Table 4.
Regression results of different funding.
| Post-allowance | Housing allowance | Team allowance | |
|---|---|---|---|
| Coefficient (SE) | Coefficient (SE) | Coefficient (SE) | |
| Personality | |||
| Age | 0.36*** (6.27) | 1.44*** (7.07) | 1.94*** (5.54) |
| Hometown | −0.72 (−0.96) | −2.09 (−0.79) | −6.79 (−1.48) |
| Gender | 0.67 (0.72) | 2.41 (0.73) | 0.10 (0.02) |
| Human capital | |||
| Initial academic degree | 0.55 (0.99) | 2.99 (1.53) | 6.16* (1.82) |
| Final academic degree | 1.31*** (4.38) | 5.33*** (5.03) | 4.34** (2.37) |
| Key university | 0.50 (1.37) | 2.80** (2.14) | −0.27 (−0.12) |
| Length of service in Guangxi | 0.04 (1.20) | −0.23** (−2.02) | −0.41** (−2.06) |
| Skills certificate | 1.48* (1.89) | 7.25*** (2.61) | 6.17 (1.29) |
| Cumulative advantage | |||
| Professional qualification | 0.54 (1.04) | 1.63 (0.89) | 0.16 (0.05) |
| Overseas talent | −0.88 (−0.76) | −0.20 (−0.05) | 0.43 (0.06) |
| Number of national-level title | 2.50*** (3.34) | 10.67*** (4.01) | 20.72*** (4.51) |
| Number of national-level funding | 3.46** (2.41) | 15.54*** (3.05) | 19.28** (2.19) |
| Number of provincial-level title | −2.73*** (−3.92) | −8.86*** (−3.59) | −5.85 (−1.37) |
| Fixed effects | |||
| Location1 | 0.39** (2.53) | −0.19 (−0.34) | 0.60 (0.63) |
| Professional field1 | 0.15** (2.34) | 0.60*** (2.66) | 0.31 (0.79) |
| Institution1 | 0.04 (0.14) | −1.12 (−1.23) | −0.07 (−0.04) |
| _cons | −22.22*** (−8.09) | −58.11*** (−5.95) | −101.55*** (−6.03) |
| N | 499 | 499 | 499 |
| Pseudo R2 | |||
*
p < 0.10,
**
p < 0.05,
***
and p < 0.01.