Analysis of Gas Prices in Howard County, Maryland
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- 3.1 Annual Analysis
3.0 Economic AnalysisTraditional economic analysis would hint to researchers to investigate key elements such as gas prices, household income, expectations, and market share or competition to determine the potential gas price within an area. RESI took a traditional approach running an ordinary least squares model based on preconceived supply and demand factors that may influence gas prices. The results and statistical significance of each variable have been recorded in the following subsections. 3.1 Annual AnalysisRetail prices of gas over eight counties within Maryland provided RESI with a potential dependent variable for analysis. RESI reviewed the prices across the eight counties for 2012 to determine if any difference existed through simple observation. Reviewing over 1,000 gas stations across eight counties, RESI determined an average annual retail price for each county during 2012 in Figure 2. Figure : Average Annual Retail Prices, 2012
Sources: RESI, OPIS Reviewing Figure 2, RESI was able to determine that some price difference existed across counties, but further analysis was necessary to determine if this was a one-time event or an annual trend. RESI performed an econometric analysis of the change in prices over time across the eight counties on an annual basis first. To determine the factors that may impact the price, RESI researched previous studies concerning the differentiation on gas prices across regions and brands. Studies stated that increases in input costs such as labor, wholesale costs, and taxes at times may add to the rapid rise in retail gasoline prices; however, as the costs decrease, gas prices will be slow to decrease in response.1 Other statistically significant variables included household income as a proxy for the wealth of a county, population change, stations per capita, and percentage of unbranded stations per capita. To conduct the analysis, data was gathered for a period from 2002 to 2012. Data presented over time is termed as “time series” data in economics. The data collected spanned across Baltimore City and Anne Arundel, Baltimore, Carroll, Frederick, Harford, Howard, and Montgomery Counties. The review of more than a single jurisdiction is termed “cross-section,” meaning that the data is reported across various jurisdictions. Using this data, RESI ran the following time series cross-section panel data model: 
Where  represents the cross-section identifier, in this case the jursidiction, and  represents the year. Variable descriptions and discussion are available in Appendices A.1 and A.3.
Figure : Annual Analysis Regression on Margin
Sources: RESI, OPIS, U.S. Census Figure 3 highlights the annual analysis on the primary economic factors for the annual regression. The model predicts approximately 87 percent of variation based on non-land planning variables. At the 95 percent confidence level, RESI’s regression shows that there is some difference on the retailer’s margins for specific variables annually. When reading the table above for the variables such as “Percent Unbranded,” the results are the following: for every one percent increase in the percent of unbranded gas stations in a region, there is a 0.18 percent decrease in margins for a retailer. The variations of the locations in this model are represented through dummy variables. For example, if a retailer is located in Anne Arundel County, then the dummy variable for Anne Arundel was equal to one. For all other dummy variables, the value was equal to zero. If all the dummy variables equal zero, then the retailer was located in Howard County. More information on interpreting dummy variables is located in Appendix A.3. Since reading the impact of dummy variables is not as straightforward as the interpretation of other numerical figures, Figure 4 displays all the margin differences for each county dummy below in reference to their difference from Howard County. One would read Figure 4 as “retailers in X county would see margins X percent less (greater) than Howard County.” Figure : Margin Differences by County
Sources: RESI, OPIS In Figure 4, one would interpret the dummy variable for Anne Arundel as “retailer margins in Anne Arundel County are 32.5 percent less than those in Howard County” after using the conversion formula. The percentage differences are presented in Figure 4 for convenience. The only jurisdiction where gasoline retailers recorded receiving higher margins on average compared to Howard County is Montgomery County. The estimates are consistent with what RESI saw in the data reported by the Oil Price Information Service (OPIS). The regression results show that the model could explain nearly 87 percent of the change in margins for a retailer. In the following section, RESI completed a more thorough analysis of a single year for 52 weeks across the eight jursidictions and added land planning components. 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:
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