Identifying the Effects of Sanctions on the Iranian
Economy using Newspaper Coverage
Dario Laudati
University of Southern California, USA

M. Hashem Pesaran
University of Southern California, USA, and Trinity College, Cambridge, UK

July 27, 2021

Abstract
This paper considers how sanctions affected the Iranian economy using a novel measure of sanctions
intensity based on daily newspaper coverage. It finds sanctions to have significant effects on exchange rates,
inflation, and output growth, with the Iranian rial over-reacting to sanctions, followed up with a rise in inflation
and a fall in output. In absence of sanctions, Iran’s average annual growth could have been around 4 5 per
cent, as compared to the 3 per cent realized. Sanctions are also found to have adverse effects on employment,labor force participation, secondary and high-school education, with such effects amplified for females.
Keywords: Newspaper coverage, identification of direct and indirect effects of sanctions, Iran output
growth, exchange rate depreciation and inflation, labor force participation and employment, secondary education, and gender bias.
JEL Classifications: E31, E65, F43, F51, F53, O11, O19, O53

We would like to thank Nick Bloom, Jeff Nugent, Adrian Pagan, Alessandro Rebucci, and Ron Smith for helpful comments.

1

Introduction

Over the past forty years Iran has been subject to varying degrees of economic and financial sanctions,and asset freezes, which began in November 1979 when the U.S. 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 politicalinfluence of Iran in the Middle East region and beyond; a process which escalated over Iran’s nuclear
program. As a result, the Iranian economy has been operating for a prolonged period under severe andoften quite harsh international restrictions, perhaps unique for a sizeable economy with deep historicalroots 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 andlasting indirect effects, such as rent-seeking, resource allocation distortions, and general costs associatedwith efforts involved in mitigating and circumventing the sanctions regimes. These indirect effects arelikely to be more serious the longer the sanctions are in place, particularly when the prospect of asanctions free outcome seems very remote.
The focus of the present paper is on the identification and quantitative evaluation of the direct andindirect effects of sanctions on the Iranian economy over the period 1989–2020, since the end of the
Iran-Iraq War and the start of the reconstruction period under President Rafsanjani. We exclude theperiod 1979–1988 due to the special circumstances of the 1979 Revolution, the hostage crises and theensuing eight year war with Iraq, which ended in August 1988. We also do not address the wider issueof the effectiveness of sanctions in achieving the foreign policy goals of the U.S. and its Western allies.
Nor do we discuss Iran’s ability to respond to sanctions in the rapidly changing geographical conditionsof the region.
We try to capture the intensity of the sanctions and the international political pressure on Iran tocomply by constructing a time series index based on daily newspaper coverage of the sanctions, theirimposition, the intensity of their use, as well as their occasional removal. The idea of a newspaper1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 evolutionof our proposed sanctions intensity index closely tracks the main sanctions time points, such as the
U.S. Iran and Libya Sanctions Act of 1996, the U.S. export ban in 1997, the U.S. investment bans andasset freezes in 2006 and 2007 ("Iran Freedom and Support Act", and Executive Order 13438), theUnited Nations nuclear Resolutions (1737, 1747) during 2006 and 2007, the U.S. Comprehensive IranSanctions, 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. See Figure 2. The sanctions intensity measure also
correlates with the U.S. Treasury "Specially Designated Nationals And Blocked Persons List" (SDN)
for Iran which has been publicly available since 2006.
We use the sanctions intensity variable in reduced form equations as well as in structural vector
autoregressive (SVAR) models to identify short run and long run effects of sanctions (direct and indirect
effects) on Iran’s rial/USD exchange rate, money supply growth, inflation and output growth, whilst
controlling for oil price changes, foreign output growth, and other global factors such as equity market
volatility. By making use of the sanctions intensity index we also avoid some of the limitations ofcomparative approaches used in the literature for policy evaluations, such as the synthetic control method(SCM) proposed by Abadie and Gardeazabal (2003), and the panel data approach proposed by Hsiao
et al. (2012). These techniques require pre-policy intervention outcomes to estimate weighted averagesof post policy outcomes for a "pre-selected" control group to be used as the basis of comparisons. In
the case of Iran there are no clear cut "sanctions on" and "sanctions off" periods, and it is unclear whichcountries should be included in the control group given the continued importance of the Iranian economy
in the region.
The main drawback of our approach is its inability to distinguish between the direct and indirect
effects of sanctions, since we have not been able to come up with satisfactory measures to control for
rent-seeking, and other economic distortions that might have been caused by the sanctions. There is no
doubt that the Iranian economy would have been subject to distortions and economic mismanagement

2even in the absence of any sanctions, and it would seem unlikely to be able to separate sanctions-induced
distortions from all other distortions either. For these reasons, our results should be approached withcaution. What we estimate can be viewed as measuring the combined effects of sanctions and sanctionsinduced distortions, broadly defined. Seen from this perspective, we find that the sanctions intensity
variable has highly statistically significant effects on 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 strong currency depreciations (with substantial overshooting), and highinflation rates are important channels through which sanctions affect the real economy. On the other
hand, the overexpansion of the money supply used to compensate underdeveloped capital and money
markets does not seem to affect the path of other domestic variables, once we control for inflation andvariations in exchange rates.
Using impulse response analysis and forecast error variance decomposition techniques, we also finda significant over-reaction of the rial to sanctions, 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 alreadybeen documented by Esfahani et al. (2013), who consider the effects of oil revenue shocks on output
growth and inflation. The forecast error variance decompositions also show that, despite the inclusion of
the sanctions intensity variable in the VAR, around 80 per cent of variations in foreign exchange and 83
per cent of variations in output growth remain unexplained, and most likely relate to many other latent
factors that drive the Iranian economy. We also estimate that in the absence of sanctions Iran’s output
growth could have been around 4

5 per cent, as compared to the 3 per cent realized.

We are also able to identify negative effects of sanctions on the labor market. The employment ratewith respect to other countries in the Middle East and North Africa (MENA) region has systematicallydecreased after sanctions were imposed, and women seem to have paid the higher price, with significant
declines in female labor force participation. From a socio-demographic standpoint, we also find thatsanctions have negatively affected secondary school education, with the number of schools and teachers
both sharply down in response to new sanctions waves. Again, gender effects seem to be at play here. We
find changes in sanctions intensity to have statistically significant negative effects on the ratio of female3

to male students. These outcomes could be due to government responses to sanctions-induced reductionsin oil income, with associated budgetary allocation away from education and female participation.
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 relatively 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. There have also been significant successes
in internet access and the associated rise of high-tech and knowledge-based companies in Iran, such as
Digikala, Snapp, and Cafe Bazaar, to name just a few. It is likely that U.S. sanctions have been partly
responsible for the rapid rise of high-tech companies in Iran over the past decade.
Our research does not encompass health outcomes because of the lack of sufficiently long time series
on the healthcare system. A number of recent on-field reports suggest that medicines (especially quality
drugs) were increasingly harder to find in Iran even before the Covid-19 pandemic.1
Overall, there seems little doubt that sanctions have harmed the Iranian economy and weakened its
socio-economic infrastructure. But removal of sanctions on their own is unlikely to ensure a period ofsustained growth and low and stable inflation, and many policy reforms are needed to address sanctionsinduced price distortions as well as other distortions due to general economic mismanagement, poor
governance, and the ambiguities that surround the relative roles of semi-government agencies and theprivate sector in the economy. Subsidies on essential food items and energy (fuel as well as electricity)
have created inefficiencies, smuggling, and damaging unintended 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.
Related literature
As noted already, in this paper we are primarily interested in the economic implications of sanctions
with a focus on the case of Iran, which is arguably the most sanctioned country in the world. Yet
1

To our knowledge there are no U.S. sanctions on the export of essential drugs to Iran. But large pharmaceutical and medical companies
tend to avoid transactions with Iran because of the highly complex financial regulatory structure which is in place as a part of U.S.
sanctions. Different laws overlap making some transactions appear legal on the assistance side but potentially illegal from a financial
perspective (Peksen (2009), Kokabisaghi (2018)).

4

there exists limited systematic knowledge of the effects of a prolonged period of sanctions on a major
economy such as Iran (Greenwald, 2020). We do not address the wider issues discussed in the literature
about the efficacy of sanctions as a foreign policy tool, although it is hoped that the economic analysis
provided in this paper could help in this regard. 2 Studies that are more directly related to our paper
either consider a specific sub-period or use shocks to oil export revenues as representing a sanction
shock. Gharehgozli (2017) considers the effects of sanctions just before JCPOA, which we discuss infurther detail in Section 3 below. Dizaji and van Bergeijk (2013) study the impact of economic sanctions
via changes in oil revenues using a VAR model. They show that sanctions are effective in the shortrun but their relevance fades with time. Similar results are reported by Esfahani et al. (2013), whofind that shocks to foreign output and oil exports are rather short-lived for Iran. This is an importantfeature of the Iranian economy which is also confirmed by our analysis using the new sanctions intensity
variable. 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 suffer less from 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 awayfrom Western economies to countries in the region (notably Iraq), China and other Asian economies.
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) andBorszik (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 and Markwardt (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 pressures and risks of currency crises.
2

The 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.

5

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 rentseeking opportunities and related economic distortions. Currently, there are three different exchange
rates for the rial. 3 Bahmani-Oskooee (1996) provides an earlier account of the gains obtainable in Iranfrom the black market premium, and the need to consider the free market rate rather than the official
one when the Iranian money demand is to be assessed; we follow this approach when conducting ouranalyses and disregard the official rate. The economic implications of multiple exchange rates in Iranare discussed in Pesaran (1992), Farzanegan (2013) and Majidpour (2013).
The role of gender inequality in Iran as compared to other Middle Eastern countries both in termsof labor force participation and education has also been studied by Esfahani and Shajari (2012) and
Majbouri (2015), among others, and Alizadeh (2017) provides a review.4
The rest of the paper is organized as follows. Section 2 offers an overview of the Iranian economy
under sanctions. Section 3 discusses alternative approaches to the analysis of policy interventions, anddevelops a structural vector autoregressive framework with latent factors used to identify the effectsof sanctions on the Iranian economy. Dynamics of sanctions and the channels through which sanctions
affect the Iranian economy are discussed in Section 4. Section 5 explains how we construct the sanctions
intensity index from six leading newspapers, and its co-movements with historical events. Section 6presents the estimates of the structural model and reports the related impulse responses and forecast
error variance decompositions of sanction and domestic shocks, as well as providing an estimate of
sanctions-induced output losses. Additional economic and socio-demographic results are obtained fromthe reduced form analyses in Section 7. Section 8 ends with some concluding remarks. An online
supplement provides details on the construction of our sanctions intensity variable, all data sources,
3

4

The three exchange rates are: (i) The official exchange rate to import essential items such as medicine, grain and sugar; (ii) The Nimarate, officially set at 2 per cent above the official rate by Bank Markazi daily, but in practice it is subject to much higher premiums andis reserved for non-oil exporters; (iii) The free market rate used for all other transactions.
We do not address the effects of economic sanctions on government and military expenditures. Interested readers may wish to consult
the papers by Dizaji (2014), Dizaji et al. (2014), Farzanegan (2011), Farzanegan (2019), Dizaji and Farzanegan (2021), Farzanegan(2014) and Farzanegan and Alaedini (2016).

6

further methodological notes and empirical analyses, and a comprehensive list of sanctions imposed
against Iran over the past forty years.

2

Sanctions and the Iranian economy: an overview

The evolution of the Iranian economy over the past forty years has been largely shaped by the Revolution
and the eight-years 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 since 1989. Initially, U.S. sanctions were much more clearly targeted. The
goal of the 1980–81 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.5
The evolution of Iran’s oil exports since the Revolution is shown in Panel A of Figure 1. Iran’s oil
exports had been already cut by half from the pre-Revolution peak of 6 millions barrels per day (mb/d).
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 U.S. andother Western 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. and U.N. sanctions in December 2006 aimed at halting Iran’suranium enrichment program which had gathered pace under the newly elected President Ahmadinejad. Initially, the sanctions targeted investments in oil, gas and petrochemicals, and exports of refined
products, but then were later expanded to include banking, insurance and shipping. Sanctions also targeted the Islamic Revolutionary Guard Corps (IRGC) and its vast commercial and industrial empire
whose activities had been expanded under President Ahmadinejad’s administration (2005q3–2013q2).
Sanctions against Iran were intensified further under President Obama and their coverage expanded to5

For an overview of U.S. sanctions against Iran see also Chapter 9 of Maloney (2015).

7

imports of rugs, pistachios, and caviar, representing some of Iran’s major non-oil exports. Additional
financial sanctions were imposed on Iran from July 2013, almost immediately before President Rouhani(2013q3–2021q2) took office. The coverage of U.N. and U.S. sanctions increased well beyond the oiland gas sectors and affected all aspects of Iranian foreign trade and international finance, and even theinternational payment system of Bank Markazi (Iran’s Central Bank). The extensive coverage of the
sanctions, their multilateral nature, coupled with the start of Rouhani’s administration in 2013 paved
the way for the 2015 nuclear agreement, Joint Comprehensive Plan of Action (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-US 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 declining 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
U.S. 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 alreadybeen 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 tradefrom the West to the East (primarily China) and to neighboring countries, focussing on trading companies that operate outside the reach of the U.S. Treasury. The sources of foreign exchange were also
diversified from oil to non-oil exports of goods and services. In this regard, ironically, Iraq became oneof the most important importers of Iranian products and services (electricity, agricultural products andlight manufacturing) after the fall of Saddam in 2003. Turkey and Qatar have also played importantroles in facilitating Iran’s foreign trade and international payments. Panel B of Figure 1 shows that theshare of oil and gas exports declined steadily from 96 per cent of total exports in 1979 to around 60 per
cent in 2018, before the full impact of the U.S. withdrawal from Iran’s oil exports. Over the same period
non-oil exports have increased from 753 million dollars to 37 billion dollars.
Nevertheless, the Iranian financial system has found it difficult to adapt to the new sanctions suffi-8Figure 1: Relevant Iran’s and World macroeconomic and financial time series over the period 1979–2020
Panel A
Iranian oil exports1

Panel B
Shares of oil and gas vs. non-oil and gas exports revenues2

Panel CFree market and official FX rates in logs3

Panel DFree market FX rate and CPI in logs4

Panel E
Iran and World real output in logs5

Notes: 1. Annual data over the period 1979–2020. 2. Annual data over the period 1979–2019. 3. Quarterly data over the period 1979q2–
2020q3. Foreign exchange rates are expressed as number of Iran’s rials per U.S. dollars. 4. Quarterly data over the period 1979q2–2020q3.
CPI stands for Consumer Price Index, and it is equal to 100 in 1979q2. 5. Quarterly data over the period 1979q2–2020q1. The world real
output is a weighted average of the natural logarithm of real output for 33 major economies.
See Sections S.2.2 and S.2.3 in the data appendix of the online supplement for details on calendar conversions, and sources of the data.

9

Table 1: Free market and official foreign exchange rate depreciation, inflation, real output growth, andsanctions intensity over the period 1979q3–2021q1

Periods
Revolution and Iran-Iraq War1
(1979q3–1989q2)
Rafsanjani presidency(1989q3–1997q2)
Khatami presidency(1997q3–2005q2)
Ahmadinejad presidency(2005q3–2013q2)
Rouhani presidency2(2013q3–2021q2)
Post-revolution full sample2(1979q3–2021q1)
Post-War full sample2(1989q1–2021q1)

Free FX
depreciation

Official FX
depreciation

Inflation

Outputgrowth

19.94

0.28

18.29

-1.60

Per cent per annum
Sanctions intensity
(0,1)
Mean
Median
0.20
0.11

16.55

39.83

21.17

5.16

0.11

0.10

7.90

20.34

14.53

4.72

0.15

0.13

17.08

5.16

18.15

1.68

0.38

0.39

25.34

14.66

19.61

0.83

0.34

0.27

17.39

15.30

18.34

2.01

0.23

0.15

17.38

19.88

18.30

3.13

0.24

0.16

Notes: 1. Data on free market foreign exchange rate start in 1980q2. 2. Data 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. See Section 5 of the paper
for the sanctions intensity variable definition over the range (0,1). See Sections S.2.1, S.2.2, and S.2.3 in the data appendix of the online
supplement for details on the construction of the sanctions intensity variable, calendar conversions, and sources of the data used.

ciently 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 creating opportunities for rent-seeking
and often corrupt business practices. 6 Panel C of Figure 1 shows the evolution of the free and official
rates (in log scales), and clearly shows the episodic nature of the jumps in the free market rate, followedby the sluggish catch up of the official rate. 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. The time profiles of free market rate and consumer prices(in log scales) are depicted in Panel D of Figure 1, and show the very close association that exists between the two series. As can be seen from Table 1, over the period 1989q1–2021q1 the free market rate
has depreciated around 17.4 per cent per annum as compared to the average annual rate of inflation of
6

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).

10around 18.3 per cent over the same period, representing a gap of around 1 per cent between the two series. But according to the Purchasing Power Parity (PPP), the difference between inflation and exchange
rate depreciation should match the average annual U.S. inflation, which is estimated to be around 2.5
per cent over the same period. 7 One reason for the discrepancy (of 1.5 per cent) could be due to theofficial rate being kept systematically below the free market rate, and the lower than expected long run
inflation should be set against the cost of price distortion and corruption that multiple exchange rate
regimes entail.
It is also important to note that not all foreign exchange crises can be traced to the intensificationof sanctions. Iran has witnessed major currency crises during all the four presidencies since 1989,
whilst 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 fromthe rapid expansion of imports and low oil prices, coupled with accommodating fiscal and monetary
policies. 8 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 per cent, despite his conciliatory
foreign policy and a much lower rate of currency depreciation (7.9 per cent as compared to 17.4 per centover the full sample).
Turning to the effects of sanctions on real output, Panel E of Figure 1 shows the time profile of
Iran’s real Gross Domestic Product (GDP) and world GDP in log scales. World output is computed asa weighted average of some of the largest 33 economies with details provided in the online supplement.
Iran’s average output growth rates over various sub-periods starting with the period of the Revolution
and the Iran-Iraq War, and followed by the four presidencies since 1989 are also summarized in Table
1. After the 1.6 per cent contraction during 1979q3–1989q2, Iran’s economy recovered strongly during
President Rafsanjani’s administration and grew by an average of 5.2 per cent per annum, an upward

7

8

The PPP is a long-run relationship that relates the exchange rate between two currencies to their relative price of goods: Pt = Et Pt ; 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 Pt denote the domestic and foreign price levels.
During the reconstruction period under President Rafsanjani imports of goods and services doubled over the period 1989–1991 (risingfrom 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 thedevelopments that led to the currency crisis under President Rafsanjani, see Section 3 of Pesaran (2000).

11

trend which continued over the period of Khatami’s presidency. 9 Iran’s output growth was also inline with the growth of world output during the 1989–2005 period. This period also coincided withthe lowest levels of sanctions intensity that Iran experienced over the past forty years, and is in stark
contrast with the growth performance under Ahmadinejad and Rouhani presidencies that coincided with
new U.S. and U.N. sanctions during the period before JCPOA in 2015, and the subsequent "maximum
pressure" strategy initiated by Trump in 2018. The results in Table 1 clearly provide a prima faciecase for sanctions having important adverse effects on Iran’s output growth. It is also worth noting that
Turkey, a country often compared to Iran being of a similar size and in a similar region, grew by anaverage annual rate of 4 per cent over the 2005–2020 period as compared to Iran’s 1.3 per cent average
growth rate over the same period.
Comparing Iran’s output growth with that of world output growth over the 1989–2019 period alsosuggests an output growth shortfall of around 1 per cent per annum, which could be contributed tothe 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 growthvolatility (12.61 versus 2.69 per cent). 10 Comparing Iran and Turkey over the same period we alsofind that Turkey grew at an average annual rate of 4 per cent with volatility of 10.8 per cent, a country
also known for high inflation and repeated currency crises. 11 Finally, sanctions have most likely also
contributed to the de-coupling of the Iranian economy from the rest of the world. Again comparing Iranand 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 little doubt that sanctions have adversely affected the Iranian economy, contributing to9

Detailed analyses of reconstruction of the Iranian economy under President Rafsanjani are provided in Karshenas and Pesaran (1995)
and Chapter 5 of Maloney (2015).
10 Mohaddes and Pesaran (2013) document the high volatility of Iran’s oil export revenues as one of the factors behind Iran’s low growthand 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. 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.
11 The average annual output growth of Saudi Arabia over the 2005-2019 period was also similar to Turkey and amounted to 4.3 per cent.

12

low growth, high inflation and increased volatility. What is less clear is how to carry out a quantitativeevaluation 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 underwhich 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

Identification of sanctions effects: methodological issues

Identifying the effects of sanctions on the Iranian economy is challenging even if a reliable measureof sanctions intensity is available. As with all macro policy interventions, when identifying the effectsof sanctions we also need to take account of confounding factors that are correlated with changes insanctions intensity, and which at the same time have a causal influence on target variable(s) of interest
such as output growth, inflation, health and education outcomes. In situations where a policy intervention
has differential effects over time and across many different units such as households or firms, differencein-difference techniques are used whereby 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 theanalysis 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 and Gardeazabal (2003) and the Panel Data Approach (PDA) proposed by Hsiao
et al. (2012). 12 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 theeconomic 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. Perhaps most importantly theyboth use pre-policy outcomes to estimate the weights applied to the countries included in the control

12 Further details and extensions of SCM are discussed in Abadie et al. (2010) and Doudchenko and Imbens (2016).

13

group. The main difference between the two approaches lies in way the weights are estimated.13
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 to mimic a "synthetic"
Iran as a weighted average of GDP of these economies with their respective weights determined using the
SCM based on seven different macroeconomic indicators. She concludes that the 2011–2014 sanctions
resulted in Iran’s real GDP to fall by as much as 17 per cent, as compared to the synthetic sanctions freeIran, with all the output short fall attributed to sanctions.
We depart from the mainstream literature reviewed above and consider the following model for Iranquarterly output growth∆yt = α + λ ∆yt 1 + ψ0 st + ψ1 st 1 + β 0 xt + γ 0 ft + ut ;(1)

where ∆yt 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 ). The measurement of the sanctions intensity variable, st , will be discussed in
Section 5, and will be treated as given for now. It is assumed that part of the change in the intensity ofsanctions affects Iran’s output growth with a lag, thus distinguishing between short term, ψ0 ; and longterm, θ = (ψ0 + ψ1 )=(1λ ), effects of sanctions.

Despite the introduction of st , identification of the sanctions coefficients, ψ0 and ψ1 , depends onthe confounding observed, xt , and unobserved, ft , factors. In the present reduced-form setting, for xt
13 Gardeazabal and Vega-Bayo (2017) provide a comparative simulation analysis of SCM and PDA, with a follow up critique by Wan et al.

(2018).

14

we consider changes in international oil prices. The effects of sanctions on output growth that operate
through exchange rate, liquidity, and inflation will be addressed in Section 4. Here we are concernedwith both direct and indirect effects of sanctions on output growth and for this reason we will not be
including any of the domestic variables in xt . The main challenge is how to identify and estimate ft . In
this regard our approach is closely related to the PDA (Hsiao et al. (2012)). To this end, we consider thefollowing equations for output growth for the rest of the world∆yit = αiy + β 0iy xit + γ 0iy ft + uy;it , i = 1; 2; :::; n;where ∆yit denotes output growth in country i (excluding Iran), xit is a k
specific to country i, and ft is the m(2)
1 vector of control variables

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. 14 By
allowing the factor loadings, γ i , to differ across countries, we do not assume that all economies areequally affected by the same factors, an assumption that underlies the DiD approach. We also departfrom 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 rank condition which allows us to identify ft as weighted averages of ∆yit 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 U.S., it isadvisable to use output shares as weights. Accordingly, suppose that
xit = α ix + Γ0ix ft + ux;it , i = 1; 2; :::; n;where Γix is a k

m matrix of factor loadings, and vit is a k(3)

1 vector that follows general stationary

processes that are weakly cross-sectionally correlated. Combining (2) and (3) we have
0

B 1
@
0

1

β 0iy C

Ik

0

1 0

1

0

1

0
B αiy C B γ iy C
B uy;it Cz=+
A ft + @A;
A it @
A @
0
Γix
ux;it
α ix

n
14 A set of random variables, fu ; i = 1; 2; :::; ng is said to be weakly cross correlated if supitj ∑i=1 Cov(uit ; u jt ) < C < ∞. It then follows
n
n
1=2
1
that ∑i=1 wi uit = O p (n), for any granular weights, wi , such that wi = O(n ) and ∑i=1 w2i = O(n 1 ). An obvious example is thesimple weights wi = 1=n. For further details see Chudik et al. (2011).

15

which yields zit = ci + Ai ft + Bi uit , where
1
1
0
1
0
0
0
0
0 0
0
B 1 β iy C
B αiy + β i C
B γ iy + β iy Γix C
ci = @A ft , and Bi = @A , Ai = @
A:
Γ0ix
0 Ik
α ix

Averaging zit over i using the weights wi we now have z̄wt = c̄w + Āw ft + ∑ni=1 wi Bi uit ;where z̄wt =∑ni=1 wi zit , c̄w = ∑ni=1 wi ci , Āw = ∑ni=1 wi Ai . Suppose now that the (k + 1)
rank (that requires m

m matrix Āw is full column

k + 1) then we can solve for ft as150ft = Āw Āw

1

0

0

Āw c̄w + Āw Āw

1

0

1

0

Āw z̄wt

Āw Āw

0nĀw ∑ wi Bi uit :i=10

Under the assumptions that uit are weakly cross correlated, Āw Āw ! p > 0, as n ! ∞, then for any choiceof weights wi that are granular it is possible to consistently estimate ft up to intercepts and an mmrotation matrix using z̄wt = (∆ȳwt ; x0wt )0 . More specifically, it is easily established that ∑ni=1 wi Bi uit =O p (n 1=2 ), and we have ft = āw f + H̄w z̄wt + O p (n 1=2 ), which can be used to eliminate the unobserved
factors, ft , from Iran’s output growth equation. Specifically, we obtain
0x̄wt + ut + O p (n 1=2 ):∆yt = αyw + λ ∆yt 1 + ψ0 st + ψ1 st 1 + β 0 xt + θyw ∆ȳwt + θxw(4)Hence, for n sufficiently large, and considering that the Iranian economy is quite small relative to the restof the world, the sanctions coefficients ψ0 , and ψ1 can be identified by augmenting the output growthequations with the rest of the world average output growth, ∆ȳwt , and the cross section weighted averagesof the observed drivers of the rest of the world output growth, x̄wt .
It is interesting to note that our approach does not favor selecting a pool of countries that are close
to Iran, but recommends including all countries, possibly weighted for their relative importance in theworld economy. Selecting specific countries could bias the results by restricting the number included in0

the construction of cross section averages. The rank condition, rank Āw Āw = m ; for a given n; and as
n ! ∞, ensures that ft has a reasonably pervasive effect on most economies which in turn allows us touse ∆ȳwt , and x̄wt as a reliable proxy for ft .
The analysis of sanctions effects can also be extended to other macro variables such as inflation and
15 See Pesaran (2006) for further details in a related context.

16

unemployment, and even to some key socio-economic indicators such as life expectancy, death rate or
educational achievement, which has been made possible due to the unusually long duration of sanctionsin the case of Iran.

4

Channels and dynamics of sanctions

It is also important to consider the main channels through which sanctions affect the economy, and tolearn about the time profile of their effects. In the case of Iran, new sanctions, or even their announcement, have invariably led to a significant depreciation of the Iranian rial, reduced oil exports and foreignexchange revenues, followed by a sharp rise in price inflation and output losses within 3–6 months after
the imposition of the new sanctions. The dynamic inter-relationships of exchange rate, money supply,
inflation and output growth can be modelled using a structural vector autoregressive (SVAR for short)model augmented with the sanctions intensity variable, oil price changes and other control variables,
denoted by z̄wt above.
We denote by qt = ∆e f t ; ∆mt ; ∆pt ; ∆yt

0

an m

1 (with m = 4) vector of endogenous domestic

variables, where ∆e f t represents the rate of change of free market foreign exchange rate,16 ∆mt is thegrowth rate of money supply, ∆pt is the rate of inflation, and ∆yt is real output growth.
To distinguish between different types of shocks and their implications for the Iranian economy, in
our SVAR we assume the direction of causality goes from ∆e f t to money supply growth, to inflation,and then to output growth, as represented by the ordering of the four endogenous variables in qt . Under
this causal ordering, we are able to distinguish changes in qt that are due to variations in the intensity ofsanctions from those that are the result of domestic policy shocks. The assumed causal ordering can bejustified in terms of relative speed with which the Iranian economy responds to crises. It is the value ofthe rial in free market that weakens first, followed by a potential expansion of liquidity, a rise in the priceof 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, monetary policy tends to beaccommodating by allowing a commensurate rise in liquidity.
16 We also tried a weighted average of free market and official rate, and we found that the free market rate provides a more accurate andtimely measure of the exchange rate movements for Iran given its higher responsiveness to sanctions. Our variable is expressed as Iran’s
rials per U.S. dollar.

17

In Equation (5), we look at the explanatory variables for qt = ∆e f t ; ∆mt ; ∆pt ; ∆yt

0

A0 qt = aq + A1 qt 1 + A2 qt 2 + γ 0s st + γ 1s st 1 + Dw z̄wt + ε t ;where st is the sanctions intensity variable, and z̄wt = (∆pt0 ; ∆ȳwt ; ∆reqwt ; ∆r̄wt ; grvt ; ∆ewt )0 is a k(5)1(k = 6) vector of control variables that includes: the rate of change of oil price, ∆pt0 ; global output
growth, ∆ȳwt , global equity returns, ∆reqwt , global long term interest rates (in first difference), ∆r̄wt ,global realized volatility, grvt ; and the rate of change of the global real exchange rate, ∆ewt .17 To reflect
the assumed causal ordering, we restrict A0 to be the following lower triangular matrix
0
1
1
0
:::
0 C
B
B
.. C
B a0
1
0
. C
B
C
∆m;∆e
A0 = BC;
B a00a∆p;∆m
1
0 C
B
C
∆p;∆e
@
A
0
0
0
a∆y;∆ea∆y;∆ma∆y;∆p 1
where we expect a0∆p;∆e0, with inflation responding positively to a contemporaneous rise in e f t(rial depreciation). The contemporaneous impacts of ∆e f t ; ∆mt and ∆pt on output growth are lessclear cut, considering that all four structural equations are also conditioned on st , the variable representing sanctions intensity. The structural shocks, ε t = (ε∆e;t ; ε∆m;t ; ε∆p;t ; ε∆y;t )0 ; are assumed to beserially uncorrelated with zero means, E(ε t ) = 0, and the diagonal covariance matrix E(ε t ε t0 ) = Σ =Diag σ∆e;∆e ; σ∆m;∆m ; σ∆p;∆p ; σ∆y;∆y . 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 ε t are uncorrelated with st and z̄wt . Under these assumptions
it is now possible to distinguish between the effects of a unit change in the sanctions variable, fromdomestic policy changes initiated by a unit standard error change to the domestic shocks, ε t . Specifp
ically, for contemporaneous effects we have ∂ qt =∂ st = A0 1 γ 0s , and ∂ qt =∂ ε jt = σ j j A0 1 e j where e j( j = ∆e f ; ∆m; ∆p; ∆y) 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

17 Details on data sources and the computation of the global variables are given in Section S.2 of the online supplement.

18

model st and z̄wt as autoregressive processes:
st = as + ρs st 1 + ηt ;(6)z̄wt = azw + Azw z̄w;t 1 + vwt ;(7)

where the sanctions and global shocks, ηt and vwt , are serially uncorrelated with zero means, andvariances ωs2 and Ωw . Combine equations (5), (6), and (7), we obtain the following SVAR modelΨ0 zt = a+Ψ1 zt 1 + Ψ2 zt 2 + ut ;(8)0where zt = (qt0 ; st ; z̄0wt )0 ; a = a0q ; as ; a0zw ; ut = (ε t0 ; ηt ; v0wt )0 ; are (m + k + 1) 1 vectors, and
0
1
0
1
0
1
γ 0s
Dw C
B A0
B A1 γ 1s 0 C
B A2 0 0 C
B
C
B
C
B
C
B
B
C
Ψ0 = B
1
0 C
0 C
B 0
C ; Ψ1 = B 0 ρs
C ; Ψ2 = B 0 0 0 C ;
@
A
@
A
@
A
0
0
Ik
0
0 Azw
0 0 0

are (m + k + 1)

(m + k + 1) matrices. Standard techniques can now be applied to Equation (8) toobtain impulse response functions and error variance decompositions assuming the global shocks, vwt ,are uncorrelated with domestic and sanctions shocks (namely ε t ; and ηt ). This is a standard small open
economy assumption which applies to the Iranian economy in particular since its relative size in theworld economy is small and has been declining over the past forty years.

4.1

Impulse response analysis for SVAR model of the Iranian economy

The SVAR model can also be used to compute the time profile of the responses of the economy to shocks(sanction, domestic and foreign) using impulse response functions (IRFs). For the purpose of computing
IRFs, we drop money supply growth and foreign variables except the world output growth, as none ofthese variables will prove to be statistically significant.
4.1.1

IRFs for domestic shocks0

Our starting point is Equation (5) where qt = ∆e f t ; ∆mt ; ∆pt ; ∆yt , and there are four domestic shocks
ε t = (ε∆e;t ; ε∆m;t ; ε∆p;t ; ε∆y;t )0 . The IRFs of one standard error shock to domestic shocks are given bypp
IRFq (h; σ j j ) = E qt+h It 1 ; εt; j = σ j j

19

E (qt+h jIt 1 ) ; for j = ∆e f ; ∆m; ∆p; ∆y;where h = 0; 1; 2:::; H, is the horizon of the IRFs, σ j j = Var ε jt , and It 1 is the information set
at time t

1. The IRFs compare the expected outcome of the shock (intervention) to an alterna-

tive counterfactual in the absence of the shock. Using the reduced form version of (5), we havepp
IRFq (h; σ j j ) = σ j j (Gh A0 1 e j ); whereG` = Φ1 G` 1 + Φ2 G` 2 ; for ` = 1; 2; : : : ;(9)with G 1 = 0; and G0 = Im ; Φ j = A0 1 A j , for j = 1; 2, and e j is a m

1 selection vector of zeros

except for its jth element which is unity. See Chapter 24 of Pesaran (2015). More specifically, thepimpulse response effects of a positive one standard error shock to the jth domestic variable, σ j j , onpp
the ith variable at horizon h = 0; 1; :::; H, are given by IRFi j (h; σ j j ) = σ j j (e0i Gh A0 1 e j ), for i; j =∆e f ; ∆m; ∆p; ∆y.
4.1.2

IRFs for a shock to the sanctions intensity variable

Since global factors are assumed to be strictly exogenous to the Iranian economy and unrelated to sanctions, then without loss of generality the IRFs of sanction shocks can be obtained abstracting from theglobal shocks. Accordingly, using (5) and (6), the moving average (MA) representation of the domestic
variables can be written as
qt = G(1)A0 1 aq +where γ s = γ 0s + γ 1s ; b(L) = G(L)A0 1 (1asγ + b(L)ηt + G(L)A0 1 ε t ;1 ρs s(10)`ρs L) 1 (γ 0s + γ 1s L), G(L) = ∑∞`=0 G` L , and G` is definedby the recursions in (9). Therefore, the responses of the ith domestic variable (the ith element of qt ) to a
positive one standard error shock to the sanctions intensity variable, ωs , are given by
IRFi (h; ωs ) = ωs (e0i bh ); h = 0; 1; :::; H, i = ∆e f ; ∆m; ∆p; ∆y:
4.1.3

(11)

IRFs for a global factor shock

As noted earlier, we only consider the shock to the world output growth, ∆ywt , as the global factor in our
analysis, and consider the following general linear process for ∆ywt
∆ywt = g0 + c(L)v∆y;t :

20

(12)

Since the sanctions intensity variable and the world output growth are assumed to be uncorrelated, abstracting from the sanctions intensity variable we can re-write (5),A0 qt = aq + A1 qt 1 + A2 qt 2 + δ w ∆ywt + ε t :By combining (12) with the moving average representation of the above equation we have
qt = G(1)A0 1 (aq + δ w g0 ) + κ(L)v∆y;t + G(L)A0 1 ε t ;

(13)

1
`
where κ(L) = ∑∞`=0 κ` L = G(L)A0 δ w c(L); and G(L) is as defined above. Hence, the impulse re-sponses of the ith element of qt to a single period shock to world output growth is then given by
IRFi (h; ω∆y ) = ω∆y (e0i κ h ); h = 0; 1; :::; H, i = ∆e f ; ∆m; ∆p; ∆y;(14)

2 is the variance of v
where ω∆y∆y;t .

4.2

Forecast error variance decompositions

Another useful measure of dynamic propagation of shocks is forecast error variance decompositions(FEVDs), which measure the proportion of forecast error variance of variable qit (say, output growth)which is accounted for by a particular domestic shock, ε jt , at different horizons. We are particularlyinterested in estimating the relative importance of domestic shocks vis-à-vis sanctions or world output
shocks in explaining output growth at different horizons. To obtain the FEVDs of both types of shocks,
we first note that, by building on (10) and (13), the n-step ahead forecast errors for the vector of domestic
variables, qt , is given by
n

n

n

`=0

`=0

`=0

ξ t (n) = ∑ b` ηt+n ` + ∑ κ ` v∆y;t+n ` + ∑ G` A0 1 ε t+n ` ;where, as before, ε t is a m

1 (with m = 4) vector of domestic shocks. Using standard results from theliterature, the h-step ahead FEVD of the ith variable in qt which is accounted by the domestic shock ε jt
is given by
θi j (h) =σ j j ∑h`=0 e0i G` A0 1 e j

2

;
h
h
0 G A 1 ΣA0 1 G0 e + ω 2 h e0 b b0 e + ω 2
0κ κ 0 eee∑`=0 i ` 0
s ∑`=0 i ` ` i∆y ∑`=0 i ` ` i
0
` i

21

(15)

for i; j = ∆e f ; ∆m; ∆p; ∆y; and Σ = Diag(σ∆e∆e ; σ∆m∆m ; σ∆p∆p ; σ∆y∆y ). Similarly, the proportion of theforecast error variance of the ith variable due to sanctions intensity and world output growth shocks athorizon h are given byθis (h) =ωs2 ∑h`=0 e0i b` b0` ei
2∑h`=0 e0i G` A0 1 ΣA00 1 G0` ei + ωs2 ∑h`=0 e0i b` b0` ei + ω∆y∑h`=0 e0i κ ` κ 0` ei

;

(16)

;

(17)

and
θi∆y (h) =2ω∆y∑h`=0 e0i κ ` κ 0` ei
2∑h`=0 e0i G` A0 1 ΣA00 1 G0` ei + ωs2 ∑h`=0 e0i b` b0` ei + ω∆y∑h`=0 e0i κ ` κ 0` ei

respectively. Since all the shocks are assumed to be orthogonal, then it follows that ∑mj=1 θi j (h)+θis (h)+θi∆y (h) = 1.

5

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 theoccasional lifting of sanctions. Newspaper coverage has been used in the literature and was initiallyformalized 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 the Wall Street Journal in the U.S., and the Guardian and the Financial
Times in the U.K.. We then count the number of articles published in these newspapers that deal withsanctions and Iran. 18 We are careful not to confound our measures with articles that cover internationalsanctions against Iraq but also mention Iran. Sources and details of how the searches were carried out
are provided in Section S.2.1 of the online supplement.
One can think of our approach as measuring a proxy for an underlying latent sanctions intensity
18 The selected newspapers represent a balanced sample of the most-read and well-informed articles over the past forty 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 a more diversified sample.

22

process. The true process generates a signal, part of which is captured in daily articles published in thesix newspapers under consideration. To be specific, let n jdt 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 st . The relationship between n jdt and st is specified as
n jdt = θ j st + ζ jdt ,(18)

where θ j > 0 is loading of newspaper j on the true signal, st , and ζ jdt is an idiosyncratic serially uncorrelated error term assumed to be distributed independently of the true signal, sdt , with zero means
and finite variances. Equation (18) could be viewed as a single factor model where θ j is the newspaperspecific factor loading. The number of articles published in newspaper j correlates with the true signal
depending on the size of θ j 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 given month it ispossible to reduce the effects of the noise, ζ jdt , and obtain a consistent estimator of st , up to a scalarconstant. Both simple and weighted averages can be used. Taking a simple average across the J newspat
pers and the number of days, Dt , in month t, we have n̄t = θ̄J st + ζ t , where n̄t = J 1 Dt 1 ∑Jj=1 ∑D
d=1 n jdt ,1 J θ . We considered 6 newspapers (J = 6) over a number of pubt
st = Dt 1 ∑D
∑ j=1 jd=1 sdt , and θ̄J = J

lishing 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. SpecificallyJDtζ t = J 1 Dt 1 ∑ ∑ ζ jdt = O p J 1 Dt 1 ;j=1 d=1

and we have st = θ̄J 1 n̄t + o p (1):These monthly measures can then be time aggregated further to obtain
quarterly or annual series which are then used to identify the effects of st (up to the scaling factor θ̄J 1 )in our macro-econometric model. We could also consider a weighted average version of n̄t along thelines suggested in the literature, where the number of newspaper articles (the raw count) is weighted
by the inverse of their respective standard deviations, σ̂ jT , computed over the full data set, using σ̂ jT =q
2
T
1 T n . See Baker et al. (2016) andt(T 1) 1 ∑t=1
n jt n j , n jt = Dt 1 ∑D
∑t=1 jt
d=1 n jdt ; and n j = T
Plante (2019). But, as reported in Figure S.1 of the online supplement, the simple and weighted averages,

23

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 aresome isolated periods 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
sanctions measures: an ‘on’ measure, denoted by st;on , and an ‘off’ measure, denoted by st;o f f , and wenormalize them such that they both lie between 0 and 1, with 1 representing the maximum sanctions
intensity over the full sample. We then define a net sanctions measure as st = st;onw

st;o f f , wherew > 0 represents the weight attached to the sanctions off indicator compared to the sanctions on indicator.
b = 0:4 using a grid search method over values of w 2 (0; 1).19
The weight, w; is estimated to be w

Figure 2 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 U.N., the U.S.
and the E.U.. Important historical events are annotated in the lower part of the figure, while specifics ofparticular 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
2. 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 andSupport 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 toinvestments in the energy sector. An even larger spike occurs between 2011 and 2012, when the Obamaadministration 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 U.S. 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 E.U. initiated a total disconnect of Iranian financial institutions from the international
payments system (SWIFT) in March 2012,20 while the U.N. proceeded to extend the mandates of theirprevious resolutions between June 2011 and June 2012. The third, and most recent, spike is registered
19 Section S.2.1 in the data appendix of the online supplement provides further details on the estimation of w:b

20 SWIFT stands for the "Society for Worldwide Interbank Financial Telecommunications", and it is a vast and secure network used bybanks and other financial institutions to operate financial transactions across the globe.

24

in 2018q2 after Trump decided to unilaterally withdraw the U.S. from the JCPOA accord and begin astrategy of "maximum pressure". There are also minor spikes in 1996 when the Clinton administration
signed the "Iran and Libya Sanctions Act", and in 1997 when the U.S. introduced an export ban toreduce 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 U.S. unilaterally withdrew
from the agreement. Table 2 provides summary statistics (minimum, median, mean, maximum andstandard deviations) of st over a number of sub-periods. A number of interesting observations follow
from this table. First, the summary statistics for st over the low sanctions periods under Rafsanjani and
Khatami are very close to those recorded for the period 2015q1–2018q1 when sanctions were partiallylifted after JCPOA. Second, the peak of sanctions occurred during the internationally coordinated effortsof 2011/2012 rather than after 2018, when the U.S. began their "maximum pressure" strategy under
Trump and Bolton. In the period after 2018q2, the degree of intensity of our indicator is 82 per centof its peak in 2011. However, after 2018 the intensity of sanctions against Iran seems to have beenmuch more persistent: the mean and median are higher during the 2018q2–2020q3 period than during
2012q1–2014q4. Finally, we notice that after the Iran-Iraq War, the median of the sanctions intensity
has been only two thirds of the mean: 0:16 vs. 0:24. This feature stems from the several tail events thatcharacterize the series of sanctions against Iran, and as an overall measure the median is to be preferredto 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 sometime before subsiding. Table S.4 in the online supplement 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 inten-

25

26

Notes: 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 Section
S.2.1 in the data appendix of the online supplement for details on the construction of the sanctions intensity variable.

Figure 2: Sanctions intensity variable over the period 1989q1–2020q3

Table 2: Descriptive statistics of the sanctions intensity variable over relevant time periods
Rafsanjani & Khatami presidencies
Ahmadinejad presidency
U.N./U.S. max sanctions
JCPOA agreement
U.S. "maximum pressure"
Full sample (post Iran-Iraq War)Time period
1989q3–2005q2
2005q3–2013q2
2012q1–2014q4
2015q1–2018q1
2018q2–2020q3
1989q1–2020q3

Min
0.02
0.11
0.27
0.06
0.21
0.02

Median
0.12
0.39
0.45
0.11
0.63
0.16

Mean
0.13
0.38
0.48
0.14
0.56
0.24

Max
0.36
1.0
1.0
0.33
0.82
1.0

St. Dev.
0.07
0.17
0.18
0.07
0.21
0.19

Notes: See Section S.2.1 in the data appendix of the online supplement for details on the construction of the sanctions intensity variable.

sity based on the number of Iranian entities being sanctioned by the U.S.. We used the U.S. Treasury
data set on entries and exits of sanctioned companies, individuals and vessels. We were able to build
an indicator from 2006 to present. Although the two measures (newspaper coverage and U.S. Treasurydata) capture the sanctions phenomenon from different perspectives, they correlate rather well at 38 percent. For further details see Section S.2.1 of the online supplement.

6

A time series structural model of Iran

Equipped with the sanctions intensity measure, st , we now report the results of estimating the SVAR
model set out in Section 4 with the exchange rate variable, ∆e f t , included first followed by money
supply growth, ∆mt , inflation, ∆pt , and output growth, ∆yt . We estimated the four equations of the SVAR
model including different sub-sets of the control variables: world output growth, changes in international
oil prices, global realized volatility, world real equity returns, changes in long term interest rates, andglobal real exchange rate changes against U.S. dollar. The full set of regressions results are provided inTables S.6a to S.6d in the online supplement. As can be seen, none of the other control variables play asignificant role in our analysis with the exception of the world output growth. Accordingly, in Table 3 weprovide estimates of SVAR model including only the world output growth (∆ywt ) as a control variable.
The estimates of the effects of sanctions on the free market foreign exchange rate variable, ∆e f t , aregiven under Column (1). 21 We first note that it is moderately persistent with a persistent coefficient of
0:33, partly reflecting inefficiencies in Iran’s foreign exchange market. Second, we observe that the rial
21 Note that the exchange rate is expressed as the number of Iranian rials per one U.S. dollar, and therefore a rise in the exchange ratevariable corresponds to a depreciation of the rial.

27

depreciates strongly in the same quarter in which sanctions are raised. The median fall in its value isbetween 4:7 and 5 per cent per quarter. 22 However, there is a significant degree of overshooting, withthe sanctions variable having the opposite effect on exchange rate after one quarter. The rial appreciates
by about 3:8 per cent in the following quarter, resulting in a less pronounced overall impact of sanctions
on the rial depreciation of around 2:5 per cent per quarter, or 10 per cent per annum, which is still quite
substantial. 23 As can be seen from Table S.6a of the online supplement, these estimates are remarkably
stable and statistically significant at the 1 per cent level across all specifications regardless of the numberof global control variables included in the regression equation. In fact, none of the lagged domestic
variables (inflation, money supply growth, and output growth) have a statistically significant effect on
the exchange rate, and only foreign output growth proves to be statistically significant at 10 per centlevel. Most notably, changes in international oil prices do not have a statistically significant impact on
the exchange rate, which could be due to the fact that once we condition on the sanctions variable, arise in oil prices is less likely to benefit Iran when oil exports are severely limited due to sanctions.
The adjusted R2 of the exchange rate equations with world output growth included is around 24 percent. This is high by the standard of exchange rate equations, and is partly explained by the presenceof the contemporaneous sanctions variable in the regression. Its use for prediction requires predicting
the sanctions variable which adds another layer of uncertainty. It is also noteworthy that there is no
statistically significant evidence of residual serial correlation in any of the exchange rate equations inTable S.6a. Lack of residual serial correlation is important for a valid impulse response analysis andforecast error variance decompositions that we shall consider below.
The estimates for the money supply growth (∆mt ) equation are summarized in Column (2) of Table 3.
As can be seen, only lagged money supply growth is significant. It is also interesting that the coefficientof lagged money supply growth has a negative sign suggesting some overshooting of money supply
growth which is then reversed in the subsequent period. Notably, we do not find any feedback effects
from inflation to money supply growth.
The estimates for inflation (∆pt ) are summarized in Column (3) of Table 3. As discussed in Section
22 To assess the effect of sanctions, we need to multiply all coefficients related to s and stt 1 by the median (0:16). See Table 2. In thisway, we can assess the difference with respect to a case with no sanctions (st = 0). We consider the median rather than the averageintensity to maintain a conservative approach given that st follows a non-Gaussian process with several outliers.
23 Such overshooting is well-known in the international finance literature. See, for example, Dornbusch (1976).

28

2, inflation in Iran has been persistently high over the past forty years, and to capture its persistence it
proved necessary to include ∆pt 2 , as well as ∆pt 1 in the regression equation. It does not seem necessary to include second order lags of other variables in the inflation equation.24 Perhaps not surprisingly,
the estimates also show that exchange rate depreciation is an important determinant of inflation in Iran,a factor which is statistically significant and quantitatively important. The immediate effect of one percent depreciation of the free market exchange rate is to raise prices by around 0:15 to 0:16 per cent, asmany 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 thenet direct effects of sanctions on inflation seem to be negligible. It is also interesting and quite surprisingthat money supply growth 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 kindof 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 (4) of Table 3 provides the results for real output growth. Output growth in Iranis negatively autocorrelated, with a coefficient estimated to be around0:195 which is statisticallysignificant. 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. 25 Within two quarters the regression predicts Iran’s output
growth to slow down by about 0:9 per cent per quarter (3:6 per cent per annum). In addition to thisdirect effect, sanctions also influence output growth through exchange rate depreciation, which is also
highly statistically significant. This indirect effect amounts to around 0:125 per cent per quarter drop in
output growth when the rial depreciates by one per cent. Output growth is also negatively affected bylagged inflation, which highlights the adverse effects of high and persistent inflation without any shortterm Phillips curve type of trade off between inflation and output growth. Interestingly enough, noneof the global factors seem to have any significant effects on Iran’s output growth, partly due to Iran’s
24 See also Table S.6c of the online supplement where different sub-sets of control variables are also included in the regressions for the

inflation equation.
25 Table S.6d in the online supplement proves this to be a stable finding under different specifications when we allow for a variety ofcontrol variables.

29

relative economic and financial isolation from the rest of the global economy. See Table S.6d of theonline supplement for further details.
Table 3: Quarterly estimates of the SVAR model of Iran with domestic variables ordered as: foreignexchange rate returns, money supply growth, inflation, and output growth, estimated over the period
1989q1–2020q1

st
st 1

∆e f t

∆mt

∆pt

∆yt

(1)

(2)

(3)

(4)

0:303
(0:061)
0:245(0:063)

0:001(0:024)
0:011(0:024)
0:015(0:033)

0:033(0:012)
0:037(0:013)
0:162(0:017)
0:032(0:048)

2:059(1:123)
0:341(0:090)0:350(0:250)0:376(0:331)
0:126(0:242)0:135(0:405)0:040(0:034)
0:289(0:090)
0:132(0:118)0:064(0:086)

0:721(0:209)0:010(0:019)0:038(0:048)0:490(0:086)
0:023(0:046)0:174(0:076)

0:021(0:025)0:058(0:026)0:125(0:044)
0:097(0:094)
0:341(0:180)0:049(0:432)
0:033(0:035)0:020(0:095)
0:496(0:165)
0:195(0:088)

6:013[0:198]
0:240

7:165
[0:127]
0:047

8:236
[0:083]0:669∆e f t
∆mt
∆pt
∆ȳwt
∆e f ;t 1
∆mt 1
∆pt 1
∆yt 1
∆pt 2
Residual serial
correlation test
Adjusted R2

7:108[0:130]0:129

Notes: The variables are ordered as: ∆e f t ; ∆mt ; ∆pt ; and ∆yt ; where: ∆e f t = ln(E f t =E f ;t 1 ); E f t is the quarterly rial/U.S. dollar freemarket exchange rate; ∆mt = ln(M2t =M2;t 1 ); M2t is obtained by summing the aggregates M1 and "quasi-money"; ∆pt = ln(Pt =Pt 1 ); Pt
is the quarterly consumer price index of Iran; ∆yt = ln(Yt =Yt 1 ); Yt is the quarterly real output of Iran. st is the quarterly sanctions intensity
variable. ∆ywt is the quarterly world output growth, computed as ywt = ∑ni=1 wi yit ; with fyit gni=1 being the natural log of real output for
33 major economies, and fwi gni=1 are GDP-PPP weights. Numbers in parentheses are 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 S.2.1, S.2.2, and S.2.3 in the data appendix of the online supplement 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. oil prices)
are provided in Tables S.6a–S.6d in the online supplement.

It is also worth noting that our main findings are not much affected by re-ordering of the domestic variables. In Section S.4 of the online supplement we provide results of estimating the SVAR
model in (5), with two other orderings of the domestic variables, namely (∆pt ; ∆e f t ; ∆mt ; ∆yt ), and30

∆mt ; ∆e f t ; ∆pt ; ∆yt . The results are summarized in Tables S.7a to S.7d and S.8a to S.8d, respectively, and clearly show that money supply growth plays a minimal role in determination of inflation andexchange rate variations, and exchange rate remains the primary driver of inflation and output growth.
Overall sanctions have affected Iran in a number of ways and through different direct and indirectchannels, the most important of which is 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 importprices 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 per cent in Iran (See Table 1).
Money supply growth seems to follow patterns which are neither related to sanctions nor to any ofthe domestic variables, notably inflation, which could be due to the underdevelopment of capital andmoney markets in Iran, as highlighted recently by Mazarei (2019). These results seem quite robust toother measures of liquidity such as M1 or private sector credit.26

6.1

Impulse response analysis

The estimates of the individual equations provided in Table 3 provide a snap-shot of how sanctions interact with some of the key macroeconomic variables. However, given the dynamic and simultaneous
nature of the model, to fully understand and evaluate the nature and consequences of these interactions we need to resort to impulse response functions (IRFs) and forecast error variance decompositions(FEVDs) discussed in Sections 4.1 and 4.2, respectively. 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 bysanctions. Also, amongst the control variables, only foreign output growth seems to exert statisticallysignificant effects on inflation and output growth. For these reasons, to compute IRFs and FEVDs wewill be focussing on a parsimonious SVAR model without the money supply growth, and only including∆ywt as the control variable. We also use AR(1) models for st and ∆ywt to capture the dynamics of these
exogenous processes.27
26 Estimates based on these alternative measures of liquidity are available upon request.
27 Time series evidence in support of our choice of AR(1) specifications for s and ∆y are provided in Tables S.4twt

and S.5 of the online
supplement. It is also worth noting that the assumed AR(1) processes for st and ∆ywt only affect the IRFs and FEVDs, and do not affectthe estimates of the SVAR model.

31

The IRFs for positive one standard error (s.e.) shocks to the three sanction domestic shocks are
displayed in Figure 3. Panel A of this figure shows the results for the sanction shock.28 A single quartershock to sanctions intensity causes the foreign exchange rate to depreciate by about 3 per cent in thesame 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 per cent 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 cent per quarter (1:6 per cent per
annum). The loss in output growth is still close to 0:2 per cent per quarter two years after the shock.
The results for the foreign exchange rate shock (independent of the sanction shock) is given in Panel
B of Figure 3. This shock generates a sizeable and precisely estimated effect (of around 8 per cent 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 byaround 1:2 per cent 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 per cent on impact, whichthen moves towards zero very quickly.
Panel C of Figure 3 gives the results for an inflation shock (for example, 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 cent per quarter on impact
and then falling gradually to zero after two 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 B and C of
Figure 3. The effects of inflation shock on output growth are positive on impact but small in magnitude, and reverse quickly after one quarter, suggesting that it might not be possible to increase output by
expansionary policies.
28 One standard deviation is equal to 0.125. See Table S.4.

32

Figure 3: Impulse responses of the effects of sanctions and domestic shocks on foreign exchange, inflation, and output growth
Panel A: One positive standard error shock to the sanctions intensity variable

Panel B: One positive standard error shock to foreign exchange

Panel C: One positive standard error shock to inflation

Panel D: One positive standard error shock to output growth33

Finally, the IRFs of the effects of a positive shock to output growth are displayed in Panel D of
Figure 3. 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 exchange rate, which seems to be primarily driven by sanctions andits own dynamics. But the positive output shock has a positive, but rather moderate, effect on inflation,
lowering inflation by 0:1 per cent per quarter after two quarters. The primary effects of the output shock
are on output itself, raising output by 2:8 per cent per quarter on impact before losing momentum inless than a year. The initial very large increase in output is somewhat of an over-reaction which is thencorrected slightly, yet providing a net 2 per cent 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 thelong run.29
The impulse response analysis confirms some of the preliminary conclusions set out in Section 6.
Sanctions have their most impact on 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 affectoutput growth after one quarter but such effects are short lived.

6.2

Forecast error variance decompositions

We now turn to a quantification of the relative importance of sanctions as compared to the three domestic
shocks and the foreign output shock. Table 4 presents the results.30 Panel A of the table gives the results
for the foreign exchange variable. Not surprisingly, foreign exchange shocks are the most important,and account for 82 per cent of forecast error variance on impact and decline only slightly, falling to 79
per cent after 5 quarters. Sanctions shock accounts for 16 per cent 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 thesame intensity.
29 Figure S.3 in Section S.4of the online supplement provides an illustration of how the domestic variables respond to a positive global
output growth shock. It is found that the effects are statistically insignificant, except for a small appreciation of the Iranian rial in thefirst quarter.
30 FEVDs are computed using equations (15), (16), and (17).

34

Table 4: Forecast error variance decomposition for domestic variables in the SVAR model with a single
shock to sanctions
Panel A: Forecast error variance decomposition for foreign exchange
Quarter
ahead
0
1
2
3
4
5
6
7
8

st
0.16
0.16
0.16
0.16
0.16
0.16
0.16
0.16
0.16

Proportion explained by a shock to:∆e f t
∆pt
∆yt
0.82
0.00
0.00
0.80
0.00
0.00
0.80
0.01
0.00
0.80
0.01
0.00
0.80
0.01
0.00
0.79
0.01
0.00
0.79
0.01
0.00
0.79
0.01
0.00
0.79
0.01
0.00

∆ȳwt
0.02
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03

Panel B: Forecast error variance decomposition for inflation
Quarter
ahead
0
1
2
3
4
5
6
7
8

st
0.01
0.03
0.05
0.06
0.06
0.07
0.07
0.07
0.07

Proportion explained by a shock to:∆e f t
∆pt
∆yt
0.43
0.54
0.00
0.48
0.47
0.00
0.49
0.44
0.00
0.50
0.43
0.00
0.50
0.42
0.00
0.50
0.42
0.00
0.50
0.42
0.00
0.50
0.42
0.00
0.50
0.42
0.00

∆ȳwt
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02

Panel C: Forecast error variance decomposition for output growth
Quarter
ahead
0
1
2
3
4
5
6
7
8

st
0.00
0.02
0.04
0.04
0.05
0.05
0.05
0.05
0.05

Proportion explained by a shock to:∆e f t
∆pt
∆yt
0.04
0.03
0.93
0.04
0.05
0.88
0.04
0.05
0.87
0.04
0.05
0.86
0.04
0.05
0.85
0.04
0.05
0.85
0.04
0.05
0.85
0.04
0.05
0.85
0.04
0.05
0.85

∆ȳwt
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

Notes: st is the quarterly sanctions intensity variable. ∆e f t = ln(E f t =E f ;t 1 ); E f t is the Iran rial/U.S. dollar quarterly free market exchange
rate. ∆pt = ln(Pt =Pt 1 ); Pt is the quarterly consumer price index of Iran. ∆yt = ln(Yt =Yt 1 ); Yt is the quarterly real output of Iran. ∆ywt is
the quarterly world output growth: ywt = ∑ni=1 wi yit ; with fyit gni=1 being the natural log of real output for 33 major economies, and wi the
GDP-PPP weights.
See Sections S.2.1, S.2.2, and S.2.3 in the data appendix of the online supplement for details on the construction of the sanctions intensity
variable, calendar conversions, and sources of the data used.

35

The FEVDs of inflation, reported in Panel B of Table 4, show that foreign exchange and inflation
shocks account for the bulk of the variance, with sanction shocks accounting for the remainder. Domestic
and foreign output shocks make little contribution. On impact, inflation shock accounts for 54 per centof the variance, flattening out at 42 per cent after 4 quarters. In contrast, the contribution of the foreignexchange shock rises from 43 per cent on impact to 50 per cent after 3 quarters. The contribution of the
sanction shock is not particularly large, and starts at 1 per cent, but rises to 7 per cent after 4 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 C of Table 4. As can be seen, the output
shock is by far the most important shock and accounts for 93 per cent of forecast error variance of
output growth on impact and falls only slightly to 85 per cent after 4 quarters. In line with our estimates,
sanctions shocks do not affect output growth on impact, and end up explaining only 5 per cent of thevariance after 4 quarters. Foreign output shocks do not have any explanatory power for Iran’s output
growth. The other two domestic shocks (inflation and exchange rate) together account for 9 per cent offorecast error variance of output growth after 1 quarter.
These results show that without reducing the inherent volatility of output growth (by reforms andbetter management), it is unlikely that sanctions removal would enable the Iranian economy to return tosingle digit inflation and sustained growth.

6.3

Estimates of sanctions-induced output losses

So far we have focussed on the channels through which changes in the intensity of sanctions affect the
Iranian economy and the time profile of their propagations. Here we consider possible output losses dueto the direct and indirect effects of the sanctions. However, the output growth equation included in the
SVAR model, and its estimates presented in Column (4) of Table 3, is not suitable for this purpose, since
output growth is specified to contemporaneously depend on exchange rate and inflation, and therefore
does not take account of output losses that could arise indirectly through these variables. Instead, we
consider the reduced form output growth regressions set out in Equation (4), and focus on specifications

36

with st 1 as the intervention variable. We favor this specification over the one that includes both st andst 1 , since "sanctions news" does 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.31 The estimates of the reduced form output growth equations computed over the period
1989q1-2020q1 are summarized in Table S.9 in the online supplement, where we report both the shortand long-run effects of sanctions on output growth, whilst allowing for a host of both domestic and foreign control variables. 32 The parameter of interest is the long run effect of sanctions on output growthreported at the bottom panel of Table S.9. It is estimated to be around0:031 (0:013), which is statisti-cally significant and remarkably robust across the seven different specifications reported. 33 This in turnsuggests output growth losses of around 2 per cent per annum if we use the median value of st over the
sample under consideration, or 3 per cent if we use the mean value of st . 34 Due to the large outliers inthe sanctions intensity variable, we favor the lower estimate of 2 per cent based on the median value ofst , which in turn suggests that in the absence of sanctions and sanctions-induced mismanagement Iran’s
average annual growth over 1989q1–2020q1 could have been around 4 5 per cent, as compared to the 3
per cent realized, a counterfactual outcome which is close to the growth of emerging economies such as
Indonesia, South Korea, Thailand, and Turkey whose average annual growth rate over the same sample
period amounted to 4.8, 4.5, 4.2 and 4.0 per cent, respectively.

7

Wider economic and socio-demographic effects of sanctions

In this section we consider the effects of sanctions on the sectoral composition of output, employment,labor force participation, education, and consider if sanctions tend to have a gender bias. For this purpose
we shall be using annual data, as quarterly observations are not available for many indicators of interest.

31 We are grateful to Nick Bloom for drawing our attention to this point.
32 Amongst the domestic variables, only inflation has a statistically significant impact on output growth.

But, once again, 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. The only global factor which is statistically significant in the output growth equation is the exchange rate. The negative
effect of inflation on output growth could be due to price distortions and allocation inefficiencies that are often associated with high andpersistent levels of inflation, as has been the case in Iran. The negative effect of the global exchange rate variable on output growth ismore difficult to rationalize.
33 Similar estimates are obtained if both s and stt 1 are included in the regressions. See Table S.10 of the online supplement.
34 The median and mean values of s , are 0:16 and 0:24, respectively, as summarized in Table 2.
t

37

7.1

Differential effects of sanctions on sectoral output growths

Since sanctions primarily affect oil revenues, foreign trade and international payments, they are likely
to have differential sectoral effects with traded goods and financial sectors being more affected. In thecase of Iran the least traded goods sector is agricultural and parts of the service sector. It is, therefore,plausible to expect sanctions to have less impact on the agricultural sector as compared to the manufacturing and service sectors. To investigate the sectoral effects of sanctions, we estimate VAR(1) models
in sectoral growth rates with the results summarized in Table S.11 of the online supplement. As canbe seen, output growths of manufacturing and service sectors are affected negatively by sanctions andthese adverse effects are statistically highly significant. But the same can not be said about the output
growth of the agriculture sector, which seems to be hardly affected by sanctions. It is also interesting thatwhilst there are significant inter-linkages between manufacturing and services, there are no significant
feedbacks between agriculture and the rest of the economy. These findings are robust across the various
specifications that allow for a large combination of control variables.
We also note that sanctions have affected the manufacturing sector much harder than their effects on
the service sector. The median annual sanctions-induced fall in manufacturing growth is about 2:4 percent, as compared to 1:2 per cent per for services. Even though industries such as banking and financehave been seriously hampered by sanctions, services might have suffered less than manufacturing as aresult of the ongoing shift towards the knowledge-based economy under way in Iran.
In the online supplement we report additional results for the shares of each of these sectors out of
the total value added. 35 Our main finding is that sanctions targeting Iran have caused the agricultural
sector to become more important with respect to the overall economy, while the manufacturing sector
shrinks. Conversely, the share of services out of the total is not significantly affected. Therefore, eventhough sanctions have somewhat slowed down the growth of services value added, the share of services
out of the total value added has not declined in a statistically significant way as a result of that.
These findings seem to confirm that sanctions are reflected not only in lower output growth rates but
also in a change of the sectoral composition. The sectors that are more trade-intensive shrink the most,while a greater reliance on agriculture and home-made innovations potentially comes into play.
35 See Tables S.12 to S.14 of the online supplement.

38

7.2

Effect of sanctions on labor outcomes

It is reasonable to expect that sanctions-induced disruptions and economic slow downs have spill-overeffects on the labor market, both in terms of employment and labor force participation. To this end
we run regressions of a few labor market indicators on the sanctions intensity variable, with the resultssummarized in Tables S.15 to S.17 of the online supplement. We measure these indicators relative to thetrends in other MENA countries to take account of common socio-economic factors of the region. We
also include a number of additional control variables in the regressions to reduce possible confounding
effects.
Table S.15 gives the regressions results for the rate of change of employment in Iran relative to othercountries in the MENA region, and shows that Iran’s employment rate contracts by about 1 per centper year more than in other MENA countries as a reaction to sanctions. 36 No other control variables
have statistically significant effects except some mild positive spill-overs from Turkey output growth, afeature that may relate to the interconnections between the two economies.37
More interestingly, we find statistically significant evidence of a gender bias. Sanctions, measured attheir median value of 0:16, resulted in male labor force participation declining by 0:5 per cent per year
– an impact that is statistically significant but economically small. See Table S.16 of the online supplement. However, female labor participation was much more affected by sanctions, with the coefficient of
the sanctions variable being statistically highly significant and much larger as compared to the results formales. See Table S.17 of the online supplement. A median level rise in sanctions intensity has caused
the rate of female labor force participation in Iran to fall by about 3:8 per cent relative to other MENA
countries. To give a sense of the magnitude of such an effect, we notice that after the period of highest
international pressure 2006–2014, female labor force participation rate declined by more than 30 per
cent (from its peak of 19.5 per cent in 2005 to 13.4 per cent in 2014).
Overall, we confirm the economic intuition that links output declines to employment losses. The
worsening of labor market conditions is sizeable and statistically significant. However, to the best of
36 Recall that for assessing the effects of sanctions, we need to multiply all coefficients related to s ; and stt 1 , by their median value of

0:16, given in Table 2 of the online supplement. In this way, we compare the effects of sanctions at their median values with the nosanctions (st = 0) outcomes.
37 Similar results are obtained when using total labor force participation. See Table S.18 of the online supplement.

39

our knowledge, our study is the first to provide quantitatively robust measures of gender bias in Iranresulting from sanctions. There are many economic as well as social factors behind this outcome. One
plausible scenario points to resource allocation away from females at times of reduced oil revenues andbudget cuts, with males given priority for jobs and wages over females.

7.3

Effect of sanctions on education outcomes

Finally, we ask if the education system has also been affected by sanctions. If economic sanctions
impair the government’s budget then we would expect to see some decline in expenditures on schools
and teachers. We provide evidence for the effects of sanctions on the number of lower secondary schools
and high schools in Tables S.19 and S.20 in the online supplement. For comparison we also provide
similar results for the number of primary schools in Table S.21. These results clearly show that indeed
lower secondary schools and high schools have been adversely affected by sanctions. But, interestingly
enough, the number of primary schools does not seem to have been affected by sanctions, which could
be due to the compulsory nature of primary education in Iran, that obligates the government to provide
schools and teachers commensurate with the demographic rise in the number of students.38
To investigate whether the gender bias documented in the case of labor force participation is alsopresent in the education sector, in Table S.22 of the online supplement we show regressions of the rateof change of female-to-male student ratio on the sanctions variable, as well as a number of control
variables. We see that sanctions have depressed this ratio, an effect which is sizeable as well as being
statistically highly significant. Data from 1989 onwards confirm a sanctions-induced reduction of about
0:5 per cent per annum on average.

8

Concluding remarks

In this paper, using a novel measure of the intensity of sanctions based on newspaper coverage, we havequantified the effects of sanctions on exchange rate, inflation, output growth, employment, labor force
participation, and secondary and high school education in Iran. Our empirical analyses also show thatsanctions, perhaps unintentionally, accentuate rather than reduce gender bias, and divert resources from38 The impact on the number of teachers is very similar to the one experienced by the number of schools. See Tables S.23, S.24 and S.25

in the online supplement.

40

education to other more pressing immediate needs such as maintaining consumption at the expense ofphysical and human investments.
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 toits potential, high inflation and excess output growth volatility cannot all be traced to sanctions andhave domestic roots stemming from prolonged periods of economic mismanagement, distorted relative
prices, rent seeking, a weak banking system and under-developed financial institutions. Sanctions haveaccentuated some of these trends and delayed the implementation of badly needed reforms.
A more comprehensive analysis of sanctions also requires detailed investigation into how sanctions
and their variability over the past forty 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.
In 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 economy but
are unrelated to sanctions, such as advances in technology, world output growth, international prices,and economic performance of some neighboring economies. Using a reduced form regression of output
growth on our sanctions intensity variable we estimate Iran’s output loss to be around 2 per cent per
annum, which is considerable when cumulated over time. There is, of course, a high degree of uncertainty associated with such estimates which should be born 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 percent still lies below the average growth of
4.4 per cent experienced by Indonesia, Turkey, South Korea and Thailand over the same period.
We also recognize the importance of investigating the possible effects of sanctions on price distortions and rising income inequality and poverty. Real expenditure of households in Iran has been falling;
more in urban than in rural areas. The income gap is most pronounced between the top and the bottom
deciles of expenditure distribution. The Gini coefficient, a measure of overall inequality, in Iran has been41

rising and is almost at the same level of the Gini coefficient of the U.S., which is amongst the highest inadvanced economies. Another important area if the possible effects of sanction on price distortions, andtheir implications for productivity and employment. However, detailed studies of these important topics
are beyond the scope of the present paper.
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 per cent of Iranianhouseholds are now connected to the internet. This rapid expansion was facilitated by the governmentand security apparatus making affordable high-speed internet a reality in Iran. The Mobile Telecommunication Company of Iran, largely controlled by the Islamic Revolutionary Guard Corps now has over
43 million subscribers. Sanctions have also resulted in significant advances in the areas of missiles andother military-related technologies. It is estimated that IRGC control between 10-30 per cent of theeconomy, 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 analysis also clearly shows that sanctions can only explain a relatively small fraction of thevariance of output growth, and the cause of Iran’s low and excessively volatile output growth should befound elsewhere. This in turn suggests that lifting of sanctions will most likely have short-term positiveeffects and for long term sustainability major reforms are required, in addition to better management of
the economy. This involves wide ranging reforms of banking, the tax and subsidy system, unificationof exchange rates, and the development of a coherent system of social safety net to protect the poor.
Regional development policies should be initiated by giving priority to remote regions that have beenleft behind. Government policies should become more transparent with greater openness to private
sector initiatives and foreign investments. Insulating the economy against oil revenue volatility will also
become an urgent policy issue once sanctions are removed. In the past, the Iranian economy has beenvery sensitive to oil price volatility and, over the longer term, Iran needs a sovereign wealth fund to42smooth the impact of the volatility of oil revenues. The primary challenge is how to integrate the Iranian
economy within the regional and global economic system, so that Iran’s true economic potential can berealized.
Finally, it is important to bear in mind that Covid-19 could not have come at a worse time for
the Iranian economy. Our sample does not cover the period from March 2020 when Covid-19 effects
started to be felt in Iran. But it is clear Covid-19 could have important medium 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 andfinancial linkages. The full economic impact of Covid-19 on the Iranian economy is unknown andrequires further investigation.

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45

Online supplement to "Identifying the Effects of Sanctions on the
Iranian Economy using Newspaper Coverage"
Dario Laudati
University of Southern California

M. Hashem Pesaran
University of Southern California, USA, and Trinity College, Cambridge, UK

July 27, 2021

S.1

Introduction

This online supplement is composed of four main sections. Section S.2 gives details of data sources andconstruction of some of the key variables used in our analysis. Sub-section S.2.1 provides details of how
the proposed sanctions intensity variables are constructed. Sub-section S.2.2 gives information on conversion of data from the Iranian calender to the Gregorian calender. Sub-section S.2.3 provides detailsof data sources for the socio-economic variables. In Section S.3 we present details of the bootstrapping
procedure used to compute error bands for the impulse response functions (IRFs). Section S.4 reports
other empirical results such as the AR specifications for the world output growth, additional IRFs fora shock to the global output growth not presented in the paper, and further estimates on the impact ofsanctions on the Iranian economy and its education system. Finally, a comprehensive list of all major
sanctions against Iran from November 1979 to January 2021 is provided in Table S.28.

S.2

Data appendix

S.2.1

Sanctions intensity variables

Our sanctions intensity variable, st , is based on newspapers coverage of sanction events against Iran.
Articles were retrieved from the platform ProQuest (www.proquest.com) which covers the whole period
of interest 1979q1–2020q3. ProQuest has detailed newspapers archives with good search capabilities.
The only exception to ProQuest was the Financial Times Historical Archive accessed through Gale
Historical Newspapers (www.gale.com/intl/primary-sources/historical-newspapers), which helped to filla gap left by ProQuest for articles published in the Financial Times before 1996.
Criteria of inclusion
We focused on six major newspapers: the New York Times, the Washington Post, the Los Angeles Times,
the Wall Street Journal, the Guardian, and the Financial Times. We only selected articles published in
the newspapers print version thus disregarding blogs, websites and other digital formats which are only

S.1

available more recently; however, we did allow for all types of articles to be included, e.g. we included
both editorials and main articles.
ProQuest has both a general ProQuest Central database, holding information for the relatively more
recent publications, and several historical newspaper-specific collections for the most highly printed
world outlets, ProQuest Historical Newspapers, which proved useful in order to extend our series backto 1979. Accordingly, we used the ProQuest Central data for the maximum period available for eachnewspaper, and complemented each series with the ad-hoc historical data sets before such dates. See
Table S.1 for details. As mentioned already, the only exception was the articles published in the Financial
Times before 1996, for which there does not exist a historical archive on ProQuest, and instead Gale
Historical Newspapers were used.
To create the index of sanctions imposed on Iran ("sanctions on"), articles were required to include
the following terms: "economic*", "sanction*", "against", "Iran*", with the additional feature of excluding articles in which "lift*" was present. The star at the end of the previous words allowed the searchengine to pick words beginning with the same initial letters thus including terms such as: "sanctioning","Iranian", "lifting" etc.. Although the number of potential synonyms and keywords to describe the phenomenon is virtually very high, this set of words seemed to capture rather well the extent to which Iranwas mentioned as target of international measures. We also found that further complicating the search
did not produce sensible results, as the new commands often could not be recognized by the searchengine.
The search was carried out for each newspaper series separately by specifying the name of the newspaper in the options list "Publication title – PUB". For some newspapers the search engine produced
a handful of duplicates of the same articles despite the option "Exclude duplicate documents" under"Result page options" had being ticked. To address this issue, all articles were manually checked before
starting the download in order to avoid double-counting of articles.1
For the period 1990q3–1991q2, the search commands for sanctions against Iran were updated to
exclude also the word "Iraq". This adjustment was necessary in order to avoid confounding noise dueto the events of the Iraq invasion of Kuwait in August 1990, and the subsequent Gulf War period, fromJanuary to February 1991. These events received massive press coverage, which led Iran to be mentioned
for geopolitical reasons, not because of sanctions. Also, some newspapers reported two additional small
spikes not strictly related to Iran: (i) For the terrorist attacks happened between December 1985 (inRome and Vienna airports) and April 1986 (in a West Berlin discotheque); (ii) For the "1998 Coimbatore
bombings" attacks in southern India. In both cases, Iran was not the target of new sanctions therefore amanual check deletion of these small number of occurrences had to be carried out.
The intensity variable to capture the partial lifting of sanctions ("sanctions off") included the words
beginning with "economic*", "sanction*", "against", "Iran*" but now allowing also for at least one of the1

The extent of this technical hurdle varied considerably amongst outlets. It was particularly severe for journals such as the Los Angeles
Times, while virtually non-existent for other newspapers such as the New York Times.

S.2

following words: "lift*", "waive*" and "accord*". An exception was made for the Historical Databaseof the Financial Times, which does not support sophisticated search structures. Therefore, a simple
research allowing for "sanctions against Iran" and "deal*" was conducted to capture the highest numberof articles, which were subsequently checked and skimmed manually to meet our criteria of inclusion.
A detailed chronological study of economic sanctions against Iran allowed us to restrict our searchof "sanctions off" on two time periods only. First, in 1981 when the Algiers Accords were signed andthe "Tehran hostage crisis" ended; second, from 2016q1 to 2018q2, when the Joint Comprehensive Planof Action (JCPOA) was enacted by all world major powers before U.S. President Trump withdrew thecountry from the agreement. Accordingly, for construction of the sanction-off index we focussed on the
periods 1981q1–1981q4 and 2015q1–2018q2 in order to avoid unnecessary noise for the time in between
and after Trump’s announcement. The "sanctions off" period of our indicator was extended to one year
before the actual implementation of the JCPOA in order to allow for possible anticipatory effects.
Sanctions intensity variable construction
Having obtained a number of daily articles related to the sanctions imposed ("sanctions on") and lifted("sanctions off"), we proceeded with the following steps in order to build our estimator, st (w) = st;onw st;o f f . Here we focus on the construction of st;on . The same procedure was used to construct st;o f f :First, we computed a monthly series for each of our J newspapers (J = 6) by averaging our daily
series over the number of articles per month. In turn, we carried out a simple average across newspapers,
which led us to have a single monthly series of "sanctions on" articles; subsequently, we averaged themonthly observations over each quarter to obtain the quarterly series. The "sanctions on" average wasthen divided by its maximum value over the period 1989q1–2020q3 in order to obtain the indicatorst;on ; so that st;on index was defined on the (0; 1) range. We obtained a second variable st;o f f from our"sanctions off" raw count by following the same steps just described. Finally, we estimated the weight,
w 2 (0; 1); with a grid search in order to derive our final sanctions intensity variable st = st;on w st;o f f .
The grid search was performed by running the regressions:∆yt = β0 + β1 ∆yt 1 + β2 st 1 (w) + εt ;over the period 1989q1–2020q1, with ∆yt being Iran’s quarterly real output growth, and with a step
size of our grid equal to 0:1. The optimal weight was estimated as ŵ = 0:4; although the shape of thelikelihood was rather flat.2
As a robustness check, we created a standardized version of our indicator by following the approachadvanced by Baker et al. (2016). We divided each of the J newspapers monthly raw series by theirrespective standard deviations. 3 The final standardized intensity variable was obtained as before by
averaging across newspapers at monthly frequency, taking the simple mean for each quarter (for both2
3

The annual series for st was computed as a simple average of the quarterly intensity variable over each year.
For a measure of "sanctions on", we considered the standard deviations over the entire period 1979m1–2020m9. For "sanctions off",the monthly raw counts during 1981m1–1981m12 and 2015m1–2018m6 were divided by the standard deviations over their respective
periods.

S.3

Table S.1: Sources of newspaper articles over the period 1979m1–2020m9

PeriodNew York Times
Los Angeles Times
Washington Post
Wall Street Journal
Guardian
Financial Times

Historical dataset
1979m1–1980m12
1979m1–1984m12
1979m1–2002m12
1979m1–1983m12
1979m1–1996m12
1979m1–1995m12

Modern dataset
1981m1–2020m9
1985m1–2020m9
2003m1–2020m9
1984m1–2020m9
1997m1–2020m9
1996m1–2020m9

Notes: "Historical data set" is the ProQuest Historical Newspapers data set for all newspapers except the Financial Times, for which
information have been retrieved from Gale Historical Newspapers. "Modern data set" is ProQuest Central database for all newspapers
considered.

sanctions "on" and "off"), dividing each series by their respective maxima over the period 1989q1–
2020q3, and subtracting the "standardized sanctions off" series from the "standardized sanctions on".
We found these weighted sanction on and sanction off series to be very close to the ones based on simple
averages, and as a result the grid search applied to the weighted series also resulted in the estimateŵ = 0:4: Even though this procedure was meant to avoid newspapers with a larger number of articles
per issue to carry unwarranted weight, the two series co-move almost perfectly (ρ = 0:998). See Figure
S.1. This finding is consistent with Plante (2019), who adjusts for the number of total articles per monthand finds that his two measures correlate at 0.97.
U.S. Treasury sanctions variable construction
We also constructed a measure of sanctions intensity based on the U.S. Treasury "Specially DesignatedNationals And Blocked Persons List (SDN)". The online database of the Treasury keeps track only of theentities currently sanctioned. To compile a complete time series list of all Iranian entities, individuals,
and vessels being sanctioned by the U.S., we used yearly pdf files available in the online archive of
the U.S. Treasury. In this way, we were able to follow over time each entity entering and exiting thedatabase. 4 The list of sanctioned entities can be retrieved from 1994 onwards but the number of entries
for Iran up to 2005 is negligible. This is in line with the historical record of U.S. sanctions against Iran.
Therefore, we focussed on building our entry-exit matrix from 2006 onwards.
To construct the U.S. Treasury sanctions variable, we first summed the total number of Iranian
entities, individuals, and vessels being hit by U.S. sanctions. 5 In the SDN lists, entities refer to companies (and institutions) of Iranian nationality, foreign companies having offices in Iran, and – in lightof secondary sanctions – all other foreign companies doing business with sanctioned Iranian compa4
5

The documents specify the exact day in which entities enter/exit the list during the year considered.
Notice that SDN lists specify to which sanctions programs each entry belongs. In other words, according to whether the aim is to hit
entities related to Iran vis-à-vis other nations (say, North Korea) different codes are attached to them.

S.4

Figure S.1: Sanctions intensity variable and standardized sanctions intensity variable over the period
1989q1–2020q3

Notes: See Section 5 of the paper for the sanctions intensity variable definition over the range (0,1). See Section S.2.1 in the data appendix
of the online supplement for details on construction of both the sanctions intensity variables.

nies. Iranian individuals, or foreigners doing business with sanctioned Iranians, were tracked by First
and Last Name, and Passport number or National ID – when available. For vessels, we did not confine
ourselves to vessels name or national flag given that these attributes were often changed. Instead, theInternational Maritime Organization (IMO) unique identification number proved to be important andcompletely reliable to follow vessels history.
The number of Iranian entries added to the SDN list allowed us to build an "SDN sanctions on"time series; similarly, the entries removed from the list provided the information for an "SDN sanctions
off" index. We obtained our final "U.S. Treasury sanctions variable" by attaching a weight to the "SDN
sanctions off" count equal to the newspaper-based indicator (w = 0:4) and subtracting it from the "SDN
sanctions on" count. The final series was then re-scaled by dividing it for its maximum value. See Figure
S.2. The correlation between the U.S. Treasury measure and st is equal to 38 per cent over the period
2006q1–2020q3. Notice that the series based on SDN has inevitably negative values over the JCPOA
period regardless of the weight one is willing to choose. This feature is due to the fact that no new
Iranian entities were added, while a large number of previously sanctioned entities were removed.6

6This could also be considered as a shortcoming of using such measure given that – in our framework – a negative value of the sanctions
intensity variable means an attempt to subsidize the Iranian economy through transfers, something far from the actual process happening
over the period 2016q1–2018q2.

S.5

S.6

Notes: The U.S. Treasury sanctions variable is computed from the number of newly introduced and removed entries in the "Specially Designated Nationals And Blocked Persons List"(SDN) of the U.S. Department of the Treasury. Major sanctions-related historical events are indicated by arrows and brackets. See Section S.2.1 for details of the construction of the
sanctions variables.

Figure S.2: Sanctions intensity variable and the U.S. Treasury sanctions variable over the period 2006q1–2020q3

S.2.2

Conversions from Iranian to Gregorian calendar

The data we use in our analysis are in Gregorian calendar. However, data retrieved from Iranian sources,
namely from the Central Bank of Iran and the Statistical Center of Iran, follow the Iranian calendar
format. The Iranian year starts on March 21st of the corresponding Gregorian year. Accordingly, we
carried out three calendar conversions in order for the Iranian data to be in line with the ones in the
Gregorian format. In the following expressions, Gy ; Gq ; and Gm stand for the variables transformed in
the Gregorian calendar at yearly, quarterly, and monthly frequencies, respectively, while Iy ; Iq ; and Im
are the data in the original Iranian format. For annual statistics, the following formula was applied: 781
80
8Iy 1 + 285
Gy = 365
365 Iy : For quarterly data, we converted the Iranian series according to: Gq = 9 Iq 1 + 9 Iq :Finally, for the monthly time series – we applied the following transformation: Gm = 13 Im 1 + 23 Im :S.2.3

Economic and socio-demographic variables

In this section, we will refer to some of the Iranian data as being retrieved from the "Quarterly Iran
Data Set 2020". In this case, we extend and update the data for Iran in the GVAR Data Set compiled
by Mohaddes and Raissi (2020) until 2018q2 (and available upon request); more recent observations for
Iran were added by splicing forward the previously available series with new observations from Iraniansources. In this respect, the conversions mentioned in Section S.2.2 were applied. All data from theCentral Bank of Iran (CBI) were obtained from the Economic Time Series Database. For global factors
we will refer to the "GVAR Data Set 2020". In this case, we use the latest version of the GVAR Data Set
provided by Mohaddes and Raissi (2020), which we extend to include observations for 2020q1.9
Quarterly data
The quarterly real output of Iran was obtained by splicing forward the GVAR series in the Quarterly
Iran Data Set 2020 available until 2018q2 with the "Iran’s Quarterly National Accounts" released by theStatistical Center of Iran until 2020q1.
Iran’s inflation was computed as first difference of the natural logarithm of Iran consumer priceindex (CPI). CPI data from the GVAR series in the Quarterly Iran Data Set 2020 available until 2018q2
were extended forward with data from the Statistical Center of Iran, which provides Iranian monthly
inflation bulletins. After having converted the monthly series to the Gregorian calendar, it was possible
to compute the quarterly inflation rate, and splice forward the Quarterly Iran Data Set until 2021q1. The
CPI was then rebased to have value equal to 100 in 1979q2.
The official foreign exchange statistics from 1979q2 to 2020q3 were retrieved in quarterly format
from Bank Markazi (Iran’s Central Bank), and converted to the Gregorian calendar. The free market
foreign exchange rate in quarterly format from 1979q2 to 2017q4 was also retrieved from Bank Markazi.
For 2018 onward the series were spliced forward with data from bonbast.com – a highly cited website
7
8
9

Eighty days of the Gregorian year (from Jan. 1st to Mar. 21st ) were to be attributed to the previous Iranian year.
In the following expression, 8=9 represents the eighty days out of the approximately ninety days within a given quarter.
Mohaddes and Raissi (2020) data set ends in 2019q4. The extended data set is available upon request.

S.7

tracking the Iran’s rial free market rate against all major currencies. In this regard, bonbast.com presents
information for "buy" and "sell" rates at daily frequency. We used the average of buy and sell rates. In
this way we were able to extend the historical series from Bank Markazi until 2021q1.
Monetary statistics were also downloaded from the Bank Markazi website. The monetary aggregate
M2 was computed as the sum of M1 and "quasi-money". Data were available at quarterly frequency,
and – before converting them to the Gregorian calendar – the observations from 2015q2 onwards had tobe multiplied by 1,000 given a change of format from billions to trillions of rials.
In order to account for global factors, we augmented our analyses with several variables. ∆pt0 is therate of change of the oil price (first difference of the natural logarithm). The oil price considered was the
Brent crude (U.S. dollars/barrel). Data at quarterly frequency until 2020q1 were taken from the GVAR
Data Set 2020. Observations for 2020q2 and 2020q3 were obtained by splicing the series with data fromthe U.S. Energy Information Administration (series name: "Europe Brent Spot Price FOB, Dollars per
Barrel"). The E.I.A. provided information at monthly frequency therefore we first averaged the oil prices
over each quarter, and then spliced forward our GVAR time series.
The quarterly global realized volatility, grvt ; was taken directly from the GVAR Data Set 2020 for
the whole period 1979q2–2020q1; details about its construction can be found in Chudik et al. (2020).
We used the GVAR Data Set 2020 and followed the procedure indicated by Chudik et al. (2020) also
for the construction of the other global factors. The factors we considered are: the world real output
growth, ∆ywt ; the rate of change of the world real exchange rate against the U.S. dollar, ∆ewt ; the world
real equity returns, ∆reqwt ; and the per cent change of the world nominal long-term interest rate, ∆rwt .
These control variables were obtained by taking the first difference of the following weighted crosssectional averages: ywt = ∑ni=0 wi yit ; ewt = ∑ni=0 wi eit ; reqwt = ∑ni=0 wi eqit ; rwt = ∑ni=0 wi rit ; where yit ; eit ;eqit ; rit are: the log of real output, the log of the real exchange rate against the U.S. dollar, the log of real
equity prices, and the nominal long term interest rates of country i in quarter t. The sample included 33of the world major economies, and the weights, wi , were computed as the GDP-PPP average by country
i out of the overall world average output over the period 2014–2016:
wi =

2016
YitPPP∑t=2014:2016
YitPPP∑ni=0 ∑t=2014(S.1)

The GDP-PPP measure allows for international comparisons, and it was retrieved at yearly frequency
from the World Bank Open Data repository. The 33 countries are: Argentina, Australia, Austria, Belgium, Brazil, Canada, China, Chile, Finland, France, Germany, India, Indonesia, Italy, Japan, South
Korea, Malaysia, Mexico, the Netherlands, Norway, New Zealand, Peru, the Philippines, South Africa,
Saudi Arabia, Singapore, Spain, Sweden, Switzerland, Thailand, Turkey, the U.K., and the U.S.A..
For some of the 33 countries, real equities returns, eqit ; and nominal long term interest rates, rit ;were not available. As such, to compute reqwt and rwt we focussed on the countries for which we had
information, and rescaled the weights accordingly. In particular, the historical real equity prices, eqit ;S.8

were available for 26 out of 33 countries (excluded were Brazil, China, Indonesia, Mexico, Peru, Saudi
Arabia, and Turkey). For the long run interest rates, rit ; data were available for 18 of the 33 countries(excluded were Argentina, Brazil, China, Chile, Finland, India, Indonesia, Malaysia, Mexico, Peru, thePhilippines, Saudi Arabia, Singapore, Thailand, and Turkey).
We also considered real output growth for oil exporters, ∆yt0 ; and Turkey’s output growth, ∆ytTur . The
oilreal output for oil exporters is the cross-sectional weighted average yt0 = ∑ni=0
w0i y0it , with the countries
noil, werecomposing noil being: Brazil, Canada, Mexico, Norway, and Saudi Arabia. The weights, w0i i=1
computed following the same procedure described in Equation (S.1) but restricting the sample from n to
noil . Turkey real output growth is ∆ytTur = ln(YtTur =YtTur1 ); with YtTur being Turkey real output in quartert. The output series were retrieved from the GVAR Data Set 2020.
We also retrieved information on the U.S. CPI from the OECD accessed through the Federal Reserve
Bank of St. Louis (FRED) data set. Table S.2 provides an overview of the data sources.
Annual data
Economic variables – We cumulated quarterly series, whenever available, to obtain annual series, suchas Iran real output, Yt ; Iran consumer price index, Pt ; Brent crude oil price, Pt0 ; world, oil exporters, andTurkey real output data fywt ; yt0 ; ytTur g; and the global factors fewt ; reqwt ; rwt ; grvt g.
Data on value added by sectors (agriculture, manufacturing and mining, and services) were retrievedat annual frequency from the Bank Markazi. The series at constant 2004 prices was spliced forward
in 2010 with the series at constant 2011 prices. Before splicing, each data series was converted to the
Gregorian calendar format. For sectoral composition analyses: agriculture, manufacturing, and services
shares of value added were computed as a fraction of the total value added represented by the sum of thethree components.
Total exports and imports revenues in millions of dollars were retrieved from the Bank Markazi atannual frequency, and converted to the Gregorian calendar format. Oil and gas exports revenues, andnon-oil exports revenues (in millions of U.S. dollars) were also obtained from Bank Markazi website.
However, it is important to bear in mind that for both oil and gas, and non-oil exports, Bank Markazi
holds two data sets based on the "fourth edition" and "fifth edition" directives of the annual IMF Balanceof Payment statistics.10 We followed the fourth edition, which was available for almost the whole of theperiod under consideration (until 2018), and obtained data for 2019 by splicing forward the series withinformation from the fifth edition.11
The historical data series on annual oil exports in thousands of barrels per day was also compiled
from the Bank Markazi. But this series ended in 2018 therefore – after having converted it to the
10 Under the fourth edition oil exports category was defined as “Crude oil, oil products, liquefied gas and natural gas”. In the fifth edition,

the description is: “Value of crude oil, oil products, natural gas, natural gas condensate and liquids (Tariff codes: 2709, 2710 and2711) exported by National Iranian Oil Company (NIOC), National Iranian Gas Company (NIGC), National Iranian Oil Refining andDistribution Company (NIORDC), petrochemical companies, and others (customs and non-customs)”. Tariff codes refer to unique
identifying numbers adopted by the UN Statistics Division.
11 The correlation of oil and non-oil exports growth under the two systems of reference was 99.5 per cent and 92.8 per cent, respectively,
for the overlapping period 1997–2018 for which we had data for both series.

S.9

Gregorian calendar – we spliced it forward with IMF data "Crude Oil Exports for Iran" series for theyears 2019 and 2020, which we retrieved from FRED.
Education variables – Bank Markazi "Economic Time Series Database" allows to retrieve further statistics on education such as the number of schools (from 1979 to 2018), and the number of teachers (from1979 to 2019). In each case, it was possible to obtain data on the number of schools and teachers bothat aggregate level and divided by grade: primary, lower secondary, and high school. Furthermore, we
retrieved data on the overall number of students by gender. Information on the number of students bygender for each school grade was discontinued in 2013. The total number of schools was computed as
the sum of primary, lower secondary, and high schools. When using the series on the number of teacher
employed, we always divided it by the yearly Iran population in the 25–64 age group, in order to control
for population increases.
The "Female-to-male students enrollment ratio", ∆ f mt ; is the log difference of the ratio between
female and male students. Female and male enrolled students were divided by the female and malepopulation in the 5–19 age groups, respectively, to account for general population increases by gender.
Specifically, ∆ f mt = ln(FMt =FMt 1 ); with FMt = (S f t =Pop f t )=(Smt =Popmt ) where S f t and Smt are
the number of female and male students, respectively, and Pop f t and Popmt are Iran female and malepopulation in the 5–19 age group.
Other socio-demographic variables – Several other yearly socio-economic statistics were obtained fromthe "World Development Indicators" of the World Bank. More specifically, the male and female labor force participation rate, 12 the employment rate, total national population for a number of world
economies (listed below), and Iran male and female population in the age brackets 5–9, 10–14, 15–19,
20–24, 15–64, as fraction of total Iran’s male and female population. The absolute values for the population by sex and age bracket were computed by multiplying each fraction by the total number of males
and females in Iran each year, while the population in the 25–64 age group was obtained by subtracting
the population in the 15–24 age groups from the ones in the 15–64 age groups (which was available).
The information about age brackets was used as demographic control when estimating the rate of change
in the number of students enrolled in schools by gender. Similarly, the 25-64 population was used as ademographic control for the number of teachers employed in Iran.
We also constructed geographic regional aggregates for labor statistics, in particular for Middle Eastand North Africa (MENA). Each aggregate annual labor series, say xwt , was built as a weighted average ofei xit . Weights, w
ei ; were based on the country annual population
countries data series, xit ; i.e. xit = ∑ni=0 w
average out of the total population for the specific regional aggregate considered over the period 2014–

12 The overall labor force for both Iran and MENA countries was computed by summing the male and female labor force participation

rates.

S.10

2018:
ei =
w

2018
Popit
∑t=2014:n2018∑i=0 ∑t=2014 Popit

The MENA region in our statistics includes the following 17 countries: Bahrain, Iraq, Israel, Jordan,
Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, Syrian Arab Republic, United Arab Emirates, Yemen,
Algeria, Egypt, Libya, Morocco, and Tunisia.
Table S.2: Sources of quarterly data
Data series

SourceIranian variables1
Consumer price index

Quarterly Iran Data Set 2020

Foreign exchange rate, Free Market

Central Bank of Iran and bonbast.com

Foreign exchange rate, Official rate

Central Bank of IranMoney supply: M1 and Quasi-money

Central Bank of IranReal output

Quarterly Iran Data Set 2020

Global and regional control variables2

GVAR Data Set 2020 and World Bank

Notes: 1. The Quarterly Iran Data Set 2020 extends and updates the GVAR Data Set compiled by Mohaddes and Raissi (2020), whose
observations for Iran are available up to 2018q2. Such version of the data base including Iran is available upon request. The most recent
observations for the consumer price index taken from the Statistical Center of Iran can be retrieved from the monthly inflation bulletins
available at www.amar.org.ir. The data provided by the Central Bank of Iran on foreign exchange rates are available from the Economic
Time Series Database: tsd.cbi.ir, under "External Sector/Value of Financial Assets (Exchange Rate and Coin Price)". Recent data on freemarket foreign exchange data can be retrieved from www.bonbast.com. Money supply statistics are available under "Monetary and Credit
Aggregates" at tsd.cbi.ir. The data used to extend the Iran’s real output series are taken from the Statistical Center of Iran and can be
retrieved under "Iran’s Quarterly National Accounts (base year = 1390)" from www.amar.org.ir.
2. Raw data for each country composing the global and regional averages were retrieved from the GVAR Data Set compiled by Mohaddes
and Raissi (2020) and available at www.mohaddes.org/gvar. We extended the original data set from 2019q4 to 2020q1 – data are available
upon request. The World Bank data (data.worldbank.org) have been used to construct the GDP-PPP weights for each country (codeindicator: "NY.GDP.MKTP.PP.CD"). The variables included in this set of controls are: global nominal long term interest rate, global real
equity price, global real exchange rate, global real output, global realized volatility, oil exporters real output, oil price (Brent crude), andTurkey real output. For oil price, the observations for 2020q2 and 2020q3 were obtained from the U.S. Energy Information Administration(series name: "Europe Brent Spot Price FOB, Dollars per Barrel") available at www.eia.gov. Information on the U.S. consumer price index
was retrieved from the FRED data base fred.stlouisfed.org (series name: "Consumer Price Index: Total All Items for the United States,
Index 2015=100, Quarterly, Seasonally Adjusted").
See Section S.2 for further details on variables construction.

S.11

Table S.3: Sources of annual data
Data series

SourceIranian variables
Consumer price index

Quarterly Iran Data Set 2020

Crude oil exports (th. barrels/day)1

Central Bank of Iran and IMF

Education statistics: Schools, students by gender,
teachers1

Central Bank of Iran

Exports revenues (non-oil, oil & gas, and total)1

Central Bank of Iran

Foreign exchange rate, Free Market

Central Bank of Iran and bonbast.com

Foreign exchange rate, Official rate

Central Bank of Iran

Import revenues1

Central Bank of IranLabor statistics: Employment rate, Labor force participation rates2

World Bank, World Development Indicators

Money supply: M1 and Quasi-money

Central Bank of IranPopulation statistics2

World Bank, World Development Indicators

Real output

Quarterly Iran Data Set 2020

Value added by sector1

Central Bank of Iran

Global and regional control variables

GVAR Data Set 2020 and World Bank

Notes: 1. Crude oil exports (th. barrels/day) data from the Central Bank of Iran are available from the Economic Time Series Database:tsd.cbi.ir under "Energy Sector" statistics. The observations from the IMF were retrieved through the FRED data base of the Federal
Reserve Bank of St. Louis: fred.stlouisfed.org (series name: "Crude Oil Exports for Iran"). Data on education were retrieved from "Human
resource and Employment" statistics available on the Central Bank of Iran repository tsd.cbi.ir. Export and import revenues are available
from the same website under "External Sector/Balance of Payments (Manual)" statistics. Value added data are also taken from the same
source under "National Accounts (1383=100)" and "National Accounts (1390=100)" statistics.
2. Labor and national population statistics are from the World Development Indicators of the World Bank for both Iran and other MENA
countries accessed through data.worldbank.org. Employment rate (ages 15+, total) indicator code: "SL.EMP.TOTL.SP.ZS". Female labor
force participation rate (per cent of female population ages 15+) indicator code: "SL.TLF.CACT.FE.ZS". Male labor force participation
rate (per cent of male population ages 15+) indicator code: "SL.TLF.CACT.MA.ZS". National total population statistics indicator code:"SP.POP.TOTL".
See notes in Table S.2 for details on the sources of the other variables. See Section S.2 for further details on variables construction.

S.3

Computation of IRFs, FEVDs and their error bands by bootstrap

S.3.1

IRFs and FEVDs alternative computation

To compute the IRFs and FEVDs, we provide an alternative computation approach with respect to theone described in the paper. We confirm that we obtained the same numerical results as when we used
the formulae in the paper, which we had included for pedagogic reasons.
Re-write Equation (S.2) as:eezt = Ψ
0

1

e 1e
e 2e
e
et ;
a+Ψ
zt 1 + Ψ
zt 2 + u
S.12

0

et = (ε∆e f t ; ε∆pt ; ε∆yt ; εst ; ε∆ywt )0 : The IRF can be computed bywith ezt = ∆e f t ; ∆pt ; ∆yt ; st ; ∆ywt ; and ufollowing the approach described in the paper as:p
e 0 1 e j );σ j j (Fh Ψ

IRFz (h) =where e j is a (m + 2)

1 selection vector of zeros except for its jth element, which is unity, ande 1 F` 1 + Φ
e 2 F` 2 ; for ` = 1; 2; : : :F` = Φ

e 0 1Ψ
e 1; Φ
e 0 1Ψ
e 2 ; with F 1 = 0, and F0 = Im+2 . Consequently, the impulse response
e1 = Ψ
e2 = Ψ
where Φeffects of a positive one standard error change in the jth domestic shock, ε jt , on the ith variable (the ithelement of ezt ) are given by:
IRFi j (h) =pe 1 e j ); for h = 0; 1; :::; H, i; j = ∆e f t ; ∆pt ; ∆yt ; st ; ∆ywt .
σ j j (e0i Fh Ψ
0

The forecast errors can be now written more succinctly as:ne 1u
ξet (n) = ∑ F` Ψ
0 et+n ` ;`=0

et is a vector of (m + 2) 1 shocks. Similarly, the proportion of the forecast error
where, as before, uvariance of the ith variable due to a shock to the jth variable at horizon h is given by:
θi j (h) =e 0 1e j
σ j j ∑h`=0 e0i F` Ψ2

e 0 1 ΣΨe 00 1 F0 ei∑h`=0 e0i F` Ψ`

, for i; j = ∆e f t ; ∆pt ; ∆yt ; st ; ∆ywt ;with Σ = Diag(σ∆e∆e ; σ∆p∆p ; :::; σ∆y∆y ): It can be proved that ∑mj=1 θi j (h) + θis (h) + θi∆yw (h) = 1:

S.3.2

Bootstrapping procedure

In order to compute the impulse response functions (IRFs) and the associated confidence bands, wefollowed a bootstrap procedure by simulating the in-sample values of zt in Equation (8), which wereport here for convenience:
Ψ0 zt = a+Ψ1 zt 1 + Ψ2 zt 2 + ut :(S.2)

In Equation (S.2), zt = (qt ; st ; z̄wt )0 is a vector of m domestic policy variables (qt ), the sanctions intensity
variable (st ), and the k global factors (z̄wt ); a is a (m + k + 1) 1 vector of constants, and ut are theresiduals of the system. In order to generate our bootstrap replications, we proceed as follows:no(r)
1. Generate the simulated residuals ut ; r = 1; 2; :::; R by resampling with replacement from theestimated residuals of each equation separately fb
ut ;t = 3; 4; :::; T g, where R = 1; 000 is the numberof random samples.

S.13

(r)

(r)

2. Let z1989q1 = z1989q1 , z1989q2 = z1989q2 8r, and compute:(r)bzt = Ψ
01

b 1 z(r) + Ψ
b 2 z(r) + ut(r)
b
a+Ψ
t 1
t 2

t = 1989q3; :::; 2020q1

3. Use the data computed at point 2 to estimate the bootstrapped coefficients for each replication:(r)b
zt = Ψ
0

1;(r)

(r)

(r)

b z(r) + Ψb z(r) + ut(r) :ba(r) + Ψ
1 t 1
2 t 2
(r)

(r)

The procedure just described can help evaluating zt (baseline) vis-à-vis other scenarios zt (Shock)with the major shock profiles derived in the paper.

S.4

Additional empirical results

In this section we provide additional empirical results in support of our analyses. Table S.4 provides
estimates of AR(1) and AR(2) processes for the sanctions intensity index, st : As explained in the paper,
the process is highly persistent, and a first-order specification describes the process sufficiently well,with additional lags not being statistically significant. Table S.5 gives the estimates of first- and secondorder autoregressive processes (AR) for the world output growth, and shows that the AR(1) specification
used in the paper provides a reasonable approximation.
In the main paper we presented estimates of SVAR model under our preferred ordering, namely
with the exchange rate variable (∆e f t ) included first, followed by money supply growth (∆mt ), inflation(∆pt ), and output growth (∆yt ), including the world output growth as the control variables. Tables S.6a
to S.6d display the regression results including all the control variables, and a number of their sub-sets.
As can be seen, the estimates of the effects of sanctions on domestic variables are highly stable andconsistent across all specifications. It is also worth noting that changes in international oil prices do nothave a statistically significant impact on the exchange rate, which could be due to the fact that once wecondition on the sanctions variable, a rise in oil prices is less likely to benefit Iran when oil exports areseverely limited due to sanctions. Also, none of the global factors seem to have any significant impact
on Iran’s output growth, partly due to Iran’s relative economic and financial isolation from the rest of the
global economy.
Tables S.7a to S.7d and S.8a to S.8d present results of the SVAR model given by Equation (5) of thepaper, but with the different orderings of the domestic variables. Tables S.7a to S.7d provide the results
when the domestic variables are ordered with inflation (∆pt ) first followed by the rate of change of thefree market foreign exchange rate (∆e f t ), money supply growth (∆mt ), and output growth (∆yt ). Tables
S.8a to S.8d give the results when the variables are ordered as ∆mt ; ∆e f t ; ∆pt ; and ∆yt . As can be seen,
re-ordering of the variables do not alter our main findings summarized in Section 6 of the paper.
Figure S.3 displays the impulse response results for one positive standard error shock to the global

S.14

output growth on Iran’s free market foreign exchange rate depreciation, inflation, and output growth. 13
As such, it complements the IRFs results from our SVAR model presented in Figure 3 of the paper.
Following one quarter shock to global output growth, the Iran’s rial appreciates by about 1.5 per centin the same quarter. However, the results are not particularly persistent, and become quantitatively lessimportant three quarters ahead, and lose statistical significance about five quarters ahead. The effects ofglobal output growth on both inflation and Iran’s output growth, on the other hand, are not statisticallysignificant. These results are in line with Iran’s relative economic isolation from the main advanced
economies. Most of the global shocks are reflected in the movements of the free market foreign exchange
rate, while the domestic economic mismanagement is a factor that can not be underplayed to explain the
dynamics of the Iranian economy.
Table S.10 gives the estimates of the reduced form output growth equation given by (4), where we
include both current lagged values of the sanctions intensity variable st . As can be seen, the estimatesare very close to the ones presented in the paper, where only the lagged value of st was included.
As reported in the paper, sanctions have adversely affected the growth of manufacturing and services,
but seem to have had little impact on the growth of the agricultural sector. Here we also present results on
the effects of sanctions on the rate of change of the shares of agriculture, manufacturing and mining, andservices value added out of the total. To this end we estimate VAR(1) models in two of the three sharevariables at a time, since the sum of the shares add up to unity. The estimation results are summarizedin Tables S.12, S.13 and S.14. As can be seen, sanctions have reduced the share of the manufacturingsector, whilst resulting in an increase in the share of the agricultural sector. The share of the services
do not seem to be much affected by the sanctions. Overall, it is the manufacturing sector which is mostaffected by the sanctions.
In terms of labor statistics, we provide a robustness check for the effects of sanctions on the labor
force participation rate in Iran with respect to other MENA countries. See Table S.18. We noticethat sanctions induced a median decline which is perfectly consistent with the results obtained for theemployment rate shown in Table S.15 of the paper.
Finally, we provide additional robustness checks on sanctions-induced education outcomes by considering changes in the number of teachers employed across different education grades, and the total
number of schools and teachers. The estimates are summarized in Tables S.23, S.24, S.25, S.26 andS.27. These results support our main conclusion that the primary effects of sanctions has been on lower
secondary and high school grades, with little impact on primary education (which is compulsory in Iran).
In terms of the total number of schools and teachers, we find that sanctions have adversely affected thetotal number of schools, but the effects of sanctions on the total number of teachers is less clear cut.

13 For a theoretical derivation, see Equation (14) in the main text.

S.15

Table S.4: Quarterly estimates of the sanctions intensity variable AR(1) and AR(2) models over theperiod 1989q1–2020q3
st

Constant

0.063(0.018)(2)
0.639(0.089)0.139(0.089)0.055(0.019)Adjusted R2
S.E. of regression (ω̂s )

0.551
0.125

0.557
0.125

st 1

(1)
0.743
(0.059)st 2

Notes: Standard errors in parentheses. ***p < 0:01, **p < 0:05, *p < 0:1. See Section S.2.1 in the data appendix of the online supplementfor details on the construction of the sanctions intensity variable.

Table S.5: Quarterly estimates of the world real output growth AR(1) and AR(2) models over the period
1989q1–2020q1∆ȳwt

Constant

0:005(0:001)(2)
0:439(0:118)
0:073(0:118)0:005(0:002)Adjusted R2
Residual Std. Error

0:125
0:006

0:121
0:006

∆ȳw;t 1(1)
0:468(0:108)∆ȳw;t 2

Notes: ∆ywt is the quarterly world output growth: ywt = ∑ni=1 wi yit ; with fyit gni=1 being the natural log of real output for 33 major
economies, and wi the GDP-PPP weights. See Section S.2.3 in the data appendix of the online supplement for details on the construction
and sources of the data used.

S.16

Table S.6a: Quarterly estimates of the equation for the rate of change of the free market foreign exchange
rate in the SVAR model of Iran with domestic variables ordered as: foreign exchange rate returns, money
supply growth, inflation, and output growth, estimated over the period 1989q1–2020q1∆e f tst
st 1∆e f ;t 1
∆mt 1
∆pt 1
∆yt 1

(1)

(2)

(3)

(4)

(5)

(6)

(7)

0:311
(0:061)
0:243(0:064)
0:335(0:091)
0:298(0:251)
0:304(0:332)0:135(0:245)

0:303(0:061)
0:245(0:063)
0:341(0:090)0:350(0:250)0:376(0:331)
0:126(0:242)
2:059(1:123)

0:295(0:061)
0:238(0:064)
0:345(0:090)0:366(0:251)
0:366(0:331)
0:133(0:242)
2:513(1:205)0:054(0:052)

0:296(0:061)
0:240(0:063)
0:337(0:090)0:321(0:251)
0:413(0:330)0:146(0:241)
3:467(1:348)0:037(0:053)
0:365(0:237)

0:297(0:060)0:236(0:063)
0:327(0:089)0:379(0:251)
0:357(0:329)0:113(0:239)3:211(1:344)
0:079(0:058)
0:387(0:235)0:748(0:427)0:304(0:061)
0:242(0:063)
0:328(0:089)0:385(0:251)
0:391(0:332)
0:114(0:240)
3:048(1:359)0:076(0:058)
0:542(0:299)0:739(0:428)0:126(0:150)0:303(0:061)
0:243(0:063)
0:335(0:090)
0:406(0:254)
0:411(0:334)
0:108(0:241)
3:105(1:366)
0:094(0:065)0:550(0:300)0:799(0:439)0:117(0:151)
7:731(12:260)

6:968[0:138]
0:225

6:013
[0:198]
0:240

5:356
[0:253]
0:240

3:905
[0:419]
0:249

5:079
[0:279]
0:263

5:171
[0:270]
0:261

5:465
[0:243]
0:257

∆ȳwt
∆pt0
grvt
∆ēwt
∆reqwt∆r̄wt
Residual serial
correlation test
Adjusted R2

Notes: The variables are ordered as: ∆e f t ; ∆mt ; ∆pt ; and ∆yt ; where: ∆e f t = ln(E f t =E f ;t 1 ); E f t is the quarterly rial/U.S. dollar freemarket exchange rate; ∆mt = ln(M2t =M2;t 1 ); M2t is obtained by summing the aggregates M1 and "quasi-money"; ∆pt = ln(Pt =Pt 1 ); Pt
is the quarterly consumer price index of Iran; ∆yt = ln(Yt =Yt 1 ); Yt is the quarterly real output of Iran. st is the quarterly sanctions intensity
variable. ∆ywt is the quarterly world output growth, computed as ywt = ∑ni=1 wi yit ; with fyit gni=1 being the natural log of real output for 33
major economies, and fwi gni=1 are GDP-PPP weights. ∆pt0 = ln(Pt0 =Pt0 1 ); Pt0 is the quarterly oil price (Brent crude). grvt is the quarterly
global realized volatility. ∆ewt is the quarterly rate of change of the global real exchange rate vis-à-vis the U.S. dollar: ewt = ∑ni=1 wi eit ,eit is the natural log of the real exchange rate of country i in quarter t. ∆reqwt is the quarterly rate of change of the global real equity priceindex: reqwt = ∑ni=1 wi reqit ; reqit is the natural log of the real equity price of country i in quarter t. ∆rwt is the quarterly change of the
global nominal long term interest rate: rwt = ∑ni=1 wi rit ; rit is the long term nominal interest rate of country i in quarter t. Numbers inparentheses are 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 S.2.1, S.2.2, and S.2.3 in the data appendix of the online supplement for details on the construction of the sanctions intensity
variable, calendar conversions, and sources of the data used.

S.17

Table S.6b: Quarterly estimates of the equation for the money supply growth in the SVAR model of Iranwith domestic variables ordered as: foreign exchange rate returns, money supply growth, inflation, andoutput growth, estimated over the period 1989q1–2020q1
∆mt
st
st 1
∆e f t∆e f ;t 1
∆mt 1
∆pt 1
∆yt 1

(1)

(2)

(3)

(4)

(5)

(6)

(7)

0:001
(0:024)
0:011(0:024)
0:016(0:032)
0:039(0:034)
0:285(0:089)0:126(0:117)0:064(0:086)

0:001(0:024)
0:011(0:024)
0:015(0:033)
0:040(0:034)
0:289(0:090)
0:132(0:118)0:064(0:086)0:135(0:405)0:001(0:024)
0:012(0:024)
0:015(0:033)
0:040(0:034)
0:289(0:090)
0:132(0:119)0:064(0:087)0:127(0:439)0:001(0:019)0:002(0:024)
0:009(0:024)
0:023(0:033)
0:040(0:034)
0:304(0:090)0:110(0:119)0:070(0:086)
0:272(0:494)
0:006(0:019)0:145(0:085)

0:002(0:024)
0:009(0:024)
0:023(0:034)0:040(0:034)
0:304(0:092)0:110(0:120)0:070(0:087)
0:273(0:498)0:006(0:021)
0:145(0:086)
0:006(0:156)

0:002(0:024)
0:008(0:024)
0:023(0:034)0:040(0:034)
0:304(0:092)
0:108(0:121)
0:070(0:087)
0:265(0:504)
0:006(0:021)
0:154(0:110)
0:006(0:157)
0:007(0:055)

0:002(0:025)0:008(0:025)
0:023(0:034)
0:039(0:035)
0:302(0:093)
0:106(0:122)
0:070(0:087)
0:271(0:508)0:005(0:024)
0:155(0:111)
0:001(0:162)
0:006(0:055)
0:606(4:461)

7:051[0:133]
0:054

7:165
[0:127]
0:047

7:174
[0:127]
0:038

6:363
[0:174]
0:054

6:411
[0:171]
0:045

6:653
[0:155]
0:037

6:650
[0:156]
0:029

∆ȳwt
∆pt0
grvt
∆ēwt
∆reqwt∆r̄wt
Residual serial
correlation test
Adjusted R2

Notes: See the notes to Table S.6a for further details on the construction and sources of data used.

S.18

Table S.6c: Quarterly estimates of the equation for the inflation rate in the SVAR model of Iran withdomestic variables ordered as: foreign exchange rate returns, money supply growth, inflation, and output
growth, estimated over the period 1989q1–2020q1
∆pt
st
st 1
∆e f t∆mt∆e f ;t 1
∆mt 1
∆pt 1
∆yt 1
∆pt 2

(1)

(2)

(3)

(4)

(5)

(6)

(7)

0:033
(0:013)
0:034(0:013)
0:153(0:018)0:026(0:050)0:003(0:020)0:016(0:050)0:456(0:090)0:026(0:048)0:180(0:080)0:033(0:012)
0:037(0:013)
0:162(0:017)
0:032(0:048)0:010(0:019)0:038(0:048)0:490(0:086)
0:023(0:046)0:174(0:076)
0:721(0:209)0:032(0:013)
0:036(0:013)
0:164(0:017)
0:031(0:048)0:010(0:019)0:042(0:049)0:484(0:087)
0:025(0:046)
0:181(0:077)
0:801(0:225)0:009(0:010)0:032(0:013)
0:036(0:013)
0:163(0:017)
0:033(0:049)0:010(0:020)0:043(0:049)0:484(0:087)
0:024(0:047)0:179(0:078)0:777(0:258)0:010(0:010)0:009(0:045)

0:033(0:012)
0:036(0:013)
0:168(0:017)
0:032(0:049)0:008(0:019)0:056(0:049)0:460(0:087)
0:022(0:046)
0:199(0:078)0:751(0:256)
0:018(0:011)
0:002(0:045)0:138(0:081)

0:030(0:013)
0:033(0:013)
0:166(0:017)
0:032(0:048)0:006(0:019)0:054(0:049)0:443(0:088)0:023(0:046)
0:207(0:078)0:795(0:257)
0:019(0:011)
0:049(0:057)0:141(0:081)
0:038(0:028)

13:954[0:007]
0:638

8:236
[0:083]
0:669

9:993
[0:041]
0:669

10:393[0:034]
0:666

10:984[0:027]
0:672

9:239
[0:055]
0:674

0:030
(0:013)
0:033(0:013)
0:167(0:017)
0:031(0:048)
0:007(0:019)0:059(0:050)0:441(0:088)0:023(0:046)
0:216(0:079)0:813(0:259)0:023(0:012)
0:046(0:057)0:156(0:084)0:040(0:028)1:802(2:287)
9:526[0:049]
0:673

∆ȳwt
∆pt0
grvt
∆ēwt
∆reqwt∆r̄wt
Residual serial
correlation test
Adjusted R2

Notes: See the notes to Table S.6a for further details on the construction and sources of data used.

S.19

Table S.6d: Quarterly estimates of the equation for the output growth in the SVAR model of Iran withdomestic variables ordered as: foreign exchange rate returns, money supply growth, inflation, and output
growth, estimated over the period 1989q1–2020q1
∆yt
st
st 1
∆e f t∆mt∆pt∆e f ;t 1
∆mt 1
∆pt 1
∆yt 1

(1)

(2)

(3)

(4)

(5)

(6)

(7)

0:021
(0:025)
0:057(0:025)0:123(0:042)
0:096(0:094)
0:335(0:171)
0:033(0:034)
0:022(0:094)0:490(0:157)
0:195(0:087)

0:021(0:025)0:058(0:026)0:125(0:044)
0:097(0:094)
0:341(0:180)0:033(0:035)0:020(0:095)
0:496(0:165)
0:195(0:088)0:049(0:432)

0:020(0:025)
0:057(0:026)
0:127(0:045)
0:097(0:095)
0:348(0:181)
0:035(0:035)
0:016(0:096)
0:499(0:166)
0:196(0:088)0:145(0:467)
0:010(0:019)0:021(0:025)0:058(0:026)0:130(0:045)
0:088(0:096)
0:344(0:182)
0:034(0:035)0:024(0:097)
0:502(0:167)
0:199(0:088)0:278(0:524)
0:008(0:019)0:050(0:088)0:016(0:025)
0:055(0:026)
0:113(0:046)
0:086(0:095)
0:304(0:181)
0:034(0:035)
0:051(0:097)
0:495(0:165)0:210(0:088)0:313(0:519)0:009(0:021)
0:039(0:088)
0:291(0:159)0:017(0:025)
0:056(0:026)
0:113(0:046)
0:085(0:096)
0:300(0:183)
0:034(0:035)
0:051(0:098)
0:495(0:166)0:210(0:088)0:298(0:527)
0:009(0:021)
0:053(0:112)
0:291(0:160)0:011(0:055)

7:157[0:128]
0:137

7:108
[0:130]
0:129

6:991
[0:136]
0:124

7:143
[0:129]
0:119

7:813
[0:099]
0:137

7:815
[0:099]
0:129

0:017
(0:026)
0:057(0:026)0:116(0:046)
0:084(0:096)
0:305(0:183)
0:038(0:035)0:040(0:098)
0:509(0:167)0:208(0:088)0:335(0:529)0:001(0:024)
0:058(0:113)
0:261(0:164)
0:006(0:056)
3:734(4:487)
7:782[0:100]
0:127

∆ȳwt
∆pt0
grvt
∆ēwt
∆reqwt∆r̄wt
Residual serial
correlation test
Adjusted R2

Notes: See the notes to Table S.6a for further details on the construction and sources of data used.

S.20

Table S.7a: Quarterly estimates of the equation for the inflation rate in the SVAR model of Iran withdomestic variables ordered as: inflation, foreign exchange rate returns, money supply growth, and output
growth, estimated over the period 1989q1–2020q1
∆pt
st
st 1
∆e f ;t 1
∆mt 1
∆pt 1
∆yt 1
∆pt 2

(1)

(2)

(3)

(4)

(5)

(6)

(7)

0:015
(0:015)0:004(0:016)
0:050(0:024)
0:036(0:061)
0:396(0:114)0:009(0:061)
0:194(0:102)

0:017(0:015)0:004(0:016)
0:049(0:024)
0:027(0:062)
0:411(0:114)
0:007(0:061)
0:192(0:102)
0:381(0:275)

0:017(0:015)0:004(0:016)
0:049(0:024)
0:027(0:062)
0:411(0:115)
0:007(0:062)
0:192(0:102)
0:385(0:296)
0:001(0:013)

0:017(0:015)0:004(0:016)
0:046(0:025)0:020(0:062)
0:411(0:115)
0:003(0:062)
0:181(0:103)
0:217(0:334)
0:003(0:013)
0:064(0:059)0:017(0:015)0:004(0:016)
0:047(0:025)0:019(0:063)
0:410(0:117)
0:003(0:062)
0:182(0:105)0:214(0:337)
0:004(0:015)0:064(0:059)0:009(0:108)0:020(0:015)
0:007(0:016)
0:048(0:025)
0:021(0:063)
0:384(0:117)0:005(0:062)
0:194(0:104)
0:292(0:338)0:005(0:015)0:135(0:074)
0:015(0:108)0:059(0:037)

3:293[0:510]
0:406

3:802
[0:433]
0:411

3:941
[0:414]
0:406

4:168
[0:384]
0:407

4:101
[0:392]
0:401

4:563
[0:335]
0:409

0:021
(0:016)
0:007(0:016)
0:048(0:025)0:020(0:064)
0:384(0:118)0:004(0:062)
0:196(0:106)
0:295(0:341)
0:007(0:016)0:135(0:075)
0:018(0:111)
0:059(0:038)0:443(3:081)
4:734[0:316]
0:404

∆ȳwt
∆pt0
grvt
∆ēwt
∆reqwt∆r̄wt
Residual serial
correlation test
Adjusted R2

Notes: The variables are ordered as: ∆pt ; ∆e f t ; ∆mt ; and ∆yt ; where: ∆pt = ln(Pt =Pt 1 ); Pt is the quarterly consumer price index of Iran;∆e f t = ln(E f t =E f ;t 1 ); E f t is the quarterly rial/U.S. dollar free market exchange rate; ∆mt = ln(M2t =M2;t 1 ); M2t is obtained by summing
the aggregates M1 and "quasi-money"; ∆yt = ln(Yt =Yt 1 ); Yt is the quarterly real output of Iran. See the notes to Table S.6a for further
details on the construction and sources of data used.

S.21

Table S.7b: Quarterly estimates of the equation for the rate of change of the free market foreign exchange
rate in the SVAR model of Iran with domestic variables ordered as: inflation, foreign exchange rate
returns, money supply growth, and output growth, estimated over the period 1989q1–2020q1∆e f tst
st 1∆pt∆e f ;t 1
∆mt 1
∆pt 1
∆yt 1

(1)

(2)

(3)

(4)

(5)

(6)

(7)

0:286
(0:048)0:250(0:050)2:541(0:293)
0:257(0:072)
0:175(0:198)
1:696(0:306)
0:082(0:192)

0:272(0:046)
0:255(0:048)
2:672(0:282)
0:263(0:068)0:246(0:189)
1:877(0:295)
0:066(0:183)
3:102(0:854)0:265(0:046)
0:248(0:048)
2:668(0:281)
0:267(0:068)
0:261(0:189)
1:865(0:295)
0:073(0:182)
3:515(0:912)
0:049(0:039)0:266(0:046)
0:248(0:048)
2:637(0:283)
0:264(0:068)0:241(0:190)1:870(0:295)
0:079(0:182)
3:945(1:021)
0:041(0:040)0:169(0:180)0:267(0:045)0:245(0:047)
2:626(0:279)0:255(0:067)
0:295(0:189)
1:812(0:291)
0:049(0:180)
3:704(1:011)
0:081(0:043)
0:190(0:178)
0:696(0:321)

0:266(0:046)
0:244(0:048)
2:631(0:282)
0:254(0:068)0:294(0:190)
1:811(0:293)
0:049(0:181)3:724(1:027)
0:081(0:044)0:172(0:229)0:697(0:322)
0:014(0:114)

11:560[0:021]
0:523

8:001
[0:092]
0:568

8:665
[0:070]
0:571

7:670
[0:104]
0:570

8:223
[0:084]
0:584

8:175
[0:085]
0:580

0:265
(0:046)
0:245(0:048)
2:629(0:283)
0:261(0:068)0:312(0:192)
1:827(0:294)0:044(0:181)
3:774(1:032)
0:097(0:049)0:180(0:230)0:750(0:331)
0:022(0:115)
6:829(9:235)
7:930[0:094]
0:578

∆ȳwt
∆pt0
grvt
∆ēwt
∆reqwt∆r̄wt
Residual serial
correlation test
Adjusted R2

Notes: See the notes to Table S.6a for further details on the construction and sources of data used.

S.22

Table S.7c: Quarterly estimates of the equation for the money supply growth in the SVAR model of Iranwith domestic variables ordered as: inflation, foreign exchange rate returns, money supply growth, andoutput growth, estimated over the period 1989q1–2020q1
∆mt
st
st 1
∆pt
∆e f t∆e f ;t 1
∆mt 1
∆pt 1
∆yt 1

(1)

(2)

(3)

(4)

(5)

(6)

(7)

0:005
(0:025)
0:016(0:025)0:120(0:169)0:002(0:041)
0:041(0:034)
0:285(0:089)0:198(0:154)0:064(0:086)

0:007(0:025)0:018(0:025)0:151(0:177)0:010(0:044)0:044(0:034)
0:292(0:090)0:226(0:162)
0:064(0:086)
0:245(0:426)

0:007(0:025)0:018(0:025)0:151(0:179)0:010(0:044)0:044(0:035)
0:292(0:091)
0:226(0:163)
0:064(0:087)0:246(0:461)
0:0002(0:019)0:004(0:025)
0:016(0:025)0:160(0:177)
0:003(0:044)0:044(0:034)0:307(0:090)0:209(0:162)
0:070(0:086)
0:152(0:512)
0:007(0:019)0:147(0:085)0:005(0:025)
0:016(0:025)0:163(0:179)0:005(0:045)0:044(0:034)
0:309(0:092)
0:210(0:163)
0:071(0:087)0:155(0:515)0:008(0:021)
0:147(0:086)
0:023(0:158)0:004(0:025)0:015(0:025)0:168(0:181)
0:005(0:045)0:044(0:035)0:308(0:092)
0:209(0:163)
0:071(0:087)0:137(0:523)
0:008(0:021)
0:162(0:110)0:024(0:158)
0:013(0:055)0:004(0:025)0:015(0:026)0:167(0:182)
0:005(0:046)
0:044(0:035)0:307(0:094)0:207(0:165)0:071(0:088)0:142(0:527)
0:007(0:024)
0:163(0:111)
0:020(0:163)
0:012(0:056)
0:449(4:468)

6:516[0:164]
0:050

6:331
[0:176]
0:044

6:329
[0:176]
0:036

4:946
[0:293]
0:052

4:989
[0:288]
0:044

5:283
[0:259]
0:036

5:266
[0:261]
0:027

∆ȳwt
∆pt0
grvt
∆ēwt
∆reqwt∆r̄wt
Residual serial
correlation test
Adjusted R2

Notes: See the notes to Table S.6a for further details on the construction and sources of data used.

S.23

Table S.7d: Quarterly estimates of the equation for the output growth in the SVAR model of Iran withdomestic variables ordered as: inflation, foreign exchange rate returns, money supply growth, and output
growth, estimated over the period 1989q1–2020q1
∆yt
st
st 1
∆e f t∆mt∆pt∆e f ;t 1
∆mt 1
∆pt 1
∆yt 1

(1)

(2)

(3)

(4)

(5)

(6)

(7)

0:021
(0:025)
0:057(0:025)0:123(0:042)
0:096(0:094)
0:335(0:171)
0:033(0:034)
0:022(0:094)0:490(0:157)
0:195(0:087)

0:021(0:025)0:058(0:026)0:125(0:044)
0:097(0:094)
0:341(0:180)0:033(0:035)0:020(0:095)
0:496(0:165)
0:195(0:088)0:049(0:432)

0:020(0:025)
0:057(0:026)
0:127(0:045)
0:097(0:095)
0:348(0:181)
0:035(0:035)
0:016(0:096)
0:499(0:166)
0:196(0:088)0:145(0:467)
0:010(0:019)0:021(0:025)0:058(0:026)0:130(0:045)
0:088(0:096)
0:344(0:182)
0:034(0:035)0:024(0:097)
0:502(0:167)
0:199(0:088)0:278(0:524)
0:008(0:019)0:050(0:088)0:016(0:025)
0:055(0:026)
0:113(0:046)
0:086(0:095)
0:304(0:181)
0:034(0:035)
0:051(0:097)
0:495(0:165)0:210(0:088)0:313(0:519)0:009(0:021)
0:039(0:088)
0:291(0:159)0:017(0:025)
0:056(0:026)
0:113(0:046)
0:085(0:096)
0:300(0:183)
0:034(0:035)
0:051(0:098)
0:495(0:166)0:210(0:088)0:298(0:527)
0:009(0:021)
0:053(0:112)
0:291(0:160)0:011(0:055)

7:157[0:128]
0:137

7:108
[0:130]
0:129

6:991
[0:136]
0:124

7:143
[0:129]
0:119

7:813
[0:099]
0:137

7:815
[0:099]
0:129

0:017
(0:026)
0:057(0:026)0:116(0:046)
0:084(0:096)
0:305(0:183)
0:038(0:035)0:040(0:098)
0:509(0:167)0:208(0:088)0:335(0:529)0:001(0:024)
0:058(0:113)
0:261(0:164)
0:006(0:056)
3:734(4:487)
7:782[0:100]
0:127

∆ȳwt
∆pt0
grvt
∆ēwt
∆reqwt∆r̄wt
Residual serial
correlation test
Adjusted R2

Notes: See the notes to Table S.6a for further details on the construction and sources of data used.

S.24

Table S.8a: Quarterly estimates of the equation for the money supply growth in the SVAR model of Iranwith domestic variables ordered as: money supply growth, foreign exchange rate returns, inflation, andoutput growth, estimated over the period 1989q1–2020q1
∆mt
st
st 1
∆e f ;t 1
∆mt 1
∆pt 1
∆yt 1

(1)

(2)

(3)

(4)

(5)

(6)

(7)

0:006
(0:021)
0:015(0:022)
0:044(0:032)0:290(0:088)0:131(0:116)
0:061(0:086)

0:005(0:022)
0:015(0:022)
0:045(0:032)
0:294(0:089)0:137(0:117)
0:062(0:086)0:165(0:398)0:005(0:022)
0:015(0:023)
0:045(0:032)
0:294(0:089)0:137(0:118)0:062(0:086)0:164(0:429)0:0001(0:018)0:005(0:022)
0:014(0:023)
0:048(0:032)
0:311(0:089)0:120(0:118)0:067(0:086)
0:193(0:480)
0:006(0:019)0:137(0:084)0:005(0:022)
0:014(0:023)
0:047(0:032)
0:313(0:090)0:118(0:119)0:068(0:086)
0:201(0:485)0:008(0:021)
0:136(0:085)
0:023(0:154)0:005(0:022)
0:014(0:023)
0:047(0:032)
0:312(0:091)
0:117(0:120)0:068(0:087)
0:195(0:492)
0:008(0:021)
0:141(0:108)
0:023(0:155)0:004(0:054)0:005(0:022)
0:014(0:023)
0:047(0:033)
0:311(0:092)
0:116(0:121)
0:067(0:087)
0:198(0:495)
0:007(0:023)
0:142(0:109)0:020(0:159)0:004(0:055)
0:427(4:442)

7:577[0:108]
0:060

7:715
[0:103]
0:053

7:720
[0:102]
0:045

7:444
[0:114]
0:058

7:503
[0:112]
0:050

7:676
[0:104]
0:042

7:718
[0:102]
0:033

∆ȳwt
∆pt0
grvt
∆ēwt
∆reqwt∆r̄wt
Residual serial
correlation test
Adjusted R2

Notes: The variables are ordered as: ∆mt ; ∆e f t ; ∆pt ; and ∆yt ; where: ∆mt = ln(M2t =M2;t 1 ); M2t is obtained by summing the aggregates
M1 and "quasi-money"; ∆e f t = ln(E f t =E f ;t 1 ); E f t is the quarterly rial/U.S. dollar free market exchange rate; ∆pt = ln(Pt =Pt 1 ); Pt is thequarterly consumer price index of Iran; ∆yt = ln(Yt =Yt 1 ); Yt is the quarterly real output of Iran. See the notes to Table S.6a for further
details on the construction and sources of data used.

S.25

Table S.8b: Quarterly estimates of the equation for the rate of change of the free market foreign exchange
rate in the SVAR model of Iran with domestic variables ordered as: money supply growth, foreignexchange rate returns, inflation, and output growth, estimated over the period 1989q1–2020q1∆e f tst
st 1∆mt∆e f ;t 1
∆mt 1
∆pt 1
∆yt 1

(1)

(2)

(3)

(4)

(5)

(6)

(7)

0:310
(0:062)0:241(0:064)
0:134(0:264)
0:329(0:092)
0:260(0:263)
0:286(0:335)0:143(0:246)

0:302(0:061)
0:244(0:064)0:116(0:262)
0:336(0:091)
0:316(0:263)
0:360(0:334)
0:134(0:244)
2:040(1:128)0:295(0:061)
0:236(0:064)0:116(0:262)
0:340(0:091)
0:331(0:263)
0:350(0:334)0:140(0:244)
2:494(1:210)0:054(0:052)

0:295(0:061)
0:237(0:064)
0:180(0:263)
0:329(0:091)
0:265(0:264)
0:391(0:333)
0:158(0:242)
3:502(1:352)
0:035(0:053)
0:390(0:240)0:296(0:061)
0:234(0:063)
0:174(0:260)0:319(0:090)0:324(0:264)
0:337(0:331)
0:125(0:241)
3:246(1:348)0:078(0:058)
0:411(0:238)0:744(0:428)0:303(0:061)
0:240(0:063)
0:175(0:261)
0:320(0:090)0:330(0:265)0:371(0:334)
0:126(0:241)
3:082(1:364)
0:075(0:058)0:567(0:302)
0:735(0:429)0:127(0:151)

6:955[0:138]
0:220

6:089
[0:193]
0:235

5:471
[0:242]
0:235

4:159
[0:385]
0:246

5:183
[0:269]
0:259

5:145
[0:273]
0:257

0:302
(0:061)
0:241(0:064)0:177(0:261)
0:327(0:091)
0:351(0:267)
0:391(0:336)0:120(0:242)
3:140(1:370)0:093(0:065)
0:576(0:303)
0:795(0:440)0:117(0:152)
7:807(12:290)
5:412[0:248]
0:253

∆ȳwt
∆pt0
grvt
∆ēwt
∆reqwt∆r̄wt
Residual serial
correlation test
Adjusted R2

Notes: See the notes to Table S.6a for further details on the construction and sources of data used.

S.26

Table S.8c: Quarterly estimates of the equation for the inflation rate in the SVAR model of Iran withdomestic variables ordered as: money supply growth, foreign exchange rate returns, inflation, and output
growth, estimated over the period 1989q1–2020q1
∆pt
st
st 1
∆e f t∆mt∆e f ;t 1
∆mt 1
∆pt 1
∆yt 1
∆pt 2

(1)

(2)

(3)

(4)

(5)

(6)

(7)

0:033
(0:013)
0:034(0:013)
0:153(0:018)0:026(0:050)0:003(0:020)0:016(0:050)0:456(0:090)0:026(0:048)0:180(0:080)0:033(0:012)
0:037(0:013)
0:162(0:017)
0:032(0:048)0:010(0:019)0:038(0:048)0:490(0:086)
0:023(0:046)0:174(0:076)
0:721(0:209)0:032(0:013)
0:036(0:013)
0:164(0:017)
0:031(0:048)0:010(0:019)0:042(0:049)0:484(0:087)
0:025(0:046)
0:181(0:077)
0:801(0:225)0:009(0:010)0:032(0:013)
0:036(0:013)
0:163(0:017)
0:033(0:049)0:010(0:020)0:043(0:049)0:484(0:087)
0:024(0:047)0:179(0:078)0:777(0:258)0:010(0:010)0:009(0:045)

0:033(0:012)
0:036(0:013)
0:168(0:017)
0:032(0:049)0:008(0:019)0:056(0:049)0:460(0:087)
0:022(0:046)
0:199(0:078)0:751(0:256)
0:018(0:011)
0:002(0:045)0:138(0:081)

0:030(0:013)
0:033(0:013)
0:166(0:017)
0:032(0:048)0:006(0:019)0:054(0:049)0:443(0:088)0:023(0:046)
0:207(0:078)0:795(0:257)
0:019(0:011)
0:049(0:057)0:141(0:081)
0:038(0:028)

13:954[0:007]
0:638

8:236
[0:083]
0:669

9:993
[0:041]
0:669

10:393[0:034]
0:666

10:984[0:027]
0:672

9:239
[0:055]
0:674

0:030
(0:013)
0:033(0:013)
0:167(0:017)
0:031(0:048)
0:007(0:019)0:059(0:050)0:441(0:088)0:023(0:046)
0:216(0:079)0:813(0:259)0:023(0:012)
0:046(0:057)0:156(0:084)0:040(0:028)1:802(2:287)
9:526[0:049]
0:673

∆ȳwt
∆pt0
grvt
∆ēwt
∆reqwt∆r̄wt
Residual serial
correlation test
Adjusted R2

Notes: See the notes to Table S.6a for further details on the construction and sources of data used.

S.27

Table S.8d: Quarterly estimates of the equation for the output growth in the SVAR model of Iran withdomestic variables ordered as: money supply growth, foreign exchange rate returns, inflation, and output
growth, estimated over the period 1989q1–2020q1
∆yt
st
st 1
∆e f t∆mt∆pt∆e f ;t 1
∆mt 1
∆pt 1
∆yt 1

(1)

(2)

(3)

(4)

(5)

(6)

(7)

0:021
(0:025)
0:057(0:025)0:123(0:042)
0:096(0:094)
0:335(0:171)
0:033(0:034)
0:022(0:094)0:490(0:157)
0:195(0:087)

0:021(0:025)0:058(0:026)0:125(0:044)
0:097(0:094)
0:341(0:180)0:033(0:035)0:020(0:095)
0:496(0:165)
0:195(0:088)0:049(0:432)

0:020(0:025)
0:057(0:026)
0:127(0:045)
0:097(0:095)
0:348(0:181)
0:035(0:035)
0:016(0:096)
0:499(0:166)
0:196(0:088)0:145(0:467)
0:010(0:019)0:021(0:025)0:058(0:026)0:130(0:045)
0:088(0:096)
0:344(0:182)
0:034(0:035)0:024(0:097)
0:502(0:167)
0:199(0:088)0:278(0:524)
0:008(0:019)0:050(0:088)0:016(0:025)
0:055(0:026)
0:113(0:046)
0:086(0:095)
0:304(0:181)
0:034(0:035)
0:051(0:097)
0:495(0:165)0:210(0:088)0:313(0:519)0:009(0:021)
0:039(0:088)
0:291(0:159)0:017(0:025)
0:056(0:026)
0:113(0:046)
0:085(0:096)
0:300(0:183)
0:034(0:035)
0:051(0:098)
0:495(0:166)0:210(0:088)0:298(0:527)
0:009(0:021)
0:053(0:112)
0:291(0:160)0:011(0:055)

7:157[0:128]
0:137

7:108
[0:130]
0:129

6:991
[0:136]
0:124

7:143
[0:129]
0:119

7:813
[0:099]
0:137

7:815
[0:099]
0:129

0:017
(0:026)
0:057(0:026)0:116(0:046)
0:084(0:096)
0:305(0:183)
0:038(0:035)0:040(0:098)
0:509(0:167)0:208(0:088)0:335(0:529)0:001(0:024)
0:058(0:113)
0:261(0:164)
0:006(0:056)
3:734(4:487)
7:782[0:100]
0:127

∆ȳwt
∆pt0
grvt
∆ēwt
∆reqwt∆r̄wt
Residual serial
correlation test
Adjusted R2

Notes: See the notes to Table S.6a for further details on the construction and sources of data used.

S.28

Figure S.3: Impulse responses of the effects of world output growth shocks on foreign exchange, inflation, and Iran output growth
One positive standard error shock to the global output growth

Notes: The structural VAR system studied in Equation (8) is composed of five variables. The current figure supplements the four panels
available in Figure 3.

S.29

Table S.9: Estimates of reduced form Iran’s output growth equation estimated over the period 1989q1–
2020q1∆yt
st 1 (βst 1 )∆yt 1 (λ∆yt 1 )∆e f ;t 1
∆mt 1
∆pt 1

(1)

(2)

(3)

(4)

(5)

(6)

(7)

0:037
(0:016)0:186(0:089)0:000(0:033)
0:068(0:092)
0:264(0:120)0:035(0:016)
0:188(0:089)0:002(0:033)
0:077(0:092)
0:250(0:121)
0:375(0:413)

0:035(0:016)
0:188(0:089)0:002(0:033)
0:077(0:093)
0:250(0:122)
0:353(0:443)
0:003(0:019)0:035(0:016)
0:190(0:090)0:002(0:033)
0:082(0:094)
0:255(0:123)
0:245(0:501)
0:001(0:020)0:041(0:088)0:037(0:016)
0:206(0:088)
0:003(0:033)0:110(0:093)
0:283(0:121)
0:119(0:494)0:020(0:021)
0:030(0:087)0:370(0:158)0:037(0:016)
0:206(0:089)0:003(0:033)
0:109(0:093)
0:287(0:122)0:140(0:501)
0:021(0:021)
0:052(0:110)0:372(0:158)0:018(0:055)0:038(0:016)
0:204(0:089)0:006(0:033)
0:101(0:094)
0:295(0:123)
0:119(0:503)
0:014(0:024)
0:055(0:111)
0:349(0:163)
0:014(0:056)
2:932(4:542)

0:031(0:013)
0:093

0:029
(0:013)
0:091

0:029
(0:013)
0:084

0:029
(0:013)
0:077

0:031
(0:013)
0:112

0:031
(0:013)
0:105

0:031
(0:013)
0:100

∆ȳwt
∆pt0
grvt
∆ēwt
∆reqwt∆r̄wt
βst 1 =(1λ∆yt 1 )Adjusted R2

Notes: βst 1 and λ∆yt 1 are the coefficients of st 1 and ∆yt 1 ; respectively; βst 1 =(1 λ∆yt 1 ) represents the long run effect of sanctions
on output growth. See Chapter 6 of Pesaran (2015). See the notes to Table S.6a for further details on the construction and sources of data
used.

S.30

Table S.10: Estimates of reduced form Iran’s output growth equation including contemporaneous sanction variable and estimated over the period 1989q1–2020q1∆yt
st (βst )st 1 (βst 1 )∆yt 1 (λ∆yt 1 )∆e f ;t 1
∆mt 1
∆pt 1

(1)

(2)

(3)

(4)

(5)

(6)

(7)

0:015
(0:022)
0:025(0:023)
0:191(0:089)0:002(0:033)
0:070(0:092)
0:257(0:121)

0:013(0:022)
0:025(0:023)
0:192(0:089)0:004(0:033)
0:079(0:092)
0:244(0:122)
0:357(0:415)0:014(0:023)
0:024(0:024)
0:193(0:090)0:003(0:033)
0:078(0:093)
0:243(0:123)
0:322(0:447)
0:004(0:019)0:014(0:023)
0:024(0:024)
0:194(0:090)0:004(0:034)
0:083(0:094)
0:249(0:124)
0:216(0:505)
0:002(0:020)0:041(0:089)0:014(0:022)
0:026(0:023)
0:211(0:089)0:001(0:033)
0:111(0:093)
0:276(0:122)
0:089(0:498)0:019(0:021)
0:030(0:087)
0:371(0:158)0:013(0:023)
0:027(0:023)
0:211(0:089)0:001(0:033)0:110(0:093)
0:280(0:123)
0:106(0:505)0:019(0:021)
0:046(0:111)
0:372(0:159)0:013(0:056)

0:014(0:023)
0:027(0:024)0:208(0:089)0:004(0:034)
0:102(0:094)
0:287(0:124)0:084(0:508)0:012(0:024)
0:049(0:112)
0:349(0:163)
0:009(0:056)
2:982(4:556)

0:040(0:016)
0:034(0:014)
0:088

0:038
(0:017)
0:032(0:014)
0:086

0:038
(0:017)
0:032(0:014)
0:079

0:038
(0:017)
0:032(0:014)
0:072

0:040
(0:017)
0:033(0:013)
0:107

0:040
(0:017)
0:033(0:014)
0:100

0:041
(0:017)
0:034(0:014)
0:095

∆ȳwt
∆pt0
grvt
∆ēwt
∆reqwt∆r̄wt
βst + βst 1(βst + βst 1 )=(1Adjusted R2

λ∆yt 1 )Notes: βst ; βst 1 and λ∆yt 1 are the coefficients of st ; st 1 and ∆yt 1 ; respectively; (βst + βst 1 )=(1 λ∆yt 1 ) represents the long run effectof sanctions on output growth. See Chapter 6 of Pesaran (2015). See the notes to Table S.6a for further details on the construction andsources of data used.

S.31

S.32

-0.154***(0.057)-0.352*(0.188)-0.215(0.186)
1.271***(0.300)
yes
yes

0.385

-0.010(0.058)0.034(0.189)0.086(0.187)-0.048(0.302)yes
yes-0.020(1)∆m f gt

0.545-0.074***(0.028)-0.074(0.092)-0.226**(0.091)0.878***(0.147)yes
yes∆svcst0.102-0.008(0.051)
0.113(0.169)-0.111(0.178)-0.084(0.266)
yes
yes
yes
yes
yes

∆agrt

0.378

-0.158***(0.054)-0.284(0.180)-0.335*(0.189)1.235***(0.283)
yes
yes
yes
yes
yes

(2)
∆m f gt

0.598-0.076***(0.025)-0.034(0.083)-0.314***(0.087)0.866***(0.130)
yes
yes
yes
yes
yes

∆svcst

0.167

0.019
(0.050)0.189(0.164)-0.106(0.167)-0.244(0.262)
yes
yes
yes
yes
yes
yes

∆agrt

0.368

-0.143***(0.055)-0.242(0.182)-0.332*(0.186)1.145***(0.292)
yes
yes
yes
yes
yes
yes

(3)
∆m f gt

0.601-0.067***(0.025)-0.009(0.083)-0.313***(0.084)0.812***(0.132)
yes
yes
yes
yes
yes
yes

∆svcst0.038(0.054)0.231(0.162)-0.104(0.173)-0.348(0.270)
yes
yes
yes
yes
yes
yes
yes
yes
0.133

∆agrt

-0.154***(0.059)-0.284(0.177)-0.291(0.190)1.238***(0.296)
yes
yes
yes
yes
yes
yes
yes
yes
0.365

(4)
∆m f gt-0.075***(0.027)-0.028(0.082)-0.307***(0.087)0.858***(0.136)
yes
yes
yes
yes
yes
yes
yes
yes
0.585

∆svcstNotes: ∆agrt ; ∆m f gt ; and ∆svcst are the yearly rates of change of the value added of agriculture, manufacturing and mining, and the services sector, respectively: ∆agrt =ln(Agrt =Agrt 1 ); ∆m f gt = ln(M f gt =M f gt 1 ); ∆svcst = ln(Svcst =Svcst 1 ); with Agrt ; M f gt ; Svcst being the value added of the three sectors aforementioned in year t. See the notes
to Table S.6a for further details on the construction and sources of data used.

∆pt0
grvt
∆r̄wt
∆ēwt
∆reqwt∆ȳwt∆ȳt0∆ytTurAdjusted R2∆svcst 1∆m f gt 1∆agrt 1st

∆agrt

Table S.11: Effects of sanctions on Iran’s sectoral output growths estimated over the period 1989–2019

S.33

0.182

0.105*(0.057)0.598***(0.188)0.708**(0.289)0.147-0.066*(0.035)-0.326***(0.115)-0.176(0.178)

g
∆m
f gt

0.256

0.123**(0.054)
0.662***(0.178)0.711***(0.271)
yes

ft
∆agr

(2)

0.162

-0.074**(0.035)-0.352***(0.114)-0.177(0.173)
yes

g
∆m
f gt

0.235

0.125**(0.054)0.671***(0.178)0.698***(0.270)
yes
yes

ft
∆agr

(3)

0.159

-0.070**(0.034)-0.341***(0.112)-0.193(0.171)
yes
yes

g
∆m
f gt

0.218

0.144***(0.055)0.715***(0.175)0.748***(0.266)
yes
yes
yes
yes

ft
∆agr

(4)

0.139

-0.076**(0.035)-0.364***(0.111)-0.197(0.169)
yes
yes
yes
yes

g
∆m
f gt

0.122**(0.056)
0.596***(0.185)0.608**(0.272)
yes
yes
yes
yes
yes
yes
yes
yes
0.207

ft
∆agr

(5)

-0.063*(0.037)-0.274**(0.122)-0.165(0.179)
yes
yes
yes
yes
yes
yes
yes
yes
0.048

g
∆m
f gt

g
f t and ∆m
Notes: ∆agr
f gt are the yearly rates of change of the value added of the agriculture, and manufacturing and mining sectors as a fraction of total value added, respectively:
gt =Agr
gt 1 ); ∆mggggt ; Mg
f t = ln(Agr∆agrf gt = ln(Mf gt =Mf gt 1 ); with Agr
f gt ; being the value added of the two sectors aforementioned divided by the sum of agriculture, manufacturing andmining and services value added in year t. See the notes to Table S.6a for further details on the construction and sources of data used.

∆ȳwt
∆pt0
∆ȳt0
∆ytTur
grvt
∆r̄wt
∆ēwt
∆reqwtAdjusted R2g∆m
f gt 1

ft 1
∆agr

st

ft
∆agr

(1)

Table S.12: Effects of sanctions on the rate of change of value added of the agriculture and manufacturing sectors as a share of total value
added over the period 1989–2019

S.34

0.188

0.111*(0.058)0.387**(0.157)-1.703**(0.680)

(1)

0.101

0.017
(0.012)0.077**(0.033)0.084(0.144)

gt
∆svcs

0.247

0.125**(0.055)0.443***(0.151)-1.633**(0.644)
yes

ft
∆agr

(2)

0.085

0.018
(0.012)0.082**(0.034)0.090(0.143)
yes

gt
∆svcs

0.228

0.129**(0.055)0.459***(0.151)-1.612**(0.639)
yes
yes

ft
∆agr

(3)

0.103

0.016
(0.012)0.075**(0.033)0.081(0.139)
yes
yes

gt
∆svcs

0.222

0.152***(0.055)0.492***(0.148)-1.794***(0.631)
yes
yes
yes
yes

ft
∆agr

(4)

0.070

0.016
(0.012)0.080**(0.033)0.092(0.139)
yes
yes
yes
yes

gt
∆svcs

0.130**(0.057)0.423***(0.161)-1.488**(0.638)
yes
yes
yes
yes
yes
yes
yes
yes
0.216

ft
∆agr

(5)

0.014
(0.013)0.064*(0.038)0.085(0.151)
yes
yes
yes
yes
yes
yes
yes
yes
-0.076gt∆svcs

gt =Agr
gt 1 ); ∆svcs
g t are the rates of change of the value added of the agriculture and services sectors as a fraction of total value added, respectively: ∆agr
gt =f t and ∆svcs
f t = ln(AgrNotes: ∆agr
g t =Svcs
g t 1 ); with Agr
gt ; Svcs
g t ; being the value added of the two sectors aforementioned divided by the sum of agriculture, manufacturing and mining and services value added inln(Svcs
year t. See the notes to Table S.6a for further details on the construction and sources of data used.

∆ȳwt
∆pt0
∆ȳt0
∆ytTur
grvt
∆r̄wt
∆ēwt
∆reqwtAdjusted R2

gt 1
∆svcs

ft 1
∆agr

st

ft
∆agr

Table S.13: Effects of sanctions on the rate of change of value added of the agriculture and services sectors as a share of total value added
over the period 1989–2019

S.35

0.147

-0.072**(0.036)0.859***(0.306)2.436***(0.863)

0.098

0.018
(0.012)-0.241**(0.105)-0.474(0.296)

gt
∆svcs

0.136

-0.075**(0.036)
0.881***(0.304)2.460***(0.852)
yes

g
∆m
f gt

(2)

0.071

0.018
(0.012)-0.245**(0.105)-0.479(0.295)
yes

gt
∆svcs

0.123

-0.072**(0.036)0.824***(0.307)2.343***(0.853)
yes
yes

g
∆m
f gt

(3)

0.080

0.017
(0.012)-0.218**(0.105)-0.421(0.292)
yes
yes

gt
∆svcs

0.121

-0.082**(0.036)
0.945***(0.304)2.686***(0.853)
yes
yes
yes
yes

g
∆m
f gt

(4)

0.051

0.017
(0.013)-0.242**(0.106)-0.473(0.296)
yes
yes
yes
yes

gt
∆svcs

-0.067*(0.039)0.698*(0.357)2.007**(0.963)
yes
yes
yes
yes
yes
yes
yes
yes
0.029

g
∆m
f gt

(5)

0.014
(0.014)-0.183(0.127)-0.337(0.343)
yes
yes
yes
yes
yes
yes
yes
yes
-0.101

gt
∆svcs

g
g
g t are the rates of change of the value added of the manufacturing and mining, and services sectors as a fraction of total value added, respectively: ∆mNotes: ∆m
f gt and ∆svcs
f gt =gg
g t =Svcs
g t 1 ); with Mgg t ; being the value added of the two sectors aforementioned divided by the sum of agriculture, manufacturing and miningg t = ln(Svcsln(Mf gt =Mf gt 1 ); ∆svcs
f gt ; Svcs
and services value added in year t. See the notes to Table S.6a for further details on the construction and sources of data used.

∆ȳwt
∆pt0
∆ȳt0
∆ytTur
grvt
∆r̄wt
∆ēwt
∆reqwtAdjusted R2

gt 1
∆svcs

g
∆m
f gt 1

st

g
∆m
f gt

(1)

Table S.14: Effects of sanctions on the rate of change of value added of manufacturing and services sectors as a share of total value added
over the period 1989–2019

Table S.15: Effects of sanctions on the rate of change of employment rate in Iran relative to other MENA
countries estimated over the period 1995–2019

st∆ẽt 1
∆yt

(1)
0:067
(0:028)0:408(0:180)0:170(0:111)(2)
0:061(0:027)
0:443(0:176)
0:119(0:112)
0:022(0:014)

∆ẽt
(3)
0:057
(0:028)0:439(0:177)
0:081(0:123)
0:013(0:017)
0:297(0:368)

0:236

0:287

0:274

∆pt0
∆ȳwt
∆ȳt0

(4)
0:055
(0:029)0:420(0:184)
0:087(0:125)
0:011(0:019)0:079(0:554)
0:217(0:408)∆ytTurAdjusted R2

0:245

(5)
0:054
(0:026)
0:373(0:168)0:103(0:114)
0:002(0:017)
0:536(0:542)
0:335(0:373)
0:235(0:106)
0:381

Notes: The dependent variable is the rate of change of yearly employment rate in Iran relative to that of other MENA countries, namely
MENA =∆eet = ∆et ∆etMENA ; where ∆et = ln(Et =Et 1 ) and ∆etMENA = ln(EtMENA =EtMENA
1 ). Et is Iran’s employment rate in year t, Et
nMENA
nMENA
∑ j=1 w j E jt ; where E jt j=1 are the employment rates of 17 MENA countries (excluding Iran) in year t, and w j the population weights
for each country. See the notes to Table S.6a for further details on the construction and sources of data used. See Section S.2.3 in the data
appendix of the online supplement for details on labor statistics.

Table S.16: Effects of sanctions on the rate of change of male labor force participation in Iran relative toother MENA countries estimated over the period 1995–2019st∆lef m;t 1
∆yt

(1)
0:034
(0:015)
0:522(0:170)0:106(0:061)(2)
0:028(0:013)
0:557(0:141)
0:057(0:053)
0:021(0:006)∆lef mt(3)
0:026(0:013)
0:554(0:140)0:034(0:057)
0:016(0:008)0:178(0:169)

0:299

0:520

0:523

∆pt0
∆ȳwt
∆ȳt0

(4)
0:025
(0:013)
0:535(0:145)
0:037(0:058)0:014(0:008)0:049(0:253)0:129(0:185)∆ytTurAdjusted R2

0:510

(5)
0:024
(0:013)0:512(0:143)
0:041(0:057)
0:012(0:008)0:183(0:268)0:163(0:184)
0:069(0:052)
0:529

Notes: The dependent variable is the rate of change of yearly male labor force participation rate in Iran relative to that of other MENA
MENA = ln(LF MENA =LF MENA ). LF is Iran’s
MENA ; where ∆l f = ln(LF =LF
countries, namely ∆lef mt = ∆l fmt ∆l fmt
mt
mt
mt
m;t 1 ) and ∆l f mt
mt
m;t 1
nMENA
nMENA
MENA
male labor force participation rate in year t, LFmt
= ∑ j=1 w j LFjmt ; where LFjmt j=1 are the male labor force participation ratesof 17 MENA countries (excluding Iran) in year t, and w j the population weights for each country. See the notes to Table S.6a for further
details on the construction and sources of data used. See Section S.2.3 in the data appendix of the online supplement for details on labor
statistics.

S.36

Table S.17: Effects of sanctions on the rate of change of female labor force participation in Iran relative
to other MENA countries estimated over the period 1995–2019st∆lef f ;t 1
∆yt

(1)
0:269
(0:062)
0:535(0:143)
0:583(0:256)(2)
0:238(0:048)0:624(0:111)
0:393(0:201)
0:098(0:024)∆lef f t(3)
0:232(0:049)0:617(0:112)
0:327(0:220)0:084(0:031)
0:491(0:641)

0:602

0:770

0:765

∆pt0
∆ȳwt
∆ȳt0

(4)
0:229
(0:050)0:607(0:116)
0:332(0:225)0:079(0:033)
0:150(0:960)0:344(0:708)∆ytTurAdjusted R2

0:755

(5)
0:229
(0:046)
0:588(0:107)
0:353(0:207)
0:065(0:031)
0:920(0:957)
0:525(0:656)
0:387(0:186)
0:793

Notes: The dependent variable is the rate of change of yearly female labor force participation rate in Iran relative to that of other MENA
countries, namely ∆lef f t = ∆l f f t ∆l f fMENA; where ∆l f f t = ln(LFf t =LFf ;t 1 ) and ∆l f fMENA= ln(LFfMENA=LFfMENAttt;t 1 ). LFf t is Iran’s
nMENA
nMENA
MENA
female labor force participation rate in year t, LFf t= ∑ j=1 w j LFj f t ; where LFj f t j=1 are the female labor force participation
rates of 17 MENA countries (excluding Iran) in year t, and w j the population weights for each country. See the notes to Table S.6a forfurther details on the construction and sources of data used. See Section S.2.3 in the data appendix of the online supplement for details onlabor statistics.

Table S.18: Effects of sanctions on the rate of change of labor force participation in Iran relative to other
MENA countries estimated over the period 1995–2019st∆lef t 1
∆yt

(1)
0:077
(0:023)
0:557(0:153)
0:193(0:093)(2)
0:067(0:018)0:620(0:123)
0:121(0:076)
0:034(0:009)∆lef t(3)
0:064(0:018)
0:613(0:122)
0:084(0:082)
0:026(0:011)
0:278(0:240)

0:472

0:668

0:673

∆pt0
∆ȳwt
∆ȳt0

(4)
0:062
(0:019)0:598(0:127)0:087(0:083)
0:024(0:012)
0:121(0:360)
0:157(0:266)(5)
0:062(0:018)0:568(0:119)0:093(0:078)0:019(0:012)
0:386(0:363)
0:226(0:250)0:136(0:071)

0:662

0:706

∆ytTurAdjusted R2

Notes: The dependent variable is the difference between the rate of change of yearly labor force participation rate in Iran and other
MENA countries: ∆lef t = ∆l ft ∆l ftMENA ; with ∆l ft = ln(LFt =LFt 1 ) and ∆l ftMENA = ln(LFtMENA =LFtMENA). LFt is Iran’s labor force
1
nMENA
MENA
participation rate in year t, LFtMENA = ∑nj=1
w j LFjt ; with LFjt j=1
being the labor force participation rate of 17 MENA countries (Irannot included) in year t, and w j the population weights for each country. See the notes to Table S.6a for further details on the construction
and sources of data used. See Section S.2.3 in the data appendix of the online supplement for details on labor statistics.

S.37

Table S.19: Effects of sanctions on the rate of change of the number of lower secondary schools in Iranestimated over the period 1989–2018

st 1
∆yt

(1)
0:217
(0:052)
0:124(0:167)

∆pt0
∆ȳwt
∆ȳt0

Lower secondary schools
(2)
(3)
(4)
0:221
0:227
0:222
(0:052)(0:050)(0:054)
0:166
0:130
0:117
(0:167)(0:163)(0:172)
0:036
0:066
0:062
(0:027)(0:032)(0:035)
1:072
1:288(0:637)(0:979)0:233(0:791)∆ytTurAdjusted R2

0:413

0:429

0:467

0:447

(5)
0:223
(0:055)0:114(0:181)
0:062(0:036)
1:265(1:068)0:233(0:808)
0:011(0:186)
0:423

Notes: The dependent variable is the rate of change of the number of lower secondary schools in Iran. See the notes to Table S.6a forfurther details on the construction and sources of data used. See Section S.2.3 in the data appendix of the online supplement for details on
education statistics.

Table S.20: Effects of sanctions on the rate of change of the number of high schools in Iran estimatedover the period 1989–2018

st 1
∆yt

(1)
0:253
(0:074)
0:031(0:238)(2)
0:248(0:075)0:010(0:243)
0:035(0:039)High schools
(3)
0:254
(0:075)0:044(0:244)
0:006(0:047)
1:025(0:955)

0:281

0:275

0:280

∆pt0
∆ȳwt
∆ȳt0

(4)
0:272
(0:080)
0:004(0:256)
0:010(0:053)
0:256(1:456)0:830(1:177)(5)
0:266(0:081)
0:047(0:266)
0:012(0:053)
0:647(1:571)
0:842(1:189)0:195(0:274)

0:265

0:249

∆ytTurAdjusted R2

Notes: The dependent variable is the rate of change of the number of high schools in Iran. See the notes to Table S.6a for further details
on the construction and sources of data used. See Section S.2.3 in the data appendix of the online supplement for details on education
statistics.

S.38

Table S.21: Effects of sanctions on the rate of change of the number of primary schools in Iran estimatedover the period 1989–2018
(1)
0:041
(0:033)
0:040(0:105)

st 1
∆yt
∆pt0
∆ȳwt
∆ȳt0

Primary schools
(2)
(3)
(4)
0:041
0:040
0:047
(0:034) (0:034) (0:037)
0:040
0:045
0:064
(0:109) (0:112) (0:118)
0:001
0:003
0:003
(0:018) (0:022) (0:024)
0:130
0:441
(0:438) (0:669)
0:336(0:541)∆ytTurAdjusted R2

0:007

0:031

0:068

(5)
0:051
(0:037)
0:037(0:121)
0:002(0:024)
0:686(0:715)
0:329(0:542)
0:122(0:125)

0:095

0:097

Notes: The dependent variable is the rate of change of the number of primary schools in Iran. See the notes to Table S.6a for further details
on the construction and sources of data used. See Section S.2.3 in the data appendix of the online supplement for details on education
statistics.

Table S.22: Effects of sanctions on the number of female to male students across all grades (rate ofchange) estimated over the period 1989–2019

st
∆yt

(1)
0:028
(0:008)0:005(0:027)

∆pt0
∆ȳwt
∆ȳt0

Female-to-male student ratio
(2)
(3)
(4)
0:028
0:028
0:028
(0:008)(0:008)(0:008)0:004
0:003
0:0001(0:029)(0:029)(0:031)
0:001
0:001
0:0002(0:005)(0:006)(0:006)
0:042
0:093(0:111)(0:170)0:052(0:130)∆ytTurAdjusted R2

0:358

0:335

0:313

0:290

(5)
0:028
(0:009)0:004(0:032)0:0005(0:006)
0:063(0:184)
0:054(0:132)
0:015(0:033)0:267

Notes: The dependent variable is the rate of change of the ratio of Iran’s females students (as a proportion of the Iran’s female population
ages 5–19) to the males students (as a proportion of the Iran’s male population ages 5–19). See the notes to Table S.6a for further details
on the construction and sources of data used. See Section S.2.3 in the data appendix of the online supplement for details on education
statistics.

S.39

Table S.23: Effects of sanctions on the rate of change of the number of primary school teachers in Iranestimated over the period 1989–2019
(1)
0:026
(0:069)0:186(0:217)

st 1
∆yt
∆pt0
∆ȳwt
∆ȳt0

Primary school teachers
(2)
(3)
(4)
0:025
0:024
0:005
(0:070) (0:071) (0:071)
0:195
0:181
0:252
(0:225) (0:232) (0:229)
0:008
0:017
0:047
(0:038) (0:047) (0:049)
0:323
1:350
(0:934) (1:368)
1:744(1:069)∆ytTurAdjusted R2

0:044

0:081

0:117

(5)
0:013
(0:072)
0:205(0:236)
0:045(0:049)
1:814(1:471)
1:743(1:073)
0:228(0:258)

0:050

0:059

Notes: The dependent variable is the rate of change of the number of primary school teachers in Iran as a proportion of the Iran 25–64
population. See the notes to Table S.6a for further details on the construction and sources of data used. See Section S.2.3 in the data
appendix of the online supplement for education statistics.

Table S.24: Effects of sanctions on the rate of change of the number of lower secondary school teachers
in Iran estimated over the period 1989–2019

st 1
∆yt

(1)
0:222
(0:069)0:054(0:218)

∆pt0
∆ȳwt
∆ȳt0

Lower secondary school teachers
(2)
(3)
(4)
0:228
0:233
0:221
(0:068)(0:065)(0:068)
0:114
0:047
0:019
(0:218)(0:212)(0:219)
0:052
0:096
0:084
(0:037)(0:043)(0:047)
1:545
2:215(0:856)(1:306)
0:699(1:020)∆ytTurAdjusted R2

0:286

0:312

0:365

0:352

(5)
0:229
(0:069)0:023(0:226)
0:082(0:047)
1:801(1:407)0:700(1:027)
0:203(0:247)0:344

Notes: The dependent variable is the rate of change of the number of lower secondary school teachers in Iran as a proportion of the Iran
25–64 population. See the notes to Table S.6a for further details on the construction and sources of data used. See Section S.2.3 in the data
appendix of the online supplement for education statistics.

S.40

Table S.25: Effects of sanctions on the rate of change of the number of high school teachers in Iranestimated over the period 1989–2019

st 1
∆yt

(1)
0:170
(0:081)
0:185(0:257)

∆pt0
∆ȳwt
∆ȳt0

High school teachers
(2)
(3)
(4)
0:163
0:164
0:190
(0:081)(0:082)(0:084)
0:121
0:106
0:170
(0:259)(0:267)(0:269)
0:056
0:047
0:019
(0:044)(0:054)(0:057)
0:337
1:189(1:077)(1:608)
1:591(1:257)∆ytTurAdjusted R2

0:163

0:182

0:154

0:173

(5)
0:185
(0:086)
0:199(0:281)
0:018(0:058)0:912(1:751)
1:592(1:277)0:136(0:307)0:145

Notes: The dependent variable is the rate of change of the number of high school teachers in Iran as a proportion of the Iran 25–64
population. See the notes to Table S.6a for further details on the construction and sources of data used. See Section S.2.3 in the data
appendix of the online supplement for education statistics.

Table S.26: Effects of sanctions on the rate of change of the total number of teachers in Iran estimatedover the period 1989–2019

st 1
∆yt

(1)
0:089
(0:054)
0:163(0:172)

∆pt0
∆ȳwt
∆ȳt0

Total number of teachers
(2)
(3)
(4)
0:089
0:090
0:108
(0:056) (0:056)(0:057)
0:158
0:133
0:177
(0:178) (0:182)(0:184)
0:004
0:013
0:031
(0:030) (0:037)(0:039)0:575
0:457(0:735)(1:098)
1:077(0:858)∆ytTurAdjusted R2

0:124

0:093

0:079

0:099

(5)
0:112
(0:058)0:154(0:191)
0:030(0:040)0:680(1:193)
1:076(0:870)0:109(0:209)0:072

Notes: The dependent variable is the rate of change of the total number of teachers in Iran. The total number of teachers is computed as thesum of primary, lower secondary, and high schools teachers, as a proportion of the 25–64 Iran population. See the notes to Table S.6a forfurther details on the construction and sources of data used. See Section S.2.3 in the data appendix of the online supplement for education
statistics.

S.41

Table S.27: Effects of sanctions on the rate of change of the total number of schools in Iran estimatedover the period 1989–2018

st 1
∆yt

(1)
0:102
(0:030)0:059(0:097)

∆pt0
∆ȳwt
∆ȳt0

Total number of schools
(2)
(3)
(4)
0:102
0:104
0:111
(0:031)(0:031)(0:033)
0:056
0:047
0:065
(0:100)(0:102)(0:107)
0:002
0:006
0:012
(0:016)(0:020)(0:022)
0:280
0:019(0:399)(0:609)0:323(0:492)∆ytTurAdjusted R2

0:307

0:281

0:266

0:249

(5)
0:112
(0:034)
0:055(0:112)
0:011(0:022)
0:115(0:661)
0:320(0:501)
0:048(0:115)
0:222

Notes: The dependent variable is the rate of change of the total number of schools. The total number of schools is computed as the sumof primary, lower secondary, and high schools. See the notes to Table S.6a for further details on the construction and sources of data used.
See Section S.2.3 in the data appendix of the online supplement for education statistics.

S.42

S.43

Table S.28: Chronology of major sanctions events against Iran over the period from November 1979 to January 2021

S.44

Table S.28: Chronology of major sanctions events against Iran over the period from November 1979 to January 2021

S.45

Table S.28: Chronology of major sanctions events against Iran over the period from November 1979 to January 2021

S.46

Table S.28: Chronology of major sanctions events against Iran over the period from November 1979 to January 2021

S.47

Table S.28: Chronology of major sanctions events against Iran over the period from November 1979 to January 2021