Abstract
The price elasticity of gasoline demand is a key parameter in evaluation of various policies. However, most of the literature uses aggregate data to identify this elasticity. Temporal and spatial aggregation make such elasticity estimates biased. We employ a unique dataset of all gasoline transactions in Iran during a 4-month period around an unexpected exogenous price change to identify that price elasticity. We also identify a significant withholding behavior by consumers in response to anticipated price changes. The consumers reduce or postpone their purchases when they expect a decrease in prices. Controlling for date fixed effects would eliminate homogeneous withholding responses. However, heterogeneous responses to this anticipated price change would lead to an overestimation of price elasticity. After controlling for date, individual, and location fixed effects as well as the withholding behavior, we estimate a robust significant price elasticity of − 0.085. Aggregation of the same data by week, month, and city yields an estimate of − 0.3, indicating a significant bias in earlier studies.
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Notes
Iran has 11% of world proven oil reserves (ranked 4th) and has 18% of gas reserves (ranked 1st).
Quotas are delivered on the first day of each Persian calendar month which corresponds to about 21st of each Gregorian calendar month.
We drop the following card types (dropped cards in parenthesis): taxis (350 thousand cards), motorcycles (5.6 million cards), and cars with specific monthly quotas beyond 60 L (3 million cards). Private cars with unusual quotas constitute many types including diplomatic and veteran-owned vehicles that might have different behavioral responses and hence are removed from our sample.
This is equivalent to 45 cents per gallon. The same quality gasoline had an average price of 3.36 dollars per gallon in the US. We assume an exchange rate of 33,320 Rials per US dollar in this period.
This abrupt announcement resulted in some limited unrest and queuing at pump stations. But since the time between announcement and implementation was less than four hours, there was limited leverage to hoard gasoline.
During this period, the wholesale price of gasoline in the Persian Gulf was 75 cents per liter. Therefore, both prices were substantially subsidized, though the high price called as the unsubsidized price by consumers.
Premium quality gasoline is priced at 10% higher than the corresponding regular quality gasoline.
Gas stations provide “station cards” for fueling of cars with no cards (stolen, malfunctioning, etc.). These station cards have an unlimited quota, but the supply is only at the higher price.
The government announced the price increase just three hours before the implementation.
On a given day a cardholder can only engage in three fueling transactions with a daily limit of 180 L.
For simplicity we exclude \({g}_{ijt}\), but one can simply add this covariate to the aggregate specification.
This equation corresponds to Equation 20 in Levin et al. (2017). They identified three sources of gap between aggregate elasticity and city-day elasticity. In our setup, we assume the aggregation does not change the weights to each observation. If aggregation puts different weights to each observation compared with the dis-aggregated estimates, then the difference in weights times the actual elasticity estimate is another source of deviations. Their exercise to measure the source of bias reveals that this channel as well as the second source is insignificant and economically small.
We also use other time-fixed effects like day of month (30 dummy), day of week (7 dummy) and month of sample (4 dummy) fixed effects instead of day of sample fixed effects in individual-level observations. The results are similar to the reported elasticity in Column (5) of Table 4. Also, multiple location fixed effects like city and province fixed effects do not change the elasticity of demand. To capture regional trends, we also include month by province fixed effect that do not change the results. These results are not shown for brevity.
We also execute an exercise when average daily prices of gas station are used as IV's for the individual prices. This specification will remove individual variation and keep station-level variation in prices. In a specification similar to Column (5) of Table (4), we find an elasticity of − 1.7% that is smaller than our benchmark. We interpret this result due to the weak IV and the removal of useful exogenous individual-level variation created by the unexpected price increase in 24 April 2014.
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Vesal, M., Tavakoli, A.H. & Rahmati, M.H. What do one hundred million transactions tell us about demand elasticity of gasoline?. Empir Econ 62, 2693–2711 (2022). https://doi.org/10.1007/s00181-021-02122-3
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DOI: https://doi.org/10.1007/s00181-021-02122-3