Employee motivation, external orientation and the technical efficiency of foreign-financed firms in...

Copyright # 2005 John Wiley & Sons, Ltd.

MANAGERIAL AND DECISION ECONOMICS

Manage. Decis. Econ. 26: 175–190 (2005)

Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/mde.1203

Employee Motivation, External Orientationand the Technical Efficiency of Foreign-Financed Firms in China: A Stochastic

Frontier Analysis

Vincent Moka,* and Godfrey Yeungb

aSchool of Accounting and Finance, Hong Kong Polytechnic University, Hong KongbSchool of Social Sciences, University of Sussex, United Kingdom

By using a stochastic frontier model, we have identified several firm-specific attributes asdeterminants of technical efficiency in foreign-financed manufacturing firms in southern

China. The empirical results suggest a strong association between efficiency and employee

motivation, which includes the use of bonus incentives and flexibility in employment policy. In

terms of the external orientation behavior of firms, the findings do not support the export/efficiency relationship. Sample firms with a high degree of export-orientedness were less

efficient, possibly due to the high transaction costs in China of exportation. As for the effects

of expatriate input on production, our empirical evidence revealed that firms with a relativelyhigh expatriate ratio performed less efficiently than others did. These two findings may have

significant implications for the marketing strategies and management (including the

localization) of human resources of foreign-financed firms in China. Copyright # 2005

John Wiley & Sons, Ltd.

INTRODUCTION

China has enjoyed considerable success in attract-ing foreign direct investment (FDI) since theimplementation of economic reforms in 1979.According to the Ministry of Foreign Trade andEconomic Cooperation, the total utilized value ofFDI reached US$445 billion between 1979 and2002. In 2002, the inflow of FDI in China wasestimated to be US$53 billion, accounting for9.4% of the total FDI in the world.1 This showsthat China is a very popular destination forforeign investors. In a study of the effect of FDIon China’s economic growth by a group of multi-variance models, Wei (2002) finds that the inflowof FDI has been a significant driver of growth in

southeastern China, such as in Guangdong and

Fujian provinces. In terms of promoting China’s

exports, foreign-financed firms accounted for 50%

of China’s total exports in 2001. In terms of job

creation, the inflow of FDI has supported 23

million jobs, accounting for over 10% of the total

workforce in Chinese townships and cities in

2002.2 The investigation of the determinants of

the performance of foreign-financed firms in China

is thus of paramount interest and significance.However, much of the existing literature has

been confined to estimates of the productivity ofstate-owned or collective enterprises in China. Thetypical examples are Groves et al. (1994), Liu andLiu (1996), Wu (1996), Young (2000), etc. Severalstudies, such as those of Beamish (1993), andJefferson et al. (2000), have revealed disappointingperformances from foreign-financed firms in China.Yeung and Mok (2002) provide a glimpse of how

*Correspondence to: School of Accounting and Finance, HongKong Polytechnic University, Kowloon, Hong Kong. E-mail:[email protected]

Chinese government policies affect the competitive-ness of foreign-financed firms in China. However,none of the above studies have provided an econo-metric investigation of the determinants of techni-cal efficiency in foreign-financed firms in China.

Based on a sample of 23 foreign-financedmanufacturing firms in Guangdong provincespanning the period 1998–1999, this study usesthe stochastic frontier approach to examinewhether technical efficiency is a key issue inexplaining variations in performance among for-eign-financed firms in southern China. The samplesize was small. However, given the significant roleof foreign-financed firms in the economic perfor-mance of Guangdong, the findings of this paperadd to the growing literature on the determinationof efficiency in manufacturing firms in China, andthe extent of the role played by such firms.

A discussion of the conceptual issues ofvariables and of the stochastic frontier model willbe provided in the next two sections, before thefindings are analyzed and the implications of thepaper are discussed.

CONCEPTUAL INVESTIGATION

Many time-invariant factors affect the efficiency offirms in production. They are normally firm-specific attributes; e.g., employee motivation andexternal orientation. We shall also examine theeffects of firm size on efficiency. In this paper,employee motivation encompasses bonus incen-tives and employment flexibility, while externalorientation refers to the input of expatriates (withmanagerial, marketing and technical expertise)and export-orientedness. These selected firm-spe-cific attributes are generally regarded as having aneffect on the productive efficiency of foreign-financed firms in China. The following is abackground survey of the conceptual investiga-tion. A more detailed discussion of the associationbetween the selected firm-specific attributes andthe efficiency of firms in production as evidencedby our sample firms is provided in the section onempirical analysis.

Employee Motivation

Bonuses are often used as a means to motivateworkers to perform better (Groves et al., 1994;

Laffont and Tirole, 1996). We use bonus per capitato represent the performance-related incentiveschemes of foreign-financed firms. It is hypothe-sized that there is a positive relation betweenbonus per capita and the efficiency of firms. Inother words, the higher the bonus, the higher thelevel of efficiency the firms are able to achieve, viceversa, ceteris paribus. On employment flexibility,China’s labor system used to be highly centralized,which meant that the government was responsiblefor the assignment of jobs. Enterprises oftencomplained about the lack of autonomy in theiremployment policies, especially in the dismissal ofworkers. There was no exception for foreign-financed firms.3 The lack of mobility in the labormarket obviously reduced an enterprise’s perfor-mance; i.e., workers were not afraid of redundancyand thus had less incentive to work harder. Thisrather rigid labor system was often blamed for thepoor economic performance of enterprises,whether state-owned or foreign-financed. Withthe relaxation of government controls over em-ployment, enterprises enjoyed a much higher levelof autonomy in managing human resources. Inother words, they had the flexibility to dismissindividual employees without the need to obtainprior approval from the relevant authorities. Withthe de facto collapse of the household registrationsystem (hukou) and the development of commod-ity and labor markets, the rise in the number ofmigrant workers produced an abundant supply oftemporary workers, thus improving the employ-ment flexibility of enterprises in China.4 This typeof employment flexibility, measured in this paperby the ratio of temporary workers to the totalpopulation of workers, is expected to have apositive effect on enterprise efficiency.5 However, itmay be argued that temporary workers may have aweak sense of belonging to enterprises and are thusassociated with a higher labor turnover rate,compared with their permanent worker counter-parts. If this is the case, the productivity oftemporary workers may be in doubt: a high ratioof temporary workers to total labor force maynegatively affect enterprise efficiency. We shallexamine this issue by looking at our sample firms.

External Orientation

The first firm-specific attribute included in theexternal orientation of foreign-financed firms is theinput of foreign managerial, marketing and

V. MOK AND G. YEUNG176

Copyright # 2005 John Wiley & Sons, Ltd. Manage. Decis. Econ. 26: 175–190 (2005)

technical expertise. It is hypothesized that foreign-financed firms will transfer managerial, marketingand technical know-how to China and that thiswill result in improvements in enterprise perfor-mance (Graham and Krugman, 1991, p. 58;Blomqvist, 1996, p. 224). Facilitating the inflowof the know-how is one of the objectives of theeconomic reforms in China. However, the aboveargument may not necessarily always hold true. Ifforeign-financed firms are top-heavy with expatri-ates, there may be some drawbacks, includingproblems with localization and cultural conflicts.Along this line of argument, a high proportion ofexpatriates in foreign-financed firms may insteadreduce productive efficiency. It is however verydifficult to accurately quantify the impact offoreign managerial and technical expertise at thelevel of the firm (Helleiner, 1989). Therefore, a‘second best’ benchmark based on available dataon the ratio of expatriates to the total populationof employees is used in this paper.6 The secondfirm-specific attribute is the degree of export-orientedness. Theoretically, firms with a higherpercentage of exports are likely to be more efficientdue to their exposure to competitive internationalmarkets. Therefore, firms that export a greaterportion of their output are hypothesized to bemore efficient than firms that export less. However,Yeung and Mok (2002) note that foreign-financedfirms in China experienced high transaction costsin exporting their products.7 This implicitlysuggests that the export-orientedness may nothave a positive relation with foreign-financed firmsin China. This hypothesis will be investigated inthis paper.

Besides employee motivation and external or-ientation, we also study the effects of firm size onefficiency. Economies of scale are one of the mostcommonly used firm-specific attributes in thediscussion of enterprise efficiency. According toFordism, the size of a firm is essential for achievinglarge-scale production and thus for reaping thebeneficial effects of economies of scale andachieving a higher level of efficiency; i.e., a firm’ssize is positively related to economies of scale(Dicken, 1998). There are various sources ofeconomies of scale, including technical economies,managerial economies, marketing economies andfinancial economies. This refers to the preferentialaccess by large firms to new technologies, manage-rial expertise, inputs (including discounts on bulkpurchasing) and financial resources, due to the fact

that large firms are big players in the factor (laborand capital) and financial markets. Theoretically,these types of economies of scale should havepositive effects on the efficiency of firms.

STOCHASTIC FRONTIER PRODUCTION

FUNCTION

To perform the empirical investigations of thedeterminants of technical efficiency in foreign-financed firms in China, we use the stochasticfrontier approach modeled as follows:8

Qit ¼ f ðXit; bÞeeit ; ð1Þ

where Qit is the output in real terms of the ithsample firm at time t, Xit is a vector of inputs forthe ith sample firm at time t, b is a vector ofunknown parameters to be estimated, eit is arandom disturbance term.

Following Aigner et al. (1977), the randomdisturbance term is split into two error terms:

eit ¼ vit � uit;

vit is assumed to be normally and identicallydistributed (NID) with a zero mean and a varianceof s2v , which captures the effects of random shocksand statistical noise. uit is assumed to be a non-negative random variable and is obtained bytruncating the random variable, that is NID½mit;s

2u�, and uit is independent of vit. uit is the

inefficiency measurement and its mean ðmitÞ isdetermined by a number of factors that impact onthe firm.

According to Battese and Coelli (1995), mit ismodeled as an explicit function of variables thatexplain the level of technical inefficiency.

mit ¼ d0 þ d1Z1t þ � � � þ diZit þ dtTt; ð2Þ

where Zi are firm-specific attributes and di areunknown parameters to be estimated. Equation (2)analyzes whether and to what extent firm-specificattributes affect the level of technical inefficiency.The trend variable Tt in the inefficiency functionspecifies the change in inefficiency over time underthe period of investigation.

In the early empirical studies using the stochas-tic frontier approach, for example, Pitt and Lee(1981) first estimated the stochastic frontier andpredicted the technical inefficiency of each samplefirm. These were subsequently regressed against aset of firm-specific variables in an attempt to

EMPLOYEE MOTIVATION, EXTERNAL ORIENTATION AND TECHNICAL EFFICIENCY 177


identify some of the reasons explaining thedifferences in the predicted inefficiencies amongthe sample firms. This two-step procedure contra-dicts the assumptions of identically distributedtechnical inefficiency effects, which is required toobtain predictions for their unknown values. Toovercome this problem, Battese and Coelli (1995)propose a one-step procedure allowing the estima-tion of firm-level inefficiencies and the identifica-tion of efficiency determinants in one step.

The technical efficiency of the firm i is defined asTEit = exp(�uit). It is the ratio of observed outputto the stochastic frontier output, which has notechnical inefficiency.9 Hence, the technical effi-ciency for a firm is inversely related to theinefficiency measurement in Equation (2). It rangesfrom zero to 1, where unity implies that the firm isfully technical efficient.

On the estimation of the stochastic frontier inEquation (1) and the inefficiency function inEquation (2), the method of maximum likelihoodis used for the estimation of their parameters(Battese and Coelli, 1993, 1995). The likelihoodfunction and its partial derivatives with respect tothe parameters of the model are given in Batteseand Coelli (1993). To simply the search for asuitable starting value in the iterative process ofmaximization, s2v and s2u are replaced by s2 ¼

s2v þ s2u and g ¼ s2u=ðs2u þ s2vÞ, respectively. With

this form of parameterization, the value of g fallsinto the range of zero and one. This provides aconvenient method for performing hypothesistesting to determine whether the mean responseproduction function model is an adequate repre-sentation for the sample data, given the assump-tions of the stochastic frontier model that isdefined. If there is no measured technical effi-ciency, i.e. s2u ¼ 0, then g is zero, and it followsthat the mean response production functionestimation by ordinary least squares (OLS), wherethe estimated residuals have an expected valueof zero, will be an appropriate model for ana-lysis. On the other hand, if g is large, this impliesthat the inefficiency function is a significantcomponent in the estimation of the productionfunction by stochastic frontier approach as com-pared to the mean response production functionapproach.

Data measurement

The data set includes information about oneoutput, three basic inputs and several firm-specificattributes that allow an inter-firm comparison tobe made. Table 1 sets out a description of thevariables and summary statistics of the data. Qi is

Table 1. Description and Summary Statistics of Variables

Variable Description of variable

Q=gross output Gross output at 1990 prices (10 000 yuan)K=capital Net fixed assets at 1990 prices (10 000 yuan)L=labor Total number of employeesM=raw materials Raw materials at 1990 prices (10 000 yuan)BONUS Bonus per capita at 1990 prices (10 000 yuan)TEMPWORK Ratio of temporary workers to the total number of employeesF EXPAT Ratio of expatriates to the total number of employeesEXPORT Ratio of exports to gross outputSIZE Size of a firm = 1 if large, = 0 otherwiseTREND Trend variable

Variable Mean Standard deviation

Q 16 311 19 232K 4914 5140L 1337 1898M 6083 7232Employee motivation:BONUS 0.021 0.043TEMPWORK 0.338 0.399External orientation:F EXPAT 0.008 0.011EXPORT 0.449 0.299

Note: Number of firms=23.



the gross industrial output of firm i at the constant1990 prices. Many similar studies have used ‘value-added’ as the output variable. However, McGuck-in and Nguyen (1993) explain that there is asignificant upward bias in estimation when ‘value-added’ is used. Chow and Fung (1997) argue thatthe use of gross industrial values will alleviate thisproblem. In this paper, we use gross industrialvalues as the measurement for output, and labor,capital and raw materials as the basic inputs. Li isthe number of workers and staff. Mi is the rawmaterials deflated by the ex-factory output priceindex, compiled from various issues of theStatistical Yearbook of China (State StatisticsBureau, various issues).10 Capital, Ki, is measuredby the net value of fixed assets deflated by a capitalprice index. The price index of machinery andequipment at base year 1990 was used as thecapital price index. There are two commonproblems in handling the measurement of capitalstock in China. First, capital stock includes ‘non-productive’ welfare facilities, such as the schoolingand medical facilities (McGuckin et al., 1992),provided by state-owned and collective enterprises.However, in the case of foreign-financed firms inChina, the proportion of ‘non-productive’ facilitiesin capital stock is basically negligible as foreign-financed firms do not bear a similar responsibility.Hence, no adjustment of capital stock is needed inthis regard. Second, Chinese firms adopt theperpetual inventory method in calculating thevalues of fixed assets by adding the investment ofeach year to the amount of fixed assets from theprevious year, less depreciation. The capital datawe used were the fixed assets net of depreciation.To maintain a consistent ‘comparability’ of thecapital stock in the two years of observations inour data set, we did not adjust the capital stockwith respect to the second problem of theperpetual inventory method. Hence, this is thelimitation of the capital measurement in our study.

For the firm-specific attributes, we includeemployee motivation and external orientation.The former encompasses BONUS andTEMPWORK , the latter refers to F EXPAT andEXPORT : BONUS measures the bonus percapita. TEMPWORK and F EXPAT were, re-spectively, obtained as the ratios of temporaryworkers and expatriates to the total number ofemployees. EXPORT was calculated by theproportion of output exported. All monetaryfigures were measured at 1990 prices. The model

also included the SIZE dummy variable to detectany efficiency in scale. The criterion to classifyfirm size is by employment size. Firms with morethan 501 employees on their payrolls are classi-fied as large-scale firms (10 cases), whereas thosewith 51–500 workers are medium-sized firms(13 cases).11 In addition, the TREND variablewas included to capture the technical change in thestochastic frontier and to account for changes ininefficiency over time.

With all the variables defined, the estimation ofEquation (1) requires specifications for the produc-tion technology form and the error components.For simplicity, we employ the Cobb–Douglasproduction specification for the production func-tion. The Cobb–Douglas specification is useful forexploring changes in production behavior overtime and it is specified as follows:

Qit ¼ AoKaitL

bitM

yit e

vit�uit ; ð3Þ

where Qi= gross output, Ki= capital input,Li= labor input, Mi= raw material input, anda;b; y are coefficient parameters.

With the inclusion of the TREND variable, thecomputational form of Equation (3) is given asfollows:

lnðQitÞ ¼ lnðA0Þ þ a lnðKitÞ þ b lnðLitÞ

þ y lnðMitÞ þ jTRENDþ vit � uit; ð4Þ

where ln denotes the natural logarithm and j is thecoefficient parameter.

The technical inefficiency function is defined asfollows:

mit ¼ d0 þ d1ðBONUSÞ þ d2ðTEMPWORKÞ

þ d3ðF EXPATÞ þ d4ðEXPORT Þ

þ d5ðSIZEÞ þ dtðTRENDÞ: ð5Þ

Since our estimation involves data from apanel that comprises firms from different typesof industries, the effects of inter-industry hetero-geneity on productivity is expected. To accountfor the heterogeneity of firms in different indus-tries, we estimate the models with industry-specificfixed effects and also study whether the coeffi-cients of the input variables are industry-specific.These procedures can also help to deal withinter-industry pricing differences while stateprices are implicitly used in the measurement ofoutput, capital and raw material costs at constantprices.12



Capturing the industry effects, the sub-modelcan be written as:

lnðQitÞ ¼ lnðA0Þ þ a lnðKitÞ þ b lnðLitÞ þ y lnðMitÞ

þ jTRENDþX4

j¼1

djDij þ vit � uit; ð6Þ

where the subscript j indicates the type of industryfor firm i. lnðA0Þ is the intercept of the Equation(6) for the fifth type of industry. D is the industrydummy and d is the coefficient parameter.13

THE DATA SET IN GUANGDONG

Data covering 2 years from 1998 to 1999 werecollected from 26 foreign-financed manufacturingfirms in Guangdong province. The data wereobtained with the help of a local research centerin Guangzhou, and contained various economicvariables which we have used in this paper.14

Observations with missing outputs or inputs weredeleted, leaving a sample of 23 firms with 45observations in an unbalanced panel data set.Although the absolute sample size is small, ourdata set covering various firm-specific attributespermits us to construct measures of key variablesthat are close to theoretical ideals. For example,we have different categories of labor data to depictthe various effects of temporary workers, andmanagerial/technical expatriates on the efficiencyof firms. This type of data was quite often absentin other similar studies; e.g., Groves et al. (1994),Liu and Liu (1996), Wu (1996), Jefferson et al.(2000), Young (2000), etc.

The manufacturing industry in Guangdong waschosen for this study for a number of reasons.First, undertaking empirical studies on firm leveldata in China is often contingent upon accessi-bility to organizations in China. Second, since theintroduction of economic reforms in China in1979, Guangdong has been given a higher level ofeconomic freedom than other provinces. It iswidely viewed as a place that is successful inattracting FDI. The available data allowed anempirical examination of the effects of variousfirm-specific attributes on industrial production.Third, most other studies have tended to use datafrom the national level to study the effects ofselected firm-specific attributes on efficiency. How-ever, in view of the vast extent of China’s industry,certain firm-specific attributes may have produced

positive effects in some provinces but not in others.Even within the same industrial sector, it is likelythat selected firm-specific attributes will exertdifferent effects on firms in different localities.Thus, based on the manufacturing industry inGuangdong, this paper seeks to determine whethercertain selected firm-specific attributes havebrought significant changes to firms at theprovincial level.

In the sample, there were 16 firms owned byinvestors originating from Hong Kong, and the restwere mainly from Taiwan, the USA, Singapore andsome European countries. The sample firms repre-sented three major investment modes: equity jointventures (15 cases), contractual joint ventures (7cases) and wholly foreign-owned firms (1 case).Most of the firms were located in Guangzhou (19cases), and others were located in the Pearl RiverDelta, including Dongguan, Panyu and Zhongshan.In terms of industrial sectors, textiles and clothing(7 cases), plastic products (6 cases), food andbeverages (3 cases) and leather products (2 cases)accounted for more than half of the total firms inthe sample. The remaining firms were engaged inthe production of metal products, toys, andelectrical appliances. In terms of employment size,there were 13 cases of medium-sized firms (51–500workers), and the rest were large-scale firms withmore than 501 employees on their payrolls.

RESULTS OF THE EMPIRICAL ANALYSIS

The econometric computation was executedusing FRONTIER 4.1 software for panel data.15

FRONTIER estimates the stochastic frontier andthe inefficiency function simultaneously in one stepwith balanced or unbalanced data. The results ofthe estimation of the equations with gross indus-trial output as the dependent variable are given inTable 2. The results of the parameter estimation inmodel (1) were obtained by using OLS estimators.

To capture the industry effects, we add fourindustry dummies in the stochastic frontier asshown in Equation (6) to control for the hetero-geneity in production. The results of the industry-specific fixed effects are presented in model (4). Inchoosing between models (2) and (4), the selectionis based on the generalized likelihood ratio (LR)test as follows:

LR ¼ �2½LR � LU�



where LR and LU denote the log-likelihoodfunctions of the restricted model and the unrest-ricted model, respectively. The LR statistic followsa chi-squared distribution with degrees of freedomequal to the number of restrictions. The teststatistics for the null hypothesis that the fourindustry dummies are jointly not significantlydifferent from zero is 18.556. This exceeds thechi-square table value, with four degrees of free-dom, of 9.488 at the 5% significance level. The testresult rejects the null hypothesis and led us toselect model (4). After we have controlled theindustry-specific fixed effects, the estimates inmodel (4) appear to be more plausible. Further-more, in examining whether the coefficients of theinput variables are industry-specific, we include theindustry interaction terms, which are obtained by

multiplying industry dummy variables, respec-tively, with the logarithm of capital, labor andraw material variables.16 The statistic of thelikelihood ratio test on the null hypothesis thatthe joint effects of the industry interaction termsare not significant is 15.054. The test statistics isless than the critical value of 21.026 under the chi-squared distribution at the 5% significant level.Hence, the null hypothesis is not rejected.17 Thisindicates that the industry interaction terms arejointly not significantly different from zero. Toaccount for technical changes, we include a timetrend variable in the stochastic frontier presentedin Equation (6). However, with the inclusion of thetime trend, the estimates of most coefficientsbecame implausible. By performing the likelihoodratio test, the test result does not reject the null

Table 2. Stochastic Frontier Model for Foreign-Financed Firms in Guangdong Province, 1998–1999

Dependent variable= real gross output OLS Model (1) ML Model (2) OLS Model (3) ML Model (4)

Stochastic frontierConstant 2.62 3.55 3.00 3.39

(0.42) (0.31) (0.39) (0.20)Capital 0.142 0.183 0.139 0.184

(0.058) (0.037) (0.052) (0.023)Labor 0.273 0.380 0.229 0.394

(0.062) (0.044) (0.060) (0.039)Raw Materials 0.465 0.276 0.483 0.313

(0.052) (0.044) (0.047) (0.031)Industry dummy 1 �0.25 �0.273

(0.15) (0.095)Industry dummy 2 �0.19 �0.249

(0.16) (0.079)Industry dummy 3 �0.79 �0.517

(0.20) (0.088)Industry dummy 4 �0.08 �0.36

(0.22) (0.11)Inefficiency function

Constant �2.6 �1.59(1.9) (0.42)

BONUS;bonus per capita �38.5 �3.8(3.7) (1.6)

TEMPWORK ; ratio oftemporary workers

�1.44 �0.66(0.78) (0.27)

F EXPAT ; ratio of expatriates 24.4 4.1(3.2) (1.8)

EXPORT ; ratio of exports 2.53 1.27(0.77) (0.33)

SIZE; firm size �0.37 0.29(0.42) (0.21)

TREND; trend variable 0.91 0.59(0.73) (1.9)

g ¼ s2u=ðs2u þ s2v Þ 0.981 0.99999

(0.018) (0.00083)Log-likelihood value �21.958 �10.145 �13.973 �0.867Mean technical efficiency 0.692 0.700

Note: Figures in parentheses are the estimated standard errors.



hypothesis that the trend variable in the stochasticfrontier is not significantly different from zero.Hence, we do not include the trend variable in thestochastic frontier.18 In sum, model (4) emerges asthe preferred specification for discussion in the restof the paper.

Apart from considering the industry-specificeffects, we need to discuss the effects of plantheterogeneity on capital and labor. Bartelsmanand Doms (2000) produced a detailed literaturesurvey on the well-known persistence of firmproductivity dispersion on a cross-sectional aswell as a time series dimension. Bailey et al.(1992;1998) noted that ‘[a]llowing for plant fixedeffects in time-series cross-sectional production orproductivity estimates will provide much of theexplanation of the productivity distribution’.Furthermore, Tybout (2000) raised the question:Is productivity dispersion higher in less developedcountries? He discussed the problems of poolingheterogeneous technologies and sorted studiesaccording to whether they employed deterministicor stochastic frontiers. He found that stochasticfrontier studies usually produce higher averageefficiency levels. Turning to our panel data, whichcovered 23 firms for two yearly observations, it isapparent that the number of cross-sections was toosmall to permit an estimation of the variancecomponent due to time series. The test for firm-specific effects thus could not be carried out.

We come now to examine whether the meanresponse production function model is an ade-quate representation for the sample data. FromTable 2, the estimate for the variance parameter, g,is 0.999 which suggests that the inefficiency effectsare likely to be significant in the analysis of theoutput value of the sample firms. We next turn totest the null hypothesis of g ¼ d0 ¼ d1 ¼ � � � d5 ¼dt ¼ 0 (i.e., no inefficiency effect) versus thealternative hypothesis that the above parametersare not all zero. The value of the LR test statistic is26.213, which is greater than the critical value of14.853 with eight degrees of freedom at the 5%significance level.19 This test result rejects the nullhypothesis of no technical inefficiency effect. Basedon these test results, we can conclude that themean response production function is not anadequate representation for the production ofthe sample foreign-financed firms in China, giventhe specification of the stochastic frontier andinefficiency function, presented in Equations (6)and (5).

Regarding the efficiency of the sample firms, themean technical efficiency in the ML model was70.0% (model (4) in Table 2). This was in line withother estimates, such as the mean technicalefficiency of 62% estimated by Wu (1996) of 87Chinese iron and steel enterprises in 1988.20

To investigate the impact of firm-specific attri-butes on technical inefficiency, we check theestimates of the parameters, d, associated withthe firm-specific attributes specified in the ineffi-ciency function. When we examine the coefficientsin the inefficiency function, we have to keep inmind that a negative sign of d indicates that thepertinent explanatory variable has a positive effecton technical efficiency, vice versa, ceteris paribus.

The estimated results in Table 2 indicate that,bonus per capita and ratio of temporary workershave positive significant effects on enterprisetechnical efficiency. The coefficients of ratio ofexpatriates and ratio of exports show that firmswith a relatively high expatriate ratios or highexport ratios are less efficient than others. Besides,the size variable is not significant, rendering nosupport for the conjecture of the size/efficiencyrelationship. The following section offers somepossible explanations of these estimated results.

DISCUSSION AND INTERPRETATION

OF RESULTS

To study the impact of employee motivation onefficiency, we first examined the impact of bonusincentives on efficiency. The empirical findingsupports the conjecture of a positive associationbetween bonus schemes and enterprise efficiency.A higher level of bonuses may have generated ahigher level of technical efficiency.21 As a cross-reference, the results are largely compatible withthe findings of Groves et al. (1994), Liu and Liu(1996), and Yan (1998) on China’s state-ownedenterprises. However, some other studies criticizethe fact that bonuses are not often able todiscriminate between the performance of indivi-duals and firms. Lee and Mark’s (1989) surveyshowed that there was an egalitarian distributionof bonuses in China’s state-owned enterpriseslargely due to manager/worker collusion. How-ever, when compared to wages, which were oftendetermined upon the type of job, work experienceand qualifications of employees, but not onindividual performance (Hussain and Zhuang,



1994), bonuses were usually in principle givenout according to some pre-determined methods ofmeasuring performance.22 At any rate, our empiri-cal evidence supports the bonus/efficiency relation-ship in foreign-financed firms on some level.

Next, we investigate the effect of employmentflexibility. The empirical results demonstrate that ahigh level of autonomy on employment policy haspositively affected enterprise performance. Thisevidence may provide a prima facie argument thatindividual dismissals might have strengthenedemployee motivation. Thus, there is evidentlyan effect of employment flexibility of some kindat work.

Turning to explain the impact of externalorientation on efficiency, the estimated resultsshow that the higher ratio of expatriates has anegative effect on efficiency. This appears to goagainst the common wisdom that foreign partnersin foreign-financed firms will bring the beneficialeffects of managerial and technical know-how tofirms. What are the possible major drawbacks forforeign-financed firms with higher ratios of ex-patriates on their payrolls? We shall discuss thisquestion in some detail. First, when firms havehigher ratios of expatriates, they may face theproblem of localization at the top level. Middle-level staff members, usually local employees, thenlack the opportunity to be promoted to seniorpositions in the firm. What are the effects ofpromotions on firms? In a study of the determi-nants and consequences of promotions, Frances-coni (2001) finds that promotions lead to anincrease in job satisfaction, especially satisfactionwith the work itself as well as with the pay.Following this line of argument, the lack ofpromotion opportunities for middle-level employ-ees can partially explain the high turnover rateamong mid-ranking managerial staff in foreign-financed firms in China.23 The likely consequenceis an interruption in daily production (Fieldsurvey, April 2003).24 Second, when high-levelexpatriate managers of various departments arestationed in foreign-financed firms in China tooversee production, they may have less contactwith the international market. They may not beable to follow the most up-to-date marketinformation. Quite often, this leads to conflictsbetween managers of marketing departments (whoare on the front lines of the international market)and other operational departments when thelatter’s information about (international) market

demand is different (Field survey, January 2002).25

The experience of a famous Europe-based trans-national corporation’s (TNC) equity joint venturesin Hangzhou serves as an example. As one of 54joint ventures and the most profitable one inChina, the (German) General Manager refused toadopt the latest production technology to facilitatethe manufacturing of GIS-circuit breakers inChina. The problem was resolved after the(Chinese) Deputy General Manager (who wasresponsible for marketing) flew to Germany toobtain the approval from headquarters on theadoption of the new technology (Field survey,August 2001). Third, tremendous differences inculture may contribute to the lack of a harmoniousworking relationship between expatriates and localmanagerial staff. Some foreign managerial andtechnical staff may be rather self-centered and evenarrogant in dealing with their local counterparts;i.e., some of them (including overseas Chinese)intend to ‘teach’ their Chinese colleagues every-thing from day one, etc. Language barrierscertainly do not help to diffuse the culturaldifferences and misunderstandings that arise be-tween expatriates and locals (this is the case insome firms financed by Hong Kong-based inves-tors as well). For instance, expatriates tend to sackworkers who either under-perform or violatecompany rules, on the spot and in front of otherworkers, while the local managers tend to talk tothe workers concerned privately (to ‘save theirface’) and either give them a ‘second chance’ or askthem to resign. Our field visit to the equity jointventure set up by the Europe-based TNC inHangzhou provides a typical illustration of thelack of a harmonious working relationship be-tween expatriates and local managerial staff. TheGeneral Manager questioned the actions of hisChinese Deputy in spending so much time visitingand dining with potential customers all overChina. Yet the Deputy General Manager com-plained that his boss did not understand the wayof doing business in China; i.e., the need tocultivate personal connections, guanxi, with po-tential customers.26 It took the foreign and localmanagers a tremendous amount of effort toreconcile their differences and focus on theoperations of the joint venture (Field survey,August 2001). The above discussion may be ableto address the question why foreign-financed firmswith high ratios of expatriates appeared to be lessefficient.



The second element of external orientation is thedegree of export-orientedness. Our empiricalresults do not find the conjecture of the export/efficiency relationship. Are there similar empiricalresults involving firms in other countries? Bleaneyand Wakelin (2002; 2003) noted that, ‘[t]he majorissue is whether firms’ efficiency is significantlyimproved by the experience of competing inforeign markets. The empirical findings on thishave been uniformly negative’. For example, theempirical results of studies of firms in the UK(Bleaney and Wakelin, 2002), the US (Bernard andJensen, 1999), and in some developing countries(Aitken et al., 1997) do not support the commonconjecture of an export/efficiency relationship. Allof these are the experiences of firms in othercountries. In the case of our sample firms in China,we suspect that the negative export/efficiencyrelationship may be partly attributed to the hightransaction costs of exportation due to variousgovernment policies. Yeung and Mok (2002)explained that the high transaction costs are dueto the ambiguity, complexity and inflexibility ofgovernment policies in the labor, capital andproducts markets. For instance, to fulfill rules onthe verification of imports and exports, foreign-financed firms have to keep detailed daily recordsof the value and quantity of the raw materials thatthey import, the products that they export, and oftheir inventories. Foreign-financed firms have toshow all these records to the local government andCustoms officials. This policy is aimed at prevent-ing foreign-financed firms from illegally resellingtariff-free raw materials in the local market andevading taxes. Nonetheless, complying with thisrule imposes tremendous administrative costs ondaily operations, thus offsetting part of the profits(and lowering the efficiency) of export-orientedforeign-financed firms.27 This, coupled with theChinese government’s opening of its local marketto firms with FDI shortly before its accession tothe World Trade Organization, means that for-eign-financed firms in China are paying moreattention to capturing a share of the local Chinesemarket.28

As shown in Table 2, the coefficient on firm sizewas positive, suggesting that large firms may beless technically efficient than smaller firms. How-ever, the estimation was statistically insignificant.In this context, there was no evidence to supportthe conjecture on the relationship between firmsize and efficiency in our sample firms. Finally, to

check whether there has been a change ininefficiency over time, we included the trendvariable in the inefficiency function. The resultsindicate that the trend variable has a positive signand that the coefficient is statistically significant bythe t-test (Table 2), implying that an increasinginefficiency over time was detected during theperiod of observation.

CONCLUSIONS AND IMPLICATIONS

While acknowledging the limitation of the paper interms of its sample size, from the results of theabove quantitative analysis, we can draw sometentative conclusions and implications of empiricalsignificance. In identifying the sources of efficiencyin the foreign-financed firms in our sample, there isempirical evidence that enterprise performancewas directly related to employee motivation. Ourfindings strongly support the use of both bonusincentives and flexibility in the management ofhuman resources to enhance enterprise perfor-mance.

Perhaps the most unexpected but interestingfindings are the effects of the external orientationbehavior of firms on efficiency. Our research foundthat sample firms with relatively high expatriateratios were less efficient. Therefore, it was notnecessarily beneficial for foreign-financed firms tobring in too many expatriates to run their firms inChina. Perhaps this reflects the lack of a harmo-nious working relationship between expatriatesand local staff, and the issue of localization.Possible conflicts might occur between the man-agement and marketing departments when ex-patriates lacked the most up-to-date marketinformation. Expatriate staff, especially thoseoriginally based in the US or Europe, oftenenjoyed favorable remuneration packages (e.g.,substantial pay rises, hardship and housing allow-ances, generous relocation grants for their families,etc.) when they were sent to China to oversee theoperations of the joint ventures. Their remunera-tion packages were about six times those of theirlocal counterparts with similar qualifications andjob responsibilities.29 Local employees might lackmotivation and thus loyalty to the company, iftheir chances of being promoted to higher posi-tions were blocked by expatriates (Lasserre andChin, 1997; 94). In addition to the higher costs of



stationing expatriates in China, the lack of aharmonious working relationship between expatri-ates and local staff may be one of the reasons whymore and more foreign-financed firms are keen tolocalize their management teams (the recruitmentof overseas Chinese may be the first step inlocalization) in China. A typical example of thelocalization of management is Shanghai Volkswa-gen, where only 19 of the 320-strong managerialforce were comprised of expatriates in 1996(Lasserre and Chin, 1997; 86).

With China’s accession to the World TradeOrganization, the already strong demand forexperienced local managerial staff is expected tobe compounded in China. With the expectation ofmore FDI, there may be a ‘bidding war’ forexperienced and qualified local managerial staff inthe Chinese market. If this ‘bidding war’ goesunchecked, it may lead to even higher turnoverrates of managerial staff and thus interrupt thedaily operations of firms. This would not onlypartially offset the cost advantages of localizationin foreign-financed firms, but also disrupt the long-term development strategies of both foreign-financed and locally funded firms in China.30 Ifthe negative productivity effects of expatriates arewidespread among foreign-financed firms in Chi-na, this may have tremendous implications for therecent drive by some Chinese firms (both privatelyowned and state-owned) to recruit overseasChinese to improve their competitiveness, a movethat has been partly spurred by China’s accessionto the World Trade Organization.31

Another external orientation factor used in thisstudy was the degree of export-orientedness. Ourfinding that did not support the export/efficiencyrelationship was not unusual. Similar evidence hasbeen observed in firms in other developed anddeveloping countries. In China, it seems thatforeign-financed firms with high ratios of exportssuffered the most from the high transaction costsof exportation, partly due to the ambiguity,complexity and inflexibility of Chinese governmentpolicies in regulating them. This is illustrated bythe requirement for these firms to submit dailyrecords of imports and exports to fulfill verifica-tion rules, etc. In other words, firms that focusedon marketing their output in the local Chinesemarket appeared to perform better. The implica-tion is in line with the observed business strategyof most foreign-financed firms in China; i.e., tocapture market share in China and to reap the

benefits of China’s rapid economic growth. TheChinese local market is expected to be a moresignificant one for the majority of firms afterChina gradually opens it up in accordance withthe World Trade Organization’s regulations onaccession.

Acknowledgements

The authors would like to especially thank George E. Batteseand Sean M. Dougherty who gave them superb advice as toseveral theoretical and methodological issues. They also thankYue-Cheong Chan, Alice Shiu and Xinpeng Xu for their helpfulsuggestions. Any errors however are the sole responsibility ofthe authors.

APPENDIX A: COLLECTION AND

CHARACTERISTICS OF SAMPLES

Sampling Plan and Data Collection

In April 2000, we requested the Social EconomicDevelopment Research Center in Guangdongprovince (hereafter named the GD Center) toassist us in conducting a questionnaire survey onvarious data relating to the output, capital, labor,wages, and bonuses of foreign-financed manufac-turing firms in the province. Using a businessdirectory containing about 1800 foreign-financedmanufacturing firms in Guangdong, we randomlyselected 160 firms from the list.32 The question-naire, a cover letter from the GD Center (includinga declaration of the anonymity of the informationprovided by the sample firms), and a pre-stampedreturn envelope was sent to the finance depart-ments of the sample firms. After two follow-ups byFAX and telephone, 26 firms returned the ques-tionnaires, resulting in a response rate of 16%. Wedid not have any influence over the replies given bythe 26 respondents to our questionnaire survey.We may then consider the respondents as randomsamples.

Data Reliability and Sample Characteristics

In studying the Chinese economy, there is always aconcern about the reliability of China’s statistics.On the one hand, provincial governments havebeen accused of exaggerating their statistics toimprove their public image. There may also be anupward bias in the statistics reported to theprovincial governments by various organizations.On the other hand, for tax reasons, firms may



under-report their economic and financial fig-ures.33 Therefore, it seems that, if independentorganizations can obtain the cooperation of firms,they may be in a better position to carry out such aquestionnaire survey.

Regarding the consistency of sample data, oneway to handle the issue is to cross-check thesample data with the records of the statisticalbureau. Besides, if the respondents of the surveyare made aware that the investigators have accessto the statistical bureau to cross-check the sampledata, this may enhance the accuracy of the data inthe questionnaires. However, the authors do nothave access to the bureau. In order to make oursample more useful, we compare the aggregatestatistics of our sample with those from theprovincial and national levels. The aggregatestatistics are presented as follows (Table A1).

The sample firms were more capital-intensivewhen compared with the average for Guangdongprovince and with the national average. In termsof capital per employee, the sample averagewas slightly higher than the national average butabout one and a half times higher than theGuangdong average. The sample’s output percapital ratio was about twice as large as theaverages for Guangdong and the nation. This

suggests that capital productivity in the samplefirms was higher.

A comparison with the Guangdong and na-tional averages may be misleading, however. Thisis because the sample has a different sectoralcomposition than the firms in the provincial andnational levels; i.e. with about 78% of samplefirms in textile and clothing (7 cases), plasticproducts (6 cases), food and beverage (3 cases) andleather products (2 cases). In addition, the samplehas no small-sized firms. Comparison by firm sizeis also not permitted because data by firm size arenot available from the Guangdong Bureau ofStatistics and the State Statistical Bureau. Thenational industrial census in 1995 also did notprovide data for foreign-financed firms by indus-trial sector. Hence, in the following, we only focuson comparing aggregate statistics by industrialsector between our sample foreign-financed firmsand foreign-financed firms in Guangdong pro-vince.

From Table A2, it is obvious that most of thefirms in the sample are more productive in the useof labor and capital than firms in the province,with the exception of those in the food andbeverage sector. In terms of the output per capitalratio, the sample averages in the textile and

Table A1. Comparison of Aggregate Statistics for the Sample Firms, Guangdong Province and theNation: Foreign-Financed Manufacturing Firms

Labor productivity(10 000 yuan/employee)

Capital/labor ratio(10 000 yuan/employee) Output/capital ratio

Sample firms1998 23.44 9.68 6.931999 22.14 9.46 6.211998–1999 22.79 9.57 6.57

Guangdong province1998 18.67 6.11 3.051999 21.26 6.24 3.411998–1999 19.96 6.18 3.23

The nation, 1999a 24.27 8.16 2.98

Sample average as percentageof provincial average1998 1.26 1.58 2.271999 1.04 1.52 1.821998–1999 1.15 1.55 2.04

Notes: The figures for Guangdong province, the nation and the sample firms refer to foreign-financed manufacturing firms only;Labor productivity measures gross output at 1990 prices per employee; All monetary figures were measured at 1990 prices.Sources: Guangdong Statistical Yearbook, pp. 283–387 of issue 1999 and pp. 374–379 of issue 2000; China Industry EconomyStatistical Yearbook 2001, pp. 72–77.aThe figures for 1998 were not available.



clothing sector and in the plastic products sectorare generally higher. By contrast, the sampleaverage in the food and beverage sector iscomparable to the provincial average, and that inthe leather products sector is slightly higher thanthe provincial average. On the whole, the aggre-gate statistics in Table A2 show that, with respectto the corresponding sectors in GuangdongProvince, the sample firms in three sectors areabove the average while those in the food andbeverage sector are slightly below the average. Thegeneral pattern is still somewhat clear. Given thelimited sample size, the empirical analysis in thepaper is modest and the results obtained are thusonly preliminary.

NOTES

1. The large amount of FDI into China in 2002 made itthe world’s top destination for such inflows andsurpassed the USA into the first position. TheEconomist, 6 September 2003; 59.

2. Xinhua News Agency, 9 October 2002.

3. The principal author conducted a field survey of theCoca-Cola Bottling Plant in Tianjin in March 2000.The Deputy General Manager explained that, in thearea of employee motivation, he had encounteredresistance when firing under-performing workers.The main sources of resistance were the trade unionsof the firm and the soft drinks industry, andsometimes, the workers’ families.

4. The household registration system was the legacy ofthe planned economy, when every household had toregister their members with the local Public SecurityBureau. Household members had to produce theirregistration booklets and vouchers before beingallowed to ‘purchase’ daily necessities, food, etc.With the development of commodity and labormarkets since the 1980s, the effectiveness of thehousehold registration system in regulating themobility of people declined over time.

5. Obviously, this ‘second best’ parameter is unable tocapture all of the effects of employment flexibility orautonomy in employment policy at the firm level.

6. Obviously, a firm with a higher ratio of expatriatesmay not necessarily indicate a corresponding higherinput of foreign managerial and technical know-how, and vice versa, ceteris paribus.

7. Foreign-financed firms in China are now no longerrestricted to exporting most of their productsoverseas under the constraints of their production

Table A2. Comparison of Aggregate Statistics by Industrial Sector Between the Sample Firms andForeign-financed Manufacturing Firms in Guangdong Province

Labor productivity(10 000 yuan/employee)

Capital/labor ratio(10 000 yuan/employee) Output/capital ratio

1998 1999 1998–1999 1998 1999 1998–1999 1998 1999 1998–1999

Textile and clothingSample firms (7 cases) 21.20 18.31 19.72 7.09 6.88 6.99 9.59 11.63 10.61Guangdong province 16.95 17.05 17.00 6.04 5.73 5.89 2.80 2.98 2.89Sample average as percentageof provincial average

1.25 1.07 1.16 1.17 1.20 1.19 3.43 3.90 3.67

Plastic productsSample firms (6 cases) 25.86 27.40 26.63 12.93 11.54 12.24 5.37 3.99 4.68Guangdong province 15.64 14.45 15.05 5.46 4.95 5.21 2.86 2.92 2.91Sample average as percentageof provincial average

1.65 1.90 1.77 2.37 2.33 2.35 1.88 1.37 1.61

Food and beverageSample firms (3 cases)a 25.64 16.13 20.89 11.03 8.55 9.79 2.46 1.91 2.19Guangdong province 22.00 24.82 23.41 10.43 10.63 10.53 2.11 2.33 2.22Sample average as percentageof provincial average

1.03 0.65 0.89 1.06 0.80 0.93 1.17 0.82 0.99

Leather productsSample firms (2 cases) 11.13 9.68 10.41 3.18 3.22 3.20 7.35 6.69 7.02Guangdong province 8.57 8.16 8.37 1.42 1.33 1.38 6.01 6.14 6.08Sample average as percentageof provincial average

1.30 1.19 1.24 2.24 2.42 2.32 1.22 1.09 1.15

Sources: Same as for Table A1.aThere were 2 cases in 1998 and 3 cases in 1999, since one firm did not provide data for 1998.



contracts and related Customs documentation. Theycan sell their products locally in the China market.

8. A detailed discussion on stochastic frontier can befound in Kumbhakar and Lovell (2000) and Coelliet al. (1998).

9. The calculation can be referred to the appendix ofBattese and Coelli (1993).

10. For a better measurement, the raw material inputvariable has to be replaced by the intermediate inputvariable. Intermediate inputs for manufacturingproduction processes typically include not onlyraw materials, but also energy and water. In ourdata set, the only available data for intermediateinputs are raw materials. Hence, a comparisonbetween the coefficient of raw material inputvariable in our study with the coefficient ofintermediate input variable in other similar studieshas to be made with caution.

11. However, it is arbitrary and may be even misleadingto use the number of workers to determine the sizeof a firm, as a large-scale and highly automated firmmight, for example, employ only 200 workers, whilea medium-sized labor-intensive clothing firm mightemploy 500 or more.

12. The authors would like to thank Sean M. Dough-erty for his suggestions for clarifying this point.

13. Industry dummy 1 equals 1 if a firm is in the textileand clothing sector (7 cases) and 0 otherwise.Similar definitions apply to industry dummy 2 todummy 4 for the plastic products sector (6 cases),the food and beverage sector (3 cases) and theleather products sector (2 cases), respectively.

14. All of the data were collected by a questionnairesurvey with the help of the Social EconomicDevelopment Research Center in Guangdong pro-vince. The research center has access to theGuangdong Provincial Government, which hassubstantial power to oversee enterprises. Set againstthis background, the Center has a rather goodaccess to collect firm-level data. Details of thesampling procedures are explained in Appendix A.

15. The program FRONTIER 4.1 was written byProfessor Tim Coelli. See Coelli (1996).

16. There are 12 industry interaction terms for theinputs and industry dummy variables; e.g.,D1K=industry dummy 1� logarithm of capital,D1L= industry dummy 1� logarithm of labor, etc.

17. For the sake of presentation, we do not report theresults of the estimation. The results are availablefrom the authors.

18. This might be due to the interaction of the two timevariables in our sample firms. The results were notshown here but they are available from the authors.Similar experience with the trend variable was alsoencountered by Liu and Liu (1996).

19. This statistic has a mixed chi-squared distribution.The critical value for the testing of the hypothesis isobtained from Kodde and Palm (1986).

20. Jone et al. (1998) reported a mean technicalefficiency ranging from 63 to 70% for a sample of247 Bulgarian industrial enterprises spanning theperiod 1989–1992. In addition, Piesse and Thirtle

(2000) found a higher level of mean technicalefficiency of 92% in a sample of 43 Hungarianmanufacturing firms in 1985–1991.

21. In our empirical results, the causality from efficiencyto bonuses was not determined. A causality test,such as a Granger test or Sims test (Granger, 1969;Sims, 1972), is required to examine the causalitybetween efficiency and bonus. However, the limitedlength of our time series data for 2 years does notpermit such a test to be conducted.

22. Zhao and Nichols (1996) provided a detaileddiscussion of the bonus structure of China’s textileindustry.

23. The lack of promotion opportunities partiallyaccounted for the 50% turnover of mid-rankingmanagerial staff of a foreign-financed food manu-facturing firm in Hangzhou in 2002 (Field survey,April 2003).

24. We have conducted various phases of fieldwork inChina. The information from the fieldwork under-taken by the authors provides a cross-reference tothe experiences of foreign-financed firms in otherparts of China.

25. This is supported by an informal interview inJanuary 2002 with a senior manager from a largetextile group listed in the Hong Kong StockExchange. The Group has a large-scale joint venturein Dongguan, Guangdong, employing over 5000workers.

26. Readers interested on the concept of ‘face’ (mianzi)can refer to Chen (1995). In addition, Davies et al.(2003) explains the significance of personal connec-tions for Chinese businesses.

27. Furthermore, the lack of coordination amongvarious bureaus hampered arbitration betweenforeign investors and government departments,which further increased the transaction costs ofexportation.

28. China formally acceded to the World TradeOrganization after the government delegate signedthe treaty in November 2001.

29. At about US$300 000/year, the remunerationpackage of a US expatriate was six times higherthan that of a Chinese executive. Other foreign-financed firms that were very keen on localizationinclude ABB, Henkel, Unilever and Hoechst, etc.(Lasserre and Chin, 1997; p. 86–87: Li and Kleiner,2001; p. 51).

30. The development of a long-term strategy for acompany normally requires a certain level ofcontinuity in the level of top management.

31. In April 2002, about 100 firms from Shanghai’sPudong district participated in a career exhibition inHong Kong for the purpose of recruiting expatriatemanagers and professionals. Some overseas Chinesefrom Singapore and North America were said tohave visited Hong Kong to explore job opportu-nities in China. See South China Morning Post, 19April 2002.

32. State Statistical Bureau (1994).33. Professor Thomas Rawski published two papers in

2001 querying the authenticity of China’s official



statistics. See Rawski (2001) and Rawski and Xiao(2001) and, among others, the debate by Shi (2002).

REFERENCES

Aigner DJ, Lovell CAK, Schmidt P. 1977. Formulationand estimation of stochastic frontier productionfunction models. Journal of Econometrics 6: 21–37.

Aitken B, Hansen G, Harrison A. 1997. Spillovers,foreign investment, and export behavior. Journal ofInternational Economics 43: 103–132.

Baily MN, Hulten C, Campbell D. 1992. The distribu-tion of productivity in manufacturing plants. Brook-ings Papers on Economic Activities: Microeconomics,187–249.

Bartelsman EJ, Doms M. 2000. Understanding produc-tivity: lessons from longitudinal microdata. Journal ofEconomic Literature 38: 569–594.

Battese GE, Coelli TJ. 1993. A stochastic frontierproduction function incorporating a model fortechnical inefficiency effects. Working Papers inEconometrics and Applied Statistics, No. 69, Depart-ment of Econometrics, University of New England,Australia.

Battese GE, Coelli TJ. 1995. A model for technicalinefficiency effects in a stochastic frontier productionfunction for panel data. Empirical Economics 20:325–332.

Beamish PA. 1993. The characteristics of joint venturesin the People’s Republic of China. Journal ofInternational Marketing 1: 29–48.

Bernard A, Jensen J. 1999. Exceptional exportersperformance: cause, effect or both. Journal of Inter-national Economics 47: 1–25.

Bleaney M, Wakelin K. 2002. Efficiency, innovation andexports. Oxford Bulletin of Economics and Statistics64(3): 3–15.

Blomqvist HC. 1996. The ‘Flying Geese’ modelof regional development: a constructive interpretation.Journal of the Asian Pacific Economy 1(2): 215–231.

Chen M. 1995. Asian Management Systems: Chinese,Japanese and Korean Styles of Business. Routledge:London.

Chow CK, Fung MK. 1997. Firm size and performanceof manufacturing enterprises in P.R. China: the caseof Shanghai’s manufacturing industries. Small Busi-ness Economics 9: 287–298.

Coelli T. 1996. A guide to FRONTIER version 4.1: acomputer program for stochastic frontier productionand cost function estimation. CEPA Working Paper96/07, Centre for Efficiency and Productivity Analysis,University of New England, Australia.

Coelli T, Rao DSP, Battese GE. 1998. An Introduction toEfficiency and Productivity Analysis. Kluwer AcademicPublishers: Boston.

Davies H, Leung T, Luk S, Wong YH. 2003. Guanxiand business practices in the People’s Republic ofChina. In Chinese Culture, Organizational Behavior,International Business Management, Alon I (ed.).Praeger Publisher: Westport; 41–55.

Dicken P. 1998. Global Shift: Transforming the WorldEconomy (3rd edn). Paul Chapman Publishing Ltd.:London.

Francesconi M. 2001. Determinants and consequencesof promotions in Britain. Oxford Bulletin of Econom-ics and Statistics 63(3): 279–310.

Graham EM, Krugman PR. 1991. Foreign DirectInvestment in the United States (2nd ed). Institute forInternational Economics: Washington DC.

Granger CWJ. 1969. Investigating causal relations byeconometric models and cross-spectral methods.Econometrica 37(3): 424–438.

Groves T, Hong Y, McMillan J, Naughton B. 1994.Autonomy and incentives in Chinese state enterprises.Quarterly Journal of Economics 109(1): 183–209.

Helleiner GK. 1989. Transnational corporations anddirect foreign investment. In Handbooks of Develop-ment Economics, vol. II, Chenery HB, Srinivasan TN(eds). North-Holland: Amsterdam; 1441–1480.

Hussain A, Zhuang J. 1994. Impact of reform on wageand employment determination in Chinese stateenterprises, 1986–1991. Programme of Research intoEconomic Transformation and Public Finance: EF/12,November, London School of Economics.

Jefferson GH, Rawski TG, Li W, Zheng Y. 2000.Ownership, productivity change, and financial perfor-mance in Chinese industry. Journal of ComparativeEconomics 28(4): 786–813.

Jones D, Klinedinst M, Rock C. 1998. Productiveefficiency during transition: evidence from Bulgarianpanel data. Journal of Comparative Economics 24:446–464.

Kodde DA, Palm FC. 1986. Wald criteria for jointlytesting equality and inequality restrictions. Econome-trica 54(5): 1243–1248.

Kumbhakar SC, Lovell CAK. 2000. Stochastic FrontierAnalysis. Cambridge University Press: Cambridge.

Laffont J, Tirole J. 1996. Using cost observation to regulatefirms. Journal of Political Economy 94(1): 614–641.

Lasserre P, Chin PS. 1997. Human resources manage-ment in China and the localization challenge. Journalof Asian Business 13(4): 85–99.

Lee K, Mark S. 1989. Socialist egalitarianism and theagency problem in China’s economic reform. SeoulJournal of Economics 2(4): 383–401.

Li L, Kleiner BH. 2001. Expatriate-local relationshipand organizational effectiveness: a study of multi-national companies in China. Management ResearchNews 24(3/4): 49–56.

Liu Z, Liu GS. 1996. The efficiency impact of theChinese industrial reforms in the 1980s. Journal ofComparative Economics 23: 237–255.

McGuckin RH, Nguyen SV, Jeffrey RT, Waite CA.1992. Post-reform productivity performance andsources of growth in Chinese industry: 1980–85.Review of Income and Wealth 38: 249–266.

McGuckin RH, Nguyen SV. 1993. Post-reform indus-trial productivity performance of China: new evidencefrom the 1985 industrial census data. EconomicInquiry 31: 323–341.

Piesse J, Thirtle C. 2000. A stochastic frontier approachto firm level efficiency, technological change, and



productivity during the early transition in Hungary.Journal of Comparative Economics 28: 473–501.

Pitt MM, Lee LF. 1981. Measurement and sources oftechnical inefficiency in the Indonesian weavingindustry. Journal of Development Economics 9: 43–64.

Rawski T. 2001. What is happening to China’s GDPstatistics? China Economic Review 12(4): 347–354.

Rawski T, Xiao W. 2001. Roundtable on Chineseeconomic statistics: introduction. China EconomicReview 12(4): 298–302.

Shi L. 2002. China’s GDP statistics are credible. BeijingReview 45(27): 12–15.

Sims CA. 1972. Money, income, and causality. TheAmerican Economic Review LXII(4): 540–552.

State Statistics Bureau. 1994. List of Three Kinds ofForeign Investment Enterprises in China. China Statis-tical Publishing House: Beijing.

State Statistics Bureau. Various issues. Statistical Yearbookof China. China Statistical Publishing House: Beijing.

Tybout J. 2000. Manufacturing firms in developingcountries: how well do they do, and why? Journal ofEconomic Literature 38: 11–44.

Wei H. 2002. Effects of foreign direct investment onregional economic growth in China. Jingji Yanjiu(Economic Research) 39(4): 19–26.

Wu Y. 1996. Technical efficiency and firm attributes inthe Chinese iron and steel industry. InternationalReview of Applied Economics 10(2): 235–248.

Yan R. 1998. Short-term results: the Litmus test forsuccess in China. Harvard Business Review 76(5):61–75.

Yeung G, Mok V. 2002. Government policy and thecompetitive advantages of foreign-financed firms inGuangdong province of southern China. Asian Busi-ness & Management 1(2): 227–247.

Young A. 2000. Gold into base metals: productivitygrowth in the People’s Republic of China during thereform period. National Bureau of Economic Research,Working Paper 7856 (http://www.nber.org/papers/w7856).

Zhao M, Nichols T. 1996. Management control of laborin state-owned enterprises: cases from the textileindustry. The China Journal 36: 1–21.



Employee motivation, external orientation and the technical efficiency of foreign-financed firms in...

Documents

Transcript of Employee motivation, external orientation and the technical efficiency of foreign-financed firms in...