Received: 7 August 2021 Revised: 16 October 2022 Accepted: 24 October 2022
DOI: 10.1002/jae.2947
RE S EARCH ART I C L E
Identifying the effects of sanctions on the Iranian economy
using newspaper coverage
Dario Laudati1 M. Hashem Pesaran1,2
1University of Southern California, Los
Angeles, California, USA
2Trinity College, Cambridge, UK
Correspondence
Dario Laudati, University of Southern
California, 3620 S. Vermont Ave.,
Kaprelian Hall 300, Los Angeles, CA
90089, USA.
Email: laudati@usc.edu
Summary
This paper focuses on the identification and quantitative estimation of sanc-
tions on the Iranian economy over the period 1989–2019. It provides a new time
series approach and proposes a novel measure of sanctions intensity based on
daily newspaper coverage. In absence of sanctions, Iran's average annual growth
could have been around 4–5%, as compared to the 3% realized. Estimates of the
proposed sanctions-augmented structural VAR show that sanctions significantly
decrease oil export revenues and result in substantial depreciation of Iranian
rial, followed by subsequent increases in inflation and falls in output growth.
Keeping other shocks fixed, 2 years of sanctions can explain up to 60% of output
growth forecast error variance, although a single quarter sanction shock proves
to have quantitatively small effects.
KEYWORDS
direct and indirect effects of sanctions, forecast error variance decompositions, impulse responses,
measures of sanction intensity, sanctions-augmented structural VAR
1 INTRODUCTION
Over the past 40 years, Iran has been subject to varying degrees of economic and financial sanctions, and asset freezes,
which began in November 1979 when the US placed an embargo on Iranian oil trade and froze $12 billion of Iranian
assets held outside Iran with the aim of securing the release of U.S. hostages. Although this particular sanction episode
was successfully negotiated in January 1981, U.S. policy towards Iran became increasingly entrenched, aimed at curtailing
the economic and political influence of Iran in the Middle East region and beyond, a process that escalated over Iran's
nuclear program. As a result, the Iranian economy has been operating for a prolonged period under severe and often quite
harsh international restrictions, perhaps unique for a sizeable economy with deep historical roots in the global economy.
Given the uncertainty and durability of sanction regimes, it is also important to bear in mind that, besides the direct
effects of sanctions (arising from loss of oil export revenues, loss of access to currency reserves, and other trade-related
losses), sanctions also result in important and lasting indirect effects, such as rent-seeking, resource allocation distortions,
and general costs associated with efforts involved in mitigating and circumventing the sanctions regimes. These indirect
effects are likely to be more serious the longer the sanctions are in place, particularly when the prospect of a sanctions
free outcome seems very remote.
The focus of this paper is on the identification and quantitative evaluation of the direct and indirect effects of sanctions
on the Iranian economy over the period 1989–2019, which intentionally excludes the period 1979–1988 due to the spe-
cial circumstances of the 1979 Revolution, the hostage crises and the ensuing eight year war with Iraq, which ended in
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium,
provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
© 2022 The Authors. Journal of Applied Econometrics published by John Wiley & Sons, Ltd.
J Appl Econ. 2022;1–24. wileyonlinelibrary.com/journal/jae 1
2 LAUDATI AND PESARAN
August 1988, as well as the post 2019 period to avoid the confounding effects of the Covid-19 pandemic. We are primarily
concerned with economic rather than international political dimensions of the sanctions, and will not be addressing the
issue of the efficacy of sanctions in achieving their political aims.1
Sanctions against Iran span a period of more than four decades over which the degree of sanctions intensity has var-
ied considerably. There are no clear “sanctions on” and “sanctions off” periods, required for application of comparative
approaches used in the literature for policy evaluations, such as the synthetic control method (SCM) proposed by Abadie
andGardeazabal (2003), and the panel data approach proposed byHsiao et al. (2012). These techniques require pre-policy
intervention outcomes to estimate weighted averages of post policy outcomes for a “pre-selected” control group to be used
as the basis of comparisons. It is also unclear which countries should be included in the control group given the continued
importance of the Iranian economy in the Middle East region.
In this paper, we propose a time series approach that takes account of variations in sanctions intensity over the past
forty years, without requiring an arbitrary classification of the time periods into sanctions on and sanctions off periods.
To this end, we construct a time series index of sanctions intensity based on daily newspaper coverage of the sanctions,
their imposition, the intensity of their use, as well as their occasional removal. Given the absence of clear “sanctions off”
periods, it follows also that simple (0,1) dummy variables may not be sufficient to capture the rich variations in sanctions
intensity that are observed over the past forty years.2
The idea of a newspaper coverage index was developed by Baker et al. (2016) for measurement of economic uncertainty,
but to our knowledge it has not been utilized in the analysis of sanctions. As we shall see, the evolution of our proposed
sanctions intensity index closely tracks themain sanctions time points.3 See Figure 1. The sanctions intensitymeasure also
correlates closely with the U.S. Treasury “Specially Designated Nationals and Blocked Persons List” (SDN) for Iran which
has been publicly available since 1994, but with usable data on Iran only since 2006.
We augment a structural vector autoregressive (SVAR)model of the Iranianmacroeconomywith our sanctions intensity
variable to identify short run and long run effects of sanction shocks on oil export revenues, Iran's rial/USD exchange
rate, money supply growth, inflation, and output growth, while controlling for foreign output growth, and other global
factors such as global equity market volatility. We also consider the effects of sanction shocks when sustained over a
prolonged period. This is particularly relevant to the case of Iran where government policy responses often have led to
large fuel and food subsidies, multiple exchange rates, and lax budgetary and credit policies, which in turn have resulted
in economicmismanagement and rent-seeking, and corruption on a large scale.4 Seen from thismore general perspective,
in addition to direct (and often immediate) effects of sanctions on oil exports and exchange rates, there are also indirect
effects that result from government policy responses, some of which are inevitable as the government tries to come to
terms with the adverse effects of the sanction, particularly on the economic conditions of the low income groups on fixed
wages and salaries. While we acknowledge such indirect effects of sanctions, it is beyond the scope of the present paper to
disentangle the direct and indirect effects of the sanctions. This drawback particularly applies to the long run effects of the
sanctions that move beyond the immediate effects on oil exports and exchange rates that are much easier to identify. At
the same time, it is true that the Iranian economy would have been subject to distortions and economic mismanagement
even in the absence of any sanctions, and it seems unlikely that one could separate sanctions-induced distortions from all
other distortions. Therefore, the estimates we present can be viewed as measuring the combined effects of sanctions and
sanctions-induced distortions, broadly defined.
1The effectiveness of sanctions in achieving foreign policy goals has been studied extensively in the literature. Hufbauer et al. (1990) examine 116
case studies covering the period from the economic blockade of Germany during World War I through the U.N.-U.S. embargo of Iraq in 1990. Further
overviews are provided in Morgan et al. (2014) and Doxey (1996). Critical assessments of sanctions as a policy tool are provided by Weiss et al. (1997),
Pape, (1997, 1998), Andreas (2005), and Peksen and Drury (2010). These studies highlight possible counterproductive effects of economic sanctions.
Naghavi and Pignataro (2015) provide a game-theoretic analysis of sanctions and its application to Iran.
2To investigate the value added of our proposed measure of sanctions intensity, as suggested by a referee, we also considered dummy variables con-
structed based on historical narratives, as well as by a discretization of our own newspapers index. In all cases we found our sanction intensity variable
performs much better than dummy variable measures in explaining the variations in key macroeconomic variables of the Iranian economy.
3The most notable are the US, Iran, and Libya Sanctions Act of 1996, the U.S. export ban in 1997, the U.S. investment bans and asset freezes in 2006
and 2007 (“Iran Freedom and Support Act”, and Executive Order 13438), the United Nations nuclear Resolutions (1737, 1747) during 2006 and 2007,
the U.S. Comprehensive Iran Sanctions, Accountability, and Divestment Act of 2010, the U.S. National Defence Authorization Act of 2012, the partial
lifting of U.N. sanctions under the Joint Comprehensive Plan of Action (JCPOA) in 2015 and its subsequent implementation in January 2016, and,
finally, President Trump's unilateral withdrawal from the JCPOA agreement in 2018.
4Subsidies on essential food items and energy (fuel aswell as electricity) have created inefficiencies, smuggling, anddamagingunintended consequences.
Subsidies on electricity, for example, have led to excessive ground water withdrawals from electricity-powered irrigation wells, and more recently for
mining crypto-currencies, one of the sources of Iran's worsening water shortages, and frequent electricity blackouts.
LAUDATI AND PESARAN 3
FIGURE 1 Sanctions intensity variable over the period 1989q1–2020q3. Note: Major events related to the Middle East are indicated by
arrows below the x axis. Major sanctions episodes related to Iran are indicated by arrows above the x-axis. See Sections S2.1 and S2.2 in the
data appendix of the supporting information for details on the construction of the sanctions intensity variable
We find that the sanctions intensity variable has highly statistically significant effects on oil exports revenues, exchange
rates, inflation, and output growth, but not on money supply growth. These estimates proved to be robust to alternative
specifications and after allowing for a host of control variables. Our results also show that large reductions in oil exports,
currency depreciations (with substantial overshooting), and high inflation are important channels through which sanc-
tions affect the real economy. But we do not findmonetary expansion to have an independent impact on the real economy,
once we control for inflation and exchange rates.
Using impulse response analysis and forecast error variance decomposition,we also find a significant drop in oil exports,
followed by an over-reaction of the rial to a positive sanctions shock, with a subsequent rise in inflation and a fall in output
shortly after. The economy adapts reasonably quickly to sanction shocks, a property that has also been documented by
Esfahani et al. (2013), who consider the effects of oil revenue shocks on output growth and inflation but do not allow for
changes in sanctions environment. Forecast error variance decompositions also show that, despite the inclusion of the
sanctions intensity variable in the SVAR, around 80% of variations in foreign exchange and 82% of variations in output
growth remain unexplained, and most likely relate to many other latent factors that drive the Iranian economy. These
estimates relate to the effects of a single-period sanctions shock as it is standard in the empirical macro literature. The
effects of sanctions on the economy become much more pronounced once we consider the effects of shocks that last for
a consecutive number of periods. We find that such shock scenarios could explain up to 80% of output growth variations
after 5 years. We also estimate that in the absence of sanctions Iran's output growth on average could have been around
4–5% per annum, as compared to the 3% realized.
Sanctions have also had a number of positive unintended consequences. Interestingly, the Iranian economy at the onset
of sanctions was as heavily dependent on oil exports as countries such as Saudi Arabia. Restricting oil exports over a rela-
tively long time period has led to important structural transformations of the Iranian economy, with significant increases
in non-oil exports, most notably petrochemicals, light manufacturing products and agricultural goods. In addition, it is
likely that U.S. sanctions have been partly responsible for the rapid rise of high-tech and knowledge-based companies in
Iran over the past decade.
Overall, there seems little doubt that sanctions have harmed the Iranian economy. But removal of sanctions on their
own is unlikely to ensure a period of sustained growth and low and stable inflation, and many policy reforms are needed
to address sanctions-induced price distortions as well as other distortions due to general economicmismanagement, poor
governance, and the ambiguities that surround the relative roles of semi-government agencies and the private sector in
the economy.
4 LAUDATI AND PESARAN
1.1 Related literature
Studies that are more closely related to our paper either consider a specific sub-period or use shocks to oil export rev-
enues as representing a sanction shock. Gharehgozli (2017) considers the effects of sanctions just before the 2015 nuclear
agreement, Joint Comprehensive Plan of Action (JCPOA), which we discuss in further detail in Section 4 below. Dizaji
and van Bergeijk (2013) study the impact of economic sanctions via changes in oil revenues using an unrestricted VAR
model. They show that sanctions have adverse output effects in the short-run but their effects fade with time. Similar
results are reported by Esfahani et al. (2013), who find that shocks to foreign output and oil exports are rather short-lived
for Iran. This is an important feature of the Iranian economy which is also confirmed by our analysis using the new
sanctions intensity variable. Farzanegan et al. (2016) develop a Computable General Equilibriummodel for Iran and con-
duct a number of different comparative static exercises, finding large effects of oil sanctions on the macro-economy and
households welfare under their perfectly competitive set up.
Haidar (2017) uses micro-data over the period 2006–2011 to find that two-thirds of Iran's sanctioned non-oil exports
were redirected to other non-sanctioning countries. It is also found that large exporters appear to be less affected by
export sanctions. Popova and Rasoulinezhad (2016) find a similar geographical redirection of Iran's non-oil exports over
the period 2006–2013 of trading partners away from Western economies to countries in the region (notably Iraq), China
and other Asian economies. Farzanegan (2014) studies the role of military spending to explain output losses due to oil
shocks. Farzanegan and Hayo (2019) analyze the effect of sanctions to expand the shadow economy. Although not strictly
quantitative in nature, a number of studies maintained that the burden of economic sanctions for Iranian growth was
high but not decisive to bring about political change in Iran. (Carswell (1981), Amuzegar (1997a), Amuzegar (1997b),
Dadkhah and Zangeneh (1998), Downs and Maloney (2011) and Borszik (2016)).
Sanctions have also played an important role in shaping Iran's monetary and financial system. Mazarei (2019) analyzes
the current state of the Iranian financial system and its fragility. Farzanegan andMarkwardt (2009) focus on the extent to
which Iran suffers from a form of “Dutch disease,” thus advocating for a sovereign oil fund to mitigate inflationary pres-
sures and risks of currency crises. Mazarei (2020) highlights the danger of inflation for Iran in the wake of sanctions and
the pandemic. There are also several studies on the determinants of inflation in Iran (not related to sanctions), which could
be of interest. See, for example, the studies by Liu and Adedeji (2000), Celasun and Goswami (2002), and Bonato (2008).
Sanctions have often led to the establishment of multiple exchange rate markets with important rent-seeking
opportunities and related economic distortions. Currently, there are three different exchange rates for the rial.5
Bahmani-Oskooee (1996) provides an earlier account of the gains obtainable in Iran from the blackmarket premium, and
the need to consider the freemarket rate rather than the official onewhen the Iranianmoney demand is to be assessed; we
follow this approach when conducting our analyses and disregard the official rate. The economic implications of multiple
exchange rates in Iran are discussed in Pesaran (1992), Farzanegan (2013) and Majidpour (2013).
Our contributions are both methodological, by expanding the program evaluation literature with a novel econometric
approach, and empirical in terms of the measurement of sanction intensity using textual analysis and its incorporation
in a quarterly macroeconometric model of Iran, which has not been done before.6
The rest of the paper is organized as follows. Section 2 offers an overview of the Iranian economy under sanctions.
Section 3 explains howwe construct the sanctions intensity index from six leading newspapers, and its comovements with
historical events. Section 4 discusses alternative approaches to the analysis of policy interventions, and develops a frame-
work with latent factors used to identify the effects of sanctions on the Iranian economy, as well as providing an estimate
of sanctions-induced output losses. Section 5 reports estimates of sanctions-augmented SVAR models and discusses the
channels through which sanctions affect the Iranian economy. Impulse responses and error variance decompositions are
presented and their robustness to a different ordering of the variables in the SVAR model are discussed. Section 6 ends
with some concluding remarks. An online supplement provides details on the construction of our sanctions intensity
variable, the data sources, with further methodological notes and empirical results. The online supplement also contains
a comprehensive list of economic and financial sanctions imposed against Iran over the past forty years.
5The three exchange rates are (i) the official exchange rate to import essential items such as medicine, grain and sugar; (ii) the Nima rate, officially set
at 2% above the official rate by Bank Markazi daily, but in practice, it is subject to much higher premiums and is reserved for non-oil exporters; (iii) the
free market rate used for all other transactions.
6Also, we are not aware of any study that is able to use data at quarterly frequency for over 40 years to evaluate the long-run effect of sanctions. This is
relevant for the dynamics of the SVAR model and increases the precision of our estimates.
LAUDATI AND PESARAN 5
2 SANCTIONS AND THE IRANIAN ECONOMY: AN OVERVIEW
The evolution of the Iranian economy over the past 40 years has been largely shaped by the Revolution and the 8-year war
with Iraq (1979–1988), prolonged episodes of economic and financial sanctions, and often very different policy responses
to sanctions and economic management under the four presidents that have held office over the period 1989–2019. Ini-
tially, U.S. sanctions were much more clearly targeted. The goal of the 1980–1981 sanctions was to negotiate the release
of U.S. hostages, and the 1987 sanctions to end hostilities in the Persian Gulf and bring about an end to the war with Iraq.
These sanctions aimed at limiting Iran's access to foreign exchange earnings through asset freezes and, more importantly,
by reducing Iran's capacity and ability to produce and export oil.7
Iran's oil exports had been already cut by half from the pre-Revolution peak of 6 millions barrels per day (mb/d).8 The
first U.S. sanctions drove Iran's oil exports to the low of 700,000 b/d before recovering somewhat after the sanctions were
lifted in January 1981. However, since the lifting of the sanctions coincided with the intensification of the war with Iraq,
oil exports did not recover fully till after the war ended in 1988. From 1989 to 2005, with improvements in the diplomatic
relationships between Iran and the US and otherWestern countries, oil exports started to rise and stabilized to around 2.5
mb/d under the presidencies of Rafsanjani (1989q3–1997q2) and Khatami (1997q3–2005q2). Oil exports began to decline
again from 2007 after the imposition of U.S. andU.N. sanctions in December 2006 aimed at halting Iran's uranium enrich-
ment program, which had gathered pace under the newly elected President Ahmadinejad (2005q3–2013q2). Initially,
sanctions targeted investments in oil, gas and petrochemicals, and exports of refined products but were later expanded
to include banking, insurance and shipping. Additional financial sanctions were imposed on Iran from July 2013. The
coverage of U.N. and U.S. sanctions increased well beyond the oil and gas sectors and affected all aspects of Iranian for-
eign trade and international finance, and even the international payment system of Bank Markazi (Iran's Central Bank).
The extensive coverage of the sanctions, their multilateral nature, coupled with the start of President Rouhani's moderate
administration (2013q3–2021q2), paved the way for the 2015 nuclear agreement (JCPOA), implemented in January 2016
which led to the easing of some of the U.S. sanctions and the lifting of U.N. and European Union sanctions against Iran.
But the benefits of the JCPOA to Iran were limited, as many non-U.S. global companies and banks were hesitant to deal
with Iran because of the remaining U.S. sanctions, as well as concerns over money laundering, opacity of ownership, and
the fragility of the Iranian banking system. As it turned out, JCPOA was also short lived, with oil exports sharply declin-
ing after May 2018, when U.S. President Trump unilaterally withdrew from JCPOA, and adopted the policy of “maximum
pressure” against Iran. With the election of President Biden in November 2020, there are negotiations for the US to return
to the 2015 nuclear agreement, although our analysis will be pre-dating these negotiations.
The U.S. sanctions against Iran were mainly of extra-territorial nature. Iran-U.S. trade had already been cut drastically
after the Revolution and did not recover after the resolution of the hostage crisis. In response to sanctions, the Iranian
government made concerted efforts to re-direct Iran's foreign trade from the West to the East and to the neighboring
countries. The sources of foreign exchange were also diversified from oil to non-oil exports of goods and services. The
share of oil and gas exports declined steadily from 96% of total exports in 1979 to around 60% in 2018, before the full
impact of the U.S. withdrawal from Iran's oil exports.9 Over the same period, non-oil exports have increased from 753
million dollars to 37 billion dollars.
In contrast, Iran was not able to adapt to financial sanctions sufficiently quickly, resulting in large depreciations of the
free market rate of the rial against the U.S. dollar, with the official rate lagging behind for a number of years, thus creat-
ing opportunities for rent-seeking and often corrupt business practices.10 Given the relevance of imports in the Iranian
economy, and the role of the U.S. dollar as the store of value and as a hedge against inflation for many Iranians, the fall
in value of the rial quite rapidly translates into higher consumer prices, with the rise in prices somewhat moderated due
to government imports of food and medicine at official rates. But as the gap between the official and market rates closes
over time, consumer prices end up reflecting the full extent of depreciation of the rial on the free market.11 As can be seen
7For an overview of U.S. sanctions against Iran, see also Chapter 9 of Maloney (2015).
8See Panel A of Figure S4.
9See Panel B of Figure S4.
10See Panel C of Figure S4. The development of the free market exchange rate, also known as the “black” market rate during the 1979–1988 period, is
discussed in Pesaran (1992).
11The time profiles of free market rate and consumer prices (in log scales) are depicted in Panel D of Figure S4. As can be seen there is a very close
association between the two series.
6 LAUDATI AND PESARAN
TABLE 1 Free market and official foreign exchange rate depreciation, inflation, real output growth, and sanctions
intensity over the period 1979q3–2021q1
Percent per annum
Free FX Official FX Output Sanctions intensity (0,1)
Periods depreciation depreciation Inflation growth Mean Median
Revolution and Iran-Iraq Wara 19.94 0.28 18.29 −1.60 0.20 0.11
(1979q3–1989q2)
Rafsanjani presidency 16.55 39.83 21.17 5.16 0.11 0.10
(1989q3–1997q2)
Khatami presidency 7.90 20.34 14.53 4.72 0.15 0.13
(1997q3–2005q2)
Ahmadinejad presidency 17.08 5.16 18.15 1.68 0.38 0.39
(2005q3–2013q2)
Rouhani presidencyb 25.34 14.66 19.61 0.61 0.34 0.27
(2013q3–2021q2)
Post-revolution full sampleb 17.39 15.30 18.34 1.98 0.23 0.15
(1979q3–2021q1)
Post-War full sampleb 17.38 19.88 18.30 3.08 0.24 0.16
(1989q1–2021q1)
Note: See Section 3 of the paper for the sanctions intensity variable definition over the range (0,1). See Sections S2.1, S2.2, S2.5, and S2.6
in the data appendix of the supporting information for details on the construction of the sanctions intensity variable, calendar
conversions, and sources of the data used.
aData on free market foreign exchange rate start in 1980q2.
bData on foreign exchange rates (free market and official rate), and inflation end in 2021q1, data on output growth end in 2020q1, data
on sanctions intensity end in 2020q3.
from Table 1, over the period 1989q1–2021q1, the free market rate has depreciated around 17.4% per annum as compared
to the average annual rate of inflation of around 18.3% over the same period, representing a gap of around 1% between
the two series. But according to the Purchasing Power Parity (PPP), the difference between inflation and exchange rate
depreciation shouldmatch the average annual U.S. inflation, which is estimated to be around 2.5% over the same period.12
It is also important to note that not all foreign exchange crises can be traced to the intensification of sanctions. Iran has
witnessed major currency crises during all the four presidencies since 1989, while only the last two currency crises can
be directly attributed to intensification of the sanctions. The currency crises during Rafsanjani and Khatami presidencies
have domestic roots resulting from the rapid expansion of imports and low oil prices, coupled with accommodating fiscal
andmonetary policies.13 As shown in Table 1, the average rate of inflation has been systematically high across all the four
presidencies and does not seem to correlate with changes in sanctions intensity. Even under Khatami's presidency the
average annual inflation still amounted to 14.5%, despite his conciliatory foreign policy and amuch lower rate of currency
depreciation (7.9% as compared to 17.4% over the full sample).
Comparing Iran's output growth with that of world output growth over the 1989–2019 period14 also suggests an output
growth shortfall of around 1% per annum, which could be contributed to the sanctions, although such a comparison
does not take account of Iran's potential as an emerging economy. Even if we exclude the war periods, we also observe a
much larger output growth volatility in Iran as compared to the volatility of world output growth volatility or a number of
emerging economies of similar size to Iran, such as Turkey or Saudi Arabia. Iran's output growth volatility (as measured
by standard deviations of output growth) was almost five times as large as the global output growth volatility (12.61%
vs. 2.69%).15 Comparing Iran and Turkey over the same period, we also find that Turkey grew at an average annual rate
12The PPP is a long-run relationship that relates the exchange rate between two currencies to their relative price of goods: Pt = EtP∗t , with Et being the
exchange rate representing the number of domestic currency units that can be bought with one unit of foreign currency, Pt and P∗t denote the domestic
and foreign price levels.
13During the reconstruction period under President Rafsanjani imports of goods and services doubled over the period 1989–1991 rising from 13.5 to 25
billion dollars, and Iran's foreign debt rose to 23.2 billion dollars by the end of 1993. For further details of the developments that led to the currency
crisis under President Rafsanjani, see section 3 of Pesaran (2000).
14World output is computed as a weighted average of some of the largest 33 economies with details provided in the supporting information.
15Mohaddes and Pesaran (2013) document the high volatility of Iran's oil export revenues as one of the factors behind Iran's low growth and high
volatility. A large part of the volatility of Iran's oil export revenues is traced to high volatility of barrels of oil exported, largely due to vagaries of sanctions.
LAUDATI AND PESARAN 7
of 4% with volatility of 10.8%, a country also known for high inflation and repeated currency crises.16 Finally, sanctions
have most likely also contributed to the de-coupling of the Iranian economy from the rest of the world. Again comparing
Iran and Turkey, we note that the correlation of Iran's output growth with the world output growth is around 0.12, barely
statistically significant, as compared with a correlation of 0.33 for Turkey.
There seems to be little doubt that sanctions have adversely affected the Iranian economy, contributing to low growth,
high inflation and increased volatility. What is less clear is how to carry out a quantitative evaluation and identification of
channels through which sanctions have affected the Iranian economy over time, in particular the dynamics of responses
and the time horizon over which the effects of sanctions filter out throughout the economy. To this end, a formal model
is required where conditions under which the effects of sanctions can be identified are made explicit, and their dynamic
implications are estimated and evaluated. It is to this task that we now turn in the rest of this paper.
3 MEASURES OF SANCTIONS INTENSITY
Sanctions against Iran have been imposed with different degrees of intensity over the past forty years. To account for
both the prolonged nature of sanctions and their time-varying intensity, we construct “sanctions on” and “sanctions off”
measures based on newspaper coverage of the imposition and the occasional lifting of sanctions. Newspaper coverage
has been used in the literature and was initially formalized by Baker et al. (2016) for measuring the effects of economic
uncertainty on macroeconomic aggregates. But, to our knowledge, the idea of using newspaper coverage to quantify
sanctions intensity is new.
We consider six of the world's major daily newspapers, namely, the New York Times, the Washington Post, the Los
Angeles Times, and theWall Street Journal in the US and the Guardian and the Financial Times in the UK.We then count
the number of articles published in these newspapers that deal with sanctions and Iran.17 We are careful not to confound
our measures with articles that cover international sanctions against Iraq but also mention Iran. Sources and details of
how the searches were carried out are provided in Section S2.1 of the supporting information.
We also considered including Iranian newspaper sources in our textual analysis but decided not to do so for three main
reasons. First, newspaper articleswritten about sanctions in Iran have a political dimension (e.g., strengthen the theocracy
by levering on the idea of the “resistance economy”), which does not necessarily relate to changes in sanctions intensity.
Second, sanctions are announced, decided, and implemented by the US and other major U.N. countries. Therefore, West-
ernmedia offer a more accurate and timely changes in new and ongoing sanctions against Iran. Third, there are not many
Iranian newspapers that reliably cover the whole 40 years time period that we are considering, and including available
data from Iranian newspapers could bias our sanctions indicator.
One can think of our approach as measuring a proxy for an underlying latent sanctions intensity process. The true
process generates a signal, part of which is captured in daily articles published in the six newspapers under consideration.
To be specific, letn𝑗dt be the number of articles published about sanctions on Iran in newspaper 𝑗 during day d of month
t, and denote the true (latent) sanctions intensity variable during month t by s∗t . The relationship between n𝑗dt and s∗t is
specified as
n𝑗dt = 𝜃𝑗s∗t + 𝜁𝑗dt, (1)
where 𝜃𝑗 > 0 is loading of newspaper 𝑗 on the true signal, s∗t , and 𝜁𝑗dt is an idiosyncratic serially uncorrelated error term
assumed to be distributed independently of the true signal, s∗dt, with zero means and finite variances. Equation (1) could
be viewed as a single factor model where 𝜃𝑗 is the newspaper-specific factor loading. The number of articles published in
newspaper 𝑗 correlates with the true signal depending on the size of 𝜃𝑗 and the variance of the idiosyncratic term. Clearly,
not all published articles capture the true signals, but by averaging across newspapers and different days in a givenmonth,
it is possible to reduce the effects of the noise, 𝜁𝑗dt, and obtain a consistent estimator of s∗t , up to a scalar constant. Both
simple and weighted averages can be used. Taking a simple average across the J newspapers and the number of days,
By comparison the volatility of oil prices is of secondary importance. This contrasts to the volatility of Saudi Arabia oil revenues, which is largely
governed by changes in international oil prices.
16The average annual output growth of Saudi Arabia over the 2005–2019 period was also similar to Turkey and amounted to 4.3%.
17The selected newspapers represent a sample of the most-read and well-informed articles over the past 40 years and provide a good blend of both
generalist press and those that focus on economic-finance issues. Also, by including two different geographic regions, we hope to cover amore diversified
sample.
8 LAUDATI AND PESARAN
Dt, in month t, we have nt = 𝜃Js∗t + 𝜁 t, where nt = J−1D−1t
∑J
𝑗=1
∑Dt
d=1 n𝑗dt, s
∗
t = D−1t
∑Dt
d=1 s
∗
dt, and 𝜃J = J
−1∑J
𝑗=1 𝜃𝑗 . We
considered six newspapers (J = 6) over a number of publishing days per month Dt, typically 26 days, resulting in about
156 data points over which to average. This in turn ensures that the idiosyncratic errors get diversified, and as a result the
average error, 𝜁 t, becomes reasonably small. Specifically
𝜁 t = J−1D−1t
J∑
𝑗=1
Dt∑
d=1
𝜁𝑗dt = Op
(
J−1D−1t
)
,
andwe have s∗t = 𝜃
−1
J nt+op(1).Thesemonthlymeasures can then be time aggregated further to obtain quarterly or annual
series which are then used to identify the effects of s∗t (up to the scaling factor 𝜃
−1
J ) in our macro-econometric model.
We could also consider a weighted average version of nt along the lines suggested in the literature, where the number
of newspaper articles (the raw count) is weighted by the inverse of their respective standard deviations, ?̂?𝑗T , computed
over the full data set, using ?̂?𝑗T =
√
(T − 1)−1∑Tt=1 (n𝑗t − n𝑗)2, n𝑗t = D−1t ∑Dtd=1 n𝑗dt, and n𝑗 = T−1∑Tt=1 n𝑗t. See Baker
et al. (2016) and Plante (2019). But, as reported in Figure S1, the simple andweighted averages, after being suitably scaled,
are very close in the case of our application.
Although most sanctions' news has been about imposing new or tightening old sanctions, there are some isolated peri-
ods where sanctions have been lifted, as in 1981 after the release of the U.S. hostages, and over the period 2016q1–2018q2
after the implementation of JCPOA. Accordingly, we construct two sanctionsmeasures: an “on”measure, denoted by st,on,
and an “off” measure, denoted by st,o𝑓𝑓 , and we normalize them such that they both lie between 0 and 1, with 1 represent-
ing themaximum sanctions intensity over the full sample. We then define a net sanctions measure as st = st,on−w× st,o𝑓𝑓 ,
where w > 0 represents the weight attached to the sanctions off indicator compared to the sanctions on indicator. The
weight, w, is estimated to be ŵ = 0.4 using a grid search method over values of w ∈ (0, 1).18
Figure 1 displays the quarterly estimates of st over the period from 1989q1 to 2020q3, which takes its maximum value at
the end of 2011 when Iran was sanctioned simultaneously by the UN, the US, and the EU. Important historical events are
annotated in the lower part of the figure, while specifics of particular sanctions are shown on the upper part of the figure.
The fact that intensity of sanctions against Iran has been quite varied can be clearly seen from Figure 1. Most notably
there are three major spikes in sanctions intensity. The first is in 2006 after Ahmadinejad was elected and Iran began its
uranium enrichment program,when the U.S. passed the “Iran Freedom and Support Act,” which extended the coercive
measures against Iran—most notably secondary sanctions on non-U.S. corporations and institutions doing business with
Iran and very strict sanctions related to investments in the energy sector. An even larger spike occurs between 2011
and 2012, when the Obama administration joined efforts with the United Nations and the European Union to tighten
the sanctions even further with the aim of bringing Iran to negotiations over the nuclear program. The US passed stiff
measures at the end of December 2011 under the “National Defense and Authorization Act for Fiscal Year 2012,” with
Iran threatening to block oil shipments through the Strait of Hormuz as a response. At the same time, the EU initiated a
total disconnect of Iranian financial institutions from the international payments system (SWIFT)in March 2012,19 while
the UN proceeded to extend the mandates of their previous resolutions between June 2011 and June 2012. The third, and
most recent, spike is registered in 2018q2 after Trump decided to unilaterally withdraw the US from the JCPOA accord
and begin a strategy of “maximumpressure.” There are alsominor spikes in 1996 when the Clinton administration signed
the “Iran and Libya Sanctions Act,” and in 1997 when the US introduced an export ban to reduce the threat of potential
weapons of mass destruction being built, and in 2010 when the CISADA (“Comprehensive Iran Sanctions Accountability
and Divestment Act”) was signed into law and the U.N. Security Council passed the fourth round of sanctions against
Iran with its 1929 resolution.
Lows of the sanctions intensity variable are recorded during the reconstruction period under President Rafsanjani and
the pragmatic rule under Khatami's administration, and more recently over the period between the JCPOA agreement
in August 2015 and January 2018, when the US unilaterally withdrew from the agreement. Table 2 provides summary
statistics (minimum, median, mean, maximum, and standard deviations) of st over a number of sub-periods. A number
18The grid search was performed by running the regressions:Δ𝑦t = 𝛽0 + 𝛽1Δ𝑦t−1 + 𝛽2st−1(w) + 𝜀t, w ∈ {0.1, 0.2, … , 0.9} over the period 1989q1–2019q4,
and w selected by the maximum likelihood method. Further details are provided in Section S2 of the supporting information.
19SWIFT stands for the “Society for Worldwide Interbank Financial Telecommunications,” and it is a vast and secure network used by banks and other
financial institutions to operate financial transactions across the globe.
LAUDATI AND PESARAN 9
TABLE 2 Descriptive statistics
of the sanctions intensity variable
over relevant time periods
Time period Min Median Mean Max St. Dev.
Rafsanjani & Khatami presidencies 1989q3–2005q2 0.02 0.12 0.13 0.36 0.07
Ahmadinejad presidency 2005q3–2013q2 0.11 0.39 0.38 1.0 0.17
U.N./U.S. max sanctions 2012q1–2014q4 0.27 0.45 0.48 1.0 0.18
JCPOA agreement 2015q1–2018q1 0.06 0.11 0.14 0.33 0.07
U.S. “maximum pressure” 2018q2–2020q3 0.21 0.63 0.56 0.82 0.21
Full sample (post Iran-Iraq War) 1989q1–2020q3 0.02 0.16 0.24 1.0 0.19
Note: See Sections S2.1 and S2.2 in the data appendix of the supporting information for details on the construction of
the sanctions intensity variable.
of interesting observations follow from this table. First, the summary statistics for st over the low sanctions periods under
Rafsanjani andKhatami are very close to those recorded for the period 2015q1–2018q1when sanctionswere partially lifted
after JCPOA. Second, the peak of sanctions occurred during the internationally coordinated efforts of 2011/2012 rather
than after 2018, when the US began their “maximum pressure” strategy under Trump and Bolton.20 In the period after
2018q2, the degree of intensity of our indicator is 82% of its peak in 2011. However, after 2018 the intensity of sanctions
against Iran seems to have beenmuchmore persistent: themean andmedian are higher during the 2018q2–2020q3 period
than during 2012q1–2014q4. Finally, we notice that after the Iran-IraqWar, themedian of the sanctions intensity has been
only two thirds of the mean: 0.16 versus 0.24. This feature stems from the several tail events that characterize the series
of sanctions against Iran, and as an overall measure the median is to be preferred to the mean.
For the analysis of the effects of sanctions on Iran, it is also important to note that st shows a considerable degree of
persistence over time. When sanctions are intensified they tend to remain high for some time before subsiding. Table S6
provides estimates of first- and second-order autoregressive processes (AR) fitted to st, and shows that an AR(1) model
captures well the sanctions intensity process, with a relatively large and highly significant AR coefficient, namely, 0.743
(0.059).
Finally, as a robustness check we also attempted to create an alternative measure of sanctions intensity based on the
number of Iranian entities being sanctioned by the US. We used the U.S. Treasury data set on entries and exits of sanc-
tioned companies, individuals and vessels. We were able to build an indicator from 2006 to present. Although the two
measures (newspaper coverage and U.S. Treasury data) capture the sanctions phenomenon from different perspectives,
they correlate rather well at 38%. For further details see Section S2.3 of the supporting information.
4 IDENTIFICATION OF SANCTIONS EFFECTS: METHODOLOGICAL
ISSUES
Identifying the effects of sanctions on the Iranian economy is challenging even if a reliable measure of sanctions intensity
is available. As with all macro policy interventions, when identifying the effects of sanctions we also need to take account
of confounding factors that are correlated with changes in sanctions intensity, and which at the same time have a causal
influence on target variable(s) of interest such as output growth and inflation. In situationswhere a policy intervention has
differential effects over time and across many different units such as households or firms, difference-in-difference tech-
niques are usedwhereby changes in outcomes during policy on and policy off periods for those affected by the intervention
are compared to corresponding changes in outcomes for a control group that is not directly affected by the intervention.
This method is clearly not applicable to the analysis of policy interventions that target a particular entity such as a region
or country, and a different approach is needed. Currently, there are two such approaches: the Synthetic Control Method
(SCM) advanced by Abadie andGardeazabal (2003) and the Panel Data Approach (PDA) proposed byHsiao et al. (2012).21
Both approaches compare outcomes for the country (region) subject to the intervention with a weighted average of
outcomes from a control group. The former was originally applied to quantify the economic costs of political instability
in the Basque Country in Spain, and the latter to evaluate the economic effects of the hand-over of Hong Kong to China
in 1997. Both studies consider discrete policy interventions and do not allow for the policy intensity to vary over time.
20John Bolton served as the 26th United States National Security Advisor from April 2018 to September 2019 under the Presidency of Donald Trump.
He has been a long-standing “policy hawk” advocating for regime change in several strategic countries not aligned with the U.S. such as Iran and North
Korea, among others.
21Further details and extensions of SCM are discussed in Abadie et al. (2010) and Doudchenko and Imbens (2016).
10 LAUDATI AND PESARAN
Perhaps most importantly they both use pre-policy outcomes to estimate the weights applied to the countries included in
the control group. The main difference between the two approaches lies in way the weights are estimated.22
The application of these approaches to the case of Iran is complicated by the fact that imposition of sanctions coincided
with the onset of the Revolution which renders the pre-sanctions period of limited relevance. Also, as noted earlier, the
scope and intensity of sanctions against Iran have undergone considerable changes over the past forty years and there are
no clear cut periods that one could identify as “sanctions on” periods to be compared to “sanctions off” periods, in which
all sanctions were levied. There is also the additional challenge of identifying countries for inclusion in the control group.
To our knowledge, the only study that applies SCM to Iran is by Gharehgozli (2017), who considers the effects of the
intensification of sanctions just before the JCPOA agreement in July 2015 on Iran's real GDP, treating the years 2011–2014
as the “sanctions on” period as compared to the preceding years 1995–2010 as the “sanctions off” period. She then selects
13 countries worldwide tomimic a “synthetic” Iran as a weighted average of GDP of these economies with their respective
weights determined using the SCMbased on seven differentmacroeconomic indicators. She concludes that the 2011–2014
sanctions resulted in Iran's real GDP to fall by as much as 17%, as compared to the synthetic sanctions free Iran, with all
the output short fall attributed to sanctions.
We depart from the mainstream literature reviewed above and consider the following reduced-form model for Iran's
quarterly output growth
Δ𝑦t = 𝛼 + 𝜆Δ𝑦t−1 + 𝜓0st + 𝜓1st−1 + 𝜷′xt + 𝜸′ft + ut, (2)
whereΔ𝑦t is the output growth, st measures the intensity of sanctions against Iran, xt and ft are respectively observed and
unobserved control variables, and ut is an idiosyncratic error term, distributed independently of (st,xt, ft). It is assumed
that part of the change in the intensity of sanctions affects Iran's output growth with a lag, thus distinguishing between
short term, 𝜓0, and long term, 𝜃 = (𝜓0 + 𝜓1)∕(1 − 𝜆), effects of sanctions. As discussed above, sanctions affect output
growth through a number of channels, most importantly oil exports, exchange rate, liquidity, and inflation to be addressed
in Section 5. However, here we are concerned with both direct and indirect effects of sanctions on output growth, and to
avoid confounding these effects wewill not be including contemporaneous values domestic variables in the output growth
equation. For example, including changes in volume of oil exports in (2) will most likely result in under-estimating the
effects of the sanctions, since one important aim of the sanctions is to reduce Iran's oil exports. The same also applies to
other domestic variables, such as exchange rate or inflation, that are affected by sanctions and their inclusion bias the
estimates of𝜓0 and𝜓1. But it is important that observed and unobserved external factors that are not affected by sanctions,
but potentially can impact Iran's output growth are included in (2). One important example is changes in international
oil prices, which affect Iran's output growth through changes in government foreign exchange revenues, but do not seem
to have been affected by sanctions, particularly due the accommodating oil production and export policies followed by
Saudi Arabia.23 Accordingly, we include changes in international oil prices as an element of xt. We could not identify
other observed external factors with obvious effects on the Iranian economy, and focussed on identification of unobserved
common factors, ft. In this regard, our approach is closely related to the PDA (Hsiao et al., 2012). To this end, we consider
the following equations for output growth for the rest of the world.24
Δ𝑦it = 𝛼i𝑦 + 𝜷′i𝑦xit + 𝜸′i𝑦ft + u𝑦,it,i = 1, 2, … ,n, (3)
whereΔ𝑦it denotes output growth in country i (excluding Iran), xit is a k×1 vector of control variables specific to country i,
and ft is them×1 vector of unobserved common factors, and u𝑦,it are idiosyncratic shocks to output growth that are serially
uncorrelated but could be weakly cross correlated.25 By allowing the factor loadings, 𝜸i, to differ across countries, we do
not assume that all economies are equally affected by the same factors, an assumption that underlies the DiD approach.
We also depart from SCM and PDA and, unlike these approaches, we do not require a “donor pool” of countries to be
selected for comparative analysis. Instead, we assume that xit also follows similar multi-factor structures, and impose a
22Gardeazabal and Vega-Bayo (2017) provide a comparative simulation analysis of SCM and PDA, with a follow up critique by Wan et al. (2018).
23See section 5.2 in Mohaddes and Pesaran (2016), where it is shown that an adverse shock to Iran's oil supply induces a rise in Saudi oil supplies.
Another reasonwhy sanctions against Iran have not led to important oil price rises is the prolonged nature of these sanctions, allowing the international
oil market to adjust to reduced oil exports from Iran.
24It is assumed that sanctions against Iran have had only negligible impacts on the rest of the world economies.
25A set of random variables, {uit , i = 1, 2, … ,n} is said to be weakly cross correlated if sup𝑗
∑n
i=1
||Cov(uit ,u𝑗t)|| < C < ∞. It then follows that∑ni=1 wiuit =
Op(n−1∕2), for any granular weights, wi, such that wi = O(n−1) and
∑n
i=1 w2i = O(n
−1). An obvious example is the simple weights wi = 1∕n. For further
details see Chudik et al. (2011).
LAUDATI AND PESARAN 11
rank condition which allows us to identify ft as weighted averages of Δ𝑦it and xit over i (excluding Iran). Any granular
weights can be used to construct these averages, such as simple averages. But in cases where n is not sufficiently large and
there are dominant economies such as the. S, it is advisable to use output shares as weights. Accordingly, suppose that
xit = 𝜶ix + 𝚪′ixft + ux,it, i = 1, 2, … ,n, (4)
where 𝚪ix is a k ×m matrix of factor loadings, and ux,it is a k × 1 vector that follows general stationary processes that are
weakly cross-sectionally correlated. Combining (3) and (4) we have(
1 −𝜷′i𝑦
0 Ik
)
zit =
(
𝛼i𝑦
𝜶ix
)
+
(
𝜸′i𝑦
𝚪′ix
)
ft +
(
u𝑦,it
ux,it
)
,
which yields zit = ci +Aift + Biuit, where
ci =
(
𝛼i𝑦 + 𝜷′i
𝜶ix
)
, Ai =
(
𝜸′i𝑦 + 𝜷
′
i𝑦𝚪′ix
𝚪′ix
)
ft, and Bi =
(
1 𝜷′i𝑦
0 Ik
)
.
Averaging zit over i using the weights wi, we now have zwt = cw + Awft +
∑n
i=1 wiBiuit,where zwt =
∑n
i=1 wizit, cw =∑n
i=1 wici, andAw =
∑n
i=1 wiAi. Suppose now that the (k+ 1) ×mmatrixAw is full column rank (that requiresm ≤ k+ 1),
and A′wAw→p > 0, as n →∞. Then, ft can be solved as26
ft = aw𝑓 +Hwzwt −Hw
( n∑
i=1
wiBiuit
)
,
where
aw𝑓 =
(
A′wAw
)−1
A′wcw and Hw =
(
A′wAw
)−1
A′w.
Under the rank condition, the terms aw𝑓 andHw tend to finite limits, while under the assumptions thatuit areweakly cross
correlated, the final term of ft tends to zero for any choice of weights wi that are granular, ft can be identified up to linear
transformations in terms of zwt =
(
Δ𝑦wt, x
′
wt
)′
=
(∑n
i=1 wiΔ𝑦it,
∑n
i=1 wix′it
)′. More specifically,∑ni=1 wiBiuit = Op(n−1∕2),
and we have ft = aw𝑓 +Hwzwt + Op(n−1∕2), which can be used to eliminate the unobserved factors, ft, from Iran's output
growth equation. Specifically, we obtain
Δ𝑦t = 𝛼𝑦w + 𝜆Δ𝑦t−1 + 𝜓0st + 𝜓1st−1 + 𝜷′xt + 𝜃𝑦wΔ𝑦wt + 𝜽′xwxwt + ut + Op(n−1∕2). (5)
Hence, for n sufficiently large, and considering that the Iranian economy is quite small relative to the rest of the world,
the sanctions coefficients 𝜓0, and 𝜓1 can be identified by augmenting the output growth equations with the rest of the
world average output growth, Δ𝑦wt, and the weighted averages of the observed drivers of the rest of the world output
growth, xwt.
It is interesting to note that our approach does not require selecting a pool of countries that are close to Iran, but recom-
mends including all countries, weighted for their relative importance in the world economy. Selecting specific countries
could bias the results by restricting the number included in the construction of cross section averages. The rank condi-
tion, rank
(
A′wAw
)
= m , for a given n, and as n →∞, ensures that ft has a reasonably pervasive effect onmost economies
which in turn allows us to use Δ𝑦wt, and xwt as reliable proxies for ft.
The analysis of sanctions effects can also be extended to othermacro variables such as inflation and unemployment, and
even to some key socioeconomic indicators such as life expectancy, death rate or educational achievement. See Section 7
in Laudati and Pesaran (2021).
26See Pesaran (2006) for further details in a related context.
12 LAUDATI AND PESARAN
4.1 Estimates of sanctions-induced output losses
Initially, we report regression results for the reduced form output growth regressions set out in Equation (5), and focus
on specifications with st−1 as the intervention variable. We favor this specification over the one that includes both st and
st−1 since “sanctions news” do not contain anticipatory effects, and one would not expect contemporaneous changes in
st to affect output growth, as time is required for the real economy to adjust to sanctions news.27 The estimates of the
reduced form output growth equations computed over the period 1989q1–2019q4 are summarized in Table 3, where we
report both the short- and long-run effects of sanctions on output growth, while allowing for a host of lagged values
of domestic variables as well as contemporaneous foreign control variables and international oil price returns.28 The
parameter of interest is the long run effect of sanctions on output growth reported at the bottom panel of Table 3. It is
estimated to be about −0.027 (0.013), which is statistically significant and remarkably robust across the seven different
specifications reported. The estimates suggest output growth losses of around 2% per annum if we use the median value
of st over the sample under consideration, or 3% if we use the mean value of st.29 Due to the large outliers in the sanctions
intensity variable, we favor the lower estimate of 2% based on the median value of st, which in turn suggests that in the
absence of sanctions and sanctions-induced mismanagement Iran's average annual growth over 1989q1–2019q4 could
have been around 4–5%, as compared to the 3% realized, a counterfactual outcomewhich is close to the growth of emerging
economies such as Indonesia, SouthKorea, Thailand, andTurkeywhose average annual growth rate over the same sample
period amounted to 4.8%, 4.5%, 4.2%, and 4.0%, respectively. Similar estimates are obtained if both st and st−1 are included
in the regressions. See Table S9. Furthermore, Tables S20 and S21 show that similar results are obtained when we use
heteroskedastic-consistent standard errors following the approach proposed by White (1980).
5 SANCTIONS-AUGMENTED STRUCTURAL VAR MODEL FOR IRAN
We now consider the main channels through which sanctions affect the Iranian economy, and provide estimates of the
time profiles of their effects. Initially, U.S. sanctions targeted the Iranian oil industry with the aim of reducing oil exports
and limiting Iran's capacity to produce oil. More recently, financial sanctions have been usedmore extensively. As a result
new sanctions, or even their announcement, have invariably led to reduced oil exports, with a significant depreciation of
the Iranian rial, followed by a sharp rise in price inflation and output losses within 3–6 months after the imposition of
the new sanctions. We model the dynamic inter-relationships of oil exports, exchange rate, money supply, inflation and
output growth using a structural vector autoregressive (SVAR for short) model augmented with the sanctions intensity
variable and the global control variables, denoted by zwt above.
We denote by qt =
(
Δx0t ,Δe𝑓 t,Δmt,Δpt,Δ𝑦t
)′ an m × 1 (with m = 5) vector of endogenous domestic variables, where
Δx0t is the oil export revenues,Δe𝑓 t represents the rate of change of freemarket foreign exchange rate,30 Δmt is the growth
rate of money supply, Δpt is the rate of inflation, and Δ𝑦t is real output growth.
To distinguish between different types of shocks and their implications for the Iranian economy, in our SVARwe assume
the direction of causality goes from Δx0t to exchange rate depreciation, to money supply growth, to inflation, and then to
output growth, as represented by the ordering of the five endogenous variables in qt. Under this causal ordering, we are
able to distinguish changes in qt that are due to variations in the intensity of sanctions from those that are the result of
domestic policy shocks.31 The assumed causal ordering can be justified in terms of relative speed with which the Iranian
27We are grateful to Nick Bloom for drawing our attention to this point.
28Among the domestic variables, only lagged inflation has a statistically significant impact on output growth. The negative effect of inflation on output
growth could be due to price distortions and allocation inefficiencies that are often associated with high and persistent levels of inflation, as has been
the case in Iran. We find that global factors such as global volatility or output growth do not affect Iran's output growth, largely due to Iran's relative
economic isolation. Among the global factors, the only factor with statistically significant impact on Iran's output growth turned out to be the global
exchange rate variable. However, the negative effect of the global exchange rate variable on output growth is more difficult to rationalize.
29The median and mean values of st , are 0.16 and 0.24, respectively, as summarized in Table 2.
30We also tried a weighted average of the freemarket and official exchange rates, but found that the freemarket rate provides amore accurate and timely
measure of the exchange rate movements for Iran given its higher responsiveness to sanctions. The exchange rate variable is expressed as the number
of Iranian rials per one U.S. dollar.
31It is also possible to use non-recursive identification schemes such as sign restrictions, or themore recently developed Bayesian approach by Baumeis-
ter and Hamilton (2015) to point identify and estimate contemporaneous effects in the SVARmodel and associated impulse responses using priors. This
could be the subject of future research. However, we do not expect that the main results of our paper that relate to the effects of sanctions to be much
affected by such alternative identification schemes.
LAUDATI AND PESARAN 13
TABLE 3 Estimates of the
reduced form Iran's output growth
equation estimated over the period
1989q1–2019q4
𝚫𝒚t
(1) (2) (3) (4) (5) (6) (7)
st−1(𝛽st−1 ) −0.033∗∗ −0.032∗∗ −0.032∗∗ −0.034∗∗ −0.034∗∗ −0.034∗∗ −0.035∗∗
(0.016) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016)
Δ𝑦t−1(𝜆Δ𝑦t−1 ) −0.204∗∗ −0.202∗∗ −0.203∗∗ −0.200∗∗ −0.214∗∗ −0.214∗∗ −0.218∗∗
(0.091) (0.092) (0.092) (0.092) (0.091) (0.092) (0.092)
Δx0t−1 0.016 0.016 0.016 0.017 0.014 0.014 0.015
(0.014) (0.014) (0.014) (0.014) (0.014) (0.014) (0.014)
Δe𝑓,t−1 −0.004 −0.004 −0.004 0.0002 0.004 0.004 0.002
(0.033) (0.033) (0.034) (0.034) (0.033) (0.034) (0.034)
Δmt−1 −0.028 −0.037 −0.041 −0.032 −0.053 −0.056 −0.063
(0.100) (0.102) (0.104) (0.104) (0.103) (0.104) (0.106)
Δpt−1 −0.239∗ −0.234∗ −0.232∗ −0.246∗∗ −0.268∗∗ −0.273∗∗ −0.274∗∗
(0.122) (0.123) (0.123) (0.124) (0.123) (0.125) (0.125)
Δ𝑦wt 0.228 0.160 0.215 −0.129 −0.162 −0.117
(0.553) (0.602) (0.604) (0.625) (0.635) (0.643)
Δreqwt 0.013 0.021 0.013 0.002 −0.0001
(0.045) (0.046) (0.045) (0.057) (0.057)
Δrwt −4.518 −4.311 −4.474 −3.490
(4.141) (4.097) (4.143) (4.611)
Δēwt −0.278∗ −0.272∗ −0.309∗
(0.148) (0.150) (0.168)
grvt −0.038 −0.044
(0.114) (0.115)
Δp0t −0.012
(0.024)
𝛽st−1∕(1 − 𝜆Δ𝑦t−1 ) −0.027∗∗ −0.027∗∗ −0.027∗∗ −0.028∗∗ −0.028∗∗ −0.028∗∗ −0.028∗∗
(0.013) (0.013) (0.013) (0.013) (0.013) (0.013) (0.013)
Adjusted R2 0.083 0.077 0.069 0.071 0.091 0.084 0.077
Note: Δ𝑦t = ln(Yt∕Yt−1), Yt is the quarterly real output of Iran. st is the quarterly sanctions intensity variable. 𝛽st−1
and 𝜆Δ𝑦t−1 are the coefficients of st−1 and Δ𝑦t−1, respectively; 𝛽st−1 ∕(1− 𝜆Δ𝑦t−1 ) represents the long run effect of
sanctions on output growth. See Chapter 6 of Pesaran (2015). Δx0t = (X0t − X0t−1)∕X
0
t−1, X
0
t is the oil exports
revenues in U.S. dollars; Δe𝑓 t = ln(E𝑓 t∕E𝑓,t−1), E𝑓 t is the quarterly rial/U.S. dollar free market exchange rate;
Δmt = (M2t −M2,t−1)∕M2,t−1, M2t is the monetary aggregateM2 obtained by summing the aggregatesM1 and
“quasi-money”; Δpt = ln(Pt∕Pt−1), Pt is the quarterly consumer price index of Iran. Δ𝑦wt is the quarterly world
output growth, computed as 𝑦wt =
∑n
i=1 wi𝑦it , with {𝑦it}
n
i=1 being the natural log of real output for 33 major
economies, and {wi}ni=1 are GDP-PPP weights. Δreqwt is the quarterly rate of change of the global real equity price
index: reqwt =
∑n
i=1 wireqit , reqit is the natural log of the real equity price of country i in quarter t. Δrwt is the
quarterly percent change of the global nominal long term interest rate: rwt =
∑n
i=1 wirit , rit is the long term
nominal interest rate of country i in quarter t. Δēwt is the quarterly rate of change of the global real exchange rate
vis-à-vis the U.S. dollar: ēwt =
∑n
i=1 wieit , eit is the natural log of the real exchange rate of country i in quarter t.
grvt is the quarterly global realized volatility. Δp0t = ln(P0t ∕P0t−1), P
0
t is the quarterly oil price (Brent crude).
Numbers in parentheses are least squares standard errors. ***p < 0.01, **p < 0.05, *p < 0.1.See Sections S2.1,
S2.2, S2.5, and S2.6 in data appendix of the supporting information for details on the construction of the
sanctions intensity variable, calendar conversions, and sources of the data used. The use of asterisks represents
the statistical significance levels of the coefficients, and it is explained in the notes with the standard notation
referred to the p values. It is crucially relevant to have them.
economy responds to crises. Oil exports are usually targeted by sanctions and their revenues fall immediately by design,
then it is the value of the rial in free market that weakens, followed by a potential expansion of liquidity, a rise in the
price of imported commodities, before the real economy starts to adjust to higher prices and interest rates. Due to the
relatively underdeveloped nature of money and capital markets in Iran, monetary policy tends to be accommodating,
typically allowing liquidity to rise in line with inflation.
We consider the following augmented SVAR model in qt
A0qt = aq +A1qt−1 +A2qt−2 + 𝜸0sst + 𝜸1sst−1 +Dw zwt + 𝜺t, (6)
14 LAUDATI AND PESARAN
where as before st is ourmeasure of sanctions intensity, and zwt = (Δ𝑦wt,Δreqwt,Δrwt, grvt,Δēwt)′ is a k×1 (k = 5) vector of
control variables that includes: global output growth, Δ𝑦wt, global equity returns, Δreqwt, global long term interest rates,
Δrwt, global realized volatility, grvt, and the rate of change of the global real exchange rate, Δēwt.32 Given the assumed
causal ordering, matrix A0 is restricted to have the following lower triangular form
A0 =
⎛⎜⎜⎜⎜⎜⎜⎜⎝
1 0 … 0
−a0Δe,Δx0 ⋱ ⋱ ⋮
−a0Δm,Δx0 −a
0
Δm,Δe
−a0Δp,Δx0 −a
0
Δp,Δe −a
0
Δp,Δm 1 0
−a0Δ𝑦,Δx0 −a
0
Δ𝑦,Δe −a
0
Δ𝑦,Δm −a
0
Δ𝑦,Δp 1
⎞⎟⎟⎟⎟⎟⎟⎟⎠
, (7)
where we expect a0Δp,Δe ≥ 0, with inflation responding positively to a contemporaneous rise in e𝑓 t (rial depreciation),
and a0Δ𝑦,Δx0 ≥ 0, with output rising as a result of higher oil revenues. The signs of the contemporaneous impacts of
Δe𝑓 t, Δmt and Δpt on output growth are less clear cut. The structural shocks, 𝜺t = (𝜀Δx0,t, 𝜀Δe,t, 𝜀Δm,t, 𝜀Δp,t, 𝜀Δ𝑦,t)′, are
assumed to be serially uncorrelated with zero means, E(𝜺t) = 0, and mutually uncorrelated with the diagonal covariance
matrix E(𝜺t𝜺′t) = Σ = Diag
(
𝜎Δx0,Δx0 , 𝜎Δe,Δe, 𝜎Δm,Δm, 𝜎Δp,Δp, 𝜎Δ𝑦,Δ𝑦
)
. Since we condition on sanctions intensity and global
indicators, the structural shocks can be viewed as “domestic” shocks attributed to policy changes that are unrelated to
sanctions. Specifically, it is assumed that 𝜺t are uncorrelated with st and zwt. Under these assumptions it is now possible
to distinguish between the effects of a unit change in the sanctions variable, from domestic policy changes initiated by a
unit standard error change to the domestic shocks, 𝜺t. Specifically, for contemporaneous effects we have 𝜕qt∕𝜕st = A−10 𝜸0s,
and 𝜕qt∕𝜕𝜀𝑗t =
√
𝜎𝑗𝑗A−10 e𝑗 whereA0 is given by (7), e𝑗 (𝑗 = Δx0,Δe𝑓 ,Δm,Δp,Δ𝑦) are the vectors of zeros except for their
𝑗-th component, which is one.
For the purpose of computing impulse responses and forecast error variance decompositions, we model st and zwt as
autoregressive processes:
st = as + 𝜌sst−1 + 𝜂t, (8)
zwt = azw +Azwzw,t−1 + vwt, (9)
where the sanctions and global shocks, 𝜂t and vwt, are serially uncorrelated with zero means, and variances 𝜔2s and 𝛀w.
Combining Equations (6), (8), and (9), we obtain the following SVAR model in zt =
(
q′t , st, z
′
wt
)′
,
𝚿0zt = a+𝚿1zt−1 +𝚿2zt−2 + ut, (10)
where a =
(
a′q, as, a′zw
)′ and ut = (𝜺′t , 𝜂t, v′wt)′, are (m + k + 1) × 1 vectors, and
𝚿0 =
(A0 −𝜸0s −Dw
0 1 0
0 0 Ik
)
, 𝚿1 =
(A1 𝜸1s 0
0 𝜌s 0
0 0 Azw
)
, 𝚿2 =
(A2 0 0
0 0 0
0 0 0
)
are (m + k + 1) × (m + k + 1) matrices. Standard techniques can now be applied to the SVAR model in (10) to obtain
impulse response functions and error variance decompositions assuming the global shocks, vwt, are uncorrelated with
domestic and sanctions shocks (namely 𝜺t, and 𝜂t).33 This is a standard small open economy assumption which applies
to the Iranian economy in particular since its relative size in the world economy is small and has been declining over the
past forty years.
5.1 Structural model estimation
We estimated the five equations of the augmented SVARmodel in (6), experimenting with different sub-sets of the control
variables: world output growth, global realized volatility, world real equity returns, changes in long term interest rates,
32Details on data sources and the computation of the global variables are given in Section S2 of the supporting information.
33Further details are provided in Section S3 of the supporting information.
LAUDATI AND PESARAN 15
and global real exchange rate changes against the U.S. dollar.34 The full set of results are provided in Tables S10a to S10e.35
As can be seen, with the exception of the world output growth, none of the other control variables play a significant role
in the regressions for inflation and output growth. Accordingly, we consider a simplified specification and in Table 4
we provide estimates of the SVAR model including only the world output growth (Δ𝑦wt) as the control variable. As can
be seen from this Table, the sanction variable is statistically significant for four out of the five domestic variables, with
changes in oil exports and output growth being affected after one quarter. In contrast, exchange rate changes and inflation
are affected significantly by the sanctions contemporaneously as well as with one quarter lag. The only variable which
seems to be unaffected by the sanctions is the money supply growth. It is also worth noting that none of regressions in the
SVAR model display residual serial correlation, which is an important consideration for impulse response and variance
decomposition analyses that follow.
To assess the quantitative importance of the sanctions, we compute the effects of sanctions bymultiplying the estimated
coefficients of st and st−1 by the median value of the sanctions variable, which is around 0.16. See Table 2. The median
presents a more robust measure of a central value for sanctions intensity as compared to the average which is likely to
be sensitive to the outlier values of st over time. Using the median we are able to provide an estimate of the effects of
moving from a no sanction case (with st = 0) to a situation where st is set to its median value. We refer to these estimates
as (counterfactual) median estimates of the sanctions.
We now consider the results of the individual equations in the SVAR model. In the case of oil exports, we note that in
addition to sanctions, changes in oil exports are also affected significantly by world output growth with some feedback
effects from the exchange rate variable. The positive impact of world output growth on Iran's oil exports makes sense and
suggests that sanctions have not been completely effective in making Iran's oil exports non-responsive to world economic
conditions. The median estimate of the effects of sanctions on oil exports is around 4.6% per quarter, or about 18.4 per
year. See Table S10a.
Turning to the estimates of the exchange rate equation (given in column 2 of Table 4), we first note that exchange rate
changes have been moderately persistent with a coefficient of 0.350 (0.094), which is statistically highly significant. In
most developed markets, we do not expect exchange rate changes to be persistent, and the result for rial points to possible
inefficiencies in Iran's foreign exchangemarket.36 Second,we observe that the rial depreciates strongly in the same quarter
in which sanctions are raised. The median fall in its value is around 4.9% per quarter. However, there is a significant
degree of overshooting, with the sanctions variable having the opposite effect on exchange rate after one quarter. The
rial appreciates by about 3.7% in the following quarter, resulting in a less pronounced overall impact of sanctions on the
rial depreciation of around 1.2% per quarter, or 4.8% per annum, which is still quite substantial.37 As can be seen from
Table S10b of the supporting information, these estimates are remarkably stable and statistically significant at the 1%
level across all specifications regardless of the number of global control variables included in the regression equation. In
fact, none of the domestic variables (oil exports, inflation, money supply growth, and output growth) have a statistically
significant effect on the exchange rate, and only global realized volatility and foreign output growth prove to be statistically
significant at 10% level but not robust across all specifications. The adjusted R2 of the exchange rate equations with world
output growth included is around 21%. This is high by the standard of exchange rate equations and is partly explained by
the presence of the contemporaneous sanctions variable in the regression. Its use for prediction requires predicting the
sanctions variable, which adds another layer of uncertainty.
The estimates for the money supply growth (Δmt) equation are summarized in column 3 of Table 4. As can be seen,
only lagged money supply growth is statistically significant, and moderately persistent with a coefficient of 0.218 (0.096).
Notably, we do not find any feedback effects from inflation to money supply growth, even when we include a second lag
of inflation to the money supply growth equation.
34To take account of possible seasonal variations all regressions are also augmented with seasonal dummies which turn out to be highly significant in
the money supply growth equation.
35The figures in parentheses in these tables report the least squares standard errors. But to check the robustness of our inference we also provide
White (1980)'s heteroskedastic robust standard errors in Section S.48 of the supporting information. As to be expected the use of robust standard errors
results in reduced level of statistical significance for most of the parameters, but as can be seen the differences are largely inconsequential. Further,
we shall be using bootstrap standard error bands in our impulse response analyses and the bootstrap procedure will automatically account for possible
heteroskedasticity and non-Gaussian errors.
36Note that the exchange rate is expressed as the number of Iranian rials per oneU.S. dollar and therefore a rise in the exchange rate variable corresponds
to a depreciation of the rial.
37Such overshooting is well known in the international finance literature. See, for example, Dornbusch (1976).
16 LAUDATI AND PESARAN
𝚫x0t 𝚫e𝒇,t 𝚫mt 𝚫pt 𝚫𝒚t
(1) (2) (3) (4) (5)
st 0.107 0.305∗ ∗ ∗ −0.002 −0.033∗∗ 0.029
(0.150) (0.064) (0.017) (0.013) (0.026)
st−1 −0.288∗ −0.233∗ ∗ ∗ 0.015 0.037∗ ∗ ∗ −0.056∗∗
(0.155) (0.067) (0.017) (0.013) (0.026)
Δx0t 0.029 0.006 −0.003 0.025∗
(0.040) (0.010) (0.007) (0.014)
Δe𝑓,t −0.007 0.163∗ ∗ ∗ −0.141∗ ∗ ∗
(0.023) (0.017) (0.045)
Δmt −0.073 0.063
(0.073) (0.142)
Δpt 0.387∗∗
(0.181)
Δ𝑦wt 8.406∗∗ −2.639∗ 0.233 0.865∗ ∗ ∗ −0.520
(3.649) (1.590) (0.389) (0.298) (0.592)
Δx0t−1 −0.051 0.044 −0.005 −0.003 0.023∗
(0.090) (0.038) (0.009) (0.007) (0.014)
Δe𝑓,t−1 −0.441∗∗ 0.350∗ ∗ ∗ −0.025 −0.009 0.041
(0.217) (0.094) (0.027) (0.020) (0.036)
Δmt−1 −0.715 0.149 0.218∗∗ −0.025 0.046
(0.930) (0.397) (0.096) (0.075) (0.144)
Δpt−1 0.052 −0.341 0.167 0.488∗ ∗ ∗ −0.505∗ ∗ ∗
(0.794) (0.338) (0.115) (0.089) (0.167)
Δ𝑦t−1 0.122 −0.145 0.025 0.042 −0.221∗∗
(0.592) (0.252) (0.063) (0.048) (0.090)
Δpt−2 −0.070 0.183∗∗
(0.104) (0.079)
Residual serial 2.406 6.212 7.640 8.061 7.240
correlation test [0.662] [0.184] [0.106] [0.089] [0.124]
Adjusted R2 0.122 0.209 0.466 0.659 0.124
Note: The variables are ordered as: Δx0t ,Δe𝑓 t , Δmt , Δpt , and Δ𝑦t , where
Δx0t = (X0t − X0t−1)∕X
0
t−1, X
0
t is the oil exports revenues in U.S. dollars;
Δe𝑓 t = ln(E𝑓 t∕E𝑓,t−1), E𝑓 t is the quarterly rial/U.S. dollar free market exchange rate;
Δmt = (M2t −M2,t−1)∕M2,t−1, M2t is the monetary aggregateM2 obtained by summing the
aggregatesM1 and “quasi-money”; Δpt = ln(Pt∕Pt−1), Pt is the quarterly consumer price
index of Iran; Δ𝑦t = ln(Yt∕Yt−1), Yt is the quarterly real output of Iran. st is the quarterly
sanctions intensity variable. Δ𝑦wt is the quarterly world output growth, computed as
𝑦wt =
∑n
i=1 wi𝑦it , with {𝑦it}
n
i=1 being the natural log of real output for 33 major economies,
and {wi}ni=1 are GDP-PPP weights. Seasonal dummies are included to allow for possible
seasonality of the variables in the regressions of the SVAR model in Equation (6) with
qt =
(
Δx0t ,Δe𝑓 t ,Δmt ,Δpt ,Δ𝑦t
)′ and zwt = (Δ𝑦wt)′. Numbers in parentheses are least squares
standard errors, and those in square brackets are p-values. ***p < 0.01, **p < 0.05, *p < 0.1.
“Residual serial correlation test” is the Breusch–Godfrey LM test of serially uncorrelated
errors with lag order of the test set to 4. See Sections S2.1, S2.2, S2.5, and S2.6 in the data
appendix of the suporting information for details on the construction of the sanctions
intensity variable, calendar conversions, and sources of the data used. Regressions results
that include other global control variables (e.g., global realized volatility) are provided in
Tables S10a–e. The use of asterisks represents the statistical significance levels of the
coefficients, and it is explained in the notes with the standard notation referred to the p
values. It is crucially relevant to have them.
TABLE 4 Quarterly estimates of the SVAR model of
Iran with domestic variables ordered as: oil exports,
exchange rate returns, money supply growth,
inflation and output growth, estimated over the
period 1989q1–2019q4
The estimates for inflation (Δpt) are summarized in column 4 of Table 4. As discussed in Section 2, inflation in Iran
has been persistently high over the past 40 years, and to capture its persistence it proved necessary to include Δpt−2, as
well as Δpt−1 in the regression equation. It does not seem necessary to include second order lags of other variables in the
inflation equation.38 Perhaps not surprisingly, the estimates also show that exchange rate depreciation is an important
38See also Table S10d where different sub-sets of control variables are also included in the regressions for the inflation equation.
LAUDATI AND PESARAN 17
determinant of inflation in Iran, a factor which is statistically significant and quantitatively important. The immediate
effect of 1% depreciation of the free market exchange rate is to raise prices by around 0.15% to 0.17%, as many imported
goods items tend to rise with the fall in exchange rate. Sanctions affect inflation indirectly through the exchange rate as
well as directly, but the direct effects of sanctions do not last and the net direct effects of sanctions on inflation seem to be
negligible. It is also interesting and quite surprising thatmoney supply growth, oil exports, or lagged output growth do not
seem to have any significant direct effects on inflation. But we do find some evidence of global output growth positively
affecting inflation, a kind of international Phillips curve effect that leads to higher international prices that are in turn
reflected in Iran's import prices and hence domestic inflation.
Finally, column 5 of Table 4 provides the results for real output growth. Output growth in Iran is negatively autocor-
related, with a coefficient estimated to be around −0.22, which is statistically significant. This contrasts the positively
autocorrelated output growth observed for many other countries. The sanctions intensity variable affects output growth
with a lag, as it takes a few months for different sectors of the economy to adjust to sanctions. After only one quarter, the
effect of sanctions on output growth is statistically highly significant.39 Within two quarters the regression predicts Iran's
output growth to slow down by about 0.9% per quarter (3.6% per annum). In addition to this direct effect, sanctions also
influence output growth through exchange rate depreciation, which is also highly statistically significant. This indirect
effect amounts to around 0.14% per quarter drop in output growth when the rial depreciates by 1%. Output growth is also
negatively affected by lower oil exports, and by lagged inflation, which highlights the adverse effects of high and persistent
inflation on output growth, without any short term Phillips curve type of trade off between inflation and output growth.
Interestingly enough, none of the global factors seem to have any significant effects on Iran's output growth, partly due
to Iran's relative economic and financial isolation from the rest of the global economy. See Table S10e for further details.
Since—in the SVARmodel—money supply growth plays aminimal role in the determination of inflation and exchange
rate variations, and exchange rate remains the primary driver of inflation and output growth, we decided to simplify the
model by dropping the money supply growth from the SVAR model. The estimation results for this simplified model are
summarized in Tables S11a to S11e, and S12a to S12e.40 As can be seen, the estimates for the four equations in the current
SVARmodel are very close to those in themodelwithmoney supply growth, confirming further thatmoney supply growth
is not essential for the analysis of the interrelationships of exchange rate, inflation and output growth in Iran, which is
the primary concern of our analysis. It is also worth noting that our main findings are not much affected by re-ordering
of the domestic variables. In Section S4 of the supporting information, we provide results of estimating the SVAR model
in (6), with the following ordering of the domestic variables: {Δe𝑓 t,Δx0t ,Δmt,Δpt,Δ𝑦t}. For this ordering, the foreign
exchange is placed first and oil export revenues second to capture the idea that the rial may react even faster than Δx0t to
announcements of new sanctions. The results are summarized in Tables S13a to S13f, and—as aforementioned—they are
largely not affected by this change. In Tables S14a to S14e, we consider the effects of re-ordering of the variables in the
case of the simplified model without the money supply growth or seasonal dummies, and it is once again confirmed that
the results are reasonably robust to the re-ordering of the variables under consideration.
Overall sanctions have affected Iran in a number of ways and through different direct and indirect channels, the most
important of which are falls in oil export revenues and the exchange rate depreciation. The exchange rate depreciation
itself could have its roots in persistently high levels of inflation, coupled with a reduction in oil revenues and anticipated
decline in private sector activity. The currency depreciation in turn leads to higher import prices and lower economic
growth.We also find that the direct effect of sanctions on inflation is rather small, compared to an average annual inflation
norm of around 18% in Iran (See Table 1).
Money supply growth seems to follow patterns which are neither related to sanctions nor to any of the domestic vari-
ables, notably inflation, which could be due to the underdevelopment of capital andmoneymarkets in Iran, as highlighted
recently by Mazarei (2019). These results seem quite robust to other measures of liquidity such as M1 or private sector
credit.41
39Table S10e shows that the results are reasonably robust to different choices of control variables.
40Dropping the money supply growth from the SVAR model, also renders the seasonal dummies statistically insignificant. Thus seasonal dummies are
not included in the SVAR model that excludes the money supply growth variable.
41Estimates based on these alternative measures of liquidity are available upon request.
18 LAUDATI AND PESARAN
5.2 Impulse response analysis
The estimates of the individual equations provided in Table 4 provide a snap-shot of how sanctions interact with some of
the keymacroeconomic variables. However, given the dynamic and simultaneous nature of themodel, to fully understand
and evaluate the nature and consequences of these interactions, we compute impulse response functions (IRFs) and
forecast error variance decompositions (FEVDs) for the augmented SVARmodel given by (6).42 We have seen that money
supply growth does not play much of a role in the determination of inflation and output growth, and is hardly affected
by sanctions. Also, among the control variables, only foreign output growth seems to exert statistically significant effects
on inflation and output growth. For these reasons, to compute IRFs and FEVDs we will be focussing on the SVARmodel
with qt =
(
Δx0t ,Δe𝑓 t,Δpt,Δ𝑦t
)′, augmented with the sanction variables and Δ𝑦wt as the foreign control variable. We also
use AR(1) models for st and Δ𝑦wt to capture the dynamics of these exogenous processes.43
The IRFs for positive one standard error (s.e.) shocks to st and qt are displayed in Figure 2.44 Panel A of this figure shows
the results for the sanction shock. One standard deviation for st is equal to 0.120, which represents half of the average
sanctions intensity over the period considered (s1989q1−2019q4 = 0.24).45 A single quarter shock to sanctions intensity causes
oil exports to decrease by almost 5% after one quarter, with some reversal thereafter. But the negative effects of sanctions
on oil export revenues continue to be important even after four quarters with losses that are still about 1%. The positive
shock to sanctions also causes the foreign exchange rate to depreciate by about 3% in the same quarter, but its effects are
rather short lived and become statistically insignificant two quarters after the shock. For inflation and output growth, the
effects of the sanction shock last much longer. Its effects on inflation are particularly persistent and last at least for four
years after the shock, although its magnitude is relatively small: 0.3% increase per quarter in the first year. The effects of
sanction shock on output growth, on the other hand, are much larger in size. A single period one standard error shock to
sanctions causes output growth to fall by more than 0.4% per quarter (1.6% per annum). The loss in output growth is still
close to 0.2% per quarter two years after the shock.
Panel B of Figure 2 displays the results for a single quarter shock to oil revenues. The effect on oil export revenues them-
selves is very large and positive, although rather short-lived, reflecting the rather volatile nature of oil export revenues.
The effects of oil revenue shock on foreign exchange rate is not that large, around 1.2 after one quarter, and then falling
to zero after four quarters. Its effects on inflation is positive but quite small, around 0.2% after two quarters. The positive
shock to oil revenues induces a rise in output of around 0.5% on impact which is statistically significant, but this effect is
short lived and tends to zero quite rapidly.
The results for the foreign exchange rate shock are given in Panel C of Figure 2. The effect of this shock on oil export
revenues is negative and amounts to −4% one quarter after the shock before reverting to zero thereafter. More interest-
ingly, one quarter exchange rate shock induces a sizeable and precisely estimated effect (of around 8% per quarter) on
exchange rate, but similar to the effects of the sanction shock, it does not last long and its effects dissipate very quickly
after two quarters. The exchange rate shock raises inflation on impact by around 1.2% per quarter, and then starts to fall
and vanishes completely after about two years. The same is not true of real output growth. The direct effects of foreign
exchange shock on output growth are negative and statistically significant but small in magnitude, around −0.50% on
impact, which then moves towards zero very quickly.
Panel D of Figure 2 gives the results for an inflation shock (e.g., due to a domestic expansionary policy). Again, because
of the highly persistent nature of inflation in Iran, the most pronounced effects of the inflation shock is on inflation
itself, raising inflation by 1.5% per quarter on impact and then falling gradually to zero after 2 years. Interestingly, the
effect of inflation shock on exchange rate is not statistically significant, suggesting that the causal link between them is
from exchange rate to inflation and not vice versa. Compare the IRFs for exchange rate and inflation in Panels C and D
of Figure 2. The effects of inflation shock on output growth are positive on impact but small in magnitude, and reverse
42Detail of the derivations of IRFs and FEVDs are given in Sections S3.1 and S3.2 of the supporting information, respectively.
43Time series evidence in support of our choice of AR(1) specifications for st and Δ𝑦wt are provided in Tables S6 and S7. It is also worth noting that the
assumed AR(1) processes for st and Δ𝑦wt only affect the IRFs and FEVDs, and do not affect the estimates of the SVAR model.
44The error bands for the point estimates shown in these figures are computed using the bootstrap procedure described in Sub-section S3.4 of the
supporting information.
45See Table 2 for the descriptive statistics of the sanctions intensity indicator, and note that one s.e. sanction shock is computed using the AR(1) spec-
ification assumed for st—it is smaller than the one standard deviation of st . Information on the size of one standard error shock in the case of the
endogenous variables in the SVAR model are provided in Table S8.
LAUDATI AND PESARAN 19
FIGURE 2 Impulse responses
of the effects of sanctions and
domestic shocks on oil exports,
foreign exchange, inflation, and
output growth
20 LAUDATI AND PESARAN
TABLE 5 Forecast error variance decomposition for domestic variables in the SVAR model with a single shock
to sanctions
Panel A: FEVD for oil exports Panel B: FEVD for exchange rate
Quarter Proportion explained by a shock to: Quarter Proportion explained by a shock to:
ahead st 𝚫x0t 𝚫e𝒇 t 𝚫pt 𝚫𝒚t 𝚫𝒚wt ahead st 𝚫x
0
t 𝚫e𝒇 t 𝚫pt 𝚫𝒚t 𝚫𝒚wt
0 0.00 0.96 0.00 0.00 0.00 0.03 0 0.17 0.00 0.82 0.00 0.00 0.01
1 0.04 0.90 0.02 0.00 0.00 0.04 1 0.17 0.01 0.80 0.00 0.00 0.02
2 0.05 0.89 0.02 0.00 0.00 0.04 2 0.17 0.01 0.80 0.00 0.00 0.02
3 0.06 0.88 0.02 0.00 0.00 0.04 3 0.17 0.01 0.80 0.01 0.00 0.02
4 0.06 0.88 0.02 0.00 0.00 0.04 4 0.17 0.01 0.80 0.01 0.00 0.02
5 0.06 0.88 0.02 0.00 0.00 0.04 5 0.17 0.01 0.80 0.01 0.00 0.02
6 0.06 0.88 0.02 0.00 0.00 0.04 6 0.17 0.01 0.80 0.01 0.00 0.02
7 0.06 0.88 0.02 0.00 0.00 0.04 7 0.17 0.01 0.80 0.01 0.00 0.02
8 0.06 0.88 0.02 0.00 0.00 0.04 8 0.17 0.01 0.80 0.01 0.00 0.02
Panel C: FEVD for inflation Panel D: FEVD for output growth
Quarter Proportion explained by a shock to: Quarter Proportion explained by a shock to:
ahead st 𝚫x0t 𝚫e𝒇 t 𝚫pt 𝚫𝒚t 𝚫𝒚wt ahead st 𝚫x
0
t 𝚫e𝒇 t 𝚫pt 𝚫𝒚t 𝚫𝒚wt
0 0.01 0.00 0.43 0.55 0.00 0.01 0 0.00 0.03 0.05 0.03 0.90 0.00
1 0.04 0.00 0.48 0.48 0.00 0.01 1 0.02 0.03 0.05 0.06 0.85 0.00
2 0.05 0.00 0.49 0.45 0.00 0.01 2 0.03 0.03 0.05 0.06 0.83 0.00
3 0.06 0.00 0.50 0.43 0.00 0.01 3 0.04 0.03 0.05 0.06 0.83 0.00
4 0.06 0.00 0.50 0.43 0.00 0.01 4 0.04 0.03 0.05 0.06 0.82 0.00
5 0.06 0.00 0.50 0.43 0.00 0.01 5 0.04 0.03 0.05 0.06 0.82 0.00
6 0.07 0.00 0.50 0.42 0.00 0.01 6 0.05 0.03 0.05 0.06 0.82 0.00
7 0.07 0.00 0.50 0.42 0.00 0.01 7 0.05 0.03 0.05 0.06 0.82 0.00
8 0.07 0.00 0.50 0.42 0.00 0.01 8 0.05 0.03 0.05 0.06 0.82 0.00
Note: st is the quarterly sanctions intensity variable. Δx0t = (X0t − X0t−1)∕X
0
t−1, X
0
t is the oil exports revenues in U.S. dollars.
Δe𝑓 t = ln(E𝑓 t∕E𝑓,t−1), E𝑓 t is the Iran rial/U.S. dollar quarterly free market exchange rate. Δpt = ln(Pt∕Pt−1), Pt is the quarterly
consumer price index of Iran. Δ𝑦t = ln(Yt∕Yt−1), Yt is the quarterly real output of Iran. Δ𝑦wt is the quarterly world output growth:
𝑦wt =
∑n
i=1 wi𝑦it , with {𝑦it}
n
i=1 being the natural log of real output for 33 major economies, and wi the GDP-PPP weights. See S2.1,
S2.2, S2.5, and S2.6 in the data appendix of the supporting information for details on the construction of the sanctions intensity
variable, calendar conversions, and sources of the data used.
quickly after one quarter, suggesting that it might not be possible to increase output by expansionary policies. The effects
on oil export revenues do not appear to be statistically significant.
Finally, the IRFs of the effects of a positive shock to output growth are displayed in Panel E of Figure 2. A positive output
shock could be due to technological advance or fundamental reforms that reduce economic distortions and enhance
production opportunities. The output shock seems to have little impact (in short or medium term) on both oil exports and
exchange rate, which seem to be primarily driven by sanctions and their own dynamics. The positive output shock also
has a minimal effect on inflation, increasing inflation by less than 0.1% per quarter after two quarters. The primary effects
of the output shock are on output itself, raising output by 2.8% per quarter on impact before losing momentum in less
than a year. The initial very large increase in output is somewhat of an over-reaction which is then corrected slightly, yet
providing a net 2% rise in output within the year of the shock. Once again this result highlights the importance of supply
side policies for improving Iran's output growth in the long run.46
The impulse response analysis confirms some of the preliminary conclusions set out in Section 5.1. Sanctions have
their most impact on oil exports, free market exchange rate, and to a lesser extent on output growth. Inflation has its
own dynamics and is hardly affected by sanctions. The roots of high and persistent inflation must be found in domestic
economic mismanagement. Also, sanctions do adversely affect output growth after one quarter but such effects are short
lived.
46In the online supplement, we provide impulse responses for a positive shock to the world output growth in Figure S5.
LAUDATI AND PESARAN 21
FIGURE 3 Forecast error variance decomposition for domestic variables in the SVAR model with a cumulative shock to sanctions, and
domestic variables ordered as oil exports, exchange rate returns, inflation, and output growth
5.3 Forecast error variance decompositions
We now turn to a quantification of the relative importance of sanctions as compared to the four domestic shocks and
the foreign output shock. Table 5 presents the results.47 In Panel A we report estimates of the FEVDs of a unit shock to
oil export revenues. As can be seen, around 96% of the forecast error variance of oil export revenues is explained by the
shock to oil revenues itself. Other factors come into play in subsequent quarters, but they explain only a small proportion
of the total forecast error variance, with sanctions explaining 6%, foreign exchange 2%, and world output growth around
4%. It is clear that a single isolated sanction shock is not enough to make a significant impact on oil export revenues, and
a prolonged period of sanctions is required for sanction effects to cumulate and lead to a sizeable effect.
Panel B of Table 5 gives the results for the foreign exchange variable. Not surprisingly, foreign exchange shocks are the
most important, and account for 82% of forecast error variance on impact and decline only slightly, falling to 80% after
one quarter. Sanctions shock accounts for 17% of the variance, with the other shocks contributing very little. Therefore,
isolated sanctions do not drive Iran's exchange rate, and only become a dominant force if we consider prolonged periods
over which sanction shocks are in place with the same intensity.
The FEVDs of inflation, reported in Panel C of Table 5, show that foreign exchange and inflation shocks account for
the bulk of the variance, with sanction shocks accounting for the remainder. Oil exports, domestic and foreign output
shocksmake little contribution. On impact, inflation shock accounts for 55% of the variance, flattening out at 42% after six
quarters. In contrast, the contribution of the foreign exchange shock rises from 43% on impact to 50% after three quarters.
The contribution of the sanction shock is not particularly large, and starts at 1%, but rises to 7% after six quarters. Once
again, we see that inflation and exchange rates in Iran are mainly driven by domestic factors. But sanctions effects could
accumulate very quickly if we consider sanctions being in place over a prolonged period of time.
Finally, the FEVDs of output growth are reported in Panel D of Table 5. As can be seen, the output shock is by far the
most important shock and accounts for 90% of forecast error variance of output growth on impact and falls only slightly
to 82% after four quarters. In line with our estimates, sanctions shocks do not affect output growth on impact, and end
up explaining only 5% of the variance after six quarters. Foreign output shocks do not have any explanatory power for
Iran's output growth. The other three domestic shocks (oil exports, inflation and exchange rate) together account for 14%
of forecast error variance of output growth after one quarter, and do not increase any further after that.
The outcome of FEVDs is very different if we consider the effects of a prolonged period of sanctions, namely if sanctions
are imposed for over 2 or more years. The results are summarized in Figure 3. When sanctions are imposed with the same
intensity for about two years, sanctions explain more than 70% of the forecast error variance of inflation and around 60%
of the forecast error variance of output growth, keeping all other shocks fixed.
47FEVDs are computed using Equations (S9), (S10), and (S11).
22 LAUDATI AND PESARAN
6 CONCLUDING REMARKS
In this paper, using a novel measure of the intensity of sanctions based on newspaper coverage, we have quantified the
effects of sanctions on oil exports, exchange rate, inflation, and output growth in Iran. In order to estimate the prolonged
effect of sanctions on the Iranian economy, we faced several measurement and econometric challenges. Iran's recent his-
tory formed by the Islamic Revolution, hostage taking and the 8 year war with Iraq, makes it hard to have a reliable “donor
pool” of countries to construct a synthetic Iran. Furthermore, Dif-in-Dif methods cannot be applied because a relevant
pre-sanctions episode is not available. Finally, the degree of intensity of sanctions imposed on Iran has varied considerably
over time while never being completely lifted. For these reasons, a novel identification strategy was provided to overcome
the difficulties that could not be addressed by using approaches such as the Synthetic Control Method and the Panel Data
Approach (Hsiao et al. 2012). In addition, we have proposed the first newspaper-based indicator to track sanctions inten-
sity. In doing so, it was possible to solve the issue of not having a “sanction off” period, something impossible to capture
with a dummy variable estimator. With a novel econometric strategy and a sanctions index at hand, we proceeded to ana-
lyze both the reduced-form long term effects of sanctions on Iranian output, and the channels through which such losses
manifested.
When evaluating the direct and indirect costs of sanctions, we have followed the literature and attempted to control for
possible confounders, namely external and domestic factors that affect the economybut are unrelated to sanctions, such as
advances in technology, world output growth, and international prices. Using a reduced form regression of output growth
on our sanctions intensity variable, we estimate Iran's output loss to be around 2% per annum,which is considerablewhen
cumulated over time. There is, of course, a high degree of uncertainty associated with such estimates which should be
borne in mind. But—even if we compare Iran's growth performance over the 1989q1-2021q1 period with that of Turkey
and other similar size emerging economies—we find that Iran's realized output growth of 3% still lies below the average
growth of 4.4% experienced by Indonesia, Turkey, South Korea and Thailand over the same period.48
A SVAR analysis augmented with the proposed sanctions variable as well as global factors, allows us to identify the
channels of transmission of sanctions to the broader economy. Oil exports revenues drop first as a direct consequence of
new sanctions, accompanied by an instantaneous depreciation of the Iranian rial vis-à-vis the U.S. dollar, which is sub-
sequently translated into higher consumer prices, and slower economic growth. Monetary policy appeared to be passive,
and accommodating the behavior of other macro-financial variables once we control for a number of factors. Overall, the
economy appeared rather isolated from global factors.
There is no doubt that sanctions have harmed the Iranian economy, but one should not underestimate the damage
done by years of economic mismanagement. Iran's low output growth relative to its potential, high inflation and excess
output growth volatility cannot all be traced to sanctions and have domestic roots stemming from prolonged periods of
economic mismanagement, distorted relative prices, rent seeking, a weak banking system and under-developed financial
institutions. Sanctions have accentuated some of these trends and delayed the implementation of highly needed reforms.
Amore comprehensive analysis of sanctions also requires detailed investigation into how sanctions and their variability
over the past 40 years have affected policy decisions at all levels, from monetary and fiscal policies to industrial, regional
and social policies. It is generally agreed that, at times of increased sanctions intensity, governments fearful of political
consequences are reluctant to curtail distortionary policies, such as large subsidies on food and energy, and they might
even accentuate them, or resort to multiple exchange rates to reduce the inflationary effects of sanctions.
Sanctions have also led to some positive unintended effects. Non-oil exports have risen from $600 million before the
Revolution to around $40 billion, resulting in greater foreign exchange diversification. The high-tech sector has seen
exponential growth over the past 10 years and is now one of the regions' fastest growing sectors. Iran's major web-based
companies have been protected by potential competition from their U.S. counterparts shown in brackets including:
Digikala (Amazon), Aparat (YouTube), Cafe Bazaar (Google Play), Snapp (Uber), Divar (Craigslist). It is estimated that
over 65% of Iranian households are now connected to the internet. This rapid expansionwas facilitated by the government
and security apparatusmaking affordable high-speed internet a reality in Iran. TheMobile Telecommunication Company
of Iran, largely controlled by the Islamic Revolutionary Guard Corps now has over 43 million subscribers. Sanctions have
48If we take the 1990 value of GDP-PPP (constant international dollars) for Iran and cumulate the potential losses over the period until 2019, we reach a
similar conclusion. In the conservative scenario in which Iran grows at 4.5% per annum rather than 3.08, its output would be 18th in the world between
Saudi Arabia and Thailand. By using a less conservative yet still plausible estimate—if Iran were to grow at 5.5%, its output would be double the level
experienced in 2019. It would be the 15th largest economy between South Korea and Spain—two developed countries by now.We thank an anonymous
referee for this idea.
LAUDATI AND PESARAN 23
also resulted in significant advances in the areas of missiles and other military-related technologies. It is estimated that
IRGC control between 10% and 30% of the economy, with large stakes in the oil and gas sectors, construction, telecom,
banking, and tourism. One could argue that IRGC has been a major beneficiary of U.S. sanctions.
Our sample does not cover the period from January 2020 when Covid-19 effects started to be felt in Iran. However, it is
clear Covid-19 could have importantmedium term consequences, particularly for the traditional service sector. The Covid
shock has been truly global—it has hit almost 200 countries with different degrees of severity, with its effects magnified
through global trade and financial linkages. The full economic impact of Covid-19 on the Iranian economy is unknown
and requires further investigation.
ACKNOWLEDGMENTS
We would like to thank three anonymous reviewers and the Editor (Michael W. McCracken) for their helpful comments
and constructive suggestions. We have also received helpful comments from Nick Bloom, Jeff Nugent, Adrian Pagan,
Alessandro Rebucci, Ron Smith, and the participants at the Monthly Webinar of the International Iranian Economic
Association and at the 92nd conference of the International Atlantic Economic Society.
OPEN RESEARCH BADGES
This article has been awarded Open Data Badge for making publicly available the digitally-shareable data necessary to
reproduce the reported results.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are openly available at http://qed.econ.queensu.ca/jae/datasets/
laudati001/.
REFERENCES
Abadie, A., Diamond, A., &Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California's
Tobacco Control Program. Journal of the American Statistical Association, 105, 493–505.
Abadie, A., & Gardeazabal, J. (2003). The economic costs of conflict: A case study of the Basque Country. American Economic Review,
93, 113–132.
Amuzegar, J. (1997a). Adjusting to sanctions. Foreign Affairs, 76, 31–41.
Amuzegar, J. (1997b). Iran's economy and the US sanctions.Middle East Journal, 51, 185–199.
Andreas, P. (2005). Criminalizing consequences of sanctions: Embargo busting and its legacy. International Studies Quarterly, 49, 335–360.
Bahmani-Oskooee, M. (1996). The black market exchange rate and demand for money in Iran. Journal of Macroeconomics, 18, 171–176.
Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131, 1593–1636.
Baumeister, C., & Hamilton, J. D. (2015). Sign restrictions, structural vector autoregressions, and useful prior information. Econometrica, 83,
1963–1999.
Bonato, L. (2008). Money and inflation in the Islamic Republic of Iran. Review of Middle East Economics and Finance, 4, 3.
Borszik, O. (2016). International sanctions against Iran and Tehran's responses: political effects on the targeted regime. Contemporary Politics,
22, 20–39.
Carswell, R. (1981). Economic sanctions and the Iran experience. Foreign Affairs, 60, 247–265.
Celasun, O., & Goswami, M. (2002). An analysis of money demand and inflation in the Islamic Republic of Iran.
Chudik, A., Pesaran, M. H., & Tosetti, E. (2011). Weak and strong cross-section dependence and estimation of large panels. The Econometrics
Journal, 14, C45–C90.
Dadkhah, K., & Zangeneh, H. (1998). International economic sanctions are not zero-sum games: There are only losers. Iranian Journal of Trade
Studies Quarterly, 1, 1–14.
Dizaji, S. F., & van Bergeijk, P. A. G. (2013). Potential early phase success and ultimate failure of economic sanctions: A VAR approach with an
application to Iran. Journal of Peace Research, 50, 721–736.
Dornbusch, R. (1976). Expectations and exchange rate dynamics. Journal of Political Economy, 84, 1161–1176.
Doudchenko, N., & Imbens, G. W. (2016). Balancing, regression, difference-in-differences and synthetic control methods: A synthesis. (22791):
National Bureau of Economic Research Working Paper.
Downs, E., & Maloney, S. (2011). Getting China to sanction Iran: The Chinese-Iranian oil connection. Foreign Affairs, 90, 15–21.
Doxey, M. P. (1996). International sanctions in contemporary perspective (2nd ed.). Palgrave Macmillan UK, London.
Esfahani, H. S., Mohaddes, K., & Pesaran, M. H. (2013). Oil exports and the Iranian economy. The Quarterly Review of Economics and Finance,
53, 221–237.
24 LAUDATI AND PESARAN
Farzanegan, M. R. (2013). Effects of International Financial and Energy Sanctions on Iran's Informal Economy. SAIS Review of International
Affairs, 33, 13–36.
Farzanegan, M. R. (2014). Military spending and economic growth: The case of Iran. Defence and Peace Economics, 25, 247–269.
Farzanegan, M. R., & Hayo, B. (2019). Sanctions and the shadow economy: Empirical evidence from Iranian provinces. Applied Economics
Letters, 26, 501–505.
Farzanegan, M. R., Khabbazan, M. M., & Sadeghi, H. (2016). Effects of oil sanctions on Iran's economy and household welfare: New evidence
from a CGE model, Economic welfare and inequality in Iran: Developments since the revolution. Palgrave Macmillan US, New York.
Farzanegan, M. R., & Markwardt, G. (2009). The effects of oil price shocks on the Iranian economy. Energy Economics, 31, 134–151.
Gardeazabal, J., & Vega-Bayo, A. (2017). An empirical comparison between the synthetic control method andHsiao et al.'s panel data approach
to program evaluation. Journal of Applied Econometrics, 32, 983–1002.
Gharehgozli, O. (2017). An estimation of the economic cost of recent sanctions on Iran using the synthetic control method. Economics Letters,
157, 141–144.
Haidar, J. I. (2017). Sanctions and export deflection: Evidence from Iran. Economic Policy, 32, 319–355.
Hsiao, C., Ching, H. S., & Ki Wan, S. (2012). A panel data approach for program evaluation: Measuring the benefits of political and economic
integration of Hong Kong with mainland China. Journal of Applied Econometrics, 27, 705–740.
Hufbauer, G. C., Schott, J. J., & Elliott, K. A. (1990). Economic sanctions reconsidered: History and current policy (2nd ed., Vol. 1). Washington,
D.C.: Peterson Institute for International Economics.
Laudati, D., & Pesaran, M. H. (2021). Identifying the effects of sanctions on the Iranian economy using newspaper coverage. (9217): CESifo.
Liu, O., & Adedeji, O. (2000). Determinants of inflation in the Islamic Republic of Iran: A macroeconomic analysis. (00/127): IMF.
Majidpour, M. (2013). The unintended consequences of US-led sanctions on Iranian industries. Iranian Studies, 46, 1–15.
Maloney, S. (2015). Iran's political economy since the revolution. Cambridge: Cambridge University Press.
Mazarei, A. (2019). Iran has a slow motion banking crisis: Peterson Institute for International Economics Policy Brief No. 19-8.
Mazarei, A. (2020). Inflation targeting in the time of sanctions and pandemic: John Hopkins University School of Advanced International
Studies report.
Mohaddes, K., & Pesaran, M. H. (2013). One hundred years of oil income and the Iranian economy: A curse or a blessing? Iran and the global
economy: Petro populism, islam and economic sanctions. Routledge, New York.
Mohaddes, K., & Pesaran, M. H. (2016). Country-specific oil supply shocks and the global economy: A counterfactual analysis. Energy
Economics, 59, 382–399.
Morgan, T. C., Bapat, N., & Kobayashi, Y. (2014). Threat and imposition of economic sanctions 1945–2005: Updating the TIES dataset. Conflict
Management and Peace Science, 31, 541–558.
Naghavi, A., & Pignataro, G. (2015). Theocracy and resilience against economic sanctions. Journal of Economic Behavior & Organization,
111, 1–12.
Pape, R. A. (1997). Why economic sanctions do not work. International Security, 22, 90–136.
Pape, R. A. (1998). Why economic sanctions still do not work. International Security, 23, 66–77.
Peksen, D., & Drury, A. C. (2010). Coercive or corrosive: The negative impact of economic sanctions on democracy. International Interactions,
36, 240–264.
Pesaran, M. H. (1992). The Iranian foreign exchange policy and the black market for dollars. International Journal of Middle East Studies,
24, 101–125.
Pesaran, M. H. (2000). Economic trends and macroeconomic policies in post-revolutionary Iran. In Alizadeh, P. (Ed.), The economy of Iran:
Dilemmas of an islamic state. I.B. Tauris, London.
Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica, 74, 967–1012.
Pesaran, M. H. (2015). Time series and panel data econometrics. Oxford: Oxford University Press.
Plante, M. (2019). OPEC in the news. Energy Economics, 80, 163–172.
Popova, L., & Rasoulinezhad, E. (2016). Have sanctions modified Iran's trade policy? An evidence of Asianization and de-Europeanization
through the gravity model. Economies, 4, Article 24.
Wan, S.-K., Xie, Y., & Hsiao, C. (2018). Panel data approach vs synthetic control method. Economics Letters, 164, 121–123.
Weiss, T. G., Cortright, D., Lopez, G. A., &Minear, L. (Eds.) (1997). Political gain and civilian pain: Humanitarian impacts of economic sanctions.
Rowman & Littlefield, Lanham: MD.
White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica,
48, 817–838.
SUPPORTING INFORMATION
Additional supporting information can be found online in the Supporting Information section at the end of the article.
How to cite this article: Laudati, D., & PesaranM. H. (2022). Identifying the effects of sanctions on the Iranian
economy using newspaper coverage. Journal of Applied Econometrics, 1-24. https://doi.org/10.1002/jae.2947