Demographic Distributional Effect of High-Speed Railway

(HSR)

Evidence from Taiwan

88th International Atlantic Economic Conference

Jiaxuan Lu University of Southern California

October 19th, 2019

The author of this study remains neutral regarding the legal and political status of Taiwan. Throughout this study, Taiwan refers to Taiwan Main Island from the geographical perspective. The author reserves his rights to define all terms used in this research.

History of HSR (HSR for Passengers) Countries with HSRs (≥ 𝟏𝟏𝟏𝟏𝟏𝟏)

Source: International Union of Railways (IUC)

Backgrounds

More HSRs in the Future Belt and Road Initiative & Eurasian Rail Bridges (Huang, 2016; Cai,

2017) California, United States

HSR’s Pros and Cons Job-Creation; Political Integration; Environmental Friendliness;

Tourism; etc. Excessive Infrastructure in Mainland China and Japan Developing Countries: e.g. Malaysia

HSR’s Demographic Distributional Effect

HSR: (Passenger) High-Speed Railway Demographic Agglomeration: The trend that there are more people moving from non-

urbanized/rural counties to urbanized counties than those in the opposite direction.

Demographic Dispersion: The trend that there are more people moving from urbanized

counties to non-urbanized/rural counties than those in the opposite direction.

Preview of Conclusion: Taiwan’s HSR promotes its demographic agglomeration.

Previous Literature (Worldwide HSRs) Mainland China: Zhang and Tao (2016) Method: Quasi-Experiment Based on Panel Data Conclusion: While HSR has promoted the economic growth of

large cities, it has hindered the development of small cities, and the small cities close to large cities have been disadvantaged most.

Japan: Sasaki et. al. (1997) Method: Simulations Based on Supply-Oriented Econometric

Models Conclusion: Shinkansen (Japan’s HSR) didn’t promote regional

demographic dispersion as expected.

South Korea: Kim (2000) Western Europe: Ureña et. al. (2009) & Monzón et. al. (2013)

Map of Taiwan’s HSR

Source: Official Website of Taiwan High Speed Rail (legend edited by the author)

Why Taiwan? • Very Recent HSR Project • Many Detailed Data • Post-Rapid-Development

Capital City: Taipei

Largest Port: Kaohsiung

Previous Literature (Taiwan’s HSR)

Feng (2003): Method: Granger Causality Test + Simultaneous Equations

Model (SEM) Conclusion (Prediction): Greater Taipei, the capital city, would

experience an increase in immigrants after HSR’s availability. In contrast, suburban/rural counties would experience a demographic decline because of HSR.

Wu et. al. (2008): Method: Multilevel Descriptive Statistics Conclusion: HSR could have been beneficial to southern Taiwan,

which is mainly consist of rural areas, if the authority implemented appropriate policies.

Previous Literature (Taiwan’s Freeway)

Pai (2009): Method: Regressions with Power and Exponential Functions Conclusion: The availability of the Formosa Freeway along the

west coast of Taiwan resulted in demographic agglomeration toward the counties around the capital city Taipei.

Chen (2011): Method: Gravity Model Conclusion: Both demographic agglomeration and dispersion

have occurred after the construction of other new expressways in Taiwan.

Research Questions/Hypotheses

This study aims to examine whether a) the migrations between different counties have

been increasing because of the availability of HSR; b) there are more increases in the migrating

population from non-urbanized counties to urbanized counties than in the opposite direction after the availability of HSR.

Both hypotheses are confirmed by my findings.

Outlines

Gravity Model + Interaction Terms Standard Deviation of County-Level Population Counterfactual Analysis (Robustness Check)

Gravity Model + Interaction Terms Gravity Model of Migration: 𝑀𝑖𝑖,𝑡 = 𝐾𝑁𝑖,𝑡−1𝛼𝑁𝑖,𝑡−1

𝛽𝑑𝑖𝑖𝛾

Where: 𝑀𝑖𝑖,𝑡 is the number of people moving from county 𝑖 to 𝑗 in year 𝑡 𝐾 is a constant; 𝑁𝑖,𝑡−1 is the population of county 𝑖 in year 𝑡 − 1; 𝑁𝑖,𝑡−1 is the population of county 𝑗 in year 𝑡 − 1;

𝑑𝑖𝑖 is the distance between counties 𝑖 and 𝑗. Why 𝒕 − 𝟏? (a) Avoid Double-Counting; (b) Avoid Reversal Causality.

Log-Linearization: 𝑙𝑙 𝑀𝑖𝑖,𝑡 = 𝑙𝑙 𝐾 + 𝛼𝑙𝑙 𝑁𝑖,𝑡−1 + 𝛽𝑙𝑙 𝑁𝑖,𝑡−1 + 𝛾𝑙𝑙 𝑑𝑖𝑖

𝛼: Elasticity of the Origin County’s Population 𝛽: Elasticity of the Destination County’s Population 𝛾: Elasticity of the Bilateral Distance Usually, 𝛼 > 0, 𝛽 > 0, and 𝛾 < 0.

Gravity Model + Interaction Terms Interaction Terms: 𝛼 ≡ 𝛼0 + 𝛼1ℎ𝑖𝑖,𝑡 𝛽 ≡ 𝛽0 + 𝛽1ℎ𝑖𝑖,𝑡 Compare 𝛼 with 𝛽! 𝛾 ≡ 𝛾0 + 𝛾1ℎ𝑖𝑖,𝑡 𝐾 ≡ 𝐾0𝑒𝛿ℎ𝑖𝑖,𝑡 Expect 𝛾1 > 0 & 𝛿 > 0. where ℎ𝑖𝑖,𝑡 is the dummy of availability of HSR between counties 𝑖 and 𝑗

in year 𝑡. If there is HSR between the two counties, ℎ𝑖𝑖,𝑡 = 1; otherwise, ℎ𝑖𝑖,𝑡 = 0.

Econometrics Equation: 𝑙𝑙 𝑀𝑖𝑖,𝑡 = 𝑙𝑙 𝐾0 + 𝛼0𝑙𝑙 𝑁𝑖,𝑡−1 + 𝛽0𝑙𝑙 𝑁𝑖,𝑡−1 + 𝛾0𝑙𝑙 𝑑𝑖𝑖 +𝛿ℎ𝑖𝑖,𝑡 + 𝛼1ℎ𝑖𝑖,𝑡𝑙𝑙 𝑁𝑖,𝑡−1 + 𝛽1ℎ𝑖𝑖,𝑡𝑙𝑙 𝑁𝑖,𝑡−1 + 𝛾1ℎ𝑖𝑖,𝑡𝑙𝑙 𝑑𝑖𝑖 +𝜃𝑖𝑖 + 𝜌𝑡 + 𝜀𝑖𝑖,𝑡

where 𝑙𝑙 𝐾0 is the intercept, 𝜃𝑖𝑖 is the fixed effect for the type of county pair 𝑖𝑗 (distinguishing pairs of metropolitan counties from other type of pairs), 𝜌𝑡 is fixed effect for year, and 𝜀𝑖𝑖,𝑡 is the error term.

Gravity Model + Interaction Terms

𝑀𝑖𝑖,𝑡

𝑀𝑖𝑖,𝑡= 𝑁𝑖,𝑡−1

𝑁𝑖,𝑡−1

𝛼0+𝛼1ℎ𝑖𝑖,𝑡−𝛽0−𝛽1ℎ𝑖𝑖,𝑡

𝑙𝑙 𝑀𝑖𝑖,𝑡

𝑀𝑖𝑖,𝑡= 𝛼0 − 𝛽0 + 𝛼1 − 𝛽1 ℎ𝑖𝑖,𝑡 𝑙𝑙

𝑁𝑖,𝑡−1𝑁𝑖,𝑡−1

Suppose 𝑁𝑖,𝑡−1 ≥ 𝑁𝑖,𝑡−1, then 𝑙𝑙 𝑁𝑖,𝑡−1𝑁𝑖,𝑡−1

≥ 0:

𝑙𝑙 𝑀𝑖𝑖,𝑡

𝑀𝑖𝑖,𝑡�

> 0, 𝛼0 − 𝛽0 + 𝛼1 − 𝛽1 ℎ𝑖𝑖,𝑡 > 0= 0, 𝛼0 − 𝛽0 + 𝛼1 − 𝛽1 ℎ𝑖𝑖,𝑡 = 0< 0, 𝛼0 − 𝛽0 + 𝛼1 − 𝛽1 ℎ𝑖𝑖,𝑡 < 0

Dispersion

Agglomeration

Equilibrium

Call this (i.e. 𝛂 − 𝜷) 𝑪𝒊,𝒋,𝒕 orally.

Administrative Division of Taiwan BLUE: HSR 2007+ RED: HSR 2016+ GREEN: NON-HSR

Estimates of 𝒍𝒍 𝟏𝒊𝒋,𝒕 = 𝒍𝒍 𝑲𝟎 + 𝜶𝟎𝒍𝒍 𝑵𝒊,𝒕−𝟏 + 𝜷𝟎𝒍𝒍 𝑵𝒋,𝒕−𝟏 + 𝜸𝟎𝒍𝒍 𝒅𝒊𝒋 +𝜹𝒉𝒊𝒋,𝒕 + 𝜶𝟏𝒉𝒊𝒋,𝒕𝒍𝒍 𝑵𝒊,𝒕−𝟏 + 𝜷𝟏𝒉𝒊𝒋,𝒕𝒍𝒍 𝑵𝒋,𝒕−𝟏 + 𝜸𝟏𝒉𝒊𝒋,𝒕𝒍𝒍 𝒅𝒊𝒋 + 𝜽𝒊𝒋 + 𝝆𝒕 + 𝜺𝒊𝒋,𝒕

Dependent Variable: 𝒍𝒍 𝟏𝒊𝒋,𝒕 (1) (2) (3) (4) (5) (6)

Intercept -8.831*** (0.344)

-8.234*** (0.355)

-4.368*** (0.390)

-5.823*** (0.348)

-5.463*** (0.357)

-2.845*** (0.389)

𝒍𝒍 𝑵𝒊,𝒕−𝟏 0.687*** (0.013)

0.664*** (0.013)

0.542*** (0.014)

0.585*** (0.013)

0.570*** (0.013)

0.490*** (0.014)

𝒍𝒍 𝑵𝒋,𝒕−𝟏 0.668*** (0.013)

0.644*** (0.013)

0.518*** (0.014)

0.565*** (0.013)

0.551*** (0.013)

0.466*** (0.014)

𝒍𝒍 𝒅𝒊𝒋 -0.650*** (-0.018)

-0.650*** (0.018)

-0.744*** (0.021)

-0.683*** (0.016)

-0.683*** (0.016)

-0.761*** (0.020)

𝒉𝒊𝒋,𝒕 0.182*** (0.031)

-15.162*** (0.767) 0.129***

(0.031) -13.351***

(0.884)

𝒉𝒊𝒋,𝒕𝒍𝒍 𝑵𝒊,𝒕−𝟏 0.498*** (0.030) 0.435***

(0.033)

𝒉𝒊𝒋,𝒕𝒍𝒍 𝑵𝒋,𝒕−𝟏 0.527*** (0.030) 0.464***

(0.033)

𝒉𝒊𝒋,𝒕𝒍𝒍 𝒅𝒊𝒋 0.180*** (0.033) 0.182***

(0.031) Fixed Effect for County Pair 𝜽𝒊𝒋 No No No Yes Yes Yes

Fixed Effect for Year 𝝆𝒕 No No No Yes Yes Yes Number of Observations 2164 2164 2164 2164 2164 2164

F-Statistic 2344.03 1795.22 1275.92 2100.98 1591.84 1041.57 𝑹𝟏 0.765 0.769 0.806 0.761 0.765 0.804

Numbers in Parentheses are Standard Errors. Significance level: * p<.05, ** p<.01, *** p<.001.

Data Source: Chinese Statistical Association (Taiwan)

𝝏𝒍𝒍 𝟏𝒊𝒋,𝒕

𝝏𝒉𝒊𝒋,𝒕= 𝜹 + 𝜶𝟏𝒍𝒍 𝑵𝒊,𝒕−𝟏 + 𝜷𝟏𝒍𝒍 𝑵𝒋,𝒕−𝟏 + 𝜸𝟏𝒍𝒍 𝒅𝒊𝒋

Hypothesis 1 Confirmed!

Gravity Model + Interaction Terms

Column 6 (the same for column 3) 𝛼0 = 0.490, 𝛽0 = 0.466, 𝛼1 = 0.435, 𝛽1 = 0.464.

𝛼0 − 𝛽0 + 𝛼1 − 𝛽1 ℎ𝑖𝑖,𝑡 = +0.024 > 0 (if ℎ𝑖𝑖,𝑡 = 0) 𝛼0 − 𝛽0 + 𝛼1 − 𝛽1 ℎ𝑖𝑖,𝑡 = −0.005 < 0 (if ℎ𝑖𝑖,𝑡 = 1)

Pre-HSR Taiwan: Demographic Dispersion Post-HSR Taiwan: Demographic Agglomeration

Hypothesis II Confirmed!

Outlines

Gravity Model + Interaction Terms Standard Deviation of County-Level Population Counterfactual Analysis (Robustness Check)

Std. Dev. of County-Level Population

𝑆𝑆𝑡 ≡1𝑛𝐶∑ 𝑁𝑖,𝑡 − ∑ 𝑁𝑖,𝑡

𝑛𝐶𝑖∈𝐶2

𝑖∈𝐶

where 𝐶 is the set of all counties in Taiwan and 𝑙𝐶 is the number of elements in set 𝐶.

𝑙𝑙 𝑆𝑆𝑡 = 𝜔0 + 𝜔1𝑡 + 𝜔2𝐻𝑆𝐻𝑡 + 𝜀𝑡 (RDD) where 𝐻𝑆𝐻𝑡 is the dummy of the appearance of HSR in Taiwan

(𝐻𝑆𝐻𝑡 = 0 when 𝑡 ≤ 2006 and 𝐻𝑆𝐻𝑡 = 1 when 𝑡 ≥ 2007),

𝜔1 = 𝜕 𝑙𝑛 𝑆𝑆𝑡𝜕𝑡

=𝜕𝑆𝑆𝑡𝜕𝑡𝑆𝑆𝑡

is the constant instantaneous growth rate

of 𝑆𝑆𝑡, 𝜔2 = 𝜕 𝑙𝑛 𝑆𝑆𝑡𝜕𝐻𝑆𝐻𝑡

=𝜕𝑆𝑆𝑡𝜕𝐻𝑆𝐻𝑡𝑆𝑆𝑡

is the increase in the constant instantaneous growth rate of 𝑆𝑆𝑡 caused by the HSR.

𝜔2 > 0: Demographic Agglomeration

Regression Discontinuity Design (RDD)

(1) (2)

Intercept 3.918*** 4.709***

𝒕 0.504%*** 0.464%***

𝟏𝑯𝑹𝒕 0.489%* Number of

Observations 17 17

F-Statistic 1683.50 1148.34

𝑹𝟏 0.991 0.994

• Significance level: * p < 0.05, ** p < 0.01, *** p < 0.001.

Data Source: Chinese Statistical Association (Taiwan)

Outlines

Gravity Model + Interaction Terms Standard Deviation of County-Level Population Counterfactual Analysis (Robustness Check)

Counterfactual Analysis Counterfactual: What if something that did not happen happened?

𝑙𝑙 𝑁𝑖,𝑡 = 𝜆0,𝑖 + 𝑙𝑙 𝑁−,𝑡′𝜆𝑖

where 𝑁𝑖,𝑡 is the log-level population size of HSR county 𝑖 in year 𝑡, 𝑁−,𝑡 is the set of the log-level population sizes of non-HSR counties in year 𝑡; 𝜆0,𝑖 and 𝜆𝑖 are 𝑖-specific intercept and coefficient.

Step 1: Use R-squared to select the best set of predictor for 𝑙𝑙 𝑁𝑖,𝑡 for 𝑡 in county 𝑖’s pre-HSR period using 𝑚 out of 𝐾 non-HSR counties, denoted the best set for each 𝑚 = 1, … ,𝐾 by 𝑀(𝑚)∗.

Step 2: From 𝑀(1)∗, … ,𝑀(𝐾)∗, choose 𝑀(𝑚)∗ with the smallest corrected Akaike Information Criterion (AICC; Hurvich and Tsai, 1989). Use this 𝑀(𝑚)∗ to estimate 𝜆0,𝑖 and 𝜆𝑖 for every HSR county 𝑖.

Step 3: Use observed 𝑁−,𝑡 (for 𝑡 in county 𝑖’s post-HSR period) and estimated 𝜆0,𝑖 and 𝜆𝑖 to predict 𝑁𝑖,𝑡 (post-HSR 𝑡) if there were no HSR station in county 𝑖 at all.

Counterfactual Analysis Focus on RED Counties BLUE: HSR 2007+ RED: HSR 2016+ GREEN: NON-HSR

All Counties in Red are Non-urbanized Areas.

Counterfactual Analysis Results

Changhua County 彰化縣

Miaoli County 苗栗縣

Yunlin County 雲林縣

𝑹𝟏 = 𝟎.𝟗𝟗𝟗𝟏 𝑨𝑨𝑪𝑪 = −𝟏𝟏𝟐

Disadvantaged

𝑹𝟏 = 𝟎.𝟗𝟗𝟗𝟗 𝑨𝑨𝑪𝑪 = −𝟏𝟏𝟎

Disadvantaged

𝑹𝟏 = 𝟎.𝟗𝟏𝟗𝟏 𝑨𝑨𝑪𝑪 = −𝟏𝟏𝟏

Disadvantaged

Data Source: Chinese Statistical Association (Taiwan)

Conclusions

Taiwan’s HSR has significantly increased the migrating population between different counties. (Hypothesis I)

There are more increases in the migrating population from non-urbanized areas to urbanized areas than in the opposite direction after HSR’s availability. (Hypothesis II)

Encountering the trend of Demographic Agglomeration, the authority of Taiwan should adopt the policies that help improve the economic vitality of the suburban areas so as to alleviate the demographic inequality.

Future Research Are the demographic effects exerted by HSR permanent

as assumed by the “Gravity Model+ Interaction Term”? In reality, this could be misleading because those effects might only

last for several years.

If there were Taiwan’s economic data (e.g. GDP) decomposed into county level, it would have been better to control the economic factors behind inter-county migrations.

Long-run effects on rural areas are also important. Studies in the future might be able to conduct counterfactual

analyses including more post-HSR observations (instead of just two years, i.e. 2016 and 2017, in my analysis).