Intercity Passenger Rails: Facilitating the Spatial Spillover Effects of Population and Employment Growth in the United States, 2000–2010

Abstract

This research examines the association that intercity passenger rails have with population and employment growth at the county level in the continental United States from 2000 to 2010. This research adopts an integrated spatial regression approach that incorporates both spatial lag and spatial error dependence. The data come from the U.S. Census Bureau, the Bureau of Transportation Statistics, the Land Developability Index, and the National Atlas of the United States. Population and employment change are regressed on intercity passenger rails, controlling for 14 socioeconomic variables. Intercity passenger rails are measured by the number of intercity passenger rail terminals in each county. The results suggest that the associations that intercity passenger rails have with population and employment change are both direct and indirect. Intercity passenger rails have a negative and direct association with population and employment change from 2000 to 2010. The Great Recession during this period may have compelled people to move out of their home county in search of jobs; having intercity passenger rails facilitated this process. The results also indicate that intercity passenger rails have a positive and indirect association with population and employment change. Population and employment change in one county influences those in the adjacent counties. This indirect association shows the spatial spillover effect of population and employment growth through passenger rails. The indirect association does not come from within the county; rather, it is a spread effect from its neighbors. This research suggests that intercity passenger rails, although built long ago, still play an important role in facilitating the spread of change and the integration of local communities into a larger regional economy.

Research on the coopetition relationship and the development of city-industry integration of urban agglomeration under the effect of HSR

“…Some of them have used a variety of approaches to study the influence of HSR construction from the viewpoint of population and employment growth, environmental impacts, spatial and temporal structural characteristics, and urban consumption. Kasu et al used an integrated spatial regression model based on spatial lags and spatial error correlation to study the relevance between intercity passenger rail and population growth and employment growth in the continental United States (Kasu and Chi, 2018). Hiramatsu studied cities in the high-speed rail route in Kyoto, Japan, and developed an interregional computational general equilibrium model based on the behavioral patterns of consumers and producers, which explained the impact of high-speed rail construction on the growth of population and employment in different cities (Hiramatsu, 2018).…”

Fishing trip cost modeling using generalized linear model and machine learning methods – A case study with longline fisheries in the Pacific and an application in Regulatory Impact Analysis

“…Although OLS with log transformation can be used to model skewed data, studies found that retransforming the log-scale dependent variable would introduce bias [ 17 – 19 ], thus bias correction could become necessary. But there is no single correct method to correct bias and some methods could be labor intensive [ 20 , 21 ]. To address non-normal distribution of fishing trip cost data, Das [ 11 ] and Kirkpatrick et al [ 22 ] used GLMs with trip-level and vessel-specific covariates to estimate and predict trip costs in the U.S. northeast and Atlantic commercial fisheries, respectively.…”