Price Elasticity of Gasoline Smuggling: a Semi-Structural
Estimation ApproachI
Hamed Ghoddusi 1 , Nima Rafizadeh 2 , Mohammad H. Rahmati
3
Abstract
We estimate the price elasticity of the demand for gasoline smuggling in Iran. For this
purpose, we employ a detailed panel of monthly gasoline consumption data from 160 distribution hubs during the period 2005-2014. We apply two different approaches which are
diff-in-diff and panel data estimations. The results suggest that the foreign-to-home gasoline
price ratios have a significant impact on the time-varying elasticity of demand. This finding
supports the hypothesis that an increase in domestic gasoline prices will significantly reduce
the demand for smuggling. In addition, as the distance of a region from its closest higherprice neighboring country diminishes, the price elasticity of demand for gasoline smuggling
for that region declines as well. Finally, we find that when the ratio of foreign to domestic
prices is higher, the gasoline demand sensitivities to price in neighboring countries go up.
Our work offers new insights regarding the welfare impact of removing fuel subsidies and
modifying fuel pricing policies.
Keywords: Gasoline Smuggling, Fuel Subsidy Removal, Price Elasticity, Gasoline Price
Differential, Panel Data Estimation
JEL Classification Codes: C23, F15, H77, Q41
I
This paper is based on the masters thesis of Nima Rafizadeh at the Sharif University of Technology
supervised by the other two co-authors.
1
School of Business, Stevens Institute of Technology, Hoboken, NJ, USA. Email: hghoddus@stevens.edu,
website: www.ghoddusi.com
2
Graduate Department of Management and Economics, Sharif University of Technology, Tehran, Iran.
Email: nima.rafizadeh90@gmail.com
3
Corresponding Author, Graduate Department of Management and Economics, Sharif University of Technology, Tehran, Iran. Email: rahmati@sharif.edu
Preprint submitted to SSRN
Electronic copy available at: https://ssrn.com/abstract=2954034
November 28, 2017
1. Introduction
Smuggling is a common problem across international borders where variation in tax or
subsidy policy might create a price differential between the source and destination regions.
A notable example is the case of smuggling the heavily-subsidized gasoline from Iran to its
neighboring countries, where the price of gasoline has typically been much higher than Iran.
The literature on smuggling1 suggests that the prerequisite for smuggling homogeneous
goods (e.g. gasoline or beverages) is a price differential for the very same good in two
areas. Since gasoline is very close to a homogeneous good, the substantial difference in the
price of gasoline between Iran and its neighboring countries has created opportunities for
an outbound smuggling of gasoline from Iran. For example, before the (partial) subsidy
removal in 2010, the price of gasoline in Iran was on average one-fifteenth of the price in
Turkey. Such a non-trivial price gap motivated individuals to earn income by the smuggling
and transferring of gasoline to Turkey2 .
The gasoline price in Iran has always been set by the government and is constant across
all parts of the country. Following a national plan to reduce and eventually eliminate fuel
subsidies, the government increased the nominal price of gasoline in three major events over
the past couple of years. The new fuel pricing policy aims to set the domestic price at the
worldwide equilibrium to narrow down the gap between domestic and foreign fuel prices. This
policy was expected to mitigate or cut incentives for selling subsidized fuel to neighboring
countries that typically have higher fuel prices. Capping the fuel smuggling was one of the
key motives behind the fuel subsidy reforms (Victor (2009) and Dartanto (2013)). The other
motives of this policy shift included controlling the ever increasing domestic demand and
releasing the fiscal burden of the gasoline subsidies from the government.
The policy has been implemented. For the past few years Iranian domestic fuel prices
1
For a more in depth understanding of smuggling, see Kanbur and Keen (1993), Trandel (1994), Haufler
(1996), Wang (1999), Ohsawa (1999), Scharf (1999) and Nielsen (2001, 2002).
2
The smuggling could be carried out through illegal border crossing or via quasi-legal means such as filling
the tanks of large trucks and pickups and withdrawing the gasoline right outside Irans border.
2
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have been closer to the fuel prices in neighboring countries. However, the effectiveness of
this policy experiment on reducing the extent of smuggled fuel has not been empirically
examined. Our paper is the first to rigorously evaluate such outcomes.
The objective of this paper is to use domestic variations in order to provide estimates of
the price elasticity of the demand for gasoline smuggling in various regions of the country.
We use the fact that gasoline prices in Iran are determined by a central government as
a key identification factor. Our research uses administrative regional-level data (for 160
distribution hubs) during the years 2005-2014. We utilize the heterogeneity of the various
provinces and their closest higher-price neighbors. As all of the provinces are forced to follow
the same gasoline price, we estimate the elasticity of the demand for gasoline smuggling as
a function of price differentials between Iran and neighboring countries, and the distance to
the border.
We offer multiple contributions to the existing research. First, we provide empirical
evidence for the impact of changing government-set fuel prices to the demand for smuggling.
To the best of our knowledge, this is the first attempt to provide such results in the context
of a resource-rich country. Removing domestic fuel subsidies and bringing prices closer to the
international levels have been the subject of recent heated debates. Due to various factors,
several oil-rich countries have already started removing or reducing domestic fuel subsidies
and narrowing the gap between domestic and international prices. However, there is not
much literature concerning ex-post empirical evaluation of the results of such policies. We
fill this gap by offering empirical evidence from Iran, a vast and largely populated oil-rich
country that undertook an ambitious fuel subsidy removal program in 2010.
Second, our paper contributes to the literature of public finance and the demand for
smuggling. Prior research has focused on various goods (especially luxury goods such as
beverages and cigarettes). We provide evidence for gasoline, a homogeneous and essential
good.
Despite the typical reliable data collection issues in developing countries, we believe that
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the granularity of our quantity and price observations in conjunction with the particular
institutional setups, which we exploit for causal identification, enable us to provide a reasonably reliable estimate of the elasticity. We also offer multiple robustness tests to address
or mitigate potential concerns.
The paper is structured as follows. Section 2 presents the literature review on the estimation of goods demanded with regard to smuggling, cross-border shopping, and subsidizing
fuel. Section 3 presents data. In Section 4 the empirical method used for the estimation
of gasoline demand is discussed. Sections 5 and 6 deal with results and robustness checks.
Finally, Section 7 draws the conclusion.
2. Literature Review
2.1. Cross-Border Shopping and Smuggling
A seminal work in this area has been conducted by Leal et al. (2010) who provide a
comprehensive review of the literature on cross-border shopping as a result of tax differentials.
According to Mikesell (1970), tax differences between various states in the United States
induced an interest in cross-border shopping from the mid-1930s. The first applied research,
however, was performed between the 1950s and 1970s.3 In such works, the most general
form for each good was calculated in a demand function with the goods sold in each region
as the dependent variable. Explanatory variables include the price and the relative taxation
of the regarded goods in each region compared to its neighbors, the income in considered
territories, the cost of travel between neighbouring territories, and a set of other standard
control variables. Fox (1986) was the first paper to explicitly incorporate variables related to
transport costs into empirical models. More recent papers have also introduced new controls
such as the number of Fridays in the month to explain the sales of alcohol (Asplund et al.
(2007)), the stock of vehicles to explain the sales of gasoline (Banfi et al. (2005)), as well as
3
For instance, see Mikesell (1970, 1971), Maliet (1955), McAllister (1961) and Hamovitch (1966).
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the expenditure on advertising by tobacco companies to explain the sales of tobacco (Coats
(1995)).
Regularly, the specifications take a multiplicative form, which favors subsequent logarithmic transformation and a direct interpretation of the estimated coefficients in terms of
elasticity. An illustration of this is provided by the work of Walsh and Jones (1988). Their results suggest that the residents of the counties that border West Virginia choose to purchase
outside their state in order to avoid paying higher taxes.
Following the methodology proposed by Walsh and Jones (1988), Asplund et al. (2007)
estimate how the monthly sales of alcohol in Sweden respond to prices in Denmark and
Germany. They conclude that in urban areas close to the border, the elasticity of domestic
demand with respect to foreign prices is approximately 30%. These estimates are reduced to
20% (10%) when the distance is widened to 150 (400) kilometers. In sum, both distance and
price differentials critically influence the size of cross-border shopping. Other works have
tested the cross-border purchases of alcoholic drinks, such as Crawford and Tanner (1995)
and Crawford et al. (1999) for the United Kingdom, Smith (1976) and Beard et al. (1997)
for the United States, and Fleenor (1999) for Canada and the United States.
There are also studies that focus on the cross-border shopping for tobacco in the United
States where smuggling tobacco has been a traditional concern of authorities (Fisher (2007)).
The existence of cross-border shopping has been demonstrated empirically by, among others,
Wertz (1971), Warner (1982), Coats (1995), Saba et al. (1995), Fleenor (1998), and more
recently by Chiou et al. (2008), Lovenheim (2008) and Merriman (2010). The results obtained
by the literature consistently show that between 2% and 6% of the cigarettes consumed in
the United States are smuggled.
With regards to cross-border fueling, Banfi et al. (2005) employ a panel dataset to estimate the impact of gasoline price differences between the border regions of Switzerland and
adjacent areas in Germany, Italy, and France on the demand for fuel in the Swiss border
areas. Their results establish a strong association between cross-border shopping and price
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differentials. If the price of gasoline in Switzerland were to be reduced by 10%, the demand
for gasoline in Swiss border areas would have increased by almost 17.5%.
2.2. Subsidy on Fuel
Oil-rich countries often fund fuel subsidies to distribute the resource rent among the people. It effectively lowers their transportation costs and increases gasoline’s affordability for
the public. However well-intentioned, the policy will distort the gasoline market with the
resulting inefficiencies, not to mention external costs associated with high gasoline consumption such as the emission of local pollutants, traffic congestion, and motor vehicle accidents
(Davis (2014)). The World Bank recommends that developing nations abandon the gasoline
subsidy (Loveless (2015)). The opportunity cost of such policy is high and rising (Coady
et al. (2010)).
The most common strategy for the removal of subsidies is to implement a phase-out
policy. The government only partially cuts the subsidy at any one time. Indonesia has done
so in 2000, 2002 and 2003 (Sindo (2014)) and Malaysia has also carried out such measures
in 2010 and 2013 (Bridel and Lontoh (2014)). When resistance starts to build up due to
negative side effects, the government will then back down on the removal plan. We call this
policy a gradual elimination of subsidies or percentage cut, while lifting the whole subsidy is
the ultimate goal of the policy. In this case, the government has greater flexibility. Several
governments have also attempted a partial subsidy removal plan with compensation benefits
paid to the people. For instance, such a policy was implemented in Indonesia in 2005 and
2008 (WorldBank (2012)), as well as Iran in 2010 (Guillaume et al. (2011)).
3. Data and Institutional Context
3.1. Background on the Iranian Gasoline Market
In Iran the national fuel price has always been set by the government in a fixed rate across
all regions. In fact, there is no equilibrium market price or regional pricing in the country. A
key institutional fact is that there are no private oil providers, and the government-affiliated
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National Iranian Oil Products Distribution Company (NIOPDC)4 is the sole importer and
distributor of fuels.
Historically, gasoline and diesel prices have been heavily subsidized in Iran, and the country had been experiencing one of the lowest average retail prices of fuels (in particular gasoline
and diesel) in the world. There is no price difference between personal and commercial users
of fuel; all vehicles pay the same price. The low price of fuels not only motivates outward
smuggling, but also contributes to the ever-increasing domestic consumption. (Guillaume
et al. (2011))
In a reaction to the rise in domestic consumption and the consequent increasing import
of gasoline, the government implemented a comprehensive plan to mitigate the growth of
fuel demand. Two major components of the plan were to increase the domestic prices (by
removing the subsidies) and to ration the subsidized fuel per each vehicle. The rationed
price was subsidized, yet it was only offered in limited quantities per vehicle5 .
The rationing plan was in place from June 2007 until May 2015. Following the national
plan to reduce fuel subsidies, the government increased the nominal price of gasoline in three
major events over the past years (June 27 2007, December 19 2010, and April 24 2014). The
rationing policy required all consumers to use their so-called “fuel card” when pumping their
vehicles at any of the fuel stations6 .
Figure 1 presents the monthly retail prices for gasoline (in USD terms) in Iran and its
neighboring countries between 2005 and 2014. The dollar price of gasoline for Iran is flat for
a long period because both the nominal price of gasoline and the exchange rate were held
fixed. After the major subsidy removal in 2010 the exchange rate was also floated; thus, the
real Iranian gasoline prices have been fluctuating since then.
4
Website: https://www.niopdc.ir
Commercial vehicles including taxis and trucks were eligible to receive a larger quantity of rationed fuel.
6
For detail discussion of the policy in 2014 see Rahmati and Vesal (2017)
5
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Figure 1: Gasoline Price in Iran and Its Neighboring Countries
3.1.1. Fuel Smuggling
Fuel smuggling is a major challenge in Iran. Unfortunately, official data on the volume
of smuggled gasoline does not exist. However, unofficial and informal estimates suggest that
the country is losing a significant volume of highly-subsidized fuels to its neighboring countries (especially Turkey) every year. For example, according to Iranian counter-smuggling
authorities the estimated value of the petroleum products smuggling in 2014 was about 3.8
billion USD 7 .
There are several methods for smuggling, including: fuel tank of personal vehicles, auxiliary tanks attached to trucks and pickups, small and mid-size boats, underground illegal
pipelines, fuel tanker trucks, and even animals carrying barrels of gasoline.
Anti-smuggling authorities have been in work to prevent illegal cross border flows. The
law suggests that the penalty for smuggling is three times of the market value of discovered goods. The Iranian police have setup stations along major smuggling routes and in
areas close to the borders. Suspicious vehicles are regularly checked for items such as auxiliary tanks and fuel containers. The stringency of anti-smuggling activities though varies
across different provinces. Anecdotal evidence suggests that in regions with a high rate
7
Source: www.mehrnews.com/news/2961496/
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of poverty and unemployment, government officials tolerate ”small-scale” (personal) smuggling functions because of insufficient social security system. Thus, they sometime disregard
small-scale activities by local residents and focus their efforts on the large-scale professional
transportation of fuel8 .
3.2. Data
3.2.1. Gasoline Consumption
The administrative gasoline consumption data is directly obtained from the NIOPDC.
The data set contains monthly observations of regular and premium gasoline consumption
in 160 distribution hubs of NIOPDC between 2005 and 2014. We also collect official gasoline
prices, which is one price in all distribution hubs.
Hubs of NIOPDC are responsible for the logistics and distribution of various grades of
gasoline to stations. There are 160 regional hubs, each covering a few urban and rural
areas. Furthermore, since premium gasoline has a higher price than regular gasoline, and
is considered a luxury good, we assume that the gasoline smuggling demand is mainly for
the regular gasoline9 . In this paper, we estimate the demand for regular gasoline. Hereafter,
whenever we talk about gasoline, we are referring to the regular one.
The administrative gasoline sales data collection is managed through a fully automated
nationwide system. All consumers are required to use their fuel card when buying gasoline
at all the fuel stations. This data is then transmitted to a central system. At the end of
each day the aggregate consumption for each region is recorded. Relying on such an accurate
data provides confidence that our consumption data is most likely free of measurement errors.
8
The province-level fixed-effects in our panel data specification attempt to partially capture the heterogeneity in anti-smuggling activities.
9
We had explored the option to use the premium gasoline as a robustness test (or the placebo effect, as
the other reviewer also suggested) from the beginning. However, after collecting and cleaning the data we
realized a great deal of data recording problems, many missing observations, sparsity, outliers, and also a
small sample size problem (the premium gasoline is not available in many locations). Moreover, the share
of the premium gasoline is only 3.5% and mainly in large cities. Despite all these challenges, we report the
estimations using the premium gasoline in Appendix B. As expected, due to the above reasons, the results
are not significant. We relate the lack of significance to the small sample size as well as data problems.
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Moreover, since the price is set by the central administrative authority, our observation of
price is also accurate. Descriptive statistics of the gasoline consumption data are summarized
in Table 1. The highest quantity of gasoline consumption is observed in Tehran in August
2008. The lowest one was in Savadkooh located in Mazandaran province (the northern strip
of Iran) in January 2006.
10
3.2.2. Gasoline Prices and the Distance from the Border
The two key variables which are expected to have significant impacts on the elasticity
of gasoline demand are: 1) the ratio of foreign to domestic price; 2) the distance from the
closest higher-price border. Variations in these variables enable us to identify foreign and
domestic demand in each region. Since we employ the price ratio, gasoline prices in Iran and
its neighboring countries have all been expressed in terms of the United States Dollars. Table
1 illustrates that gasoline price in Iran is almost always less than its neighboring countries.11
We assume that gasoline demand for domestic consumption and for smuggling use is made
up from both the rationed gasoline price and the non-rationed price; hence, the average of
the real price for rationed and non-rationed gasoline is used for domestic price. As for
the distance from the border variable, each region’s distance from its closest higher-price
neighbour has been extracted meticulously both manually and with the aid of Google Maps.
3.2.3. Further Socio-Economic Variables
One of the limitations of the current paper (as many other works in similar contexts) is
the lack of granular socio-economic data to be used as perfect controls. In certain cases, the
data series exist; however, their time frequency or spatial resolution does not fit with our
10
It should be noted that gasoline consumption data associated with September 2007 has been ruled
out due to inconsistency with other information. The gasoline consumption in that particular month was
inconsistent with the observations from other months in that year, and also with the similar months in the
previous and following years. Thus, we treated it as a possible data recording error.
11
It is possible that prices on the non-Iranian side of the border are endogenous to the volume of smuggling.
Ideally, one should use regional prices on the non-Iranian side of the border to estimate the price ratios.
However, reliable and long-spanning data on regional prices do not exist. Thus, we use the nationwide
average gasoline prices of the country as a close proxy. If the size of smuggled fuel compared to the total
fuel consumption of the region is small, the price effect will also be small and this concern will be alleviated.
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Table 1: Summary Statistics of Key Variables
Variable (Location)
Type
Measure
Mean
Max
Min
Gasoline consumption
Regional hub/Monthly
1000 L
13,912
311,621
9
Real price of rationed gasoline
in Iran
Country/Monthly
Rial/L
2,329
4,630
1,188
Real price of non-rationed gasoline
in Iran
Country/Monthly
Rial/L
4,826
8,102
1,606
Price of rationed gasoline in Iran
Country/Monthly
US Dollar/L
0.15
0.39
0.09
Price of non-rationed gasoline in Iran
Country/Monthly
US Dollar/L
0.31
0.67
0.09
Price of gasoline in Turkey (NW)
Country/Monthly
US Dollar/L
2.16
2.86
0.75
Price of gasoline in Armenia (NW)
Country/Monthly
US Dollar/L
1.12
1.50
0.43
Price of gasoline in Afghanistan (E)
Country/Monthly
US Dollar/L
1.03
1.46
0.42
Price of gasoline in Pakistan (SE)
Country/Monthly
US Dollar/L
0.95
1.28
0.34
Price of gasoline in Azerbaijan (NW)
Country/Monthly
US Dollar/L
0.77
1.59
0.30
Price of gasoline in Iraq (W)
Country/Monthly
US Dollar/L
0.69
1.09
0.28
Price of gasoline in Persian Gulf (S)
Country/Monthly
US Dollar/L
0.44
0.63
0.18
Price of gasoline in Turkmenistan (N)
Country/Monthly
US Dollar/L
0.18
0.31
0.02
Distance of each region from its the
closest higher-price neighboring countries
Region/Monthly
Kilometers
138
560
1
GDP per capita
Province/Yearly
Thousand Rials
683
3,214
215
Cars per capita
Province/Seasonal
Stock
0.105
0.195
0.047
Motorcycles per capita
Province/Seasonal
Stock
0.060
0.187
0.007
Unemployment rate
Province/Seasonal
-
0.127
0.330
0.039
GDP and gasoline price in Iran are based on real data in 2011 using consumer price index. Gasoline prices
in neighboring countries have been transformed into monthly from their annual reports using gasoline price
variation in the Persian Gulf FOB. NW, E, SE, W, S and N stand for northwest, east, southwest, west,
south, and north, respectively.
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unit of analysis. Despite these expected limitations, we did our best to collect the annual
GDP, the number of the cars and motorcycles, and the seasonal rate of unemployment at the
province level. We turn all variables to per-capita to make them comparable across different
locations.
4. Empirical Method
4.1. Identification
The key institutional feature of our identification strategy is the existence of an administratively set national price of gasoline. The non-varying prices across time and provinces
mitigate the potential endogeneity concern of equilibrium prices at the regional level. If the
price was determined through market forces, or even through an administrative mechanism
instituted at the regional level, one could argue that the demand for smuggling directly
affects the price.
We assume that the fuel prices in the neighboring countries are set based on their domestic
considerations. Therefore, we can use the exogenous variations of gasoline prices in different
countries, time, and distances as a useful variation to identify the elasticity parameters.
Moreover, we exploit the panel structure of the data and control for regional hub fixed
effects. The central object of our analysis is to identify the price elasticity of smuggling.
It expresses the sensitivity of smuggling to changes in the relative price of gasoline in the
foreign and domestic markets and will be defined explicitly in the following section.
4.2. Overview of the Model
In Iran, the government sets nationwide identical official gasoline prices for all regions.
Noticeably, as discussed in Section 3 at each time t, the gasoline price inside the country is
at least less than one of its neighboring countries, and smuggling would become profitable.
Two underlying assumptions are taken into account in this paper which support two different
empirical approaches. First, according to the literature presented in Section 2, by increasing
the distance of a region from its neighboring countries, the tendency to smuggle is expected
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to decrease. Therefore, it is expected that the demand for gasoline smuggling is insignificant
after a certain threshold. Under this assumption, the difference-in-difference approach is applicable because the smuggling behavior is different in bordering areas (treatment) compared
with central ones (control).
However, if the demand for gasoline smuggling is accrued from the demand in all regions,
then the difference-in-difference approach can no longer be applied. Therefore, it is necessary to assume a structure for the demand to separate domestic from smuggling use. The
structure we assume is that the propensity of the demand for smuggling is a linear function
of the price ratios of the two countries and the distance from the closest higher-price border.
Interestingly, we show that even with different gasoline demand functions for each of the two
countries, this structure allows us to identify the smuggling elasticity as a function of price
ratios and distances. In other words, the structural model can be pinned down to a reduced
form estimation; in a way that the elasticity can be estimated using reduced form coefficients. The coefficient of distance in this semi-structural approach provides an implicit test
for the underlying assumption behind the difference-in-difference approach. This method is
explained in Section 4.4.
For simplicity, region i which is located inside the country is shown by Hi and its closest
higher-price neighboring country at time t, is indicated by Fit .
4.3. Difference in Difference Approach
From a theoretical perspective, aggregate gasoline consumption in a country responds
when the price has changed in its neighboring countries. However, the effect of this channel
is not significant in provinces and distribution hubs (Hi ), which have a distance from Fit
¯ We implicitly assume that national factors (e.g.
more than a threshold as denoted by d.
GDP growth, travel days, gasoline prices, etc) affect symmetrically all distribution hubs.
Notwithstanding, the demand for smuggling only shows up in border hubs. Therefore, the
foreign gasoline demand which stimulates smuggling can be identified through using a diff-
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in-diff approach for hubs close and far from the borders (treatment vs. control).
Th
E[ln(Dit
)|Pith , Pitf , Xit ] = Π0 + Π1 ln(Pith ) + Π2 ln(
Pitf
Pitf
¯ + γ3 Xit (1)
)
+
Π
ln(
) ∗ Dummy(dit , d)
3
Pith
Pith


 1 if dit 6 d¯
¯
Dummy(dit , d) =
 0 if d > d¯

it
(2)
Where DitT h is the logarithm of the gasoline demand function in region i at time t that
includes both domestic and smuggling uses. Domestic and foreign prices are denoted by (Pith )
and (Pitf ), respectively. Xit is other covariates including regional hub fixed effects, month
fixed effects, GDP per capital, vehicle per capita, and unemployment rate.
Domestic use would not change by variation in foreign prices. Therefore, we can claim
that after controlling for month fixed effects the elasticity of demand with respect to foreign
prices solely represents the elasticity demands for smuggling in each region i at time t as
follows:12
it =
T h)
∂ ln(Dit
∂ ln(Pitf )
=
T h)
∂ ln(Dit
∂
Pf
ln( Pith )
it
¯
= Π2 + Π3 ∗ Dummy(dit , d)
(3)
4.4. Semi-Structural Approach
Consider a hypothetical isolated region i where there is no smuggling and its gasoline
demand at time t is equal to Dith . If smuggling takes place in this region, then its total
demand would be DitT h . Obviously, DitT h > Dith , because smuggling would add up to domestic
uses. However, the amount of demand related to smuggling is cannot be distinguished, so in
order to identify the smuggling effect, we take the part of consumption in Hi into account
that is demanded by smuggling, i.e. DitT h − Dith . Such demand is resulted from the higher
price in Fit , and the smuggler demands for gasoline to gain a profit by selling it across
12
Notice that this variation in time and spatial of elasticity stems from the change in dit as the closest
higher-price may be changed as gasoline prices vary independently in the neighboring countries.
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borders. Accordingly, while Hi is encountered with smuggling, the total gasoline demand in
Hi at time t is influenced by two prices: Pith and Pitf .
When the smuggling in Hi is zero, the amount of gasoline demand in Hi at time t is:
h
E[ln(Dit
)|Pith , Xit ] = β0 + β1 ln(Pith ) + γ1 Xit
(4)
which is in fact the domestic gasoline demand requested by the consumer in Hi and is
only a price function in (Pith ). Similarly, the amount of gasoline used by foreign consumers
in Fit which is simply a function of price in Fit (Pitf ) is:
f
E[ln(Dit
)|Pitf , Yit ] = β2 + β3 ln(Pitf ) + γ2 Yit
(5)
We define Sit as the influence ratio of gasoline demand in Fit from the lower price in
Hi . In fact, the Sit function is the likelihood in which each representative consumer in Fit
influenced by the lower price in Hi resorts to smuggling. We describe its aggregate as the
ratio of the Fit demand influence of price in Hi . Two basic presuppositions are considered
to obtain this ratio. First, Sit increases due to an increase in the marginal revenue derived
from smuggling. Secondly, Sit decreases as long as gasoline smuggling costs increases from
Hi to Fit . The smuggling likelihood function for the consumer i who is located at distance
d from the closest bordering region, which has lower prices than the region of the consumer
itself is defined as follows:
Sit = φ + α ln(
Pitf
) − δ ln(dit )
Pith
(6)
Lovenheim (2008) uses similar probability function for the demand of smuggling cigarettes,
however he stated that the revenue from smuggling derived from the difference between the
price logarithms in the region that purchase took place and residential prices. The cost of
smuggling varies with the distance logarithm from the border. Moreover, φ denotes the fixed
cost sustained by each consumer for smuggling.
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In Equation 6, with an increase in Pitf , the amount of Sit increases as well, so we hypothesize that α would be positive. φ, which is the smuggling fixed cost, has an inverse relationship
with Sit . So, its coefficient’s sign should be negative. Eventually, with an increase in the
distance of Hi and Fit , the smuggling appears to become much more costly. Consequently,
the demand influence of foreign price reduces, and the δ coefficient’s sign in the function Sit
is positive.
The final step in the modeling of the total gasoline demand in Hi that is DitT h , is to
extract a demand function that responds to smuggling in addition to domestic demand. As
mentioned, when no smuggling activity is present, the demand available in Fit is obtained
through Equation 5. However, when Fit is encountered with smuggling, the Fit region is
influenced by the price available in Hi with the likelihood Sit . Hence, the mathematical
expectation for the demand function of region Fit is as follows:
Tf
f
fs
E[ln(Dit
)|Pith , Pitf , Yit ] = (1 − Sit ) E[ln(Dit
)|Pitf , Yit ] + Sit E[ln(Dit
)|Pith , Yit ]
(7)
Where Ditf s is the demand which is in Fit , but with price Pith . Thus, in the case of
smuggling, compared to smuggling free situations, the mathematical expectation for the
gasoline demand function in Fit alters as follows:
Tf
f
fs
f
E[ln(Dit
)|Pith , Pitf , Yit ] − E[ln(Dit
)|Pitf , Yit ] = Sit (E[ln(Dit
)|Pith , Yit ] − E[ln(Dit
)|Pitf , Yit ])
(8)
Any changes in the mathematical expectation of the gasoline demand function is going
to be responded with smuggling from the resources inside Hi . In other words, at time t,
besides the mathematical expectation for demand available in Hi for domestic consumption,
the Fit region also requests gasoline from Hi according to Equation 8. Hence, the overall
demand function which exists for gasoline in Hi at time t is as follows:
fs
f
Th
h
E[ln(Dit
)|Pith , Pitf , Xit ] = E[ln(Dit
)|Pith , Xit ] + Sit (E[ln(Dit
)|Pith , Yit ] − E[ln(Dit
)|Pitf , Yit ])
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(9)
By replacing Equations 4, 5, and 6 in the above equation, the reduced form of the total
gasoline demand function in Hi at time t is obtained as follows:
Th
E[ln(Dit
)|Pith , Pitf , Xit ] = β0 + β1 ln(Pith ) − φβ3 ln(
Pitf 2
Pitf
Pitf
)
−
αβ
[ln(
)]
+
δβ
ln(
) ∗ ln(dit ) + γ1 Xit
3
3
Pith
Pith
Pith
(10)
By defining the following equalities, the reduced form of the gasoline demand function is
turned to Equation 11:
A0 = β0 , A1 = β1 , A2 = −φβ3 , A3 = −αβ3 , A4 = δβ3
Th
E[ln(Dit
)|Pith , Pitf , Xit ] = A0 + A1 ln(Pith ) + A2 ln(
Pitf
Pitf 2
Pitf
)
+
A
[ln(
)]
+
A
ln(
) ∗ ln(dit ) + γ1 Xit
3
4
Pith
Pith
Pith
(11)
Using the structural model and the intuition behind its deep parameters, we can determine the coefficient sign of the reduced form model. According to the law of demand, price
and the demand for gasoline should have an inverse relationship.13 Hence, in Equations 4
and 5, β1 and β3 are negative. Now, the coefficient sign for A1 , A2 , A3 and A4 are extracted
mathematically. With the aid of other coefficients stated in Equation 6 for Sit , the coefficient
signs in the paper’s presuppositions are as follows:
β1 < 0, A1 = β1 −→ A1 < 0
φ < 0, β3 < 0, A2 = −φβ3 −→ A2 < 0
α > 0, β3 < 0, A3 = −αβ3 −→ A3 > 0
δ > 0, β3 < 0, A4 = δβ3 −→ A4 < 0
It is expected that with an increase in gasoline prices in a country, its demand goes down.
Hence, the A1 coefficient sign, which became mathematically negative, is compatible with
our intuition. Similar to Equation 3, and based on Equation 11, gasoline demand elasticity
13
We assume that gasoline is not a Giffen good.
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with the proportion of foreign price to the domestic one in each i region at time t is as
follows:
ζit =
T h)
∂ ln(Dit
Pf
= A2 + 2A3 ln(
∂ ln( Pith )
Pitf
) + A4 ln(dit )
Pith
(12)
it
As it appears, Equation 12 is the sum of three phrases. It is recognized that when the
foreign price exceeds the domestic price, the impetus for gasoline smuggling appears. It
was also stated earlier that irrespective of the price differential and the distance from the
border in each region, smuggling has a fixed cost (A2 ) which is almost the same in all the
regions. Therefore, the coefficient sign is intuitively expected to be negative. In addition,
for fixed domestic prices and a specific location, as the price of foreign gasoline increases,
then the demand would be more elastic to foreign prices. Therefore, the A3 coefficient sign
is anticipated to be positive. For the third phrase, it can be said that according to earlier
assumptions, the more distance from the closest higher-price neighboring country, the less
the elasticity. That is, an increase of one percent in the ratio of the foreign price to the
domestic price has less influence on gasoline demand in faraway areas from the border, and
the A4 coefficient sign is anticipated to be negative.
4.4.1. Discussion about variable of distance
There might be a concern of the logarithm functional form we assumed in Equation 6 for
distance. So, we also consider the linear relationship between smuggling demand as follows:
Th
E[ln(Dit
)|Pith , Pitf , Xit ] = A0 +A1 ln(Pith )+A2 ln(
Pitf
Pitf 2
Pitf
)+A
[ln(
)]
+A
ln(
)∗(dit )+γ1 Xit (13)
3
4
Pith
Pith
Pith
For robustness check, we also describe a dummy variable as in Equation 15 for the certain
intervals of the border of the neighboring countries to capture non-linearity in demand by
18
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distance. Then, Equation 14 is applied for the gasoline demand in each i region at time t:
Pf
Pf
it
it
E[ln(DitT h )|Pith , Pitf , Xit ] = η0 + η1 ln(Pith ) + η2 ln( Pith ) + η3 [ln( Pith )]2 +
Pf
η4 ln( Pith )
it
∗ Dummy(dit , (d0 , d1 )) + ... +
Pf
ηn+3 ln( Pith )
it
Dummy(dit , (dp , dq )) =
(14)
∗ Dummy(dit , (dn−1 , dn )) + γXit


 1 if dit ∈ [dp , dq )
(15)

 0 if dit ∈
/ [dp , dq )
5. Result
5.1. Estimation
Gasoline prices are the same across all Iranian regions and hubs at any given time period.
Moreover, the price in each month is less than at least one of Iran’s neighboring countries.
Therefore, the assumptions presented in Section 4 for the modeling of the gasoline demand
function are satisfied. As a result, we can estimate the coefficients presented in Section 4 using
the data set introduced in Section 3. Moreover, in all the following approaches, standard
errors for panel specifications are robust and clustered at the level of the cross-sectional unit
to allow for arbitrary serial correlation.
5.1.1. Estimation Results of The Diff-in-Diff Approach
Pf The coefficient associated with the log of price ratio ln( Pith ) is a key indicator for the
it
estimation of the elasticity of smuggling demand. The estimation results presented in Table
2 suggest that this coefficient is not significant in any of the various specifications (reported
in various columns of Table 2).14
Pf
The small and insignificant estimates of the coefficient of the log of price ratio ln( Pith )
it
may stem from the inconsistent underlying assumption that smuggling took place with higher
14
It should be noted that the domestic price (Pith ) is constant across all hubs at a given time t. Hence, the
domestic price is perfectly co-linear with the monthly dummy and thus, can not be identified. We dropped
it from those regressions with month fixed effects, and reported those results.
19
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Variables
Table 2: Demand Estimation with Diff-in-Diff Approach
Dependent Variable: Dit (Gasoline consumption in the regions of Iran)
(1)
(2)
(3)
(4)
Pf
ln( Pith )
-0.00283
(0.0575)
it
Pf
ln( Pith ) ∗ Dummy(dit , 1)
it
0.00994
(0.0555)
0.0284
(0.0588)
0.0134
(0.0670)
(5)
0.0676
(0.111)
-0.209***
(0.0633)
Pf
ln( Pith ) ∗ Dummy(dit , 50)
-0.0447
(0.0288)
it
Pf
ln( Pith ) ∗ Dummy(dit , 100)
-0.0572**
(0.0274)
it
Pf
ln( Pith ) ∗ Dummy(dit , 200)
-0.0185
(0.0238)
it
Pf
ln( Pith ) ∗ Dummy(dit , 400)
-0.0692
(0.0930)
it
ln[(GDP per Capita)it ]
0.184
(0.143)
0.195
(0.145)
0.208
(0.146)
0.199
(0.145)
0.199
(0.145)
ln[(Cars per Capita)it ]
-0.100
(0.0834)
-0.0943
(0.0831)
-0.0895
(0.0830)
-0.0897
(0.0836)
-0.0909
(0.0835)
ln[(Motorcycles per Capita)it ]
0.00986
(0.0604)
0.0206
(0.0609)
0.0225
(0.0604)
0.0214
(0.0610)
0.0196
(0.0609)
-0.0358***
(0.0127)
-0.0363***
(0.0128)
-0.0348***
(0.0131)
-0.0373***
(0.0127)
-0.0375***
(0.0128)
15.00***
(2.271)
14.81***
(2.277)
14.61***
(2.302)
14.77***
(2.295)
14.79***
(2.281)
Yes
Yes
0.9522
Yes
Yes
0.9521
Yes
Yes
0.9522
Yes
Yes
0.9520
Yes
Yes
0.9521
ln[(Unemployment Rate)it ]
Constant
Region Fixed Effects
Month Fixed Effects
R-Squared
Number of observations in each column: 16,552. Standard errors are robust and clustered by region to allow
for arbitrary serial correlation. *** and **: indicate 1% and 5% significance levels, respectively. Standard
errors are in parentheses.
20
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intensity in the border regions. In contrast, we find that the demand declines more in
border regions when the rent from smuggling increases (the sign of the coefficient for the
interaction of the log of price ratio and the dummy for distance when threshold equals to 1 km
Pf
ln( Pith )∗Dummy(dit , 1) is negative in Column 1 of Table 2). In other words, the border areas
it
cannot be actually specified as the “treatment” group in response to exogenous domestic
price increases. To validate whether there exists a variation in gasoline consumption between
“treatment” and “control” in response to shocks, Figure 2 plots the average consumption
for hubs in various distance thresholds similar to Columns 1-5 in Table 2. This figure, and
the estimates in Table 2 indicate that if there are any differences between regions, though
insignificant, there would appear to be less smuggling in areas near the border. Apparently,
this finding contradicts the initial assumption that the smuggling reduces as the hub is
located far from border. For seeing the frequency of distance of hubs from the closest higherprice border, we calculate the weighted average of the distance of hubs from the border for
the period between 2005 and 2014, and report them in Table 3.15
Table 3: Frequency of Hubs When Weighted Average of Distance from the Border is Equal or Less than d¯
d¯ (km)
1
50
100
200
400
Frequency of Hubs
5
45
76
112
156
Total number of hubs: 160
5.1.2. Estimation of Semi-Structural Approach
Table 4 shows the estimates of Equation 11 as the main finding of this paper. Column 1
reports the estimates of the panel model with a limited number of fixed effects. There exist
unobserved time-invariant hub specific factors that appear to be correlated with price ratios.
Excluding these fixed effects would bias the coefficients of Column 1 toward zero. Therefore,
Pf Pf
the coefficients of the log of price ratio ln( Pith ) , the log of quadratic price ratio [ln( Pith )]2 ,
it
it
f
Pit
and the interaction of the log of price ratio and the log of distance ln( P h ) ∗ ln(dit ) are
it
15
Refer to Appendix C to see the distribution of distance of hubs from the closest higher-price border.
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Figure 2: Average of Gasoline Consumption Over Time for Various Thresholds
Dotted lines are the average of gasoline consumption for hubs with a distance less than the indicated threshold
in each plot. The solid lines are the same averages for hubs in the other side of the threshold. All figures
are normalized to their first observations. The vertical lines show major policy changes (significant price
increases and rationing). Also, increases in the amount of nominal prices are in parentheses.
22
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not significant. However, after adding month and region fixed effects we obtain promisingly
significant results in the third column of Table 4.
In the fixed effects estimation, heterogeneous intercepts are allowed for each hub. These
intercepts capture the specific characteristics of each region such as the tendency to break
the law (and enter the smuggling business), time invariant climate factors, natural barriers
for smuggling, the stringency of anti-smuggling activities, outside options for employment,
etc. We see that in comparison to the OLS method, the fixed effect estimation generates
significant results. As described in Section 4 we would expect that the signs of the coefficients
Pf of the log of domestic price ln(Pith ) and the log of price ratio ln( Pith ) to be negative and the
it
f
Pit
2
sign of the coefficient of the log of quadratic price ratio [ln( P h )] to be positive. However,
it
the sign of the coefficient of the interaction of the log of price ratio and the log of distance
Pf
ln( Pith ) ∗ ln(dit ) is not in line with our prior assumptions.
it
The positive coefficient for the interaction of the log of price ratio and the log of distance
Pf
ln( Pith ) ∗ ln(dit ) suggests that the smuggling elasticity rises as the distance to the border
it
increases, keeping all other variables fixed. This finding initially appears to be puzzling,
because we expect higher elasticity in areas close to the border. Importantly, the positive
coefficient suggests that when the price increases in the neighboring country, those areas
that are far from the border are more sensitive to such price changes. In Appendix A, we
have provided a demonstrative theoretical framework that highlight this matter.
5.1.3. The Specification of Distance
We are also interested in investigating the effects of the distance level rather than its
logarithm. For this purpose, Equation 13 is estimated by including cross-sectional fixed
effects as well as time period fixed effects in the model. Results in the first column of Table
5 illustrate that in comparison to the distance logarithm specification, the significance of the
coefficient of the distance level is reduced from the 1% level to the 10% level. It suggests that
a non-linear relationship between the distance and smuggling activities is more intuitive.
We specify three dummies for the distance from the border to include hubs that are at
23
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Variables
Table 4: Demand Estimation with The Semi-Structural Approach
Dependent Variable: Dit (Gasoline consumption in the regions of Iran)
(1)
(2)
ln(Pith )
(3)
0.0568
(0.176)
-0.142***
(0.0489)
0.0995
(0.312)
-0.122***
(0.0464)
-0.221***
(0.0793)
0.0210
(0.0882)
0.0191*
(0.0115)
0.0424***
(0.0144)
ln( Pith ) ∗ ln(dit )
0.0128
(0.0556)
0.0217***
(0.00708)
0.0226***
(0.00698)
ln[(GDP per Capita)it ]
0.228
(0.180)
0.101
(0.103)
0.201
(0.143)
ln[(Cars per Capita)it ]
0.0128
(0.267)
-0.0226
(0.0719)
-0.0867
(0.0826)
ln[(Motorcycles per Capita)it ]
-0.148
(0.176)
-0.0105
(0.0552)
0.00831
(0.0595)
ln[(Unemployment Rate)it ]
-0.117
(0.156)
-0.0644***
(0.0116)
-0.0368***
(0.0125))
11.36***
(3.349)
16.33***
(1.483)
14.97***
(2.257)
No
No
0.0193
Yes
No
0.9469
Yes
Yes
0.9523
Pf
ln( Pith )
it
Pf
[ln( Pith )]2
it
Pf
it
Constant
Region Fixed Effects
Month Fixed Effects
R-squared
Number of observations in each column: 16,552. Standard errors are robust and clustered by region to allow
for arbitrary serial correlation. *** , ** and *: indicate 1%, 5% and 10% significance levels, respectively.
Standard errors are in parentheses.
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a maximum of 100km from the border, between 100km and 200km, and beyond 200km.
Only the estimate associated with the first dummy is significant, suggesting that smuggling
activities are sensitive to the distance at the border regions. Consistent with the earlier
findings the sign is negative. One plausible explanation is that smugglers who are living
close to the border have a lower price elasticity compared to the professional smugglers that
benefit from pumping of hubs in areas far from the border. The cost of smuggling also
increases as the distance from the border increases. Thus, the demand for smuggling in
central areas is probably more sensitive to the price differentials.
5.2. Disaggregated Elasticity Estimations
After reporting the aggregate estimates of elasticity (for all time periods and over all
regional hubs), we focus on the elasticity over time and across different regions. We conduct
a system estimation for individual hubs based on the specification in Equation 12.
16
5.2.1. Time-Varying Elasticity
Figure 3 presents the weighted average over all regions of the time-varying price elasticity
of smuggling demand for gasoline. Four critical incidents are worth noticing. A good match
between the timeline of real world events and our estimated values provide strong anecdotal
support to the validity of our estimations.
The first incident (fuel rationing) takes place around July 2007 and is related to a pure
quantity effect. We observe that prior to the enforcement of the fuel rationing policy (July
2007), the level of smuggling elasticity is substantial. However, right after the start of the
rationing policy (by obliging the consumers to use their electronic fuel card) a significant
drop (almost to zero) is observed.
16
Some of the model-predicted smuggling elasticity values are negative (particularly in hubs that are close
to the border.) These few negative values should be interpreted with some caution. As the theoretical
model of Appendix B highlights, in the presence of fixed legal and transportation costs the smuggling only
takes place when the price differential is larger than a certain threshold. The estimated intercept for the
main equation is negative; thus, the predicted values of model are only valid when the price differential is
sufficiently larger.
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Table 5: Demand Estimation with The Semi-Structural Approach, Alternative Distance Estimation
Dependent Variable: Dit (Gasoline consumption in the regions of Iran)
Variables
(1)
(2)
Pf
ln( Pith )
it
Pf
[ln( Pith )]2
it
Pf
ln( Pith ) ∗ (dit )
-0.156**
(0.0685)
-0.0899
(0.0696)
0.0459***
(0.0152)
0.0493***
(0.0161)
0.000208*
(0.000116)
it
Pf
ln( Pith )*Dummy(dit , (1, 100))
-0.0916***
(0.0315)
it
Pf
ln( Pith )*Dummy(dit , (100, 200))
0.00314
(0.0289)
it
Pf
ln( Pith )*Dummy(dit , (200, 560))
-0.0303
(0.0224)
it
ln[(GDP per Capita)it ]
0.207
(0.144)
0.219
(0.144)
ln[(Cars per Capita)it ]
-0.0828
(0.0825)
-0.0864
(0.0833)
ln[(Motorcycles per Capita)it ]
0.0126
(0.0598)
0.00852
(0.0601)
-0.0377***
(0.0125)
-0.0360***
(0.0131)
14.89***
(2.282)
14.77***
(2.280)
Yes
Yes
0.9522
Yes
Yes
0.9524
ln[(Unemployment Rate)it ]
Constant
Region Fixed Effects
Month Fixed Effects
R-Squared
Number of observations in each column: 16,552. Standard errors are robust and clustered by region to allow
for arbitrary serial correlation. *** , ** and *: indicate 1%, 5% and 10% significance levels, respectively.
Standard errors are in parentheses.
26
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Figure 3: Weighted Average of monthly Elasticity of Gasoline Smuggling Demand in Iran
The solid line is the estimate of the smuggling elasticity and the dotted line is one standard error of the
elasticity computed by bootstrap (50 replications). The vertical lines show major policy changes (significant
price increases and rationing). Also, increases in the amount of the nominal price are in parentheses. Finally,
the dashed line is corresponding to the secondary vertical axis and is the gasoline price in Iran.
The second interesting event window happens around mid-2008, when due to the drop in
the global crude oil prices, gasoline prices in the neighboring countries also declined (while
the domestic prices were kept constant).
Third, the domestic gasoline prices experienced a more than two-fold increase in December 2010. We see that the elasticity of gasoline smuggling has reached its lowest bound in the
past ten years, and that the average of monthly gasoline smuggling elasticity in the country
is almost zero in the late 2011.
Finally, starting in mid-2012, a significant deprecation of the Iranian currency reduced
the dollar value of domestic gasoline prices and created a new wave of gasoline smuggling.
Smuggling activities became profitable again and a slow upward trend in elasticity is observed
which lasts until May 2014.
Two observations are worth noting. First, in order to estimate smuggling, the fact that
the major variation over time comes mainly from price differentials between Iran and neighboring countries actually works to our advantage. In the absence of smuggling, we would
not expect the fuel prices in a neighboring country to affect the sales of gasoline in Iran;
whereas, we observe a significant sensitivity of our estimated parameter to the prices in
27
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neighboring countries. Second, the time-varying elasticity estimates in Figure 3 are entirely
estimated off of cross-sectional variation in the pairing of hubs to the neighboring countries.
Thus, the correct matching of the smuggling source and the destination country is crucial
for obtaining correct results. If the pairing is not reflective of actual smuggling patterns,
this will introduce measurement error in the foreign price (Pitf ), which would attenuate the
estimated smuggling elasticity.
5.2.2. Location-Specific Elasticity
We also report estimates of smuggling elasticity at the provincial level. One expects that
the elasticity must be higher in locations closer to major illegal export distinctions (e.g.
Turkey in the case of our study). The weighted average of price elasticity of the demand
for gasoline smuggling in each province (for the period 2005 and 2014) is shown in Figure
4. Gasoline smuggling intensity is distributed heterogeneously with respect to the distance
from the border and the price differential with the closest higher-price neighbor country.
The highest elasticity is estimated for West Azerbaijan (WA) with an approximate elasticity
of 0.048. This province is the source for a large-scale smuggling to Turkey and Iraq. Razavi
Khorasan (RK) and North Khorasan (NK) provinces with the elasticity of 0.037 and 0.033
come next. It might be interesting to know that the highest monthly elasticity of gasoline
smuggling demand in Iran (0.14) belongs to Xoy, a small city located in West Azerbaijan
that is 45 kilometers away from Turkey’s border. Note that in the model, the elasticity of
smuggling is determined by the sum of two effects: 1) the “distance to the border” (- up to a
point), 2) the “relative price at the closest higher-price border” (always +). What happens
is that fuel prices are much higher in Turkey than in other countries; thus, the relative price
effect dominates the (decreasing) distance effect and makes the elasticity for provinces close
to Turkey very high.
A meticulous question that arises after seeing Figure 4 is why are not enforcement efforts
reallocated from low-smuggling hubs to high-smuggling hubs? First of all, we are not sure
if the authorities can easily identify those hubs (our study might help them in this regard).
28
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Second, the hubs far from the border are typically located in urban areas (and connected to
many individual gas stations). In practice it is almost impossible to identify a “smuggler”
from a legit commercial or individual buyer of the fuel.
Figure 4: Weighted Average Gasoline Smuggling Elasticity By Province
Legend shows the intervals and number of provinces in each. Numbers in neighboring countries are average
gasoline price per liter in terms of US dollar. Results are from 2005-2014. WA, KR and NK stand for West
Azerbaijan, Razavi Khorasan and North Khorasan, respectively.
6. Discussion and Robustness Checks
In this section we offer multiple robustness checks by changing critical specifications of
the model.
17
17
As a robustness test, we included the quadratic price ratio term interacted with the log of distance in
the semi-structural approach, but the results did not change in a significant way.
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6.1. Effect of Octane
To properly interpret the price differentials between Iran and its neighboring countries,
one should consider the octane level of gasoline produced and sold in these markets. Table 6
reports the usual gasoline octane level in the countries presented in our study. We see that
except for Turkey, all other countries have an equal or a lower octane number compared to
Iran. Though not always but typically a higher octane may imply a better type of gasoline,
if it were true, the smuggled gasoline from Iran could be considered as a premium good in
those destinations. Additionally, the Iranian gasoline should be sold at a discount in the
Turkish market. Thus, the estimate for the elasticity in the Turkish market might be biased
downward.
Table 6: Gasoline’s Octane Rating in Iran and Its Neighboring Countries
Country
Iran Turkey Pakistan Azerbaijan Iraq Persian Gulf
Octane Rating
92
95
90
92
87
91
6.2. Unemployment Rate
The first robustness check considers the role of the unemployment rate. The negative
sign of the estimated unemployment rate coefficient indicates that the gasoline demand is
reduced by an increase in the unemployment rate. This influence might be different for
regions closer to the border. Raising the unemployment rate in border areas leads to a
reduction in income, so it may increase the incentive for the local population to smuggle
gasoline. Therefore, unlike the inner provinces, a higher rate of unemployment in border
provinces may indeed increase the recorded sales of gasoline in that region.
To test for this alternative hypothesis, we conduct an analysis which is demonstrated in
the first column of Table 7 . We define a dummy variable to represent provinces closer to the
border and add an interaction term with the unemployment rate to the regression equation.
If a higher local unemployment indeed encourages smuggling, we expect to see a positive
sign for the coefficient of the interaction term. However, the negative sign of the estimated
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term suggests that gasoline demand diminishes with the increase of the unemployment rate
even in regions near the border. Therefore, we conclude that either the local demand effect
dominates that of the other variables, or that the province-level unemployment is not a
significant determinant of the incentive for smuggling.
6.3. Reverse Smuggling
Among Iran’s neighboring countries, Turkmenistan has always had a lower gasoline price
throughout the time span of this study. Thus, a (reverse) smuggling route from Turkmenistan to Iran is conceivable. To gauge the effect of this possibly contaminating channel,
we re-estimate the model after dropping certain cities. These cities include cities closed to
Turkmenistan and cities that are closer to Afghanistan, but are also close enough to Turkmenistan as well. The results reported in the second column of Table 7 illustrate that the
significance level and the coefficients size of the previous estimations are not different.
6.4. Domestic Price: Rationed or Non-Rationed?
Following what we have stated in the data section, we assume that the demand for
domestic consumption and smuggling are fulfilled through both rationed and non-rationed
gasoline18 . Hence, as an additional robustness check, the average price for rationed and
non-rationed gasoline is used. Moreover, the rationed (i.e. heavily subsidized) gasoline
price is used for domestic demand and the non-rationed gasoline price (less subsidized) is
applied for foreign demand estimation. The third column of Table 7 illustrates that the
coefficients have not changed compared to previous estimations. Moreover, the coefficients
still are significance at the same level, and only the significance of the coefficient for the log
Pf of price ratio ln( Pith ) is reduced from the 1% level to 5% level in comparison to the previous
it
estimation.
18
Except for a short period of time in 2007, the rationing did not put any quantity restrictions on the
consumer. It was possible to purchase any quantity of gasoline beyond the subsidized ration by paying a
higher official price. This policy of dual prices lived for a few years and then was abandoned in 2015 toward
a unified subsidy-free policy.
31
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6.5. Measurement Errors in Covariates
Because we control for both region fixed effects and time fixed effects, aggregate variables
like GDP per capita and unemployment rate are demeaned. Thus, their deviations help to
identify their coefficients. It is likely that if these deviations are just measurement errors
then including them in our estimation cuts the significance of other variables.19 In order to
investigate this concern, we estimate the model by ignoring these two control variables. It
is observed in the fourth column of Table 7 that in comparison to the previous estimation
results, all the three important coefficients for the price elasticity of smuggling demand for
gasoline have preserved their coefficient signs and significance levels.
6.6. Non-linearity in Distance
Another concern is that the socio-economic conditions may be non-linear by distance so
the way we model them in Equation 11 undermines these effects. In particular, the nonlinearity in the socio-economic conditions of hubs that are located very close to the border
compared with hubs in border regions may derive our results. Hence, as the last robustness
check, we omit the areas with a distance greater than d¯ and estimate the model with the
¯ 50km, 100km, 200km,
remaining data. In Table 8, we consider the following amounts for d:
300km, and 400km. Results in Table 8 indicate that the coefficient of interaction of the
Pf
log of price ratio and the log of distance ln( Pith ) ∗ ln(dit ) is not significant for the areas
it
with a distance of less than 100km. In the areas with greater distances, however, all three
important coefficients in the elactisity for smuggling are significant at the 1% level, and with
the expected signs.
7. Conclusion
This paper investigates the price elasticity of the demand for gasoline smuggling in Iran.
Toward this goal, a panel of monthly gasoline consumption data has been used that contains
19
Fisher and Schlenker (2012) highlight the same issue in the context of climate change
32
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Variables
Table 7: Semi-Structural Approach Estimation - Robustness Check
Dependent Variable: Dit (Gasoline consumption in the regions of Iran)
(1)
(2)
(3)
Pf
ln( Pith )
(4)
-0.212***
(0.0791)
-0.261***
(0.101)
-0.156**
(0.0699)
-0.207***
(0.0781)
0.0414***
(0.0144)
0.0517***
(0.0154)
0.0347***
(0.0109)
0.0390***
(0.0148)
0.0213***
(0.00712)
0.0250***
(0.00728)
0.0155***
(0.00568)
0.0233***
(0.00706)
ln[(GDP per Capita)it ]
0.177
(0.142)
0.222
(0.157)
0.205
(0.143)
ln[(Cars per Capita)it ]
-0.0919
(0.0824)
-0.108
(0.0924)
-0.0836
(0.0829)
-0.0567
(0.0809)
ln[(Motorcycles per Capita)it ]
0.00149
(0.0590)
0.00334
(0.0625)
0.00875
(0.0600)
0.00879
(0.0607)
ln[(Unemployment Rate)it ]
-0.0273**
(0.0132)
-0.0385***
(0.0124)
-0.0386***
(0.0126)
15.31***
(2.262)
14.75***
(2.452)
15.25***
(2.556)
17.75***
(1.288)
Yes
Yes
16,552
0.9524
Yes
Yes
14,305
0.9485
Yes
Yes
16,552
0.9523
Yes
Yes
16,552
0.9520
it
Pf
[ln( Pith )]2
it
Pf
ln( Pith ) ∗ ln(dit )
it
ln[(Unemployment Rate)it ]
*Dummy(dit , 1)
Constant
Region Fixed Effects
Month Fixed Effects
Observations
R-squared
-0.294***
(0.0685)
Standard errors are robust and clustered by region to allow for arbitrary serial correlation. *** , ** and *:
indicate 1%, 5% and 10% significance levels, respectively. Standard errors are in parentheses.
33
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Variables
Table 8: Semi-Structural Approach Estimation - Distance Robustness Check
Dependent Variable: Dit (Gasoline consumption in the regions of Iran)
(1)
(2)
(3)
(4)
dit 6 50
dit 6 100
dit 6 200
dit 6 300
Pf
ln( Pith )
(5)
dit 6 400
-0.238**
(0.101)
-0.295***
(0.106)
-0.314***
(0.106)
-0.234***
(0.0884)
-0.225***
(0.0799)
0.0798***
(0.0246)
0.0838***
(0.0273)
0.0591***
(0.0190)
0.0438***
(0.0157)
0.0455***
(0.0141)
0.000439
(0.0185)
0.0123
(0.0157)
0.0281***
(0.0103)
0.0245***
(0.00730)
0.0220***
(0.00685)
ln[(GDP per Capita)it ]
0.163
(0.131)
0.262
(0.195)
0.241
(0.174)
0.220
(0.156)
0.206
(0.147)
ln[(Cars per Capita)it ]
-0.150**
(0.0718)
-0.0932
(0.131)
-0.143
(0.117)
-0.0920
(0.0923)
-0.0891
(0.0834)
ln[(Motorcycles per Capita)it ]
0.257***
(0.0984)
0.0401
(0.0873)
0.0296
(0.0695)
0.00247
(0.0620)
0.00701
(0.0597)
-0.0619***
(0.0164)
-0.0508***
(0.0165)
-0.0308*
(0.0164)
-0.0295**
(0.0129)
-0.0348***
(0.0131)
11.02***
(1.518)
10.77***
(3.367)
13.08***
(2.810)
14.68***
(2.461)
14.84***
(2.320)
Yes
Yes
4,346
0.9640
Yes
Yes
7,325
0.9343
Yes
Yes
10,899
0.9431
Yes
Yes
14,694
0.9489
Yes
Yes
16,112
0.9516
it
Pf
[ln( Pith )]2
it
Pf
ln( Pith ) ∗ ln(dit )
it
ln[(Unemployment Rate)it ]
Constant
Region Fixed Effects
Month Fixed Effects
Observations
R-squared
Standard errors are robust and clustered by region to allow for arbitrary serial correlation. *** , ** and *:
indicate 1%, 5% and 10% significance levels, respectively. Standard errors are in parentheses.
34
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observations for 160 distribution hubs of the National Iranian Oil Products Distribution
Company (NIOPDC) between 2005 and 2014. The aggregate estimation results of the elasticity of monthly gasoline smuggling demand shows the significant effect of gasoline price
ratios between Iran and its neighboring countries. On the other side, as the distance of a
region from its closest higher-price neighboring country diminishes, the price elasticity of
demand for gasoline smuggling for that region declines as well.
Our paper takes the first step to provide reliable quantitative evidence to support the
effect of domestic price adjustments on the tendency to smuggle fuel. Our results suggest
that adjusting domestic fuel prices to bring them closer to the international equilibrium can
generate considerable fiscal savings. The key point of our study is that smuggling acts as
a “variable” component of government budget. The higher the level of domestic prices, the
lower is the demand for smuggling (thus, the lost subsidy to neighboring countries) as well as
the domestic demand for gasoline. Both channels reduce the subsidy burden. The removal
of subsidy basically replaces a variable level of subsidies with a time-invariant (in nominal
terms) cash transfer.
This paper can be extended in multiple directions. First, the unique method presented
in the current paper can also be used to estimate the elasticity of domestic demand for
gasoline. Second, our results do not include the public finance aspects of the problem.
Future work can provide estimates of government subsidies and the fiscal saving and welfare
implications of the price adjustment. Our results can also be combined with more granular
socio-economic variables at the household level to examine the distributional effects of timevarying smuggling activities on households involved with those activities.
Our model estimates the “changes in smuggling as a response to “changes in the relative
price. However, it is not possible to estimate the baseline magnitude of smuggling using this
model (because one can only observe the differential response). We added some unofficial
figures to the introduction of the paper but there is no reliable estimate of the total volume
of the smuggled gasoline. An important follow-up research can focus on estimating the
35
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magnitude of the smuggled fuels.
Acknowledgment
We are grateful for comments received following the presentation of an earlier draft of this
paper at Sharif University of Technology and the members of NYC’s Dark Coffee Economics
Discussion Group. We also would like to thank Djavad Salehi-Isfahani, Mohammad Vesal,
Sorena Rahi and two anonymous reviewers for their very helpful comments on an earlier
draft. The authors are, of course, responsible for all errors and omissions.
36
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Appendix A
Relationship Between the Elasticity and the Distance
We present a stylized theoretical model of smuggling in order to derive the relationship
between the elasticity of the demand for smuggling and the distance from Hi to the border
of Fit .
Consider an economy with representative smugglers in each region. We solve the utility
maximization problems of the representative smugglers to derive the equilibrium outcome
of the smuggling intensity for a given level of price differentials (between home and foreign
countries), probability of apprehension, level of penalty, and finally the distance from the
border.
The utility function of a smuggler in region i and at time t who smuggles qit amount of
gasoline from Hi to Fit can be written as follows:
Uit (qit ) = (1 − λit ) (Pitf − Pith )qit − Φ(qit , dit ) − Zλit qit
(16)
where, λit is the probability of the apprehension and penalizing of the smuggler by the
police, Φ(qit , dit ) is the cost of transporting q units of gasoline from Hi to Fit , and Z is a
fixed cost that the smuggler is obliged to pay per unit of gasoline when he is penalized. It
should be noted that in each region i at time t, the prices, the distance and the probability
may be have different values, and hence, the utility of the smuggler can vary over time and
space.
We define ∆Pit = Pitf − Pith and then write the F.O.C in the form of (1 − λit )∆Pit =
(1 − λit )Φ1 (qit , dit ) + λit Z. We use the implicit function theorem to obtain the elasticity:
it =
∆Pit ∂(qit )
∆Pit
−1
∂ ln(qit )
=
=
∂ ln(∆Pit )
qit ∂∆Pit
qit Φ11 (qit , dit )
(17)
In order to derive the relationship between the elasticity of the demand for smuggling
37
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and distance, we use the following derivative to obtain the relationship:
∂it
∂it ∂qit
=
=
∂dit
∂qit ∂dit
∆Pit
∆P Φ111 (qit , dit )
+
2
2
qit Φ11 (qit , dit )
Φ11 (qit , dit )
!
−Φ12 (qit , dit )
Φ11 (qit , dit )
!
If one assumes that Φ11 (qit , dit ) > 0, then if Φ12 (qit , dit ) < 0, we conclude that
(18)
∂it
∂dit
> 0,
which is what we obtained from our estimates. This model corresponds to the notion that
those who are far from the border, are more sensitive to price changes.
Appendix B
Estimation of the Demand for Premium Gasoline
The premium gasoline is the higher quality version of the fuel with a higher octane. It
is sold with a significant price premium (between 10% - 30% higher) in the Iranian market.
To gain insights from a possible alternative estimation result, we repeat the semi-structural
estimation procedure by replacing the regular gasoline price and quantity data with those
of the premium gasoline. A summary of key results in this regard are reported in Table 9.
The results suggest that none of the coefficients associated with the determinants of
smuggling are significant when the estimation is done using the premium gasoline. There
are multiple plausible explanations for this negative (i.e. insignificant) result. First, the
premium gasoline is more expensive than the regular gasoline; however, the quality of the
gasoline in the destination is not verifiable and it will be sold at the same price as the
conventional one. Therefore, the price differentials between Iran and neighboring countries
will be smaller for the premium gasoline, resulting in a lower incentive to smuggle this type of
gasoline when the conventional type is available. Also, note that in the case of apprehension,
the smuggler will lose a larger value of the confiscated good. Secondly, premium gasoline is
mainly offered in larger cities far from the border. Therefore, it is more difficult to obtain a
larger volume of the premium type in hubs near the border. Finally, the total market share
of the premium gasoline is very small (only 3.5% of the total gasoline market). We also have
noted data recording problems, missing observations, outliers, and finally small sample size
problems in the premium gasoline data. As a result, the results of this estimation are likely
38
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Table 9: Demand Estimation with The Semi-Structural Approach
Dependent Variable: Dit (Premium gasoline consumption in the regions of Iran)
Variables
(1)
(2)
Pf
ln( Pith )
(3)
0.630*
(0.377)
-0.0699
(0.106)
-0.137
(0.144)
-0.117
(0.0917)
-0.0432
(0.0396)
0.0533
(0.0425)
ln( Pith ) ∗ ln(dit )
-0.0644
(0.0721)
0.0146
(0.0179)
0.0133
(0.0180)
ln[(GDP per Capita)it ]
0.770**
(0.300)
0.182
(0.254)
0.0741
(0.309)
ln[(Cars per Capita)it ]
0.00723
(0.348)
0.947***
(0.127)
0.0908
(0.144)
ln[(Motorcycles per Capita)it ]
-0.0880
(0.235)
-0.0795
(0.105)
0.0840
(0.0918)
ln[(Unemployment Rate)it ]
-0.0488
(0.213)
-0.117**
(0.0456)
-0.104***
(0.0349)
Constant
2.744
(5.204)
12.47***
(3.437)
-6.323
(4.303)
No
No
0.066
Yes
No
0.8050
Yes
Yes
0.8802
it
Pf
[ln( Pith )]2
it
Pf
it
Region Fixed Effects
Month Fixed Effects
R-squared
Number of observations in each column: 9,461. Standard errors are robust and clustered by region to allow
for arbitrary serial correlation. *** , ** and *: indicate 1%, 5% and 10% significance levels, respectively.
Standard errors are in parentheses.
39
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to be subject to small sample and measurement errors.
Appendix C
Distribution of the Distance
We plot the histogram for the weighted average of the distance of hubs from the border
for the period between 2005 and 2014 in Figure 5.
Figure 5: Histogram Plot of Weighted Average of Distance of Hubs from Border
The numerical values above the bars show the frequency of hubs in each bin.
40
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References
Asplund, M., Friberg, R., and Wilander, F. (2007). Demand and distance: Evidence on
cross-border shopping. Journal of public Economics, 91(1):141–157.
Banfi, S., Filippini, M., and Hunt, L. C. (2005). Fuel tourism in border regions: The case of
switzerland. Energy Economics, 27(5):689–707.
Beard, T. R., Gant, P. A., and Saba, R. P. (1997). Border-crossing sales, tax avoidance, and
state tax policies: an application to alcohol. Southern Economic Journal, pages 293–306.
Bridel,
A. and Lontoh,
subsidy reform.
L. (2014).
Lessons learned:
Malaysias 2013 fuel
https://www.iisd.org/GSI/sites/default/files/ffs_malaysia_
lessonslearned.pdf.
Chiou, L., Muehlegger, E., et al. (2008). Crossing the line: The effect of cross border
cigarette sales on state excise tax revenues. John F. Kennedy School of Government,
Harvard University.
Coady, D., Gillingham, R., Ossowski, R., Piotrowski, J., Tareq, S., and Tyson, J. (2010).
Petroleum product subsidies: costly, inequitable, and rising.
Coats, R. M. (1995). A note on estimating cross-border effects of state cigarette taxes.
National Tax Journal, pages 573–584.
Crawford, I., Smith, Z., and Tanner, S. (1999). Alcohol taxes, tax revenues and the single
european market. Fiscal Studies, 20(3):287–304.
Crawford, I. and Tanner, S. (1995). Bringing it all back home: Alcohol taxation and crossborder shopping. Fiscal Studies, 16(2):94–114.
Dartanto, T. (2013). Reducing fuel subsidies and the implication on fiscal balance and
poverty in indonesia: A simulation analysis. Energy Policy, 58:117–134.
41
Electronic copy available at: https://ssrn.com/abstract=2954034
Davis, L. W. (2014). The economic cost of global fuel subsidies. The American Economic
Review, 104(5):581–585.
Fisher, A.C., H. W. R. M. and Schlenker, W. (2012). The economic impacts of climate
change: evidence from agricultural output and random fluctuations in weather: comment.
American Economic Review, pages 3749–3760.
Fisher, R. (2007). State and local public finance (3rd ed.). Mason: Thompson South-Western.
Fleenor, P. (1998). How excise tax differentials affect interstate smuggling and cross-border
sales of cigarettes in the United States. Tax Foundation.
Fleenor, P. (1999). How excise tax differentials affect cross-border sales of beer in the United
States. Tax Foundation.
Fox, W. F. (1986). Tax structure and the location of economic activity along state borders.
National Tax Journal, pages 387–401.
Guillaume, M. D. M., Zytek, M. R., and Farzin, M. M. R. (2011). Iran: The chronicles of
the subsidy reform. Number 11-167. International Monetary Fund.
Hamovitch, W. (1966). Effects of increases in sales tax rates on taxable sales in new york
city. Financing Government in New York City, pages 619–634.
Haufler, A. (1996). Tax coordination with different preferences for public goods: Conflict or
harmony of interest? International Tax and Public Finance, 3(1):5–28.
Kanbur, R. and Keen, M. (1993). Jeux sans frontieres: Tax competition and tax coordination
when countries differ in size. American Economic Review, 83(4):877–92.
Leal, A., Lopez-Laborda, J., and Rodrigo, F. (2010). Cross-border shopping: a survey.
International Advances in Economic Research, 16(2):135–148.
42
Electronic copy available at: https://ssrn.com/abstract=2954034
Loveless, B. (2015). World bank economists seek oil subsidies’ end. http://www.usatoday.
com/story/money/business/2015/04/15/loveless-imf-world-bank-oilprices/
25844293/.
Lovenheim, M. F. (2008). How far to the border?: The extent and impact of cross-border
casual cigarette smuggling. National Tax Journal, pages 7–33.
Maliet, L. D. (1955). A comparative study of the Illinois retailers’ occupation tax and the
Iowa retail sales and use taxes. PhD thesis, University of Illinois at Urbana-Champaign.
McAllister, H. E. (1961). The border tax problem in washington. National Tax Journal,
14(4):362–374.
Merriman, D. (2010). The micro-geography of tax avoidance: evidence from littered cigarette
packs in chicago. American Economic Journal: Economic Policy, 2(2):61–84.
Mikesell, J. L. (1970). Central cities and sales tax rate differentials: The border city problem.
National Tax Journal, 23(2):206–213.
Mikesell, J. L. (1971). Sales taxation and border county problem. Quarterly Review of
Economics and Business, 11(1):23–29.
Nielsen, S. B. (2001). A simple model of commodity taxation and cross-border shopping.
The Scandinavian Journal of Economics, 103(4):599–623.
Nielsen, S. B. (2002). Cross-border shopping from small to large countries. Economics
Letters, 77(3):309–313.
Ohsawa, Y. (1999). Cross-border shopping and commodity tax competition among governments. Regional Science and Urban Economics, 29(1):33–51.
Rahmati, Mohammad H, A. H. T. and Vesal, M. (2017). What five hundred million transactions tell us about demand elasticity of gasoline? Sharif University of Technology.
43
Electronic copy available at: https://ssrn.com/abstract=2954034
Saba, R. R., Beard, T. R., Ekelund, R. B., and Ressler, R. W. (1995). The demand for
cigarette smuggling. Economic Inquiry, 33(2):189–202.
Scharf, K. A. (1999). Scale economies in cross-border shopping and commodity taxation.
International Tax and Public Finance, 6(1):89–99.
Sindo, K. (2014). Sejarah harga bbm subsidi di indonesia. http://economy.okezone.com/
read/2014/08/28/19/1030923/sejarah-harga-bbm-subsidi-di-indonesia.
Smith, R. T. (1976). The legal and illegal markets for taxed goods: pure theory and an
application to state government taxation of distilled spirits. The Journal of Law and
Economics, 19(2):393–429.
Trandel, G. A. (1994). Interstate commodity tax differentials and the distribution of residents. Journal of Public Economics, 53(3):435–457.
Victor, D. (2009). The politics of fossil-fuel subsidies: global subsidies initiative & the
international institute for sustainable development. geneva: Global subsidies initiative.
International Institute for Sustainable Development.
Walsh, M. J. and Jones, J. D. (1988). More evidence on the ”border tax” effect: The case
of west virginia, 1979-84. National Tax Journal, 41(2):261–265.
Wang, Y.-Q. (1999). Commodity taxes under fiscal competition: Stackelberg equilibrium
and optimality. The American Economic Review, 89(4):974–981.
Warner, K. E. (1982). Cigarette excise taxation and interstate smuggling: an assessment of
recent activity. National Tax Journal, 35(4):483–490.
Wertz, K. L. (1971). Cigarette taxation by the american states. National Tax Journal, pages
487–492.
44
Electronic copy available at: https://ssrn.com/abstract=2954034
WorldBank (2012). Bantuan Langsung Tunai (BLT) temporary unconditional cash transfer.
Social assistance program and public expenditure. Public expenditure review (PER), page
review no. 2.
45
Electronic copy available at: https://ssrn.com/abstract=2954034