Research on Ethnic Enterprises: A Case Study of Wufeng Tujia in China Sun Junfang Master course student Graduate School of Economics, Kyoto University 1

Contents 1.Introduction 2.Present status and overview 3.Model and methodology 4.Data 5.Results and discussion 6.Conclusion 2

1. Introduction  1.1 Background and aim  We take the case study of Tujia to explore the determinants of production efficiency of China’s ethnic enterprise.  1.2 Past studies  Yang (2006): “Analysis on financing dilemma of private economy in ethnic areas”.  Omarjan and Onishi (2008): “Research on ethnic entrepreneurs in Xinjiang Uygur Autonomous Region”. 3

2. Current status  2.1 Wufeng Tujia Autonomous County  Location: at the junction of two provinces.  Natural conditions: mountainous terrain.  Population: 209,476 in  Ethnic groups: 14, Tujia %.  Economic development: backward. 4

 2.2 Private enterprises in Wufeng County  Development :  Features of Wufeng County’s 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 enterprisesEmployeesRegistered capital , ,880, ,210,000 5

3. Model & methodology  Cobb-Douglas production function  Dependent variable: Y  Independent variables: L, K, Secondary, Tertiary, Ethnicity, Proportion/Debt, Eyears, Location  Hypothesis: Ethnicity(+/-), Proportion(+), Debt(+), Eyears(+), Location(+)  Methods: OLS, WLS 6

Equations (1) (2) (3) (4) 7

4. Data  Cross-section data for 2010  52 private enterprises, from field survey.  T-test and Z-test  the average education years of Tujia entrepreneurs are significantly less than that of Han entrepreneurs. 8

5. Results & discussion  Estimation results of equations (1) and (2), using OLS. Table 4 and Table 5  lnL: significant  lnK: significant  Proportion variable: significant  Debt dummy variable: significant the enterprise which is able to obtain bank loans has better performance. 9

10 Independent variables C4.953 ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ (6.266)(5.859)(5.993)(6.509)(5.838)(5.677) lnL0.342 ＊＊ ＊＊＊ ＊＊ ＊＊ ＊＊ ＊＊ (2.568)(2.770)(2.661)(2.330)(2.397)(2.331) lnK0.552 ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ (6.441)(6.729)(6.730)(6.523)(6.158)(6.035) Secondary (-1.308)(-1.287)(-0.469)(-0.417)(-0.408) Tertiary (-0.359)(-0.530)(0.155)(0.265)(0.278) Ethnicity (-1.270)(-1.256)(-1.067)(-1.068) Proportion0.670 ＊＊ ＊＊ ＊＊ (2.210)(2.176)(2.135) Eyears (0.880)(0.896) Location0.052 (0.225) Adj.R F-statistic Table 4 Estimates of production function: equation (1) Independent variables C4.953 ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ (6.266)(5.859)(5.993)(6.591)(5.915)(5.748) lnL0.342 ＊＊ ＊＊＊ ＊＊ ＊＊ ＊＊ ＊＊ (2.568)(2.770)(2.661)(2.301)(2.366)(2.296) lnK0.552 ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ (6.441)(6.729)(6.730)(6.568)(6.207)(6.083) Secondary (-1.308)(-1.287)(-0.423)(-0.376)(-0.367) Tertiary (-0.359)(-0.530)(0.206)(0.309)(0.324) Ethnicity (-1.270)(-1.271)(-1.087)(-1.090) Debt0.358 ＊＊ ＊＊ ＊＊ (2.401)(2.355)(2.317) Eyears (0.855)(0.878) Location0.060 (0.261) Adj.R F-statistic 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.

5. Results & discussion (conti.)  To address heteroskedasticity problem, use WLS to estimate equations (3) and (4). Table 6 and Table 7  lnL, lnK, Proportion, Debt : significant  Eyears variable: significant  Ethnicity dummy variable: significant  Location: significant at 10%. 11

12 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 variables C4.979 ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ (46.911)(24.434)(46.532)(36.208)(24.155)(20.783) lnL0.322 ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ (11.989)(8.126)(8.393)(11.614)(11.169)(11.044) lnK0.554 ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ (44.111)(26.323)(34.000)(34.628)(34.785)(34.682) Secondary ＊＊＊ ＊＊＊ ＊＊＊ ＊＊ ＊＊ (-5.088)(-5.269)(-4.742)(-2.295)(-2.559) Tertiary ＊＊ (-0.778)(-2.346)(-0.149)(1.024)(0.266) Ethnicity ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ (-6.241)(-4.332)(-3.993)(-4.154) Proportion0.615 ＊＊＊ ＊＊＊ ＊＊＊ (12.654)(9.482)(8.308) Eyears0.035 ＊＊ ＊＊＊ (2.253)(2.779) Location0.070 (1.615) Adj.R F-statistic Independent variables C4.979 ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ (46.911)(24.434)(46.532)(33.344)(21.911)(20.136) lnL0.322 ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ (11.989)(8.126)(8.393)(10.983)(9.819)(9.612) lnK0.554 ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ (44.111)(26.323)(34.000)(32.651)(29.237)(28.918) Secondary ＊＊＊ ＊＊＊ ＊＊＊ ＊ ＊＊ (-5.088)(-5.269)(-3.928)(-1.957)(-2.195) Tertiary ＊＊ (-0.778)(-2.346)(-0.212)(0.845)(0.164) Ethnicity ＊＊＊ ＊＊＊ ＊＊＊ ＊＊＊ (-6.241)(-4.326)(-3.919)(-4.180) Debt0.317 ＊＊＊ ＊＊＊ ＊＊＊ (9.454)(7.483)(6.792) Eyears0.032 ＊ ＊＊ (1.944)(2.531) Location0.081 ＊ (1.903) Adj.R F-statistic

 Ethnicity dummy variable: significantly negative The 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 Results & discussion (conti.)

6. Conclusion First, 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. 14

Thank you！ 15