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Table 4 Estimation results by quantile regression: full model

From: Efficient scale of prefectural government in China

Quantiles

0.10

0.25

0.5

0.75

0.90

Constant

8437.364

10865.07

14691.87

18867.71

25474.17

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

186.2419

200.8443

472.8929

1246.982

2323.414

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

2790.369

2072.114

1612.773

−256.541

−2870.05

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

−582.815

−790.664

−1124.87

−1489.6

−2170.34

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

17.69066

25.17713

37.11554

52.52024

88.6423

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

−0.1602

−0.24401

−0.36007

−0.5324

−0.96903

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

−6867.2

−9253.76

−13284.8

−17565.5

−24829.6

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

−25.0469

−22.1984

−14.4934

−13.016

7.5451

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

0.27828

0.22899

0.12407

0.16248

0.016699

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

−0.00109

−0.00086

−0.00042

−0.00069

−0.00042

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

0.12069

0.29961

0.4339

0.43212

0.36434

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

−2.17E−06

−4.66E−06

−7.83E06

−8.46E06

−7.46E−06

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

8.35E−12

1.92E−11

4.93E11

9.98E11

8.28E−11

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

1.17E−17

−5.10E−18

−9.84E17

−3.44E16

−2.85E−16

Adj R2

0.46307

0.47224

0.48437

0.48294

0.47717

  1. Adj R 2 is adjusted R 2. Italicized values mean statistically significant at the 5 % level