Analysis of Gas Prices in Howard County, Maryland
Download 0.59 Mb.
|
- Navigate this page:
- Figure : Regression Analysis for All Counties, 2012
- Statically Significant at 5% (Y/N)
3.2 2012 Cross-Sectional AnalysisTo determine the impact of the land planning characteristics on gas prices, RESI decided to look more closely at a one-year period across gas stations and jurisdictions. For this analysis, RESI created new variables to account for specific data within 2012. New variables introduced included the following:
Unlike the other jurisdictions RESI examined, Howard County does not contain any incorporated areas. As a result, RESI omitted the gas stations in incorporated areas from the analysis. Reviewing the data, RESI determined that the gas prices were highest within Montgomery County, followed by Howard County. Further review of Howard County revealed that Columbia, Maryland, had the highest gas prices within Howard County. The gas prices for stations in Columbia, Maryland, for 2012 at times equaled or exceeded those of Montgomery County retailers’ prices in some instances. The station density variable, stations per 1,000 residents, was dropped from this analysis due to multicollinearity issues between the variable and “distance to next station.”2 Due to the limitation of annual data for the land planning piece, RESI decided to use observed weekly data for 2012 across the eight regions and averaged the pricing data over the 52 weeks. A total of over 900 gas stations were observed in the model for 2012.3 The model presented in Section 3.1 transformed into the following: 
For more information or a reference to the definition of the variables above, please refer to beginning of the report for the acronyms and abbreviations list. RESI performed the basic analysis for all counties and all brands first. The results are reported in Figure 5. Figure : Regression Analysis for All Counties, 2012
Sources: RESI, OPIS, U.S. Census Figure 5 shows the impact multipliers for given variables against the log variable “margin.” In this linear-linear model, one can read the above impacts as follows: “For every additional mile away from a major highway a gas station is, then the margins decrease by approximately $0.01.” Rack price is a little harder to determine, since rack prices hardly ever change by one dollar in the short term. Instead, one is more likely to see a 10 cent increase in the rack price of unleaded gasoline during a short period. Given this, one would say “If rack prices increase by $0.10, then gas retailer’s margins will decrease by approximately $0.11.”4 To read the dummy variable “Dummy—located within Columbia, MD” above, the reader would interpret it as follows: “If a retailer is located within Columbia, their margins will be approximately $0.11 higher than other retailers within the eight jurisdictions observed in this model.”
“If a retailer is located within a conditional use permit zone, margins on average margins will be approximately $0.01 higher than those located within permitted zones.” In conclusion, the variables included in both the 2012 cross-sectional and annual analyses are consistent across the two analyses, with the exception of percent unbranded. Directory: Media Media -> Applications to Drill Media -> Unicef voices of Youth Chat Theme: Children and aids nigeria and Zimbabwe 13 October 2006 Background Media -> Tsunami Terror Alert: Voices of Youth Media -> Biblical Eschatology Presentation by: D. Paul Beck May 4, 2016 Ground Rules Media -> Guide to completing the collection using the Omnibus system Media -> The milk carton kids Media -> Events Date and Location Media -> The Gilded Age: The First Generation of Historians by H. Wayne Morgan University of Oklahoma, April 18, 1997 Media -> Analysis of Law in the United Kingdom pertaining to Cross-Border Disaster Relief Prepared by: For the 30 June 2010 Foreword Media -> Cuba fieldcourse 2010 Download 0.59 Mb. Share with your friends:
|