Exploring the Impact of Transportation Infrastructure Construction on China's Economic Growth

1.Introduction

With the rapid growth of China's economy, domestic infrastructure construction has been newly developed. In 2008, the international financial crisis broke out under the influence of factors such as the poor operation of capital market funds, and China launched an economic stimulus plan to meet the challenges brought by that crisis; the government invested a considerable portion of funds in infrastructure construction projects, which strongly promoted the development of China's infrastructure construction. The construction of transportation infrastructure has driven the flow of goods and promoted the employment of workers. For a long time, the relationship between transportation infrastructure construction and economic development has been widely discussed, and scholars at home and abroad have also given arguments and explanations on different aspects.

1)Boartnet (1997) examined the relationship between transportation investment and economic development in all California counties from 1968 to 1988 and found that there were differences in the impact of transportation on regional economies. Some areas have benefited and some have been damaged.

2)Ming Liu and Yulin Liu found that when the level of transportation infrastructure reaches a certain extreme, continued investment will significantly reduce the pulling effect of transportation on the economy. Conversely, underinvestment in transportation infrastructure will also constrain local economic development.

3）Tan Jiangrong found that there is a lag in the impact of transportation infrastructure construction on economic development, and the role of transportation infrastructure in promoting economic development will gradually increase with the extension of time.

As China's economy enters a new stage, whether the Chinese government should continue to invest in transportation infrastructure has become a question. Due to the further economic development of each region, is there a difference in the economic boosting effect of transportation infrastructure? Is there a difference in the impact of railroad and road construction on the economy? Based on these questions, we choose the GDP and transportation infrastructure construction data from 2011 to 2020 to conduct the study.

2.Data and descriptive statistics

df = pd.read_csv("/work/data/data_new.csv") df2 = df.query('province!="China"')

My data has 320 entries, recording data on road and rail mileage and economic development in 23 Chinese provinces. The average road mileage is about 290,000 km and the average railroad mileage is about 7,400 km. In terms of economic development indicators, the GDP per capita is 55,000 RMB.

df.loc[:, df2.columns != 'year'].describe().round(2)

3.Exploratory data analysis

1. China's overall GDP from 2011-2010

The figure shows China‘s overall GDP from 2011 to 2010

px.line(df2, x='year', y='GDP', hover_name='province', line_group='province', color='region', )

px.line(df2, x='year', y='GDP', hover_name='province', color='province', facet_col= 'region', facet_col_wrap= 2, height= 800)

df_map = pd.read_csv("/work/data/data_new.csv") df2020=df_map.query("year==2020") df_map=df2020[['省份','GDP']] map = ( Map() .add('', df_map.values.tolist(), 'china',is_roam=False,is_map_symbol_show=True) .set_global_opts( # 设置标题 title_opts=opts.TitleOpts(title="China GDP Map"), # 设置标准显示 visualmap_opts=opts.VisualMapOpts(max_=110000, is_piecewise=False), ) # 系列配置项 # 关闭标签名称显示 .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) ) map.render_notebook()

Through the map above, we can find that there is a regional imbalance in China's economic development, with the eastern and central provinces being more economically developed.

tl = Timeline() # 播放的速度，单位毫秒（ms） tl.add_schema(play_interval=300, symbol='emptydiamond') for i in range(2011, 2021): dfi=df2.query('year==@i') map0 = ( Map() .add('', dfi[['省份','GDP']].values.tolist(), 'china',is_roam=False,is_map_symbol_show=False) .set_global_opts( title_opts=opts.TitleOpts(title='{}National provincial GDP(Trillion)'.format(i)), visualmap_opts=opts.VisualMapOpts(max_=110000,is_piecewise=True), ) .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) ) tl.add(map0, '{}年'.format(i)) tl.render_notebook()

With the above MAP, we can find that from 2011 to 2020, the economic development of the central and eastern provinces is faster.

dfbar=df2.sort_values(by='Road mileage',ascending=True) fig = px.scatter(dfbar, #dataframe x = 'province', #x-values column y = 'Road mileage', #y-values column facet_col= 'region', facet_col_wrap= 2, animation_frame = 'year', ) fig.show()

dfbox=df2.sort_values(by='Railroad mileage',ascending=True) px.box( dfbox, x="Railroad mileage", color="region", hover_name= 'province', animation_frame = 'year' )

px.box( df2, x="Road mileage", color="region", hover_name= 'province', animation_frame = 'year' )

China's rail and road mileage has grown in the decade from 2011 to 2020.

tl = Timeline() # 播放的速度，单位毫秒（ms） tl.add_schema(play_interval=300, symbol='emptydiamond') for i in range(2011, 2021): dfi=df2.query('year==@i') sort_info = dfi.sort_values(by='Road mileage', ascending=True) map0 = ( Bar() .add_xaxis(sort_info['province'].values.tolist()) .add_yaxis('', sort_info['Road mileage'].values.tolist()) .reversal_axis() .set_series_opts(label_opts=opts.LabelOpts(position="right")) .set_global_opts( title_opts=opts.TitleOpts(title='{}National provincial road mileage'.format(i)), datazoom_opts=[opts.DataZoomOpts(is_show=True, orient='vertical')], ) ) tl.add(map0, '{}年'.format(i)) tl.render_notebook()

tl = Timeline() # 播放的速度，单位毫秒（ms） tl.add_schema(play_interval=300, symbol='emptydiamond') for i in range(2011, 2021): dfi=df2.query('year==@i') sort_info = dfi.sort_values(by='Railroad mileage', ascending=True) map0 = ( Bar() .add_xaxis(sort_info['province'].values.tolist()) .add_yaxis('', sort_info['Railroad mileage'].values.tolist(),itemstyle_opts = opts.ItemStyleOpts(color="blue"),) .reversal_axis() .set_series_opts(label_opts=opts.LabelOpts(position="right")) .set_global_opts( title_opts=opts.TitleOpts(title='{}National provincial railroad mileage'.format(i)), datazoom_opts=[opts.DataZoomOpts(is_show=True, orient='vertical')], ) ) tl.add(map0, '{}年'.format(i)) tl.render_notebook()

Transportation infrastructure development varies widely from province to province in China, but is generally growing.

4.Regression analysis

4.1Model

Based on the data I developed the following model with the independent variable of rail miles/road miles and the dependent variable of GDP.

Model:ln(GDP)=α+βln(Road mileage)+u

4.2Visualiztion

4.2.1China

df1 = df.query('province=="China"') fig=px.scatter(df1, x='Road mileage', y='GDP', log_x='true', log_y='true', hover_name='province', trendline="ols", trendline_scope="overall" ) fig.show()

According to the country's economic development plan, China is divided into the central, eastern, western, and northeastern parts.

4.2.2Road mileage and GDP

df2 = df.query('province!="China"') fig=px.scatter(df2, x='Road mileage', y='GDP', log_x='true', log_y='true', color='province', hover_name='province', trendline="ols", trendline_scope="overall", facet_col= 'region', facet_col_wrap= 2, ) fig.show()

4.2.3Railroad mileage and GDP

df2 = df.query('province!="China"') fig=px.scatter(df2, x='Road mileage', y='GDP', log_x='true', log_y='true', color='province', hover_name='province', trendline="ols", trendline_scope="overall", facet_col= 'region', facet_col_wrap= 2, ) fig.show()

4.2.4Conclusion

根据散点图，可以发现，铁路里程和公路里程和GDP数据存在正相关关系在中国的各个区域。

4.3Regression analysis of GDP and road mileage

4.3.1Regression analysis of GDP and road mileage

y = df2['lnGDP'] X = df2['lnRM'] X_withconst = sm.add_constant(X) OLS = sm.OLS(y, X_withconst).fit() print(OLS.summary())

4.3.2Regression analysis of GDP and railroad mileage

y = df2['lnGDP'] X = df2['lnRRM'] X_withconst = sm.add_constant(X) OLS = sm.OLS(y, X_withconst).fit() print(OLS.summary())

4.3.3Regression analysis of GDP and road mileage in the Center

dfcenter=df2.query('region=="center"') y = dfcenter['lnGDP'] X = dfcenter['lnRM'] X_withconst = sm.add_constant(X) OLS = sm.OLS(y, X_withconst).fit() print(OLS.summary())

4.3.4Regression analysis of GDP and railroad mileage in the Center

y = dfcenter['lnGDP'] X = dfcenter['lnRRM'] X_withconst = sm.add_constant(X) OLS = sm.OLS(y, X_withconst).fit() print(OLS.summary())

4.3.5Regression analysis of GDP and road mileage in the east

dfeast=df2.query('region=="East"') y = dfeast['lnGDP'] X = dfeast['lnRM'] X_withconst = sm.add_constant(X) OLS = sm.OLS(y, X_withconst).fit() print(OLS.summary())

4.3.6Regression analysis of GDP and railroad mileage in the east

dfeast=df2.query('region=="East"') y = dfeast['lnGDP'] X = dfeast['lnRRM'] X_withconst = sm.add_constant(X) OLS = sm.OLS(y, X_withconst).fit() print(OLS.summary())

4.3.7Regression analysis of GDP and road mileage in the west

dfwest=df2.query('region=="west"') y = dfwest['lnGDP'] X = dfwest['lnRM'] X_withconst = sm.add_constant(X) OLS = sm.OLS(y, X_withconst).fit() print(OLS.summary())

R4.3.8egression analysis of GDP and railroad mileage in the west

dfwest=df2.query('region=="west"') y = dfwest['lnGDP'] X = dfwest['lnRRM'] X_withconst = sm.add_constant(X) OLS = sm.OLS(y, X_withconst).fit() print(OLS.summary())

4.3.9Regression analysis of GDP and road mileage in the Northeast

dfnortheast=df2.query('region=="Northeast"') y = dfnortheast['lnGDP'] X = dfnortheast['lnRM'] X_withconst = sm.add_constant(X) OLS = sm.OLS(y, X_withconst).fit() print(OLS.summary())

4.3.10Regression analysis of GDP and railroad mileage in the Northeast

dfnortheast=df2.query('region=="Northeast"') y = dfnortheast['lnGDP'] X = dfnortheast['lnRRM'] X_withconst = sm.add_constant(X) OLS = sm.OLS(y, X_withconst).fit() print(OLS.summary())

5.Concluding remarks

Through regression analysis, I got the following conclusions.

1. There is a regional imbalance in China's economic development, with the eastern region being the most developed and the western region is less developed. This regional imbalance also exists in transportation infrastructure development.

2. In general, transportation infrastructure construction (railroad construction and road construction) positively affects economic growth without considering the effects of other control variables.

3. Overall, railroad construction impacts China's economic development more than road construction.

4. In northeastern China, road construction hurts economic growth.

5. There are regional differences in the impact of transportation infrastructure on economic development, with transportation infrastructure construction having a more substantial effect on economic development in the central and western regions.

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