by the Asian per year over the data period. The LR test and the estimates of variance ratios also
	Financial Crisis, but the internal shocks reflected the government's efforts to improve confirm the presence of a one-sided error component which represents the stochastic
	banking efficiency during the post-crisis period. Our results suggest that the tightening
	policy in the aftermath of the Financial Crisis had paid a high dividend. inefficiency component it U .
			25

  joint-stock banks in the first three years by 10 percentage points. In 1998, the efficiency of the Big Four encountered a sharp downward slump from 76.90% to 52.68%, while that of the joint-stock banks remained unchanged. During the last three years, joint-stock banks became more efficient than the Big Four. This result is different from that of the profit model in which joint-stock banks are 18% more efficient than the state-owned banks throughout the sample period. However, joint-stock banks are still more efficient than the state-owned banks by 8 percentage points on average over the sample period. This result is attributable to the different degree of government intervention. Although government can influence lending decision of both state and non-state banks, the extent of intervention on the latter tends to be less than on the former.

	Submitted Manuscript	Page 28 of 35
	0.20 0.40 0.60 0.80 1.00 1.20 efficient than the Figure 7 Average efficiency of the Big Four and joint-stock banks ዊhዊhዊhዊhዊhዊhዊhዊhዊhዊh F o r P e e r F o r P e e (loan)(1995-2001) r
	0.00 1994 As for the inefficiencies, they are also influenced by the E/A ratio and ownership 1995 1996 1997 1998 1999 2000 2001 2002 I CBC BOC CCB R e 0.70 0.80 0.90 R e ABC v i e w 0.20 0.30 0.40 0.50 0.60 v i ዊhዊhዊhዊhዊhዊhዊhዊhዊhዊh e w
	characteristic. The elasticity of E/A ratio is 0.4525 and statistically significant, 0.10
	0.00 indicating a positive impact on inefficiencies. It is consistent with the result of the profit 1994 1995 1996 1997 1998 1999 2000 2001 2002
	model. Again, ownership is found to have different impact on inefficiency with an The Big Four Shareholding banks
	estimated coefficient of -0.2756, which is small but significant. The smaller impact of
	ownership characteristic on bank inefficiencies compared to that of the profit model can
	28 29

be considered as evidence that both state-owned banks and joint-stock banks are both subject to government intervention in lending. The impact of ownership characteristic on bank efficiency from 1995 to 2001 is shown in Figure

7

. The Big Four are more Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UKSubmitted Manuscript