Quantitative leverage through qualitative knowledge: Augmenting the statistical analysis of complex causes

Sanford C. Gordon, Alastair Smith

    Research output: Contribution to journalReview articlepeer-review


    Social scientific theories frequently posit that multiple causal mechanisms may produce the same outcome. Unfortunately, it is not always possible to observe which mechanism was responsible. For example, IMF scholars conjecture that nations enter IMF agreements both out of economic need and for discretionary domestic political reasons. Typically, though, all we observe is the fact of agreement, not its cause. Partial observability probit models (Poirier 1980, Journal of Econometrics 12:209-217; Braumoeller 2003, Political Analysis 11:209-233) provide one method for the statistical analysis of such phenomena. Unfortunately, they are often plagued by identification and labeling difficulties. Sometimes, however, qualitative studies of particular cases enlighten us about causes when quantitative studies cannot. We propose exploiting this information to lend additional structure to the partial observability approach. Monte Carlo simulation reveals that by anchoring "discernible" causes for a handful of cases about which we possess qualitative information, we obtain greater efficiency. More important, our method proves reliable at recovering unbiased parameter estimates when the partial observability model fails. The paper concludes with an analysis of the determinants of IMF agreements.

    Original languageEnglish (US)
    Pages (from-to)233-255
    Number of pages23
    JournalPolitical Analysis
    Issue number3
    StatePublished - Jun 2004

    ASJC Scopus subject areas

    • Sociology and Political Science
    • Political Science and International Relations


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