Comparative Politics and the Comparative Method Comparative Perspectives Fall 2020 Marek Rybář, PhD. An overview of today‘s lecture lComparison and its goals lDifferences between social and natural sciences and the role of comparison lMethods of comparison lPitfalls and problems of comparison lStrategies of comparison l Comparison and its goals lComparing a part of natural human activity lPrices of cellphones, courses at college, job offers, income, etc. lWhat is the difference between such everyday comparison and scientific comparison? Q&A l lWhy do we compare in comparative politics? lWhat comparisons have you already carried out? Comparison and its goals lThe two differ in their goals: comparison of states, political systems, regimes etc. has these four basic goals: ldescription lclassification ltesting of hypotheses lprediction Description 1/2 lA systematic scientific exploration of a subject desperately needs a good description of phenomena under investigation lDescription of political phenomena and events in one or several countries lSometimes referred to as “old/traditional” comparison, in contrast to more scientific “new comparison” lAlmond: "evidence without inference“ lLijphart: atheoretical case study Description 2/2 lThe author describes a considerable and interesting „story“ without more general inferences and generalizations lSpecific events, important personalities who played a role in decision-making etc. lPotentially important information, data for case studies and comparisons lGeneral political phenomena (e.g. the emergence of social movements, military dictatorships etc.) Classification 1/2 lHelps categorize (classify) cases into several groups on the basis of a few similar features lSimple dichotomy (democracy vs. non-democracy) as well as more complex schemes (1 or 2 parties, several parties) lClassifications simplify the real world and outline differences among classes as a basis for comparative inquiry Classification 2/2 lInductive and deductive reasoning: Blondel vs Aristotle lBlondel: one, two, two and a half, multiparty with a dominant party, multiparty without a dominant party lAristotle: number of rulers and the character of their government lOne, several, many // good, bad lTypology: monarchy, aristocracy, politeia, tyranny, oligarchy, democracy l Hypotheses Testing lComparisons help to assess several competing explanations and to eliminate those that are not supported by the evidence: l1. Identify the key variables lSpecify the relations among them lWhen comparing empirical evidence, we generate hypotheses about the relations between variables that are subsequently tested on several/many cases Predictions 1/3 lA logical extension of testing lPredictions about development in the cases that were not included in the original set of cases lPredictions in comparative analysis are probabilistic, e.g.: lIncumbents are more likely to be re-elected than their challengers Predictions 2/3 lOR: countries that use the PR electoral systems are more likely to have more relevant political parties than countries with a single member plurality electoral system lWe can thus predict the effects of electoral system change from plurality to PR lHOWEVER: It does not mean we can predict the particular results in a specific country Predictions 3/3 lPrediction are less common in comparative politics than a few decades ago lA well-know “recent” prediction is Huntington’s assertion that conflicts are most likely to take place along civilizational “borders” lHuntington believed his prediction was more accurate than any other competing explanation Differences between social and natural sciences 1/2 lThe four goals of comparative politics (description, classification, testing of hypotheses and prediction) are also shared by natural sciences lNewton’s gravitation theory was originally formulated on the basis of empirical evidence that led to generalization and predictions lgravity (as well as other concepts) cannot be observed directly, we can only observe its consequences: it is an intellectual construct that was verified in repeated experiments; only after that a theory was formulated Differences between social and natural sciences 2/2 lExperiments are nearly impossible in comparative politics but are typical for most natural sciences lThe importance of “counterfactuals”, i.e. thought experiments in which analysts imagine the absence of particular variables in their cases li.e. they imagine an alternative course of events (one variable would be different) in a case under investigation lDemocratic transition in Spain in 1975: parliamentarism vs. presidentialism Comparison instead of experiments lWhen we emphasize the importance of an explanatory variable, we always implicitly work with counterfactuals lTo say that single member plurality electoral systems tends to produce bipartism involves considering a counterfactual situation in which a country would not have a two-party system without single member plurality electoral system lIn comparative analysis, we use a real world case(s) to replace counterfactuals: comparison substitutes experiments Question l l l lDo you know any political science laws? Comparative Politics is not strong in producing “laws” l(However, there are some exceptions): lDuverger’s law lMichels’ Iron law of oligarchy lDemocratic peace lToo few cases/too few observations lInstead of laws, CP produces understanding and explanation of phenomena about which we have “a lot” of observations and our level of certainty is considerably high How do we compare? lCase studies lSmall-N comparisons lLarge-N comparisons lDifferences rest in the level of abstraction of our conclusions lThe fewer cases we have, the less opportunity for generalizations Case studies 1/2 lWhat is comparative about single case studies? lWe can work with concepts that can be used in other cases (contexts) lWe can try to formulate conclusions about the more general aspects of our case lWe can supply a good description of the relevant context lWe can supply new classifications and generate hypotheses for subsequent comparative studies lWe can support/reject theories or explain deviant cases Case studies 2/2 lWhen analyzing one case (e.g. one country) we can increase the number of observations lCASE is not OBSERVATION lAnalyze several elections lAnalyze several regions lItaly and the civic culture lIndia and the role of protestant missionaries in democratic development Small-N Comparisons (2 - 20) lWe deliberately choose several cases from the entire population of cases lSearch for similarities and differences lContrasting similarities and differences can reveal possible explanations of our research puzzles Large-N Comparisons 1/2 lClosest to the logic of experimental methods of natural sciences lAdvantages: ability to statistically control and eliminate alternative explanations lCovers cases/countries across space and time lLaw-like generalizations Large-N Comparisons 2/2 lRisks and pitfalls : lValidity of measurement is questionable lNot suitable in analyzing processes where complex causal mechanisms are at play lNot suitable for analyzing phenomena whose meaning is strongly linked to local (i.e. unique) context Problems of comparison l1) Too few cases, too many variables l2) Questionable equivalence l3) Selection bias l4) Spuriousness l5) Ecological and individual fallacies l Too few cases, too many variables 1/4 lwhen there is more potential explanations than cases to test them lPossible solutions: l1) increase the number of cases or observations Too few cases, too many variables 2/4 lLijphart (1970) suggests: lgeographical or temporal strategy to increase the number of cases lTo reduce the number of variables by merging some of them lTo reduce the number of variables by focusing on the relevant variables (guidance offered by an existing theory) l Too few cases, too many variables 3/4 l2) use the most similar systems design (MSSD) lTo eliminate the variables that are the same across cases and to focus on those variables that are different and thus potentially cause the observed outcome lUnfortunately, when using the MSSD, we will never be able to eliminate many alternative explanations (variables) Too few cases, too many variables 4/4 l3) to minimize the number of relevant variables by employing the most different systems design (MDSD) lWe compare totally different cases with similar outcomes, focus is on the different variables across cases that potentially lead to the similar outcomes l l Equivalence lDifferent understanding of the key concepts may lead to different (non-comparable) ways of measurement lIt is important to specify what the equivalent concepts could be lConcepts must be modified to take into account cultural specificity of each case lBest if applied to cases that are well-known to the researchers Selection bias lComparison is a substitution for experiments, however, it is an imperfect substitution lExperiments select cases randomly, while in CP we choose among cases deliberately lThe most visible selection bias emerges when we use only those cases that support our argument l Selection bias lLess visible selection bias exists when we choose cases on the dependent variable: lE.g. when we only work with cases with a particular outcome: where a revolution did take place lIf there is no variation on the dependent variable, we may reach conclusions that overestimate the importance of some of our independent variables Spuriousness lExists when we omit the key variable that influences both our dependent and independent variable lThere is no perfect solution to the problem! The most similar systems design (MSSD) l l lWe identify the key characteristics that are different in otherwise similar cases; we thus expect that these different features lead to/explain the outcomes l CASE 1 CASE 2 VARIABLES a a b b c c X Non-X OUTCOMES Y Non-Y Variables Togo Ghana Similarities: Climate High Temperatures High Temperatures Per capita income Low Low Ethnicity Heterogeneous Heterogeneous Dominant Religion Christianity Christianity Other religions Islam, traditional tribal Islam, traditional tribal COLONIZING POWER France United Kingdom Outcome Regime Type Authoritarian Democratic The most different systems design lCases that are totally different, have only a few shared similarities lThey also share the same outcome l CASE 1 CASE 2 VARIABLES a d b f c m X X OUTCOMES Y Y France 1780-1790 China 1940-1945 Differences Geography Europe Asia Population < 30 mil. > 500 mil. Century 18. 20. Regime Monarchy One party state XXXXX X X Outcome Social Revolution yes yes