@article{Bullock:288512,
      recid = {288512},
      author = {Bullock, David W. and Wilson, William W.},
      title = {Factors Influencing the Gulf and Pacific Northwest (PNW)  Soybean Export Basis: An Exploratory Statistical Analysis},
      address = {2019-05-16},
      number = {1187-2019-1847},
      series = {Agribusiness and Applied Economics Report No. 788},
      pages = {39},
      month = {May},
      year = {2019},
      abstract = {Growth in the export marketing of soybeans has drawn  attention to the basis volatility in these market channels.   Indeed, there has been greater growth in soybean exports  compared to other commodities and this is due in part to  the growth of exports to China.  Concurrently, there has  been substantial volatility in the basis at the primary  U.S. export locations: the U.S. Gulf and the Pacific  Northwest (PNW).  This variability is caused by traditional  variables affecting the basis but is also influenced by  shipping costs, international competition, and inter-port  relationships.  Further, there seems to be distinct  seasonal patterns that vary across marketing years.    The  purpose of this study is to examine the impact of  supply/demand, export competition and logistical variables  on both the average level and seasonality of U.S. export  basis values for the 2004/05 through 2015/16 marketing  years (September through August for U.S. soybeans). This  study examines the impact of a wide range of supply,  demand, transportation, and other market variables upon  both the average level and seasonality (by marketing year)  of the basis at the two major U.S. export locations, Gulf  and Pacific Northwest (PNW).  The explanatory dataset  contains more variables (27) than observations (12  marketing years from 1994/95 through 2015/16); therefore,  it presents challenges from both a sparsity and a  multicollinearity perspective.  To address these issues, a  statistical regression technique, called partial least  squares (PLS) is utilized.  This technique has advantages  over using principal components regression (PCR) since  derivation of the components is directed towards maximizing  the covariance between the dependent (Y) and explanatory  (X) variable sets rather than just explaining the variance  of X. Seasonality is investigated in this study utilizing  agglomerative hierarchal clustering (AHC) to group similar  marketing years by seasonal pattern called seasonal  analogs.  These seasonal analogs were then related to the  explanatory variable set using a two-sample statistical  test (Lebart, Morineau and Piron 2000) that compares the  means of a subset and its parent set to explain the impact  of the explanatory variables. The results indicate that the  average market year level of the basis is primarily  influenced by export competition from Brazil and export  demand – particularly from China; however, domestic demand  (soybean crush) also has some influence.  Rail  transportation costs to both the Gulf and PNW have an  influence on the basis level; however, barge and ocean  freight rates appear to not have a significant influence on  the level of the basis.  Application of AHC resulted in the  identification of 5 and 4 distinct analogs (over the 12  marketing years in the dataset) for the Gulf and PNW  respectively.  Application of the two-sample mean  difference tests to the analogs indicate that the seasonal  pattern of the export basis is more heavily influenced by  internal logistical conditions (late railcar placement and  secondary railcar values), pace of farmer marketings,  transportation cost differentials (between ports), and  individual port export activity (ships in port and export  inspections) rather than international and domestic  demand.},
      url = {http://ageconsearch.umn.edu/record/288512},
      doi = {https://doi.org/10.22004/ag.econ.288512},
}