Skip to main content

Table 5 Estimation results by quantile regression: selected model

From: Efficient scale of prefectural government in China

Quantiles

0.10

0.25

0.5

0.75

0.90

OLS

Constant

9865.556

12879.36

16433.2

19494.19

23919.46

19595.94

\( \frac{NTowns}{Pop} \)

372.7542

310.7989

594.6125

1329.612

2163.313

980.2919

\( \frac{1}{Pop} \)

2965.341

3612.943

1550.574

1299.058

−3512.63

5.34297

\( \frac{UPo{p}^2}{Pop} \)

−708.287

−1004.27

−1298.16

−1540.59

−1887.98

−1590.68

\( \frac{UPo{p}^3}{Pop} \)

20.67388

32.16626

42.61537

53.8526

76.49473

55.56158

\( \frac{UPo{p}^4}{Pop} \)

−0.18161

−0.31268

−0.41115

−0.54328

−0.83928

−0.56619

\( \frac{RPop}{Pop} \)

−9113.59

−12080.4

−15627.1

−18605.9

−22888.9

−18819.8

\( \frac{Area}{Pop} \)

0.12268

0.18421

0.39747

0.27226

0.44509

0.19418

\( \frac{Are{a}^2}{Pop} \)

−2.88E−06

−2.83E−06

−7.61E06

−4.83E−06

−7.99E−06

−2.9E−06

\( \frac{Are{a}^3}{Pop} \)

1.73E−11

9.67E−12

4.85E11

6.95E11

8.22E−11

2.84E−11

\( \frac{Are{a}^4}{Pop} \)

−1.77E−17

8.49E−18

−9.67E17

−2.65E16

−2.77E−16

−9.6E−17

R 2

0.46899

0.4791

0.48777

0.48514

0.47876

0.50135

  1. Italicized values mean statistically significant at the 5 % level