Evaluating the spatial spillover effects of transportation infrastructure on agricultural output across the United States
Introduction
Since the late 1980s, research measuring the influence of transportation infrastructure on economic output and productivity at various geographical levels, i.e. national, regional, and less aggregated jurisdictional areas, has quickly emerged (Aschauer, 1989, Munnell and Cook, 1990, Moomaw et al., 1995, Fernald, 1999, Li and Shum, 2001, Lakshmanan, 2011). Among the studies, the role of spatial spillover associated with the infrastructure–economy relationship has received increasing attention. Most studies considering the economic effects of transportation infrastructure have supported a positive spillover effect (Dundon-Smith and Gibb, 1994, Pereira and Roca-Sagales, 2003, Pereira and Andraz, 2004, Cohen and Paul, 2004, Cantos et al., 2005, Gutierrez et al., 2010), while other studies have found negative spillover effects (Boarnet, 1998, Cohen and Monaco, 2007, Sloboda and Yao, 2008). A few studies have also found mixed or no spillover effects of transportation infrastructure on economies in different sectors (Holtz-Eakin and Schwartz, 1995, Kelejian and Robinson, 1997, Jiwattanakulpaisarn et al., 2010) (see Table 1 for a summary).
Both positive and negative spatial spillover of transportation infrastructure on the economic activities can be feasible. The construction of an interstate highway, for instance, could improve the network by connecting states efficiently, thus leading to the redistribution of existing resources for production (Cohen, 2007, Jiwattanakulpaisarn et al., 2009). An improved transportation network in a state potentially can provide a more efficient and integrated transportation network to the state and, consequently, contribute positively to the economic activities in its spatially related states. Conversely, economic activities could be reallocated from states with poor transportation infrastructure to states with well-maintained transportation systems. Thus, the construction or improvement of transportation infrastructure in one state could adversely affect the output of private sectors in neighboring states with less developed transportation infrastructure (Boarnet, 1998).
Previous studies evaluating the output and productivity impact of transportation infrastructure in the agricultural sector have not taken the spatial spillover effect of transportation infrastructure into account (Antle, 1983, Craig et al., 1997, Felloni et al., 2001, Benin et al., 2009, Onofri and Fulginiti, 2008, Zhang and Fan, 2004). Lack of such analysis in the U.S. agricultural sector is surprising given the importance of agriculture in the U.S. economy and its strong dependence on infrastructure. Currently, improvements in the deteriorating transportation infrastructure, primarily the road system, are long overdue across the nation and still under extensive debate due to budget deficits. Therefore, conducting a more comprehensive and broad evaluation of transportation infrastructure impacts on output in the agricultural sector is timely and critical.
The objective of this study is to provide a measure of the contribution of transportation infrastructure and its spatial spillover effect on U.S. agricultural output. Results from this research have the potential to assist policy makers in allocating transportation infrastructural investments by providing better estimates of how these investments could impact states’ agricultural sectors. To accomplish this objective, it is hypothesized that transportation infrastructure investment in a state has a statistically significant spillover effect on neighboring states. This hypothesis is empirically tested using a spatial modeling framework that considers the spatial interaction of both dependent and explanatory variables within the framework of the spatial Durbin model (SDM) (LeSage and Pace, 2009). The SDM is useful in testing the hypothesis because the model captures the contribution of transportation infrastructure in a given region to agricultural output both in and outside of the region. Moreover, the SDM is the only model that produces an unbiased estimator in all possible spatial data modeling processes, according to LeSage and Pace (2009, pp. 158–159). The omitted variables problem is also less likely to be observed in the SDM because of the inclusion of spatial dependence in the explanatory variables.
The remainder of the paper is organized as follows: Section 2 presents the empirical model specification and estimation methods of SDM, followed by a summary of data in Section 3. Empirical results are discussed in Section 4, and the last section presents the findings and associated discussion.
Section snippets
Model specification
This study adopts a Cobb–Douglas production function to represent the relationship between agricultural output and input factors in a state. Transportation infrastructure in the state is incorporated in the production function as an external factor to productivity (Boarnet, 1998, Jiwattanakulpaisarn et al., 2011). Transportation infrastructure is treated as a production function frontier “shifter,” which increases the efficiency of other inputs. This relationship is elaborated by the following
Data
This analysis covered panel data for 44 states in the United States4 during the period from 1981 to 2004. Agricultural output and input (excluding transportation infrastructures) data for each state were obtained from the Economic Research Service (ERS), U.S. Department of Agriculture
Empirical results
Table 3 presents the results of (1) a pooled OLS model, (2) an OLS model with spatial fixed effects, (3) an OLS model incorporating temporal fixed effects, and (4) an OLS model considering both fixed effects (referred to as “Model (4)” here and below), respectively. In this study, statistical significance at the 5% level is denoted with one asterisk in the tables; those variables and test statistics are henceforth referred to as “significant” in the discussion below. The null hypothesis of the
Conclusion and discussion
The effects of transportation infrastructure on agricultural output for 44 continuous U.S. states from 1981 to 2004 are evaluated in this study. The SDM based on four different weight matrices is employed to accommodate spatial dependence in both dependent and independent variables. Findings based on the model estimates suggest that road disbursement has a significant positive direct effect on a state’s agricultural output. A 1% increase in the investment and maintenance in roads in one state
Acknowledgments
The authors are grateful for the two anonymous reviewers and the Editor for their valuable comments and suggestions on the earlier version of the manuscript.
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