Assessing United Nations Economic Sanctions...
Giacomo Bagarella
Thus, the model takes the form of importsit = β1 sanctionsit + γit + αi + λt + uit
Where γit is a vector of control variables and αi is the entity FE and λt is the time FE. The error term is uit. For the purposes of this model, the dependent variable is imports from a country grouping as a share of total global trade. Variance inflation factor (VIF) tests conducted on baseline regressions without fixed effects suggest the absence of dangerous multicollinearity as all VIF values were below 4.00 and the mean VIF did not exceed 1.99. Data and variables The Uppsala University Special Program on the International Targeted Sanctions (SPITS) dataset provides the list of sanction target country cases and duration of sanctions on the government. The data available permit an analysis of sanctions regimes in 11 countries. These are the Central African Republic, Ethiopia, Guinea-Bissau, Haiti, Iran, Iraq, North Korea, Liberia, Libya, South Africa, and Sudan. No data exists for Eritrea and South Sudan, while partial data for Yugoslavia and the Federal Republic of Yugoslavia mean these are excluded as well. This study examines the relationship between UN sanctions and imports in these countries by running the aforementioned model on a balanced panel of 175 countries.7 The panel covers each year from 1990 until 2014; with 25 observations per state, there are 4,375 total country-years. The variable selection follows that of previous empirical analyses of sanction effects. Socioeconomic controls appear in virtually all of the previously cited studies; as described, the size of the economy affects sanction results, as does the level of economic development. Other shocks, such as high inflation, poor or negative GDP growth, and internal or external conflict likewise have effects from trade distinct from those of sanctions. Where these occur simultaneously, the variation associated with each factor must be disentangled. As discussed, different sanction types influence the target differently; as more fine-grained data is available, it becomes possible to account for this as well. Finally, governance and financial characteristics affect the extent to which the target country can cope with trade restrictions; as noted previously, the likelihood with which alternative trade networks can form—both private and state-run—depends on both the capacity of the state to suppress such activities and its own ability to maintain adequate resources to pay for imports. As discussed above, export controls are a much more widespread type of sanction that import controls. Therefore, the dependent variable of interest is the target country’s imports measured as a share of global GDP, which implicitly controls for changes in the world economy. Trade data on goods imports was obtained from the International Monetary Fund’s Direction of Trade Statistics database. To study differential effects, imports data is broken down by the type of economy, with advanced economies in one group and emerging and developing economies in the other.8 The model regresses imports from these groups on a dummy variable, Sanctions, 7 8
This sample excludes most small island-nations, for which no trade data was available. For a definition of these groupings, refer to World Economic Outlook (2015).
Asian Journal of Public Affairs | 2016
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