The Global Sanctions Data Base

Gabriel Felbermayr, Aleksandra Kirilakha, Constantinos Syropoulos, Erdal Yalcin, Yoto Yotov 04 August 2020



Economic sanctions are a form of foreign policy tool aimed at altering the behaviour of governments or political actors in targeted nations who violate international norms and treaties or threaten the interests of sanctioning countries. According to Hufbauer et al. (2007), who conducted pioneering studies in this area, “at the beginning of the 21st century, the same as a century earlier, economic sanctions remain an important yet controversial foreign policy tool.” Critics of international sanctions posit that they are often are ill-conceived and, in most cases, fail to achieve their objectives. In contrast, supporters claim that sanctions are effective and that they represent an efficient foreign policy response to crises in which national interests are in peril and military action is not a viable option (van Bergeijk 2012). A related view in this regard is the idea that sanctions provide a visible and potentially less expensive alternative to military intervention and to doing nothing (Blackwill and Harris 2017).

Recently imposed economic sanctions – such as those against North Korea, Russia (Bělín and Hanousek 2019), Venezuela, and the Islamic Republic of Iran (Haidar 2013) – illustrate the diversity of senders’ objectives and seem to provide support to arguments put forth both by critics and supporters. For example, in case of Iran, the US and the EU have perceived sanctions as a means to induce Iran to negotiate over its nuclear programme, as a tactic to slow the development of its nuclear programme, and as a way to force the Iranian government to change its domestic policies on human rights. Based on UN Security Council Resolution 1696, the first economic sanctions against Iran were initiated in July 2006 and then extended and tightened in subsequent years. Starting with trade sanctions on goods that could be used in Iran’s nuclear and ballistic missile programme, sanctions were extended by expanding the range of goods whose trade was disallowed and by imposing financial sanctions and travel bans for individual people.

The case of Iran illustrates how complex sanction policies can be. The debate begs the questions: Do economic sanctions work? More precisely, do they fulfil their political goals? Which types of sanctions are most effective? What are the economic costs for sanctioning and sanctioned countries? What are the implications for third countries? Unfortunately, there is little scholarly consensus on the answers to these questions.

To facilitate progress, we developed a novel data base, the Global Sanctions Data Base (GSDB) (Felbermayr et al. 2020).1 The GSDB covers 729 publicly traceable, multilateral, plurilateral, and purely bilateral sanction cases over the period 1950-2016.2 It defines sanctions as binding restrictive measures applied by the UN, other international organisations, country groups or individual nations to address different types of violations of international norms by target countries with the objective to change their behaviour or to constrain their actions. The GSDB classifies sanctions across the following three broad dimensions:

1. Sanction type: Trade Sanctions, Financial Sanctions, Travel Restrictions, Arms Sanctions Military Assistance, Other Types of Sanctions.

2. Sanction objectives: Policy Change, Destabilize Regime, Territorial Conflict, Prevent War, Terrorism, End War, Human Rights, Democracy, Other Objectives.

3. Sanction success: Partial Success/Achievement, Full Success/Achievement, Settlement by Negotiations, Enhancement/Failure, Ongoing.

Another important aspect of the GSDB is its special focus on trade sanctions and its classification of these sanctions on the basis of the direction of affected trade flows (i.e. export sanctions vs import sanctions vs sanctions in both directions of trade), the extent of their coverage, and their stringency (i.e. partial vs complete sanctions).

The GSDB is not the first dataset to provide detailed information about sanctions. Several prominent databases, including the HSE/HSEO database (Hufbauer et al. 2007), the TIES database (Morgan et al. 2014), the TSC database (Biersteker et al. 2018), and the EUSANCT database (Weber and Schneider 2018), have already offered information on different types of international sanctions. A distinguishing feature of the GSDB is that it is substantially more comprehensive in its coverage of sanction types, time, and countries than other databases. Moreover, it provides finer detail on the nature of trade sanctions that could be used to study their effectiveness. Besides having a case-level version, the GSDB is also available in a dyadic version. Hence, it can be effectively utilized in empirical models of bilateral transactions, such as the gravity framework.

In this column, we offer a brief overview of the GSDB based on some descriptive statistics. We also provide an illustrative application of the economic effects of the sanctions on Iran.

Types and evolution of sanctions

Figure 1 depicts the evolution of all identified sanctions between 1950 and 2016. For each year, panel a of Figure 1 reports the number of newly imposed sanctions and the cases that were initiated in previous years. Three distinct time intervals can be identified. Sanctions became gradually more popular from 1950 to 1990. In the early 1990s, the frequency of use of sanctions increased substantially. After a decade of calm and since 2004, the use of sanctions has become more widespread. Overall, the number of sanctions has been steadily rising over the last 65 years.

Panel b of Figure 1 presents the evolution of the number of sanctions by type. Several findings stand out. First, trade sanctions are the main type of sanctions implemented between 1950 and the late 1970s; during this period, all other types of sanctions played a minor role. Second, over the years two specific policy measures have been applied with increasing frequency: financial and arms sanctions. Travel bans, restrictions on military assistance and other sanctions have also become more popular. In contrast, the number of trade sanctions remained constant over the years, which, in combination with the increasing number of other sanctions, suggests that the number of trade sanctions is relatively smaller.

Figure 1 Sanctions over time

a) New vs existing cases

b) Frequency by type

Notes: Panel a: number of sanctions in force inherited from last year, and number of total (inherited plus new) sanctions in force per year. Panel b: number of sanctions by type (trade sanctions, arms sanctions, military assistance sanctions, financial sanctions, travel sanctions, and other sanctions), stacked.
Source: GSDB

Who sanctions whom?

Figure 2 offers two radial dendrograms by major regions and is an example of how the country dimension of the GSDB can be used. Arrows starting in a specific region and pointing on a particular region indicate the number of imposed trade sanctions.

Figure 2 Bilateral structure of sanctions

a) 2015

b) 1950

Note: These two radial dendrograms visualise trade sanctions between different regions in the world for the years 1950 and 2015. Regions are classified according to the UN Geoscheme. The list of country groups is available here.
Source: GSDB

Panel a of Figure 2 illustrates the sanction activities between regions for the year 2015. Countries from north-western Europe (NW Europe) imposed the largest number of trade sanctions in Africa (brown arrow). At the same time, however, not a single state from Africa imposed a trade ban against a north-western European state. Interestingly, some regions are barely sanctioned by other regions while at the same time others have been confronted with sanctions by almost every listed region. For example, East and South Asia have been sanctioned by almost all regions, at least in 2015. Panel b replicates the analysis for the year 1950. The figure illustrates a much smaller variety and number of sanctions policies among different regions. Mostly, trade sanctions took place between members of the Eastern and Western blocks.

The dimensions of trade sanctions

As noted above, in contrast to other databases, the GSDB captures three important dimensions of trade sanctions. First, depending on the direction of trade flows, it distinguishes between sanctions on exports from the sender to the target (i.e. export sanctions), sanctions on imports from the target to the sender (i.e. import sanctions), and sanctions that simultaneously apply to both exports and imports (i.e. bilateral trade sanctions). Second, the GSDB distinguishes between sanctions that apply only to specific goods and/or particular sector(s) of trade (i.e. partial trade sanctions) or to all sectors (i.e. complete trade sanctions).  Third, the GSDB distinguishes between sanctions imposed by one country (i.e. a unilateral sanction) versus sanctions that are imposed simultaneously by many countries (i.e. multilateral sanctions).

Between 1950 and 1990, for example, about 60% of countries that sanctioned exports imposed a partial restriction. In the ten years that followed, almost half of all export restricting countries applied complete export sanctions, whereas in recent years the imposition of partial export sanctions was on the rise again. These heterogeneous patterns illustrate the importance of accounting properly for the distinct dimensions of trade sanctions (e.g. partial vs complete and imports vs exports) in the GSDB. We confirm this argument with formal econometric analysis in the application below.

What are the objectives of sanctions?

In the public debate, sanctions are most often perceived as a means to induce a change in a sanctioned country's policy regime. However, a closer look into the official documents suggests that sanctions have a broader range of policy objectives. The GSDB identifies nine possible policy objectives.

Figure 3 Policy objectives of sanctions

Note: This figure depicts the number of observed policy objectives declared in all sanctions listed in the GSDB. For each sanction up to three objectives are documented. Because some sanction cases, especially in recent years, include more than one policy goal, the total number of observed objectives is larger than the corresponding number of sanction cases in the GSDB.  
Source: GSDB

Figure 3 depicts the distribution of sanction objectives across all sanction cases in the GSDB between 1950 and 2016. By a wide margin, the policy objective stated most often relates to human rights issues, followed by objectives related to democracy. The second most popular group of objectives states policy change, preventing wars, and ending wars. Regime destabilisation, territorial conflict-related issues, and other policy goals are observed less frequently.

In the early period of our sample, the objectives of policy change and regime destabilisation dominate. The pattern changes dramatically after the mid-1990s, when sanctions predominantly aim at improving human rights, ending wars, and solving territorial conflicts. In recent years, democracy related policy objectives have regained some relevance, but they have not reached the levels of the 80s and 90s.

Success of sanctions

Many academic and policy debates revolve around whether sanctions are effective in achieving their objectives or not. The GSDB can help answer this question by relying on official government statements or indirect confirmations in international press announcements.

Figure 4 Assessment of policy objectives in sanctions

Note: This figure depicts the yearly policy outcome registered for declared policy objectives in sanctions. For each sanction case up to three policy objectives are documented. See main text for further details and analysis.
Source: GSDB

Figure 4 traces the evolution of policy outcomes for all sanctions (regardless of objective) over the period 1950-2016. Several interesting patterns emerge. First, until the mid-1960s, almost 50% of all sanctions are classified as failed. For the same period, between 20% and 30% of sanctions are classified as totally successful. Second, from the mid-1960s until 1995, the success rate of sanctions went up steadily. Third, after 1995 there is a dramatic drop in the success rate. At the same time, almost no sanction regime is assessed as unsuccessful in recent years. Clearly, over the most recent 20 years, a large share of sanctions is still ongoing and thus not finally classified.

Utilisation of the GSDB for empirical policy analysis

The dyadic structure of the GSDB enables researchers to evaluate the effects of sanctions using the gravity model. To highlight the value of our dataset, below we focus on the case of Iran.

The sanctions on Iran are multi-dimensional because they vary in terms of country coverage (e.g. UN vs US vs EU sanctions), in terms of targets (e.g. on goods for military purposes vs all goods vs travel vs finance vs individuals) as well as over time (e.g. first EU sanctions were imposed in 2006 and they reached a peak in terms of stringency in 2012). We exploit this multi-dimensionality to identify heterogeneous effects within the sanctions on Iran.

Our empirical analysis reveals that, on average, the sanctions on Iran had a very strong negative impact on Iranian trade. The estimate is negative, large, and statistically significant at any conventional level. In terms of the volume effect, our estimate reveals that, all else equal, the sanctions reduced Iranian trade with the sanctioning countries by about 55%. Furthermore, we document the presence of wide heterogeneity of the impact of sanctions depending on the direction of trade flows and also across the sanctioning countries. We also find that the effects of sanctions to be heterogeneous even across EU members.3

Based on these results it is possible to quantify the general equilibrium welfare effects of the sanctions on Iran. To this end, we built on the gravity framework of Aichele and Heiland (2016), who use GTAP data to calibrate the multi-sector model with intermediate goods of Caliendo and Parro (2015) for 130 countries and 57 sectors.

Not surprisingly, if sanctions were terminated, the biggest winner would be Iran. Its real per capita income is predicted to rise by about 4.2%. This might seem small. The reason lies in the existence of substantial trade diversion, especially to China. As a consequence, sanctions do not seem to have reduced much Iran's overall trade openness. The country with the next largest welfare gain from the removal of the sanctions on Iran is Armenia, a neighbouring country and a traditionally close trading partner to Iran. This is intuitive, since Armenia has difficult political and economic relations with its other neighbours such as Azerbaijan and Turkey.

Figure 5 Welfare effects of sanctions: The case of Iran

Note: Percent changes in real per capita income resulting from an end of sanctions against Iran. Only countries with largest effects are shown: Iran (IRN), Armenia (ARM), Moldova (MDA), Malta (MLT), Sri Lanka (LKA), Mongolia (MNG), Malawi (MWI), Kirgizstan (KGZ), Georgia (GEO), Kenia (KEN), South Africa (ZAF), Cyprus (CYP), Cambodia (KHM), Oman (OMN), Greece (GRC).


Economic sanctions have become increasingly popular. With increasing geopolitical rivalries, this trend will probably continue. However, there is substantial uncertainty about whether and how sanctions affect economic outcomes as well as whether they bring about the intended political changes. To facilitate econometric work on the effects of sanctions, we have created the Global Sanctions Data Base. By covering all countries and the period of 1950 to 2016, it is the largest database focusing on sanctions in force (i.e. threats are excluded). It distinguishes different types of sanctions, reports the directionality of sanctions and their coverage. The GSDB also documents the extent to which sanctions have been successful and distinguishes between nine outcomes. As such, it is a rich database with a dyadic structure allowing empirical evaluation studies based on structural gravity models.


Aichele, R and I Heiland (2018), “Where is the Value Added? Trade Liberalization and Production Networks”, Journal of International Economics 115:  130-144.

Bělín, M and J Hanousek (2019), “Making Sanctions Bite: The EU–Russian Sanctions of 2014”,, 29 April.

Biersteker, T J, S E Eckert, M Tourinho and Z Hudáková (2018), “UN Targeted Sanctions Datasets (1991-2013)”, Journal of Peace Research 20(10): 1–9.

van Bergeijk, P A G (2012), “Failure and Success of Economic Sanctions”,, 25 April.

Blackwill, R and J Harris (2016), War by Other Means: Geoeconomics and Statecraft, Harvard University Press.

Caliendo, L and F Parro (2015), “Estimates of the Trade and Welfare Effects of NAFTA,” Review of Economic Studies,82(1): 1–44.

Felbermayr, G, A Kirilakha, C Syropoulos, E Yalcin and Y V Yotov (2020), "The Global Sanctions Data Base," European Economic Review, forthcoming.

Felbermayr, G, C Syropoulos, E Yalcin and Y V Yotov (2020), "On the Heterogeneous Effects of Sanctions on Trade and Welfare: Evidence from the Sanctions on Iran and a New Database", School of Economics Working Paper Series 2020-4, LeBow College of Business, Drexel University.

Haidar, J I (2013), “Sanctions and Trade Diversion: Exporter-Level Evidence from Iran”,, 9 April.

Hufbauer, G C, J J Schott, K A Elliott and B Oegg (2007), Economic Sanctions Reconsidered, 3rd Edition, Peterson Institute for International Economics.

Morgan, T C, N Bapat, and Y Kobayashi (2014), “Threat and Imposition of Economic Sanctions 1945-2005: Updating the TIES Dataset”, Conflict Management and Peace Science 31(5): 541–558.

Weber, P M and G Schneider (2018), “Making the World Safe for Liberalism? Evaluating the Western Sanctions Regime with a New Dataset”, Working Paper.


1 The Global Sanctions Data Base is publicly available here.

2 In the updated version of the GSDB we are working on, the time of coverage has been extended to the year 2019 and contains new cases for a total of 1,045.

3 A detailed discussion of our empirical analysis is available in Felbermayr et al. (2020).



Topics:  Global economy International trade Politics and economics

Tags:  economic sanctions, data base

President, Kiel Institute for the World Economy @kielinstitute and Professor, University of Kiel

PhD candidate, LeBow College of Business, Drexel University 

Trustee Professor of International Economics, LeBow College of Business, Drexel University

Professor of International Economics,HTWG-Konstanz - University of Applied Sciences

Professor, LeBow College of Business, Drexel University


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