Dataset accompanying the publication "Spatial spillover effects of agricultural transport costs in Peru" (Land 2022, 11, 58). The role of agricultural transport costs in core-periphery structures has habitually been ignored in New Economic Geography (NEG) models. This is due to the convention of treating the agricultural good as the numéraire, thus implying that agricultural transportation costs are assumed to be zero in these models. For more than three decades, this has been the standard setting in spatial equilibrium analysis. This paper examines the effects of agricultural transport costs on the spatial organisation of regional structures in Peru. In doing so, Krugman’s formulation of iceberg transport costs is modified to introduce agricultural transport costs into the dynamic of the NEG models. We use exploratory spatial flow data analysis methods and non-spatial and spatial origin-destination flow models to explore how the regional spatial structure changes when real transportation data for agricultural goods are included into the iceberg transport costs formulation. We show that agricultural transport costs generate flows that are systematically associated with flows to or from nearby regions generating thus the emergence of spatial spillovers across Peruvian regions. The results of the paper support the contention that NEG models have overshadowed the role of agricultural transport costs in determining the spatial configuration of economic activities.

Non-spatial and spatial SAR origin-destination flow models.

Copy the contents of the ZIP file “AgrFlows” in a folder. The following scripts will run when setting the Matlab path in this folder: 1) Script in file 1mcmc_2018.m calculates the coefficient estimates for the basic and spatial autoregressive models estimated by OLS and Bayesian MCMC, respectively. These results are showed in Table 4 of the paper. 2) Script in file 2calc_eff_2018_noesp.m calculates the effect estimates for the basic non-spatial models estimated by OLS. These results are showed in column 2 of Table 5 of the paper. 3) Script in file 3calc_eff_2018_esp.m calculates the effects estimates for spatial autoregressive interaction models estimated by Bayesian MCMC. These results are showed in column 3 of Table 5 of the paper.

Spatial spillover effects of agricultural transport costs in Peru. The dependent variable is approximated by the agricultural transport costs of the agricultural trade flows, which are calculated based on the Agricultural Trade Flow Registry – Ministry of Agriculture and Irrigation of Peru, 2013. The independent variables come from the National Institute of Statistics and Informatics (INEI), and the Ministry of Transport and Communications (MTC), 2013. Observations = 625 Variables = 8 Year = 2013

How to cite the database (APA style): Herrera-Catalán, P.; Chasco, C.; Torero, M. (2021). Spatial spillover effects of agricultural transport costs in Peru [Data set]. (doi: 10.23728/b2share.51be2ed099644f67a39e76e76ed6ef2c). Source: Herrera-Catalán, P.; Chasco, C.; Torero, M. Spatial Spillover Effects of Agricultural Transport Costs in Peru. Land 2022, 11, 58. https://doi.org/10.3390/land11010058