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Price gouging versus price reduction in retail gasoline markets during Hurricane Rita
Economics Letters 105 (2009) 11–13
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Economics Letters j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e c o l e t
Price gouging versus price reduction in retail gasoline markets during Hurricane Rita Henry Neilson ⁎ Rice University, 9 Sunset Blvd., Houston, TX 77005, United States
a r t i c l e
i n f o
Article history: Received 21 January 2008 Received in revised form 5 April 2009 Accepted 8 April 2009 Available online 18 April 2009
a b s t r a c t Using extensive hand-collected retail gas prices, I show that there was no price gouging in Bryan/College Station during Hurricane Rita. Instead, the retail price markup dropped by twelve cents during the hurricane, despite most stations running out of gas. © 2009 Elsevier B.V. All rights reserved.
Keywords: Price gouging Hurricanes Fair pricing Gasoline pricing JEL classification: D4 L1
1. Introduction Hurricanes bring stories of both price gouging and price reductions. For example, Rotemberg (2007) recounts the story of a Florida hotel that charged guests triple the posted price after Hurricane Charley. On the other hand, in that same paper Rotemberg also tells how other Florida hotels “lowered their rates, allowed pets into rooms…and gave free food to hungry guests,” during the same hurricane (Rotemberg, 2007, p. 13). On the Gulf Coast during 2005 when Hurricanes Katrina and Rita struck, a general air of benevolence occupied the mindsets of most people. My study looks at which of these two behaviors is prevalent in gas station owners during Hurricane Rita. To determine whether stations gouge or reduce prices, I use a unique hand-collected dataset. My dataset spans two months and the effects of Hurricane Rita. Prices were collected at least once each day and at about the same time each day from 28 stations around Bryan and College Station, Texas. In my study, I find no evidence of price gouging at the retail level during Hurricane Rita.1 On the contrary, I find evidence that stations actually reduced their markups by twelve cents during the impact period. My working definition of price gouging is an upward departure from a firm's standard pricing formula during a disaster, such as Hurricane Rita. For example, if a firm normally uses a $1.00 markup over the wholesale price but decides to use a $1.50 markup during a hurricane, that might be construed as price gouging. On the other
⁎ Tel.: +1 713 348 4662; fax: +1 713 348 5929. E-mail address: [email protected]. 1 The data are inappropriate for examining price gouging at the wholesale level. 0165-1765/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.econlet.2009.04.015
hand, if they only used a $0.50 markup during a hurricane, this could be interpreted as benevolence as in Rotemberg (2006). This working definition of price gouging is consistent with that of both the other literature and legislation. Rapp (2006) notes that there is no single definition of price gouging, though he does observe that many definitions include several common aspects: there is an increased price or an element of coercion or opportunism on the part of the seller in regard to the consumer. With regard to gas prices, price gouging has commonly been defined as an increase above a set percentage of the pre-spike average. The Federal Price Gouging Prevention Act (H.R.1252), which was passed by the House and forwarded to the Senate in May, 2007, defines price gouging as setting a price that either “is unconscionably excessive,” or “indicates the seller is taking unfair advantage of the circumstances related to an energy emergency to increase prices unreasonably.” Twenty-eight states also have similar anti-price gouging legislation.2 As for price reductions, Rotemberg (2006) creates a model in which firms are altruistic because consumers will become angry and act against a firm if they can reject the hypothesis that the firm is at least somewhat benevolent towards them. He later notes that price gouging not only angers those who actually pay more but also those who merely observe the excessive prices (Rotemberg, 2007). My study contributes valuable information to several groups. First and foremost, my study provides information to legislatures in regard to the ongoing effort to legally discourage price gouging. Second, my study gives empirical, as opposed to anecdotal, evidence supporting
2
Texas is one of the states that has passed legislation against price gouging.
12
H. Neilson / Economics Letters 105 (2009) 11–13 Table 1 Regression results for retail gasoline prices. Variable
Baseline Model
Alternate 1
Alternate 2
Constant Wholesale price Rita Supply Disruption dummy Number of stations without gas R-squared
217.99 (1.18) 0.269 (0.0053)
209.87 (1.24) 0.317 (0.0059) − 12.72 (0.792)
201.20 (3.70) 0.275 (0.0054)
0.472
0.514
− 0.556 (0.116) 0.476
Standard errors in parentheses. Dependent variable is retail price in cents. All coefficients are significant at the 1% level.
Table 2 Regression results for retail gasoline prices in logs.
Fig. 1. Gasoline prices in Bryan/College Station, September 5 – October 31, 2005.
Rotemberg's theories on price gouging. Lastly, my study fills in the gap in an otherwise comprehensive Federal Trade Commission (2006) report on post-Katrina gasoline price increases. The report was unable to investigate the effects on prices of stations running out of gas. My study has this data, and I found that the retail price markup falls by 0.56 cents for each station that leaves the market.
Variable
Baseline Model
Alternate 1
Alternate 2
Constant Wholesale price (ln) Rita Supply Disruption dummy Number of stations without gas R-squared
4.43 (0.021) 0.222 (0.0040)
4.43 (0.021) 0.222 (0.0040) − 0.028 (0.038)
4.40 (0.022) 0.228 (0.0041)
− 0.0026 (0.00043) 0.530
0.530
0.537
Standard errors in parentheses. Dependent variable is retail price in logs. All coefficients are significant at the 1% level.
College Station were spared from any direct weather effects of Hurricane Rita. 3. Results
2. Data Data were hand-collected from September 5, 2005, through the end of October of that year, for a total of 57 days of collection. Pergallon prices of regular unleaded gasoline were collected at about the same time and at least once every day. The data set included prices from 28 stations around the Bryan/College Station MSA. There were 114 observations during the data collection period, with a total of 2721 unleaded gas prices. I estimate that my daily driving route length was approximately 38 miles long. I used wholesale prices, measured by US Gulf Coast Conventional Gasoline Regular Spot Prices, as a supply side cost indicator.3 The retail prices ranged from $2.26 to $2.99 per gallon with a mean of $2.73. The wholesale prices ranged from $1.49 to $3.05 per gallon with a mean of $2.01. Thirty-eight cents of the average 72 cent retail markup from the wholesale price goes to the Texas and federal governments (Office of Highway Policy Research, 2006). Twenty-five of the 28 stations ran out of gas at least once between September 23 and September 28.4 None ran out of gas outside the six day window. Twelve stations had no gas on the day of Hurricane Rita's landfall, the most during the collection period. Rita made landfall as a Category 3 hurricane in the early morning hours of September 24, 2005, four weeks after Katrina devastated New Orleans. Less than two days before landfall, forecasters predicted that the eye of the storm would travel through Bryan and College Station, Texas. The storm was initially predicted to hit Houston and surrounding cities extremely hard, provoking a mass evacuation to more inland areas. Many evacuees chose to come to Bryan and College Station, although this number was not a significant percentage of the total. A much greater number traveled through the twin cities on their way to other destinations. Many businesses were affected in one way or another from the sudden influx of evacuees. In the end, Houston and Bryan/
3 Other variables, such as station characteristics, were collected but were not used as their effects are covered by the station-specific fixed-effects regression. See Neilson and Bruce (2007) for further details. 4 Stations were noted to have run out of gas when the displayed price was $0.00, there were bags over the pumps, or there were signs over the pumps.
Fig. 1 shows the average retail prices over time, along with the wholesale prices used and a line representing when Hurricane Rita made landfall. If there was any price gouging, the difference between the wholesale and retail prices should increase during the time of gouging. If station owners were altruistic, on the other hand, the spread between the retail and wholesale prices should decrease during spikes. When looking at the graph, the difference between the wholesale and retail prices clearly shrinks, and the average retail price even falls below the wholesale price. This is perhaps the most obvious evidence against retail price gouging, but there are more statistically accurate methods that confirm the same result. Table 1 shows the results of three station-specific fixed-effects regressions in levels. Table 2 shows the same regressions in logs. The dependent variable for all three regressions is the retail price in cents. Independent variables in all three regressions include the wholesale price in cents, six day-of-the-week dummy variables and a dummy variable representing the time of collection (AM or PM).5 The first regression, Baseline Model, contains no extra variables. This demonstrates how sellers determine their pricing strategy in a normal situation. Had Hurricane Rita not happened, this is most likely how sellers would have set their prices. However, as we all know, Hurricane Rita did happen. The two alternate regressions include an extra variable in each to describe the effects of Rita on pricing strategies, with a focus on the most likely times for price gouging to occur. The first added variable, Rita Supply Disruption, is a dummy variable with a ‘1’ for the dates during which any station in the dataset ran out of gas. It is apparent in Table 1 that during the impact period the stations that still had any gas reduced their markups by 12.7 cents. If price gouging had really occurred, that coefficient would have been positive. For most of the collection period, the value of the variable Number of Stations without Gas was equal to zero, except from September 23 through September 28. At one point in the collection period seventeen of the stations had run out of gas simultaneously. Both price gouging and 5 The coefficients of these variables are not reported here, as they are not central to the discussion. See Neilson and Bruce (2007) for further details.
H. Neilson / Economics Letters 105 (2009) 11–13 Table 3 Correlation coefficients for residuals on the day of landfall and several days later. Number of days out
Correlation coefficient
P-value
3 5 7
0.560 − 0.282 − 0.616
0.024 0.289 0.011
oligopoly theory predict that the coefficient of the variable should be positive. However, the results in Table 1 show that for each additional station that ran out of gas, the remaining sellers reduced their markups by half a cent. While this variable is not as statistically significant as the one in Alternate 1, it is still significant at the 99.9% confidence level. The regressions with logs provide mixed support for the above conclusions. The Rita Supply Disruption dummy variable, when run in the logarithmic regression series, maintains the same sign but is no longer statistically significant. The Number without Gas variable is negative and statistically significant, as with the levels regression. Furthermore, its coefficient, when evaluated with the average price at the time of the hurricane, implies a 0.73 cent drop in gas prices for each additional station that runs out of gas. Since there were relatively low markups during Hurricane Rita and at the same time many stations ran out of gas, there is a possibility that the stations with the lowest markups ran out of gas first. To test this, I computed the residuals for each station on the day before it ran out of gas. If exceptionally low markups led to stations running out of gas, one would expect to see large negative residuals on the day before the stations ran out of gas. Of the 26 values that I calculated, they ranged from −26.8 cents up to 12.4 cents and had a mean of − 0.62 cents.6 For comparison, the range of residuals for the entire sample was from −34.8 cents to 24.9 cents. There is no evidence that the stations had noticeably lower markups on the day before they ran out of gas. Another hypothesis worth exploring is that, in order to make up for lost profits, stations with relatively low markups during the hurricane increased their markups in the coming days or weeks. To test this, I
6
One station ran out of gas twice.
13
computed the correlations between the residuals on the day of the hurricane and the residuals three, five, and seven days later. According to the hypothesized behavior, low residuals on the day of landfall should correlate with high later markups, so the correlation coefficients should be negative. Only the sixteen stations that still had gas on September 24th, 2005, the day Rita made landfall, were used, and the correlation coefficients are reported in Table 3. As shown in the table, the evidence supports the hypothesis for seven days out from landfall, but not earlier. 4. Conclusions In my study I found evidence consistent with Rotemberg's (2006) fair pricing model among gas station owners in Bryan/College Station, Texas, during Hurricane Rita. Using station-specific fixed-effects regression analysis, I showed that not only was there no evidence of retail price gouging, but that there was actually evidence supporting the opposite. Gas station owners were surprisingly altruistic during that time of crisis. Acknowledgments I would like to thank William Neilson, Julio Rotemberg, Don Bruce, and an anonymous referee for helpful comments. References Neilson, H., D. Bruce, 2007, The geographic extent of gasoline markets, working paper, University of Tennessee. Rapp, G., 2006. Gouging: terrorist attacks, hurricanes, and the legal and economic aspects of post-disaster price regulation. Kentucky Law Journal 84, 536–550. Rotemberg, J., 2006, Fair pricing, working paper, Harvard University. Rotemberg, J., 2007, Behavioral aspects of price setting, and their policy implications, working paper, Harvard University. U.S. Federal Trade Commission, 2006, Investigation of Gasoline Price Manipulation and Post-Katrina Gasoline Price Increases (Washington, DC). U.S. Office of Highway Policy Information, 2006, Monthly Motor Fuels Reported by States, October 2005, Federal Highway Administration Publication No. FHWA-PL06-020 (Washington, DC).
Contents lists available at ScienceDirect
Economics Letters j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e c o l e t
Price gouging versus price reduction in retail gasoline markets during Hurricane Rita Henry Neilson ⁎ Rice University, 9 Sunset Blvd., Houston, TX 77005, United States
a r t i c l e
i n f o
Article history: Received 21 January 2008 Received in revised form 5 April 2009 Accepted 8 April 2009 Available online 18 April 2009
a b s t r a c t Using extensive hand-collected retail gas prices, I show that there was no price gouging in Bryan/College Station during Hurricane Rita. Instead, the retail price markup dropped by twelve cents during the hurricane, despite most stations running out of gas. © 2009 Elsevier B.V. All rights reserved.
Keywords: Price gouging Hurricanes Fair pricing Gasoline pricing JEL classification: D4 L1
1. Introduction Hurricanes bring stories of both price gouging and price reductions. For example, Rotemberg (2007) recounts the story of a Florida hotel that charged guests triple the posted price after Hurricane Charley. On the other hand, in that same paper Rotemberg also tells how other Florida hotels “lowered their rates, allowed pets into rooms…and gave free food to hungry guests,” during the same hurricane (Rotemberg, 2007, p. 13). On the Gulf Coast during 2005 when Hurricanes Katrina and Rita struck, a general air of benevolence occupied the mindsets of most people. My study looks at which of these two behaviors is prevalent in gas station owners during Hurricane Rita. To determine whether stations gouge or reduce prices, I use a unique hand-collected dataset. My dataset spans two months and the effects of Hurricane Rita. Prices were collected at least once each day and at about the same time each day from 28 stations around Bryan and College Station, Texas. In my study, I find no evidence of price gouging at the retail level during Hurricane Rita.1 On the contrary, I find evidence that stations actually reduced their markups by twelve cents during the impact period. My working definition of price gouging is an upward departure from a firm's standard pricing formula during a disaster, such as Hurricane Rita. For example, if a firm normally uses a $1.00 markup over the wholesale price but decides to use a $1.50 markup during a hurricane, that might be construed as price gouging. On the other
⁎ Tel.: +1 713 348 4662; fax: +1 713 348 5929. E-mail address: [email protected]. 1 The data are inappropriate for examining price gouging at the wholesale level. 0165-1765/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.econlet.2009.04.015
hand, if they only used a $0.50 markup during a hurricane, this could be interpreted as benevolence as in Rotemberg (2006). This working definition of price gouging is consistent with that of both the other literature and legislation. Rapp (2006) notes that there is no single definition of price gouging, though he does observe that many definitions include several common aspects: there is an increased price or an element of coercion or opportunism on the part of the seller in regard to the consumer. With regard to gas prices, price gouging has commonly been defined as an increase above a set percentage of the pre-spike average. The Federal Price Gouging Prevention Act (H.R.1252), which was passed by the House and forwarded to the Senate in May, 2007, defines price gouging as setting a price that either “is unconscionably excessive,” or “indicates the seller is taking unfair advantage of the circumstances related to an energy emergency to increase prices unreasonably.” Twenty-eight states also have similar anti-price gouging legislation.2 As for price reductions, Rotemberg (2006) creates a model in which firms are altruistic because consumers will become angry and act against a firm if they can reject the hypothesis that the firm is at least somewhat benevolent towards them. He later notes that price gouging not only angers those who actually pay more but also those who merely observe the excessive prices (Rotemberg, 2007). My study contributes valuable information to several groups. First and foremost, my study provides information to legislatures in regard to the ongoing effort to legally discourage price gouging. Second, my study gives empirical, as opposed to anecdotal, evidence supporting
2
Texas is one of the states that has passed legislation against price gouging.
12
H. Neilson / Economics Letters 105 (2009) 11–13 Table 1 Regression results for retail gasoline prices. Variable
Baseline Model
Alternate 1
Alternate 2
Constant Wholesale price Rita Supply Disruption dummy Number of stations without gas R-squared
217.99 (1.18) 0.269 (0.0053)
209.87 (1.24) 0.317 (0.0059) − 12.72 (0.792)
201.20 (3.70) 0.275 (0.0054)
0.472
0.514
− 0.556 (0.116) 0.476
Standard errors in parentheses. Dependent variable is retail price in cents. All coefficients are significant at the 1% level.
Table 2 Regression results for retail gasoline prices in logs.
Fig. 1. Gasoline prices in Bryan/College Station, September 5 – October 31, 2005.
Rotemberg's theories on price gouging. Lastly, my study fills in the gap in an otherwise comprehensive Federal Trade Commission (2006) report on post-Katrina gasoline price increases. The report was unable to investigate the effects on prices of stations running out of gas. My study has this data, and I found that the retail price markup falls by 0.56 cents for each station that leaves the market.
Variable
Baseline Model
Alternate 1
Alternate 2
Constant Wholesale price (ln) Rita Supply Disruption dummy Number of stations without gas R-squared
4.43 (0.021) 0.222 (0.0040)
4.43 (0.021) 0.222 (0.0040) − 0.028 (0.038)
4.40 (0.022) 0.228 (0.0041)
− 0.0026 (0.00043) 0.530
0.530
0.537
Standard errors in parentheses. Dependent variable is retail price in logs. All coefficients are significant at the 1% level.
College Station were spared from any direct weather effects of Hurricane Rita. 3. Results
2. Data Data were hand-collected from September 5, 2005, through the end of October of that year, for a total of 57 days of collection. Pergallon prices of regular unleaded gasoline were collected at about the same time and at least once every day. The data set included prices from 28 stations around the Bryan/College Station MSA. There were 114 observations during the data collection period, with a total of 2721 unleaded gas prices. I estimate that my daily driving route length was approximately 38 miles long. I used wholesale prices, measured by US Gulf Coast Conventional Gasoline Regular Spot Prices, as a supply side cost indicator.3 The retail prices ranged from $2.26 to $2.99 per gallon with a mean of $2.73. The wholesale prices ranged from $1.49 to $3.05 per gallon with a mean of $2.01. Thirty-eight cents of the average 72 cent retail markup from the wholesale price goes to the Texas and federal governments (Office of Highway Policy Research, 2006). Twenty-five of the 28 stations ran out of gas at least once between September 23 and September 28.4 None ran out of gas outside the six day window. Twelve stations had no gas on the day of Hurricane Rita's landfall, the most during the collection period. Rita made landfall as a Category 3 hurricane in the early morning hours of September 24, 2005, four weeks after Katrina devastated New Orleans. Less than two days before landfall, forecasters predicted that the eye of the storm would travel through Bryan and College Station, Texas. The storm was initially predicted to hit Houston and surrounding cities extremely hard, provoking a mass evacuation to more inland areas. Many evacuees chose to come to Bryan and College Station, although this number was not a significant percentage of the total. A much greater number traveled through the twin cities on their way to other destinations. Many businesses were affected in one way or another from the sudden influx of evacuees. In the end, Houston and Bryan/
3 Other variables, such as station characteristics, were collected but were not used as their effects are covered by the station-specific fixed-effects regression. See Neilson and Bruce (2007) for further details. 4 Stations were noted to have run out of gas when the displayed price was $0.00, there were bags over the pumps, or there were signs over the pumps.
Fig. 1 shows the average retail prices over time, along with the wholesale prices used and a line representing when Hurricane Rita made landfall. If there was any price gouging, the difference between the wholesale and retail prices should increase during the time of gouging. If station owners were altruistic, on the other hand, the spread between the retail and wholesale prices should decrease during spikes. When looking at the graph, the difference between the wholesale and retail prices clearly shrinks, and the average retail price even falls below the wholesale price. This is perhaps the most obvious evidence against retail price gouging, but there are more statistically accurate methods that confirm the same result. Table 1 shows the results of three station-specific fixed-effects regressions in levels. Table 2 shows the same regressions in logs. The dependent variable for all three regressions is the retail price in cents. Independent variables in all three regressions include the wholesale price in cents, six day-of-the-week dummy variables and a dummy variable representing the time of collection (AM or PM).5 The first regression, Baseline Model, contains no extra variables. This demonstrates how sellers determine their pricing strategy in a normal situation. Had Hurricane Rita not happened, this is most likely how sellers would have set their prices. However, as we all know, Hurricane Rita did happen. The two alternate regressions include an extra variable in each to describe the effects of Rita on pricing strategies, with a focus on the most likely times for price gouging to occur. The first added variable, Rita Supply Disruption, is a dummy variable with a ‘1’ for the dates during which any station in the dataset ran out of gas. It is apparent in Table 1 that during the impact period the stations that still had any gas reduced their markups by 12.7 cents. If price gouging had really occurred, that coefficient would have been positive. For most of the collection period, the value of the variable Number of Stations without Gas was equal to zero, except from September 23 through September 28. At one point in the collection period seventeen of the stations had run out of gas simultaneously. Both price gouging and 5 The coefficients of these variables are not reported here, as they are not central to the discussion. See Neilson and Bruce (2007) for further details.
H. Neilson / Economics Letters 105 (2009) 11–13 Table 3 Correlation coefficients for residuals on the day of landfall and several days later. Number of days out
Correlation coefficient
P-value
3 5 7
0.560 − 0.282 − 0.616
0.024 0.289 0.011
oligopoly theory predict that the coefficient of the variable should be positive. However, the results in Table 1 show that for each additional station that ran out of gas, the remaining sellers reduced their markups by half a cent. While this variable is not as statistically significant as the one in Alternate 1, it is still significant at the 99.9% confidence level. The regressions with logs provide mixed support for the above conclusions. The Rita Supply Disruption dummy variable, when run in the logarithmic regression series, maintains the same sign but is no longer statistically significant. The Number without Gas variable is negative and statistically significant, as with the levels regression. Furthermore, its coefficient, when evaluated with the average price at the time of the hurricane, implies a 0.73 cent drop in gas prices for each additional station that runs out of gas. Since there were relatively low markups during Hurricane Rita and at the same time many stations ran out of gas, there is a possibility that the stations with the lowest markups ran out of gas first. To test this, I computed the residuals for each station on the day before it ran out of gas. If exceptionally low markups led to stations running out of gas, one would expect to see large negative residuals on the day before the stations ran out of gas. Of the 26 values that I calculated, they ranged from −26.8 cents up to 12.4 cents and had a mean of − 0.62 cents.6 For comparison, the range of residuals for the entire sample was from −34.8 cents to 24.9 cents. There is no evidence that the stations had noticeably lower markups on the day before they ran out of gas. Another hypothesis worth exploring is that, in order to make up for lost profits, stations with relatively low markups during the hurricane increased their markups in the coming days or weeks. To test this, I
6
One station ran out of gas twice.
13
computed the correlations between the residuals on the day of the hurricane and the residuals three, five, and seven days later. According to the hypothesized behavior, low residuals on the day of landfall should correlate with high later markups, so the correlation coefficients should be negative. Only the sixteen stations that still had gas on September 24th, 2005, the day Rita made landfall, were used, and the correlation coefficients are reported in Table 3. As shown in the table, the evidence supports the hypothesis for seven days out from landfall, but not earlier. 4. Conclusions In my study I found evidence consistent with Rotemberg's (2006) fair pricing model among gas station owners in Bryan/College Station, Texas, during Hurricane Rita. Using station-specific fixed-effects regression analysis, I showed that not only was there no evidence of retail price gouging, but that there was actually evidence supporting the opposite. Gas station owners were surprisingly altruistic during that time of crisis. Acknowledgments I would like to thank William Neilson, Julio Rotemberg, Don Bruce, and an anonymous referee for helpful comments. References Neilson, H., D. Bruce, 2007, The geographic extent of gasoline markets, working paper, University of Tennessee. Rapp, G., 2006. Gouging: terrorist attacks, hurricanes, and the legal and economic aspects of post-disaster price regulation. Kentucky Law Journal 84, 536–550. Rotemberg, J., 2006, Fair pricing, working paper, Harvard University. Rotemberg, J., 2007, Behavioral aspects of price setting, and their policy implications, working paper, Harvard University. U.S. Federal Trade Commission, 2006, Investigation of Gasoline Price Manipulation and Post-Katrina Gasoline Price Increases (Washington, DC). U.S. Office of Highway Policy Information, 2006, Monthly Motor Fuels Reported by States, October 2005, Federal Highway Administration Publication No. FHWA-PL06-020 (Washington, DC).