Research on Ethnic Enterprises:

A Case Study of Wufeng Tujia in ChinaSun JunfangMaster course studentGraduate School of Economics, Kyoto University

*

ContentsIntroductionPresent status and overviewModel and methodologyDataResults and discussionConclusion*

1. Introduction1.1 Background and aimWe take the case study of Tujia to explore the determinants of production efficiency of Chinas ethnic enterprise.1.2 Past studiesYang (2006): Analysis on financing dilemma of private economy in ethnic areas.Omarjan and Onishi (2008): Research on ethnic entrepreneurs in Xinjiang Uygur Autonomous Region.

*

2. Current status2.1 Wufeng Tujia Autonomous CountyLocation: at the junction of two provinces.Natural conditions: mountainous terrain.Population: 209,476 in 2009.Ethnic groups: 14, Tujia - 84.77%.Economic development: backward.

*

2.2 Private enterprises in Wufeng County

Development :

Features of Wufeng Countys private enterprises: (1) The vast majority of them are owned by Tujia. (2) They generally are small scale. (3) They mainly concentrated in Secondary industry.

*

Private enterprisesEmployees Registered capital1989438339,00020041871965212,880,0002010267-703,210,000

3. Model & methodologyCobb-Douglas production functionDependent variable: YIndependent variables: L, K, Secondary, Tertiary, Ethnicity, Proportion/Debt, Eyears, LocationHypothesis: Ethnicity(+/-), Proportion(+), Debt(+), Eyears(+), Location(+)Methods: OLS, WLS

*

Equations(1)(2)(3)(4)*

4. DataCross-section data for 201052 private enterprises, from field survey.

T-test and Z-testthe average education years of Tujia entrepreneurs are significantly less than that of Han entrepreneurs.

*

5. Results & discussionEstimation results of equations (1) and (2), using OLS. Table 4 and Table 5 lnL: significantlnK: significantProportion variable: significant Debt dummy variable: significant the enterprise which is able to obtain bank loans has better performance. *

*Table 4 Estimates of production function: equation (1)

Table 5 Estimates of production function: equation (2)

The table presents regression coefficients. And we report the t statistics in parentheses. indicates significance at ten percent. indicates significance at five percent. indicates significance at one percent.

Independent variables123456C4.9534.7225.0655.3485.0945.065(6.266)(5.859)(5.993)(6.509)(5.838)(5.677)lnL0.3420.3680.3520.3010.3120.309(2.568)(2.770)(2.661)(2.330)(2.397)(2.331)lnK0.5520.5790.5750.5430.5270.525(6.441)(6.729)(6.730)(6.523)(6.158)(6.035)Secondary-0.325-0.318-0.119-0.106-0.105(-1.308)(-1.287)(-0.469)(-0.417)(-0.408)Tertiary-0.085-0.1260.0370.0640.068(-0.359)(-0.530)(0.155)(0.265)(0.278)Ethnicity-0.267-0.253-0.220-0.223(-1.270)(-1.256)(-1.067)(-1.068)Proportion0.6700.6620.681(2.210)(2.176)(2.135)Eyears0.0310.032(0.880)(0.896)Location0.052(0.225)Adj.R20.8520.8550.8570.8680.8680.865F-statistic147.67776.44062.27257.09048.79941.784

Independent variables123456C4.9534.7225.0655.3675.1215.086(6.266)(5.859)(5.993)(6.591)(5.915)(5.748)lnL0.3420.3680.3520.2950.3060.302(2.568)(2.770)(2.661)(2.301)(2.366)(2.296)lnK0.5520.5790.5750.5420.5260.524(6.441)(6.729)(6.730)(6.568)(6.207)(6.083)Secondary-0.325-0.318-0.106-0.095-0.094(-1.308)(-1.287)(-0.423)(-0.376)(-0.367)Tertiary-0.085-0.1260.0490.0740.079(-0.359)(-0.530)(0.206)(0.309)(0.324)Ethnicity-0.267-0.254-0.222-0.225(-1.270)(-1.271)(-1.087)(-1.090)Debt0.3580.3530.364(2.401)(2.355)(2.317)Eyears0.0300.031(0.855)(0.878)Location0.060(0.261)Adj.R20.8520.8550.8570.8710.8700.867F-statistic147.67776.44062.27258.22949.71742.590

5. Results & discussion(conti.)To address heteroskedasticity problem, use WLS to estimate equations (3) and (4). Table 6 and Table 7lnL, lnK, Proportion, Debt : significantEyears variable: significantEthnicity dummy variable: significantLocation: significant at 10%.

*

*Table 6 Estimates of production function: equation (3)

Table 7 Estimates of production function: equation (4)

The table presents regression coefficients. And we report the t statistics in parentheses. indicates significance at ten percent. indicates significance at five percent. indicates significance at one percent.

Independent variables123456C4.9794.6595.0535.6925.4375.235(46.911)(24.434)(46.532)(36.208)(24.155)(20.783)lnL0.3220.3650.3270.3280.3130.308(11.989)(8.126)(8.393)(11.614)(11.169)(11.044)lnK0.5540.5810.5860.5220.5070.509(44.111)(26.323)(34.000)(34.628)(34.785)(34.682)Secondary-0.313-0.288-0.172-0.102-0.128(-5.088)(-5.269)(-4.742)(-2.295)(-2.559)Tertiary-0.049-0.130-0.0050.0400.011(-0.778)(-2.346)(-0.149)(1.024)(0.266)Ethnicity-0.355-0.347-0.345-0.337(-6.241)(-4.332)(-3.993)(-4.154)Proportion0.6150.5390.556(12.654)(9.482)(8.308)Eyears0.0350.047(2.253)(2.779)Location0.070(1.615)Adj.R20.8520.8550.8560.8670.8650.862F-statistic4268.864887.1253904.0321013.8093667.9951076.888

Independent variables123456C4.9794.6595.0535.6855.4455.265(46.911)(24.434)(46.532)(33.344)(21.911)(20.136)lnL0.3220.3650.3270.3200.3090.300(11.989)(8.126)(8.393)(10.983)(9.819)(9.612)lnK0.5540.5810.5860.5240.5110.511(44.111)(26.323)(34.000)(32.651)(29.237)(28.918)Secondary-0.313-0.288-0.175-0.104-0.129(-5.088)(-5.269)(-3.928)(-1.957)(-2.195)Tertiary-0.049-0.130-0.0080.0390.008(-0.778)(-2.346)(-0.212)(0.845)(0.164)Ethnicity-0.355-0.350-0.348-0.342(-6.241)(-4.326)(-3.919)(-4.180)Debt0.3170.2760.294(9.454)(7.483)(6.792)Eyears0.0320.044(1.944)(2.531)Location0.081(1.903)Adj.R20.8520.8550.8560.8690.8670.864F-statistic4268.864887.1253904.0321164.9701064.791650.324

Ethnicity dummy variable: significantly negativeThe performance of Tujia enterprises is not as good as that of Han enterprises. Pure difference: even if we added some other variables, the coefficient of Ethnicity dummy variable remains statistically significant. *5. Results & discussion(conti.)

6. ConclusionFirst, the performance of Tujia enterprises is not as good as that of Han enterprises; and this is their pure difference.Second, the private enterprise which is able to obtain bank loans has better performance.Third, the owners of private enterprises having a higher education level make their enterprises perform better. Furthermore, the private enterprises located closer to the big city perform better.

*

Thank you*