1. Do High Speed Railways Lead to Economic Growth in China
Do High Speed Railways Lead to Economic Growth in China? A Spatial Panel Data Study on China’s Prefecture-level City
Li Hongchang, Beijing Jiaotong University
Jack Strauss University of Denver
Hu Shunxiang, Beijing Jiaotong University Liu Lihong Beijing Jiaotong University
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2. OVERVIEW: Infrastructure Spending Does infrastructure spending generate economic growth at the regional level? Hard to measure intra-city construction Use a new dataset on the impact of high speed railroads (HSR) on Chinese prefecture-level cities Examines Accessibility – measured by the weighted travel time (Jing Shi and Nian Zhou, 2013) Does increases in accessibility generate/predict future increases in economic growth? Or does economic growth lead to boosts in accessibility?

3. Granger Causality Does increase in one variable lead to (cause, forecast) an increase in another variable holding constant changes in the lagged dependent variable? GGDPt = r1GGDPt-1 + b1ACCt-1 + g1ISt-1 ACCt = r1GGDPt-1 + b1ACCt-1 + g1ISt-1 GGDP is GDP growth, ACC is accessibility, IS is a control variable for industrial structure. USE 3 lags to eliminate autocorrelation If b1>0 , then increases in HSR increase GDP growth or HSR GRANGER CAUSES GDP GROWTH If r1=0, then there is one-way Granger Causation from HSR to increases in economic activity. If r1>0, then there is two-way Granger Causation and positive feedback

4. Chinese Railroad Construction Growing 20% per year Is it hitting diminishing returns

5. Mechanism of HSR on Urban Economic Growth

6. Accessibility Considering the factors of time and price, the general weighted travel time between two cities can be expressed as: gwtijt: is the general weighted travel time between city i and city j; Tijt,k: is the shortest travel time from city i to city j via transport means k; Mjt: is the weighted value calibrating the value of the shortest travel time Tijt (see formula (2)); n is the number of subject cities; Tjt is the value of general weighted travel time of city j; Fijt,k is the travel expenditure from city i to city j via transport means k.

7. When calculating accessibility, population or GDP is usually used to reflect the comprehensive scale of each region, therefore GDP and population can be used in calculation of Mjt: Mjt=Git*POPjt GDPjt is GDP of city j in year t;
POPjt is the population of city j as of the end of year t used to measure the scale of labor market and job opportunities in city j. The measurement of HSR effect on the travel time of a city should take into account the values of both business travel time and personal travel time, hence the formula for calculating is TVjt` is the value of business travel time; TVjt`` is the value of personal travel time; r is the percentage of business travel; d is the ratio between the values of business travel time and personal travel time.
After more manipulations:
is the average travel time of city i calculated by general weighted avg
the accessibility indicator expressed by general weighted travel time is

8. Formula for calculating the value of business travel time is
Formula for calculating the value of business travel time is (5) WH is the average statutory annual work hours in China, which is around 2,000 hours. Formula for calculating the value of personal travel time is (6) is per capita disposable income of urban residents. Formula (5) brings the average travel time between two cities taking into account both time and price factors. The average travel time calculated by general weighted average method is presented as: （7） is the average travel time of city i calculated by general weighted average method. This means the accessibility indicator expressed by general weighted travel time is (8)  

9. Dynamic Panel Estimation Problems
Dynamic Panels (lagged dependent variable) possess problems. Imposition of common coefficient to increase power leads to bias in r (Nickell bias).
Subtracting time series averages eliminates this (and the fixed effect) but then creates a MA(1) series and thus creates a different bias.
GMM is advocated Arrelano Bond (1-step) or Arrelano Bover (orthogonal diffdences). Uses dynamic lags.
Problem with GMM. Use of instruments – must be exogeneous. Lagged dynamic are, but then you lose 1-2 observations ( ) observations. Instrument selection and lag length of lags (art, not science).
Heteroskedasticity correction (White)

10. Use OLS
Simulation results by Judson and Owen (1999) show that for T=5, N=100, the bias for a b=.2 (.8) is (-.005) for OLS and .006 (-.027) for fixed effects.
In contrast, the bias for the r =.2 (.8) is .225 (.049) for OLS and (-.504) for fixed effects.
Judsen and Owen (1999) also posit that the bias of the beta estimate is more severe than the bias of r.
Kiviet (2005) also shows the bias of b is substantially less than the bias of r; however, his simulations are not for large N.
We also conduct Monte Carlo Simulations for a T=5, N=100 and find the beta bias for fixed effects to be less than 2%.

11. Monte Carlo Yit = a + r1Yit-1 + b1Xit-1
OLS r=.2
0.435
0.447
0.454
0.496
0.494
0.504
0.571
0.229
0.197
0.175
0.158
0.146
0.043
0.041
0.040
b=.8
0.773
0.763
0.759
0.757
0.758
0.750
0.739
0.738
0.255
0.079
0.179
0.124
0.058
0.039
0.029
0.014
LSDV r =.2
0.035
0.121
0.184
0.185
0.048
0.127
0.186
0.181
0.115
0.125
0.080
0.034
0.028
0.018
0.008
0.792
0.800
0.804
0.805
0.802
0.789
0.803
0.249
0.156
0.062
0.169
0.109
0.045
0.037
0.023
0.010

12. Table 2 Granger Causality between GDP Growth and ACC – GMM Estimation
PANEL A
DIFF
ORTH
DIFFA
DAGG
GDP
Coeff
T stats
GDP(-1)
0.24
4.70
0.22
3.83
.27
-2.64
-0.53
-2.75
GDP(-2)
0.25
4.59
0.23
4.13
.32
-0.22
-0.02
-0.19
GDP(-3)
0.19
3.76
3.62
.26
0.89
0.05
0.79
ACC(-1)
-0.33
-2.42
-0.37
-2.60
-0.30
0.17
0.30
ACC(-2)
0.21
1.04
1.07
-0.27
-.55
0.07
0.4
ACC(-3)
1.28
9.74
1.30
9.71
4.53
0.28
2.42
IS(-1)
0.20
4.75
4.12
2.81
2.76
3.33
IS-2)
-0.04
-1.44
-0.74
0.14
2.99
3.02
IS(-3)
0.00
0.09
0.06
2.39
-0.62
0.98
2.20

13. Reverse Causation ACC Coeff T stats GDP(-1) -0.21 -9.99 -0.16 -11.35
-0.04
-2.21
0.11
1.99
GDP(-2)
0.01
0.34
0.02
1.13
0.06
3.04
-0.10
-2.85
GDP(-3)
0.03
2.26
0.04
2.51
3.22
-0.01
-1.21
ACC(-1)
0.30
7.69
0.35
11.85
-0.11
-4.16
0.58
4.08
ACC(-2)
0.15
2.64
0.10
2.07
0.37
2.76
-1.51
ACC(-3)
-0.29
-10.19
-0.34
-17.11
0.24
8.45
-0.18
-4.51
IS(-1)
3.17
3.49
-0.03
-1.68
0.00
0.16
IS-2)
-3.36
-0.06
-7.05
0.53
-3.27
IS(-3)
6.94
8.27
0.12
5.69
-5.52

14. Granger Causality between GDP Growth and ACC OLS Estimates
Panel A
White
PCSE
LSDV
LSDV DAGG
GDP
Coeff
T stats
GDP(-1)
0.10
1.31
0.67
0.04
0.66
-.21
-7.10
GDP(-2)
-0.06
-1.76
-0.56
-0.11
-1.95
-4.63
GDP(-3)
-0.02
-0.60
-0.25
0.05
1.64
-0.04
-5.17
ACC(-1)
-0.45
-4.06
-3.90
-7.32
0.20
5.72
ACC(-2)
0.79
3.87
4.56
0.62
7.22
0.30
10.40
ACC(-3)
0.75
4.77
2.93
0.70
4.01
0.35
8.23
IS(-1)
0.09
3.45
3.85
6.40
0.13
7.86
IS-2)
-0.01
-0.40
-0.29
0.00
-0.26
1.33
IS(-3)
0.06
25.44
4.71
11.50
2.63
Panel A
White
PCSE
LSDV
LSDV DAGG
GDP
Coeff
T stats
GDP(-1)
0.10
1.31
0.67
0.04
0.66
-.21
-7.10
GDP(-2)
-0.06
-1.76
-0.56
-0.11
-1.95
-4.63
GDP(-3)
-0.02
-0.60
-0.25
0.05
1.64
-0.04
-5.17
ACC(-1)
-0.45
-4.06
-3.90
-7.32
0.20
5.72
ACC(-2)
0.79
3.87
4.56
0.62
7.22
0.30
10.40
ACC(-3)
0.75
4.77
2.93
0.70
4.01
0.35
8.23
IS(-1)
0.09
3.45
3.85
6.40
0.13
7.86
IS-2)
-0.01
-0.40
-0.29
0.00
-0.26
1.33
IS(-3)
0.06
25.44
4.71
11.50
2.63
Panel A
White
PCSE
LSDV
LSDV DAGG
GDP
Coeff
T stats
GDP(-1)
0.10
1.31
0.67
0.04
0.66
-.21
-7.10
GDP(-2)
-0.06
-1.76
-0.56
-0.11
-1.95
-4.63
GDP(-3)
-0.02
-0.60
-0.25
0.05
1.64
-0.04
-5.17
ACC(-1)
-0.45
-4.06
-3.90
-7.32
0.20
5.72
ACC(-2)
0.79
3.87
4.56
0.62
7.22
0.30
10.40
ACC(-3)
0.75
4.77
2.93
0.70
4.01
0.35
8.23
IS(-1)
0.09
3.45
3.85
6.40
0.13
7.86
IS-2)
-0.01
-0.40
-0.29
0.00
-0.26
1.33
IS(-3)
0.06
25.44
4.71
11.50
2.63

15. Reverse Causation OLS ACC Coeff T stats GDP(-1) -0.05 -2.30 -2.58
-0.07
-3.21
0.01
1.13
GDP(-2)
-0.01
-0.86
-0.57
-0.03
-2.06
-0.02
-2.56
GDP(-3)
0.00
-0.65
2.82
ACC(-1)
0.43
2.03
1.79
0.32
1.33
-3.45
ACC(-2)
-0.31
-1.59
-1.30
-0.35
-1.51
ACC(-3)
-0.27
-2.38
-1.48
-3.42
IS(-1)
0.23
-0.17
1.06
IS-2)
-0.32
-0.20
0.66
-0.91
IS(-3)
-1.80
-2.09
-1.06

16. Growth in GDP PER CAPITA - GMM
DIFF
ORTH
DIFFA
DAGG
GDPPC
Coeff
T stats
GDPPC(-1)
0.25
4.12
0.31
1.03
0.39
0.22
-0.35
-2.32
GDPPC (-2)
4.03
0.32
1.05
-0.41
0.45
-0.01
-0.10
GDPPC (-3)
0.13
2.43
0.19
0.98
-0.16
0.24
-0.04
-0.78
ACC(-1)
-0.64
-4.75
1.01
-0.55
0.51
2.29
3.19
ACC(-2)
0.48
2.05
0.62
0.94
0.60
0.87
0.16
0.41
ACC(-3)
1.08
8.43
1.14
9.62
1.09
4.81
0.09
0.37
IS(-1)
0.18
4.08
1.37
-0.15
0.11
IS-2)
-0.05
-1.68
2.12
0.07
0.96
IS(-3)
0.08
1.64
-0.23
0.15
2.09
ACC
GDPPC (-1)
-0.22
-9.98
-9.34
-0.07
0.03
-0.02
-1.49
0.01
0.57
-0.86
1.28
0.02
1.85
0.05
-1.09
0.28
7.79
0.34
12.34
-0.11
0.26
0.81
0.04
0.75
0.33
-0.30
-10.70
-16.42
-1.45
2.94
2.34
0.00
0.61
-2.26
-0.06
-6.07
-0.59
0.103
6.27
6.91
0.12

17. Growth in GDP per Capita OLS
White
PCSE
LSDV
LSDV DAGG
GDPPC
Coeff
T stats
GDP(-1)
0.04
0.43
1.17
0.00
-0.25
-7.14
GDP(-2)
-0.07
-2.34
-2.22
-0.12
-2.10
-0.24
-9.19
GDP(-3)
-0.05
-1.85
-2.17
0.01
0.22
-0.18
-4.59
ACC(-1)
-0.69
-6.05
-11.31
-0.53
-5.81
0.17
5.29
ACC(-2)
0.89
4.71
12.14
0.62
6.02
0.15
4.24
ACC(-3)
0.75
5.05
9.85
0.63
3.15
0.18
4.80
IS(-1)
0.08
2.91
4.75
0.09
3.35
4.95
IS-2)
0.001
0.003
-0.01
-0.73
3.67
IS(-3)
0.06
27.83
9.04
8.52
0.02
1.10

18. Table 6 Granger Causality between Wage Growth and ACC, GMM EstimatesWG
Coeff
T stats
WG(-1)
1.47
0.66
-0.01
-0.03
-0.23
-0.27
-0.35
-4.43
WG(-2)
2.51
0.83
0.16
0.65
-1.19
-2.27
-0.18
-4.71
WG(-3)
2.01
0.91
0.24
1.13
-1.56
-1.55
0.00
-0.16
ACC(-1)
1.18
0.44
-0.20
-0.58
-0.04
1.61
5.69
ACC(-2)
3.50
0.38
2.20
0.79
0.04
0.06
-0.19
-1.15
ACC(-3)
0.51
0.50
1.68
0.73
0.87
0.08
IS(-1)
0.34
0.88
0.02
0.37
-0.25
-0.86
-0.02
-0.71
IS-2)
0.20
0.90
0.22
2.23
-0.11
-0.97
0.01
IS(-3)
-0.55
-0.33
-2.47
-0.08
-0.46

19. Table 6 Granger Causality between Wage Growth and ACC, GMM Estimates
Coeff
T stats
WG(-1)
-0.06
-0.52
-0.01
-0.12
-0.02
-0.16
-0.17
WG(-2)
0.00
-0.05
-0.33
-0.03
-0.28
-0.56
WG(-3)
-0.07
-0.31
-0.04
-0.35
ACC(-1)
0.64
4.09
0.57
5.66
-0.19
-1.12
0.63
6.50
ACC(-2)
3.85
3.13
2.76
3.44
0.49
3.20
-0.24
-2.44
ACC(-3)
-0.68
-3.68
-0.76
-5.11
0.28
2.93
-0.44
-4.30
IS(-1)
0.03
0.90
0.02
0.68
-2.55
0.01
0.35
IS-2)
0.08
-1.99
0.32
0.59
IS(-3)
0.07
1.38
0.15
4.18
0.11
3.74

20. Table 6 Granger Causality between Wage Growth and ACC, OLS Estimates
White
PCSE
LSDV
LSDV DAGG
Wage
Coeff
T stats
WG(-1)
-0.01
2.23
0.04
1.39
-0.32
-1.87
-0.18
-5.58
WG(-2)
0.16
3.01
0.05
1.75
-0.21
-9.11
-0.12
-3.67
WG(-3)
0.24
2.21
-0.07
-4.03
0.00
0.98
-0.03
-0.97
ACC(-1)
-0.20
2.66
0.02
0.46
0.21
1.45
0.40
6.03
ACC(-2)
2.20
9.11
0.43
6.21
0.06
0.73
0.25
10.91
ACC(-3)
0.50
1.17
7.79
0.15
2.12
0.23
8.06
IS(-1)
0.38
0.03
2.64
-101
-0.02
-.55
IS-2)
0.22
1.97
1.32
1.19
IS(-3)
-0.33
0.57
3.14
0.01
1.47
-1.91

21. Evaluate Out-of-Sample (OOS) Granger Causality is a predictive test
Evaluate Out-of-Sample (OOS) Granger Causality is a predictive test. OOS avoids data overfitting, minimizes data-mining OOS does not suffer from bias in t-stats from heteroskedastity correction OOS will fail if parameter bias (can test it). OOS will fail if parameter instability. Important to check parameter instability as HSR may suffer from diminishing returns. Are the parameters the same in ? Horse-race between GMM and OLS. Evaluate parameters bias of GMM compared to OLS

22. Table 8 Out-of-sample Forecasting Results on Growth in GDP and GDP per Capita

23. Table 5 Granger Causality between Growth in GDP Per Capita and ACC, OLS Estimates

25. Monte Carlo Simulations