Political Instability Task Force

The Political Instability Task Force (PITF), formerly known as State Failure Task Force, is a U.S. government-sponsored research project to build a database on major domestic political conflicts leading to state failures. The study analyzed factors to denote the effectiveness of state institutions, population well-being, and found that partial democracies with low involvement in international trade and with high infant mortality are most prone to revolutions. One of the members of the task force resigned on January 20, 2017 in protest of the Trump administration.

The project began as an unclassified study that was commissioned to a group of academics (particularly active was the Center for Global Policy at George Mason University) by the Central Intelligence Agency's Directorate of Intelligence in response to a request from senior U.S. policy makers. The State Failure Problem Set dataset and spreadsheets were originally prepared in 1994 by researchers at the Center for International Development and Conflict Management (CIDCM) at the University of Maryland under the direction of Ted Robert Gurr and subject to the review of the State Failure Task Force. The Problem Set was subsequently reviewed, revised, and updated on an annual basis through 1999 under the direction of Ted Gurr and, beginning in 1999, Monty G. Marshall at CIDCM. In January 2001, a major review and revision of the Problem Set coding guidelines and dataset, under the direction of Monty G. Marshall, was concluded that substantially altered the case identifications and case parameters recorded in the Problem Set.

The PITF first identified over 100 "problem cases" in the world from 1955 to 2011. Four distinct types of state failure events are included in the dataset: revolutionary wars, ethnic wars, adverse regime changes, and genocides and politicides. The Problem Set data includes the following information on each case: country, month and year of onset, month and year of ending (unless ongoing as of December 31 of the current update year), type of case, and annual codes on magnitude variables. The basic structure of the data is the "case-year," that is, there is a separate case-entry for each additional year of a multi-year episode. Only the first annual record for each event contains a brief narrative description of the event.

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