A Global Ranking of Political ScienceDepartmentsSimon HixLondon School of Economics

Rankings of academic institutions are key information tools for universities, funding agencies,students and faculty. The main method for ranking departments in political science, throughpeer evaluations, is subjective, biased towards established institutions, and costly in terms oftime and money. The alternative method, based on supposedly ‘objective’ measures of outputsin scientific journals, has thus far only been applied narrowly in political science, using pub-lications in a small number of US-based journals. An alternative method is proposed in thispaper – that of ranking departments based on the quantity and impact of their publicationsin the 63 main political science journals in a given five-year period. The result is a series ofglobal and easily updatable rankings that compare well with results produced by applying asimilar method in economics.

Rankings of academic institutions are key information tools for universities,public and private funding agencies, students and faculty. For example, toinvestigate whether and why European universities lag behind their competi-tors in the US, the European Economics Association commissioned research intothe ranking of economics departments on a global scale (see, especially, Coupé,2003).

A variety of different ranking methods have been used in the natural sciencesand have started to emerge in the social sciences, especially in economics (see, for example, Scott and Mitias, 1996; Dusansky and Vernon, 1998). Allmethods have disadvantages and trade-offs. Nevertheless, the best methodstend to have three elements: (1) they rank institutions on a global scale ratherthan in a single country; (2) they use ‘objective’ measures of research outputs,such as publications in journals, rather than subjective peer evaluations; and(3) they are cheap to update, for example by allowing for mechanized annualupdates.

However, no such global, objective or easily updated method exists in politicalscience. This research aims to fill this gap by proposing and implementing anew method for ranking departments in this field. To this end, in the nextsection I review the existing methods in our discipline. In the third section Ithen propose and justify an alternative method, based on research outputs inthe main political science journals in a particular five-year period. And in thefourth section I present the results of an analysis of the content of 63 journalsbetween 1993 and 2002.

POLITICAL STUDIES REVIEW: 2004 VOL 2, 293–313

© Political Studies Association, 2004. Published by Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UKand 350 Main Street, Malden, MA 02148, USA

294 SIMON HIX

Existing Rankings of Political Science DepartmentsAs in other disciplines, two main methods have been used to rank politicalscience departments: peer assessments, and content analysis of scientific jour-nals. However, both methods, as applied thus far, have their limitations.

Peer AssessmentsThe most widely used method for ranking political science departments is peerassessments – where senior academics are asked to evaluate the quality of otherdepartments. For example, this method is used by the US National ResearchCouncil and the U.S. News and World Report to rank doctoral programs in theUS (see PS: Political Science and Politics, 1996a, b), and in the Research Assess-ment Exercise in the UK for the allocation of central government researchfunding.

The problems with this method are well known. First, peer assessments are sub-jective, by definition. No ranking method is perfectly ‘objective’. For example,the content of scientific journals is determined by the subjective judegmentsof journal editors and article reviewers. However, journal editors and articlereviewers are experts on the subjects of the papers they publish or review. Also,peer evaluation in the journal publishing process is repeated thousands oftimes, which reduces bias. In contrast, rankings based on periodic peer assess-ments rely on a small sample of academics, who cannot possibly be experts inall areas of research produced by the institutions they rank. As a result, rank-ings based on peer assessments are less ‘objective’ than rankings based on the content analysis of journals (if a sufficiently large sample of journals isused).

The resulting biases of this subjectivity have been well documented. Becausethe sample of academics has only limited information about the output of allinstitutions, they are forced to base their judgements on other factors. Thisresults in a bias towards large established departments and against new andup-and-coming departments (Katz and Eagles, 1996). The overall reputation ofthe university has an effect on the respondents’ expected performance of apolitical science department – known as the ‘halo effect’ (Lowry and Silver,1996; Jackman and Siverson, 1996).

Second, the peer assessment method is highly costly and time-consuming. Thisis because of the need either to survey a large number of senior faculty (as inthe cases of the US National Research Council and the U.S. News and WorldReport) or to prepare and read the submissions of all the universities (as in thecase of the Research Assessment Exercise). Hence, rankings based on peerassessments are invariably updated only every five years (in the case of theResearch Assessment Exercise and the U.S. News and World Report) or evenlonger (in the case of the US National Research Council).

Third, all existing peer assessment rankings are nationally specific. If similarmethods were used in different countries, a global ranking based on peerassessments could be produced. However, different methods tend to be used

POLITICAL SCIENCE DEPARTMENTS 295

in different countries. For example, in the U.S. News and World Report, depart-ments are scored out of five in a number of criteria, and then averaged (withthe top departments scoring between 4.7 and 4.9). In the Research AssessmentExercise, departments are scored in bands from 5* to 2 (with the top depart-ments scoring 5*). As a result, relative performance on a global scale is diffi-cult to establish.

Content Analysis of Scientific JournalsIn a conscious effort to improve on these peer assessment results, political scientists have begun to develop more objective methods of ranking politicalscience departments. Following the practice in other disciplines, the mostpopular method is the analysis of the content of the leading political sciencejournals (see, for example, Welch and Hibbing, 1983). The assumption behindthis method is that, in contemporary political science, the main output forresearch results is publication in a professional journal.

Publication of books is more common in political science than in economics.Hence, ideally, a ranking based on book publications as well as journal articleswould produce the best results (see Rice et al., 2002). However, analysing thecontent of books and the number of citations to particular book series is costly,since there is not a single database of book publications and book citations likethe Social Science Citation Index (SSCI) for journal publications. One solutioncould be to use peer assessments to rank book publishers (see, for example,Goodson et al., 1999). But this would go against the aim of creating a rankingusing only non-subjective assessments.

Also, a ranking based on book publications may have little added value,because there is probably a high correlation between the outputs of depart-ments in books and journals. At the individual level, some political scientistsprefer to write books while others prefer to write articles. However, top depart-ments probably produce a lot of books as well as a lot of articles, whereas less-good departments probably produce less books and articles. Hence, therankings resulting from these two measures should be similar, at least for thelarger departments.

Consequently, most researchers have analysed journal publications rather thanbook publications. But there are problems with the way this method has beenapplied thus far. First, existing studies have counted only a small number ofjournals. Miller et al. (1996) only looked at the content of the American Polit-ical Science Review (APSR); Teske (1996) looked at APSR, the Journal of Politics(JOP), and the American Journal of Political Science (AJPS) (see Garand andGraddy, 1999); McCormick and Rice (2001) counted articles in APSR, AJPS, JOP,the Western Political Quarterly (WPQ) and Polity; and Ballard and Mitchell(1998) looked at APSR, JOP, AJPS, World Politics, Comparative Politics, theBritish Journal of Political Science (BJPolS), WPQ, Polity and Political ScienceQuarterly. But even nine journals is a rather limited sample of the main jour-nals in political science. The SSCI contains 143 journals in the fields of politicalscience, international relations and public administration. With modern

296 SIMON HIX

computer technology, there is no reason why all, or at least a larger and morerepresentative sample, of these journals cannot be counted.

Second, and partly due to the limited sample of journals coded, the existingrankings based on the content of journals have tended to be biased towardsinstitutions in the US. For example, although APSR is widely respected as the top political science journal, it is nonetheless the ‘in-house’ journal of theAmerican Political Science Association. Not surprisingly, only 7 per cent of articles in APSR between 1996 and 1999 were by scholars based outside the US(Schmitter, 2002). This small number may be a fair reflection of the quality orquantity of research outside the US. However, studying the content of onejournal inevitably risks a high degree of error.

Even in Ballard and Mitchell’s (1998) study of nine journals, only one journalbased outside the US was included (BJPolS). Not surprisingly, not a single non-American department appeared in their top 50. It might be the case that nodepartment outside the US is good enough to be in the top 50. But one wouldbe more inclined to accept this conclusion if this result was based on thecontent of a larger sample of journals and more journals based outside the US.

An Alternative MethodBuilding on existing bibliometric research, the method proposed here ranksacademic institutions on the basis of the quantity and impact of articles pub-lished in the main journals in political science in a given period. To establishthis ranking, decisions were made about the following: (1) what time periodto use; (2) what to count as the ‘main’ political science journals; (3) what tocount as a publication in a journal; (4) how to measure the impact (‘quality’)of a journal; (5) how to construct a ranking from this information; and (6) howto create a ‘quasi-error’ term.

Time PeriodCreating annual rankings would have the advantage of being able to trackshort-term changes in the performance of departments. However, looking atthe content of only one year of each journal would be a small sample size, andso would produce a high degree of measurement error. Conversely, a newranking every ten years would be more accurate, but would not measure moresubtle changes. As a result, counting articles on a rolling five-year basis wouldprobably be the most effective method. This allows for a larger sample in eachjournal and allows a new ranking to be produced every year – in other words,1993–1997, 1994–1998, and so on. This is also a similar time period to otherrankings, such as the U.S. News and World Report and the Research AssessmentExercise.

The Main Political Science JournalsFour steps were taken to define the ‘main’ political science journals. Step oneinvolved the full list of journals in the field in the SSCI, which contained 143

POLITICAL SCIENCE DEPARTMENTS 297

journals in political science, international relations and public administrationin 2002.

Step two involved adding some missing journals to this list. The SSCI does notinclude all major political science journals. The Institute for Scientific Informa-tion (ISI) follows a careful procedure for selecting which journals to include inthe SSCI.1 However, several prominent international political science journalsare not listed in the SSCI. For example, whereas the main journals of the British,German and Scandinavian political science associations are in the SSCI, the mainjournals of the French, Italian and Dutch associations are not. Also, severalmajor sub-field journals were not included before 2002, such as the Journal of Public Policy, European Union Politics, Nations and Nationalism, History ofPolitical Thought, the Journal of Legislative Studies, and Democratization.Adding these journals to the SSCI list makes a total of 152 journals.2

Step three involved setting and applying two simple criteria for divining the‘main’ political science journals from this list of 152. First, many journals are infact journals in other fields of social science, such as law, economics, geogra-phy, sociology, history, psychology, social policy, communications, philosophy,or management. For the sake of simplicity, a political science journal can bedefined as a journal that is (a) edited by a political scientist and (b) has a major-ity of political scientists on its editorial board (in departments or institutes ofpolitical science, politics, government, international relations, public adminis-tration or public policy).

Second, many journals in the SSCI list have a marginal impact on the disciplineof political science. For example, almost one third of the journals had less than100 citations to articles published in any issue of these journals by the articlespublished in the over 8,000 other journals in the SSCI in 2002. Removing thesenon-political-science journals and journals that have only a marginal impact left60 journals.

Step four, however, involved adding back three journals that have a low impactbut are the national political science association journals of three countries: theAustralian Journal of Political Science, Politische Vierteljahresschrift (publishedby the German political science association) and Scandinavian Political Studies.It is reasonable to include these journals despite their low impact, since the ISIhad already decided that these are important journals. In other words, nationalpolitical science association journals are included in the analysis either if theyare in the SSCI or if they are not in the SSCI list but receive more than 100 cita-tions per year.

This left 63 journals for the analysis, which are listed in Table 1. For the 54 journals in the SSCI, data on the content of these journals between 1993 and2002 was purchased from the ISI. For the nine journals not in the SSCI and for the issues of the SSCI journals that are not in the database (for example,where a journal existed for a number of years prior to being included in theSSCI), the content was coded by hand. In total, the content of 495 annualvolumes was collected electronically and the content of 117 volumes was collected by hand.

298 SIMON HIX

Table 1: Journals included in the Analysis

Volumes codedJournal by hand Volumes in SSCI Impact Score

American Political Science Review 1993–2002 8.82American Journal of Political Science 1993–2002 6.91International Organization 1993–2002 5.21Foreign Affairs 1993–2002 4.72Journal of Politics 1993–2002 4.13International Security 1993–2002 3.93Journal of Conflict Resolution 1993–2002 3.72World Politics 1993–2002 3.66Journal of European Public Policy 1994–1996 1997–2002 3.34International Studies Quarterly 1993–2002 3.28Public Choice 1993–2002 3.22Journal of Common Market Studies 1993–2002 2.94British Journal of Political Science 1993–2002 2.84Journal of Peace Research 1993–2002 2.82Journal of Law Economics and Organization 1993–2002 2.80Comparative Political Studies 1993–2002 2.79Journal of Democracy 1993–1994 1995–2002 2.75Europe-Asia Studies 1993–2002 2.64European Union Politics 2000–2002 2.59Political Research Quarterly 1993–2002 2.58West European Politics 1993–1999 2000–2002 2.58Political Studies 1993–2002 2.56PS: Political Science and Politics 1993–2002 2.53European Journal of Political Research 1993–2002 2.46Public Administration 1993–2002 2.44Party Politics 1995–2002 2.38European Journal of International Relations 1995–1996 1997–2002 2.30Comparative Politics 1993–2002 2.27Electoral Studies 1993–2002 2.26Post-Soviet Affairs 1993–2002 2.18Review of International Studies 1993–1994 1995–2002 2.18Security Studies 1993–1995 1996–2002 2.17Politics and Society 1993–2002 2.14Governance 1993–1994 1995–2002 2.09Legislative Studies Quarterly 1993–2002 2.08Political Communication 1993 1994–2002 2.08Political Behavior 1993–1996 1997–2002 2.06International Interactions 1993–2002 2.00Journal of Theoretical Politics 1993–2002 2.00American Politics Quarterly 2001–2002 1993–2000 1.99Millennium-Journal of International Studies 1993–2002 1.96Publius-The Journal of Federalism 1993–2002 1.93Political Theory 1993–2002 1.91Journal of Public Policy 1993–2002 1.85International Affairs 1993–2002 1.82Philosophy and Public Affairs 1993–2001 1.81Political Science Quarterly 1993–2002 1.75International Political Science Review 1993–2002 1.74Democratization 1994–2002 1.70Nations and Nationalism 1995–2002 1.70Australian Journal of Political Science 1993–2002 1.69Journal of Legislative Studies 1995–2003 1.69Canadian Journal of Political Science 1993–2002 1.64Political Quarterly 1993–2002 1.64East European Politics and Societies 1993–2002 1.63Scandinavian Political Studies 1993 1994–2002 1.60Polity 1993–2002 1.53Politische Vierteljahresschrift 1993–2002 1.52Revue française de science politique 1993–2002 1.49Cooperation and Conflict 1993–2002 1.45History of Political Thought 1993–2002 1.40Acta Politica 1993–2002 1.38Rivista Italiana di Scienza Politica 1993–2002 1.33

Note: All issues of journals between 1993 and 2002 were coded. So, if a year is missing in the table, either a journal had notbeen published yet, or a journal was not published in that particular year.

POLITICAL SCIENCE DEPARTMENTS 299

Counting ArticlesSeveral different types of articles are published in these journals. All main articles and research notes were included, and all editorial comments, bookreviews and short notes were excluded. I decided to treat each article orresearch note in the same journal as equivalent regardless of its length, becauseI see no justification for assuming that a shorter article is less important thana longer article in the same journal. There were just over 18,000 such publica-tions in the 63 journals between 1993 and 2002.

Each article was then counted as follows: an article by a single author with asingle institutional affiliation, or by two or more authors from a single insti-tution, scored 1.0 for the institution; an article by two authors from two dif-ferent institutions, or by a single author with two institutional affiliations,counted as 0.5 for each institution; an article by three authors or three institutions counted as 0.333 for each institution; and so on. This method is notideal, as it undervalues collaborative research. However, the alternative isworse: in other words, counting multi-authored articles as having more valuethan single authored articles. Observations where an institutional affiliationcould not be derived from the editorial information were excluded. This left atotal of approximately 24,000 single observations for analysis.

Measuring ImpactSome articles are more significant than others. I assume that an article is as sig-nificant as the overall impact of the journal in which it is published. Two dif-ferent articles in the same journal may have vastly different impacts on thefield. Conversely, some articles may be cited because of the fame of the author.Hence, if one assumes that a journal applies a common standard for acceptanceof a paper for publication, it is reasonable to assume that all articles in a par-ticular journal are of approximately equal quality.

A common measure of the relative ‘impact’ of a journal is the average numberof citations to a journal in a given period. For example, the ISI calculates an‘impact factor’, which is the total number of citations by all other articles inthe ISI database to all articles in a journal in the previous two years, divided bythe number of articles in the journal in the previous two years.

Using a similar method, we could calculate the average annual citations to allarticles in a journal in the ten-year period. However, because it takes time foran article to be noticed, recently published articles are less cited than articlespublished several years ago. Hence, simply counting the average annual cita-tions would create a bias against recently established journals that have nothad long enough to build up their stock of citations.

However, if we assume that the evolution in the number of citations followsthe same functional form, a fixed-effect regression model of annual citationscan be estimated. This would produce a constant for each journal that is ameasure of its relative importance. But the common trend in citations for aparticular journal is non-linear: there tends to be a plateau in the number of

300 SIMON HIX

citations for several years followed by a decline in the number of citations inthe most recent years. Hence, the appropriate common functional form is anegative quadratic equation:

where j (journal) = 1, ... , 63; y (year) = 1, ... , 10; and JOURNAL is a vector of63 binomial variables, one for each journal.

Estimating this model using ordinary least-squares regression produces the fol-lowing results (t-statistics in parentheses): b2 = 17.944 (2.65), b3 = 0.709 (0.590),and 63 constants, ranging from a high of 882.49 citations per year for APSR toa low of 133.49 for the Rivista Italiana di Scienza Politica (RISP).3 An ‘impactscore’ for each journal was than produced from the constants by dividing eachjournal’s constant by 100 (see Table 1). In other words, a paper in APSR is aboutas important as seven papers in RISP.

The journal ‘impact scores’ calculated by this method are highly correlated(0.757) with the SSCI ‘impact scores’ in 2002 for the 54 journals in both the SSCIand my list.4 The high correlation between my index and the SSCI impact indexis not surprising, as both methods are based on the number of citations to arti-cles in journals in a given period. However, there are two advantages of myimpact scores over the SSCI scores. First, my method allows for impact scores tobe calculated for journals that are not included in the SSCI. Second, by assum-ing a common trend in the number of citations over time, my method correctsfor an inherent bias against new journals in the SSCI method.

Finally, it should be noted that because journals that were not main-stream political science journals were removed from the SSCI list, the rankingdoes not include outputs published elsewhere in the social sciences. This mayproduce a bias against departments that try to contribute to general socialscience rather than the narrow discipline of political science. Nevertheless, themethod used to calculate an impact score for each journal reintroduces ameasure of the breadth of a contribution, as the impact score for a journal iscalculated from all citations to articles in the journal from any journal in theSSCI.

Construction of the RankingSome people may be interested in the total output of a department, whereasothers may be interested in the average quality of these outputs or the averageproductivity of a department. For example, the central administration of a uni-versity may wish to know the relative per capita productivity of a department,whereas a prospective graduate student may seek a large department with alot of research-active staff.

So, five separate rankings were created from the data:

• Rank 1 (Quantity) – the total number of articles in the journals by scholarsfrom a particular institution in a five-year period.

ANNUAL CITES JOURNAL YEAR YEARjy y jy jy jy_ = - - +b b b e1 2 32

POLITICAL SCIENCE DEPARTMENTS 301

• Rank 2 (Impact) – the total number of articles in the journals by scholarsfrom a particular institution in a five-year period multiplied by the ‘impactscore’ of the journal in which the article was published.

• Rank 3 (Quantity/Faculty Size) – the total number of articles in the journalsby scholars from a particular institution in a five-year period (as used toproduce Rank 1) divided by the faculty size of the political science depart-ment of that institution.

• Rank 4 (Impact/Faculty Size) – the total number of articles in the journals byscholars from a particular institution in a five-year period multiplied by the‘impact score’ of the journal in which the article was published (as used toproduce Rank 2) divided by the faculty size of the political science depart-ment of that institution.

• Overall Rank – the average position of the institution in the other fourranks.

The overall ranking is consequently an unweighted sum of the other four rank-ings (compare with Coupé, 2003). Invariably, people will have different opin-ions about the relative importance of Ranks 1, 2, 3 and 4. Hence, the positionsof the institutions in each of the four individual ranks are also reported so thatan interested person can calculate a different overall rank using a different setof weighting of the other ranks.

The information on the size of a department was gathered from two sources.First, for the British universities, the data is the number of full-time staff submitted in the Politics and International Relations section of the 2001Research Assessment Exercise. Second, for all other universities (including those British universities who did not make a submission for this section in2001), we counted the number of full-time staff with a rank of full, associateor assistant professor (or equivalent) listed on a department’s website inNovember to December 2003.5 In other words, this includes only the numberof staff in a political science department plus related institutes, or the numberof political scientists in a department or faculty of social science. For example,according to the Harvard University website, the number of permanent facultyin the Department of Government plus the number of permanent faculty inthe Kennedy School of Government who describe themselves as ‘political scientists’ is 87.

Several things are worth noting here. First, this method of counting the size ofa department assumes that the number of political scientists in a particularinstitution remains constant, which clearly is not the case. Second, this methodonly counts academics in political science departments, whereas the methodfor counting research output counts anyone publishing in one of the journalsfrom a particular institution, regardless of where they are based in an institu-tion. For example, if someone from a business school, an economics depart-ment or a law department publishes in one of the journals, this person iscounted as a political scientist, but is not included as a member of the politi-cal science faculty in their institution. However, although there may be peopleoutside a political science department who do political science research, the

302 SIMON HIX

size of the political science department is probably a reasonable proxy for thesize of the overall political science community in an institution.

A Quasi-ErrorFinally, a ‘quasi-error’ in the overall rank of each institution was calculated.There are two sources of measurement error in this analysis. First, in countingthe number of articles published by an institution, an article may have beenmissed. For example, in the computer data, an article may have been misla-belled as a minor note rather than a proper article, the institutional affiliationof an author may have been entered incorrectly (although each entry in thedata was carefully checked), or an author who was listed as having no institu-tional affiliation may have been based in a particular academic institution.Second, it is extremely difficult to accurately measure the faculty size of adepartment. For example, different academic systems have different ways ofdescribing their faculty (for example, many German universities only list theirfull professors). Also, information on the departments’ websites is invariablyout of date or inaccurate.

Using these two sources of error, a ‘quasi-error’ was worked out by calculatingwhere an institution would have been placed in the overall ranking if the institution had produced one more/less article in a journal with a mean impactscore (2.52) and if the department was 5 per cent smaller/larger than it hadbeen measured.

For example, in 1998–2002, the London School of Economics, with a faculty sizeof 76, produced 143.31 articles with an impact of 338.87. This placed it 2nd inRank 1 (Quantity), 4th in Rank 2 (Impact), 31st in Rank 3 (Quantity/Faculty Size),57th in Rank 4 (Impact/Faculty Size), and 15th overall. If it had produced onemore article in a mean-impact score journal and had 5 per cent less staff, itsposition would not have changed in Ranks 1 and 2, but would have risen to24th in Rank 3, 51st in Rank 4, and 12th overall. Conversely, if it had one lessarticle and 5 per cent more staff, it would have been 18th overall. So, the quasi-error at 15th was 12–18 (or plus/minus three places).

ResultsTable 2 lists the ‘Global Top 200’ political science institutions on the basis oftheir output in the main political science journals in the five years between1998 and 2002.6 Anyone with a cursory knowledge of the discipline would recognize most of the names on the list.

One way of assessing the validity of the method is to compare the results tothose using a similar method in economics (Coupé, 2003). In the political sciencerankings for 1998–2002, there was one department outside the US in the top10, five in the top 20, fourteen in the top 50, thirty-six in the top 100, and 103in the top 200. In the comparable ranking in economics, there were no depart-ments outside the US in the top 10, one in the top 20, ten in the top 50, thirty-four in the top 100, and eighty-eight in the top 200.

POLITICAL SCIENCE DEPARTMENTS 303

One obvious criticism is that these rankings are biased towards English-speaking countries, since nine of the top 10, nineteen of the top 20, forty-eightof the top 50, ninety-one of the top 100, and 163 of the top 200 are from theUS, the UK, Australia, Canada or Ireland. However, the equivalent rankings in economics are equally as dominated by Anglo-Saxon institutions: with all of the top 10, all of the top 20, forty-seven of the top 50, eighty-seven of thetop 90, and 155 of the top 200 coming from these same five English-speakingcountries. In other words, the dominance of institutions from these countriesmay simply be a reflection of the dominant position of English as the globallanguage in the social sciences.

So, if one assumes that the global spread of good and bad departments isbroadly similar in the disciplines of political science and economics, then themethod outlined and applied here is as good as the most similar ranking ineconomics.

Table 3 shows the rank-order correlations between the five rankings, using theresults for the top 200 in the 1998–2002 period. As would be expected giventhe calculation method, there are high correlations between Ranks 1 and 3 andbetween Ranks 2 and 4. However, the correlations suggest that each rankingmethod measures something different.

Finally, Table 4 shows the ‘rolling’ rankings for the six five-year periods between1993 and 2002. One of the striking things here is the stability of the top three,with Stanford, Harvard and Columbia swapping places at the top of the list,and none of these institutions dropping below third. Only two other institu-tions remained in the top 10 throughout this period (Indiana and the Univer-sity of California, San Diego), and thirty-six institutions remained in the top 50throughout the period. The biggest climbers, who climbed more than thirtyplaces between 1993–1997 and 1998–2002, were the State University of NewYork, Binghamton (from 117th to 19th), Aberystwyth (136th to 39th), PennState (101st to 33rd), Geneva (104th to 43rd), Trinity College Dublin (96th to40th), University College London (83rd to 46th), Illinois-Urbana Champaign(80th to 44th) and Georgetown (50th to 16th). Nevertheless, almost fifty per cent (twenty-four) of the institutions in the top 50 in 1998–2002 rose orfell less than ten places from their positions in 1993–1997.

ConclusionA reasonably objective, easily updated and global ranking of departments inpolitical science can be produced by borrowing a method used in other disci-plines – that of measuring the research output of institutions in the main jour-nals in the field in a given period, and controlling for the number of full-timestaff in a department. This method produces a series of rankings that seemintuitively correct and compare well with the equivalent rankings in econom-ics, in terms of the regional and national balance of institutions in the top 10,20, 50, 100 and 200.

One possible problem with these rankings is the apparent English-languagebias in the results, which undermines the aspiration to be truly ‘global’.

304 SIMON HIX

Tab

le 2

:Th

e G

lob

al T

op

200

Po

litic

al S

cien

ce D

epar

tmen

ts,

1998

–200

2

Qu

anti

ty (

1)Im

pac

t (2

)Q

uan

tity

/Siz

e (3

)Im

pac

t/Si

ze (

4)A

vera

ge

Qu

asi-

Erro

rO

vera

llFa

cult

yN

o.

of

Art

icle

s*A

rtic

les/

Imp

act/

of

Ran

ksR

ank

Un

iver

sity

Co

un

try

Size

Art

icle

sR

ank

Imp

act

Ran

kFa

c. S

ize

Ran

kFa

c. S

ize

Ran

k1

to 4

Bes

tW

ors

t

1C

olu

mb

iaU

SA45

120.

694

420.

102

2.68

27

9.33

62

3.75

11

2H

arva

rdU

SA87

204.

371

700.

931

2.34

915

8.05

76

5.75

23

3St

anfo

rdU

SA38

90.9

46

342.

883

2.39

314

9.02

34

6.75

23

4O

hio

Sta

teU

SA43

84.9

510

327.

875

1.97

624

7.62

59

12.0

04

55

EUI

Ital

y17

62.0

817

157.

9730

3.65

22

9.29

23

13.0

05

5

6U

C,

San

Die

go

USA

3774

.22

1225

4.53

112.

006

226.

879

1615

.25

69

7U

C,

Irvi

ne

USA

3271

.34

1321

4.71

172.

229

186.

710

1916

.75

68

8In

dia

na

USA

3367

.49

1622

4.68

162.

045

216.

808

1717

.50

712

9Pr

ince

ton

USA

4987

.50

731

9.40

71.

786

396.

518

2218

.75

612

10Y

ale

USA

5291

.90

532

3.59

61.

767

416.

223

2519

.25

714

11U

C,

Ber

kele

yU

SA45

86.6

28

268.

3410

1.92

529

5.96

331

19.5

06

1212

Mic

hig

an S

tate

USA

2957

.00

2420

4.69

191.

966

257.

058

1320

.25

815

13C

hic

ago

USA

3970

.98

1423

8.39

131.

820

356.

113

2822

.50

918

14U

C,

Los

An

gel

esU

SA51

85.5

69

317.

138

1.67

848

6.21

826

22.7

510

1815

LSE

UK

7614

3.31

233

8.87

41.

886

314.

459

5723

.50

1218

16=

Geo

rget

ow

nU

SA43

77.9

111

233.

4214

1.81

236

5.42

840

25.2

513

1816

=Es

sex

UK

2355

.83

2514

0.44

342.

427

136.

106

2925

.25

1318

18M

ITU

SA30

53.9

128

201.

4220

1.79

738

6.71

418

26.0

013

2119

=A

NU

Au

stra

lia25

61.5

318

127.

5143

2.46

110

5.10

045

29.0

019

2119

=SU

NY,

Bim

gh

amto

nU

SA16

42.0

050

120.

8648

2.62

58

7.55

410

29.0

019

2119

=O

xfo

rdU

K70

122.

083

302.

999

1.74

444

4.32

860

29.0

016

21

22B

irm

ing

ham

UK

2049

.08

3311

5.82

522.

454

115.

791

3532

.75

1927

23C

amb

rid

ge

UK

1849

.25

3210

6.41

622.

736

55.

912

3333

.00

2227

24Fl

ori

da

Stat

eU

SA25

43.5

345

167.

6926

1.74

145

6.70

820

34.0

019

2925

=Sh

effi

eld

UK

2250

.41

3111

3.01

532.

291

165.

137

4335

.75

2429

25=

Was

hin

gto

nU

SA23

39.9

154

161.

1029

1.73

546

7.00

414

35.7

522

30

27M

ich

igan

USA

4867

.69

1525

0.72

121.

410

775.

223

4136

.25

2232

28=

Joh

ns

Ho

pki

ns

USA

2143

.03

4712

2.48

472.

049

205.

832

3437

.00

2432

28=

Texa

s A

&M

USA

4158

.75

2022

5.92

151.

433

745.

510

3937

.00

2233

30Em

ory

USA

3248

.83

3518

1.00

231.

526

615.

656

3739

.00

2436

31C

olo

rad

oU

SA22

42.4

149

123.

3945

1.92

828

5.60

938

40.0

025

3432

Am

eric

anU

SA20

36.9

561

126.

9644

1.84

834

6.34

823

40.5

025

3833

Pen

nsy

lvan

iaU

SA25

40.0

653

150.

6232

1.60

253

6.02

530

42.0

030

3934

Bri

sto

lU

K14

38.3

159

83.0

478

2.73

65

5.93

132

43.5

031

3835

UN

C C

hap

el H

illU

SA37

53.4

529

177.

5124

1.44

572

4.79

850

43.7

530

41

POLITICAL SCIENCE DEPARTMENTS 305

36G

eorg

e W

ash

ing

ton

USA

4459

.18

1920

5.19

181.

345

874.

663

5244

.00

3143

37=

Car

dif

fU

K10

30.3

375

74.5

290

3.03

34

7.45

211

45.0

033

3937

=U

W M

adis

on

USA

3957

.94

2216

7.32

271.

486

694.

290

6245

.00

3142

39A

ber

ystw

yth

UK

2548

.50

3810

6.76

601.

940

264.

270

6547

.25

3344

40=

TCD

Irel

and

927

.50

8871

.42

933.

056

37.

936

747

.75

3943

40=

Van

der

bilt

USA

1528

.83

8110

3.23

651.

922

306.

882

1547

.75

3446

42C

orn

ell

USA

3148

.08

3913

3.22

401.

551

594.

297

6149

.75

3648

43G

enev

aSw

itze

rlan

d12

30.1

776

74.5

889

2.51

49

6.21

527

50.2

539

4844

Illin

ois

, U

rban

a-C

ham

pai

gn

USA

4052

.28

3018

2.68

221.

307

974.

567

5551

.00

3948

45R

ice

USA

1528

.16

8799

.00

701.

877

326.

600

2152

.50

3951

46U

CL

UK

525

.67

9651

.75

117

5.13

41

10.3

501

53.7

546

5047

=SU

NY,

Sto

ny

Bro

ok

USA

1524

.33

102

109.

5255

1.62

251

7.30

112

55.0

044

5147

=U

C,

Dav

isU

SA25

36.3

063

127.

6942

1.45

271

5.10

844

55.0

042

5149

=A

rizo

na

USA

2235

.25

6710

8.51

571.

602

534.

932

4856

.25

4552

49=

Vir

gin

iaU

SA35

44.3

744

164.

1928

1.26

810

24.

691

5156

.25

4451

51D

uke

USA

4455

.73

2617

0.44

251.

267

103

3.87

475

57.2

545

5152

Osl

oN

orw

ay33

44.7

542

122.

8446

1.35

686

3.72

277

62.7

552

6753

Cla

rem

on

t G

rad

uat

eU

SA8

19.4

512

863

.30

105

2.43

112

7.91

38

63.2

552

5754

Pitt

sbu

rgh

USA

2941

.33

5111

2.75

541.

425

763.

888

7363

.50

5262

55Le

iden

Net

her

lan

ds

2238

.81

5782

.21

801.

764

423.

737

7663

.75

5263

56Io

wa

USA

2837

.33

6011

9.75

491.

333

904.

277

6465

.75

5263

57N

ew M

exic

oU

SA16

23.6

710

410

0.74

681.

479

706.

296

2466

.50

5268

58N

ew Y

ork

USA

3847

.58

4013

5.96

381.

252

107

3.57

888

68.2

552

6859

Min

nes

ota

USA

5557

.79

2319

5.42

211.

051

138

3.55

392

68.5

052

6860

Geo

rge

Mas

on

USA

3542

.50

4813

0.13

411.

214

109

3.71

878

69.0

056

70

61H

ebre

w,

Jeru

sale

mIs

rael

2943

.33

4610

0.61

691.

494

673.

469

9669

.50

5270

62A

rizo

na

Stat

eU

SA24

32.7

569

102.

8566

1.36

584

4.28

563

70.5

056

7363

Hu

llU

K21

35.7

565

75.7

687

1.70

247

3.60

886

71.2

555

7364

Mar

ylan

dU

SA45

48.9

734

156.

6731

1.08

813

03.

482

9572

.50

5573

65G

eorg

iaU

SA19

29.0

080

79.9

083

1.52

661

4.20

567

72.7

556

74

66=

SUN

Y, B

uff

alo

USA

1526

.33

9465

.79

971.

755

434.

386

5873

.00

5874

66=

Ho

ust

on

USA

2732

.08

7111

7.25

501.

188

112

4.34

359

73.0

058

7468

No

rth

wes

tern

USA

3132

.56

7014

4.52

331.

050

139

4.66

253

73.7

558

7369

Pen

nsy

lvan

iaU

SA29

38.9

256

103.

4464

1.34

288

3.56

790

74.5

057

7570

Cal

Tec

hU

SA9

14.7

516

179

.38

841.

639

498.

820

574

.75

5875

71=

Sou

ther

n C

alif

orn

iaU

SA18

25.3

399

82.1

781

1.40

778

4.56

556

78.5

062

8071

=W

arw

ick

UK

2738

.50

5887

.31

741.

426

753.

234

107

78.5

063

8073

Tel

Avi

vIs

rael

2131

.33

7275

.39

881.

492

683.

590

8778

.75

6280

74M

ann

hei

mG

erm

any

1828

.33

8565

.71

981.

574

563.

651

8480

.75

6883

75St

rath

clyd

eU

K18

28.6

682

64.3

310

01.

592

553.

574

8981

.50

6983

306 SIMON HIX

Tab

le 2

:Th

e G

lob

al T

op

200

Po

litic

al S

cien

ce D

epar

tmen

ts,

1998

–200

2: C

on

tin

ued

Qu

anti

ty (

1)Im

pac

t (2

)Q

uan

tity

/Siz

e (3

)Im

pac

t/Si

ze (

4)A

vera

ge

Qu

asi-

Erro

rO

vera

llFa

cult

yN

o.

of

Art

icle

s*A

rtic

les/

Imp

act/

of

Ran

ksR

ank

Un

iver

sity

Co

un

try

Size

Art

icle

sR

ank

Imp

act

Ran

kFa

c. S

ize

Ran

kFa

c. S

ize

Ran

k1

to 4

Bes

tW

ors

t

76M

isso

uri

USA

2127

.17

9086

.09

761.

294

994.

100

7083

.75

6881

77W

ash

ing

ton

USA

4546

.98

4113

7.04

361.

044

140

3.04

511

984

.00

7183

78A

arh

us

Den

mar

k46

54.1

727

116.

3751

1.17

811

42.

530

147

84.7

574

8379

=N

ort

h T

exas

USA

2930

.95

7410

7.74

581.

067

133

3.71

579

86.0

074

8679

=Su

ssex

UK

2735

.67

6686

.52

751.

321

933.

204

110

86.0

071

84

81U

W,

Milw

auke

eU

SA20

25.3

399

82.2

279

1.26

710

34.

111

6987

.50

7185

82A

ber

dee

nU

K17

26.4

293

58.7

211

01.

554

583.

454

9789

.50

7488

83N

ewca

stle

-Up

on

-Tyn

eU

K14

22.8

310

851

.42

119

1.63

150

3.67

382

89.7

576

9284

UC

San

ta B

arb

ara

USA

2428

.37

8484

.83

771.

182

113

3.53

593

91.7

576

8885

Gla

sgo

wU

K14

21.2

511

451

.62

118

1.51

863

3.68

781

94.0

081

100

86Le

ices

ter

UK

1321

.00

119

47.5

212

71.

615

523.

655

8395

.25

8110

287

Man

ches

ter

UK

3136

.08

6487

.43

731.

164

118

2.82

013

196

.50

8495

88R

och

este

rU

SA29

28.6

583

101.

3467

0.98

815

03.

494

9498

.50

8296

89Lo

uis

ian

a St

ate

USA

2325

.50

9777

.40

851.

109

122

3.36

599

100.

7585

103

90=

Bir

kbec

k, L

on

do

nU

K13

20.2

512

346

.20

134

1.55

857

3.55

491

101.

2584

106

90=

Ru

tger

sU

SA53

44.5

343

136.

3037

0.84

018

02.

572

145

101.

2586

100

92Sy

racu

seU

SA40

39.0

355

106.

6261

0.97

615

22.

666

139

101.

7586

102

93To

ron

toC

anad

a66

58.0

021

140.

2535

0.87

916

92.

125

183

102.

0086

102

94K

ansa

sU

SA25

27.1

591

80.2

382

1.08

613

13.

209

109

103.

2585

104

95U

C,

Riv

ersi

de

USA

1114

.62

163

56.0

611

41.

329

925.

096

4610

3.75

8510

5

96B

rad

ford

UK

1723

.17

106

52.8

111

61.

363

853.

106

116

105.

7587

107

97H

um

bo

ldt

Ger

man

y17

22.2

511

156

.11

113

1.30

996

3.30

110

410

6.00

8610

698

Wes

tern

Au

stra

liaA

ust

ralia

815

.50

152

31.8

817

41.

938

273.

985

7210

6.25

8710

999

=Ed

inb

urg

hU

K16

22.5

011

049

.86

124

1.40

679

3.11

611

510

7.00

8610

799

=Le

eds

UK

2831

.00

7374

.40

911.

107

124

2.65

714

010

7.00

8910

7

101

Du

rham

UK

814

.83

158

32.6

816

71.

854

334.

085

7110

7.25

8710

910

2A

lab

ama

USA

1317

.17

142

50.4

012

31.

321

933.

877

7410

8.00

8710

710

3Q

MU

LU

K14

21.0

011

943

.38

145

1.50

065

3.09

911

711

1.50

8810

910

4D

artm

ou

thU

SA21

21.8

311

270

.28

941.

040

141

3.34

710

111

2.00

9210

910

5So

uth

Car

olin

aU

SA36

30.1

776

103.

6163

0.83

818

22.

878

129

112.

5096

107

POLITICAL SCIENCE DEPARTMENTS 307

106

Mo

ntr

eal

Can

ada

2224

.90

101

63.4

610

31.

132

121

2.88

512

811

3.25

9510

910

7Tu

fts

USA

1819

.83

127

60.4

310

81.

102

125

3.35

710

011

5.00

9611

410

8M

ax P

lan

ckG

erm

any

3229

.83

7876

.63

860.

932

161

2.39

515

411

9.75

103

121

109

Texa

s, A

ust

inU

SA59

41.3

351

133.

3839

0.70

122

22.

261

168

120.

0010

311

711

0G

ron

ing

enN

eth

erla

nd

s15

18.2

513

546

.27

133

1.21

710

83.

085

118

123.

5010

312

6

111

Sou

tham

pto

nU

K19

21.0

811

550

.93

120

1.10

912

22.

681

138

123.

7510

712

711

2=G

eorg

ia S

tate

USA

2223

.17

106

57.1

711

11.

053

137

2.59

914

312

4.25

108

126

112=

Ken

tuck

yU

SA23

21.0

011

969

.08

950.

913

162

3.00

312

112

4.25

107

126

114

Live

rpo

ol

UK

1618

.75

130

47.3

112

91.

172

115

2.95

712

512

4.75

107

129

115

Bri

tish

Co

lum

bia

Can

ada

2927

.33

8966

.94

960.

942

159

2.30

816

212

6.50

108

131

116

Am

ster

dam

Net

her

lan

ds

6248

.66

3710

9.31

560.

785

193

1.76

322

212

7.00

108

126

117

Man

ches

ter

Met

rop

olit

anU

K8

12.3

318

329

.16

186

1.54

160

3.64

585

128.

5010

813

611

8N

otr

e D

ame

USA

4834

.83

6810

7.03

590.

726

214

2.23

017

412

8.75

108

129

119

Den

ver

USA

610

.83

199

25.1

821

21.

805

374.

197

6812

9.00

107

133

120

SUM

Y, A

lban

yU

SA25

23.8

310

361

.18

106

0.95

315

72.

447

152

129.

5011

013

3

121

Ken

tU

K16

18.5

013

445

.13

136

1.15

611

92.

821

130

129.

7510

813

312

2Ex

eter

UK

1619

.25

129

42.9

414

61.

203

110

2.68

413

713

0.50

108

133

123

Hel

sin

kiFi

nla

nd

1720

.25

123

43.5

414

41.

191

111

2.56

114

613

1.00

108

133

124=

Bro

wn

USA

2018

.67

132

58.8

910

90.

934

160

2.94

512

613

1.75

110

133

124=

Wes

t V

irg

inia

USA

1718

.67

132

47.0

613

11.

098

129

2.76

813

513

1.75

110

133

126

No

ttin

gh

amU

K21

21.0

811

550

.78

121

1.00

414

52.

418

153

133.

5011

013

412

7Li

verp

oo

l Jo

hn

Mo

ore

sU

K4

8.50

236

20.3

524

12.

125

195.

088

4713

5.75

108

149

128=

CU

NY

USA

5036

.33

6295

.90

710.

727

212

1.91

820

113

6.50

117

134

128=

Lafa

yett

eU

SA9

13.0

017

729

.04

188

1.44

473

3.22

710

813

6.50

108

143

130

Mu

rdo

chA

ust

ralia

712

.50

182

22.8

522

31.

786

393.

264

105

137.

2511

014

3

131

East

An

glia

UK

1416

.00

148

38.8

115

01.

143

120

2.77

213

413

8.00

116

139

132

Mel

bo

urn

eA

ust

ralia

2021

.33

113

44.7

613

81.

067

133

2.23

817

313

9.25

115

142

133

Ko

nst

anz

Ger

man

y29

25.5

097

60.9

210

70.

879

169

2.10

118

613

9.75

119

138

134

QU

BU

K22

20.8

112

249

.31

126

0.94

615

82.

241

172

144.

5012

214

313

5=U

CD

Irel

and

1313

.50

173

38.5

015

21.

038

142

2.96

212

314

7.50

126

147

135=

St A

nd

rew

sU

K11

14.5

016

528

.97

189

1.31

895

2.63

414

114

7.50

122

151

137

Twen

teN

eth

erla

nd

s12

15.1

715

529

.81

184

1.26

410

62.

484

149

148.

5012

415

613

8Te

xas

Tech

no

log

ical

USA

2118

.75

130

47.2

413

00.

893

167

2.25

016

914

9.00

131

151

139

Tru

man

Sta

teU

SA8

11.0

019

725

.07

213

1.37

581

3.13

411

315

1.00

126

158

140

Bre

men

Ger

man

y15

16.5

014

633

.67

163

1.10

012

72.

245

170

151.

5013

115

8

308 SIMON HIX

Tab

le 2

:Th

e G

lob

al T

op

200

Po

litic

al S

cien

ce D

epar

tmen

ts,

1998

–200

2: C

on

tin

ued

Qu

anti

ty (

1)Im

pac

t (2

)Q

uan

tity

/Siz

e (3

)Im

pac

t/Si

ze (

4)A

vera

ge

Qu

asi-

Erro

rO

vera

llFa

cult

yN

o.

of

Art

icle

s*A

rtic

les/

Imp

act/

of

Ran

ksR

ank

Un

iver

sity

Co

un

try

Size

Art

icle

sR

ank

Imp

act

Ran

kFa

c. S

ize

Ran

kFa

c. S

ize

Ran

k1

to 4

Bes

tW

ors

t

141

Will

iam

an

d M

ary

USA

1312

.67

180

38.7

615

10.

975

154

2.98

212

215

1.75

130

152

142

Kee

leU

K31

26.0

095

55.8

711

50.

839

181

1.80

221

715

2.00

134

149

143

Sim

on

Fra

ser

Can

ada

2018

.00

137

44.0

013

90.

900

165

2.20

017

615

4.25

134

158

144

Bo

sto

nU

SA33

23.3

310

564

.04

101

0.70

722

11.

941

199

156.

5013

515

814

5N

UST

No

rway

1815

.83

150

41.5

714

70.

879

169

2.30

916

115

6.75

134

159

146

Mis

siss

ipp

iU

SA12

10.1

720

440

.14

148

0.84

817

73.

345

102

157.

7513

416

414

7N

ijmeg

enN

eth

erla

nd

s16

15.9

114

934

.62

161

0.99

414

92.

164

177

159.

0013

416

114

8U

pp

sala

Swed

en38

27.0

892

65.3

199

0.71

321

71.

719

231

159.

7513

515

814

9N

ebra

ska

USA

1512

.25

184

43.5

814

30.

817

187

2.90

512

716

0.25

135

160

150

Loyo

laU

SA14

14.2

016

631

.76

175

1.01

414

32.

269

166

162.

5013

516

8

151

Gri

ffith

Au

stra

lia16

17.0

014

331

.62

177

1.06

313

51.

976

196

162.

7513

516

815

2Io

wa

Stat

eU

SA27

16.8

314

463

.75

102

0.62

325

22.

361

157

163.

7514

216

815

3=Fl

ori

da

USA

4729

.83

7873

.26

920.

635

246

1.55

924

416

5.00

144

167

153=

UW

EU

K13

13.1

717

630

.42

181

1.01

314

42.

340

159

165.

0013

717

115

5C

op

enh

agen

Den

mar

k38

28.2

586

57.1

611

20.

743

207

1.50

425

716

5.50

143

167

156

Okl

aho

ma

USA

3522

.83

108

63.4

010

40.

652

238

1.81

121

316

5.75

143

167

157

Ber

nSw

itze

rlan

d17

14.8

315

836

.23

157

0.87

217

32.

131

181

167.

2514

117

115

8V

ien

na

Au

stri

a23

17.4

214

045

.44

135

0.75

720

01.

976

196

167.

7514

417

115

9Ill

ino

is,

Ch

icag

oU

SA22

14.6

716

250

.50

122

0.66

723

32.

295

164

170.

2514

317

416

0N

ew S

ou

th W

ales

Au

stra

lia17

15.3

315

332

.11

173

0.90

216

41.

889

204

173.

5014

717

7

161

Sou

ther

n I

llin

ois

USA

1712

.58

181

39.9

014

90.

740

208

2.34

715

817

4.00

150

176

162

No

ttin

gh

am T

ren

tU

K17

14.8

315

832

.75

166

0.87

217

31.

926

200

174.

2514

717

816

3Sy

dn

eyA

ust

ralia

2017

.33

141

35.1

715

90.

867

175

1.75

922

517

5.00

148

174

164

Ree

dU

SA4

5.50

340

20.7

723

81.

375

815.

193

4217

5.25

138

197

165

Port

lan

d S

tate

USA

36.

0031

714

.64

319

2.00

023

4.88

049

177.

0013

919

7

166

Stir

ling

UK

79.

0022

319

.57

253

1.28

610

02.

796

133

177.

2513

918

816

7Ta

sman

iaA

ust

ralia

1414

.00

170

27.7

619

81.

000

147

1.98

319

517

7.50

146

179

168

Rea

din

gU

K16

13.3

717

532

.64

168

0.83

618

32.

040

189

178.

7515

018

0

POLITICAL SCIENCE DEPARTMENTS 309

169

Lan

cast

erU

K18

15.2

315

432

.26

172

0.84

617

81.

792

218

180.

5015

318

017

0=Sc

ien

ce-P

oFr

ance

8548

.83

3591

.03

720.

574

277

1.07

134

218

1.50

160

175

170=

Bat

hU

K19

14.5

816

435

.18

158

0.76

719

81.

852

206

181.

5015

718

3

172

INSE

AD

Fran

ce4

5.50

340

18.5

226

71.

375

814.

630

5418

5.50

143

206

173=

Bo

wd

oin

USA

158.

5023

649

.60

125

0.56

728

13.

307

103

186.

2515

019

517

3=TU

Dar

mst

adt

Ger

man

y5

6.50

299

18.5

126

81.

300

983.

702

8018

6.25

143

207

175

GIIS

Swit

zerl

and

57.

5826

715

.64

308

1.51

664

3.12

811

418

8.25

143

207

176

Go

then

bu

rgSw

eden

1814

.17

167

31.7

117

60.

787

191

1.76

222

318

9.25

159

195

177

Wes

tmin

iste

rU

K8

9.33

216

18.4

627

11.

166

117

2.30

816

219

1.50

155

207

178

De

Mo

ntf

ort

UK

1311

.00

197

25.9

721

00.

846

178

1.99

819

319

4.50

161

200

179

Leh

igh

USA

76.

8329

122

.35

227

0.97

615

23.

193

112

195.

5015

320

818

0Q

uee

nsl

and

Au

stra

lia22

16.5

014

632

.35

171

0.75

020

31.

470

267

196.

7516

919

9

181

Lun

dSw

eden

3421

.00

119

44.8

613

70.

618

253

1.31

928

719

9.00

172

204

182

Leu

ven

(K

UL)

Bel

giu

m24

15.6

715

136

.30

156

0.65

323

71.

513

254

199.

5017

220

518

3U

CLA

NU

K6

7.00

283

17.7

527

91.

167

116

2.95

812

420

0.50

153

217

184

Tub

ing

enG

erm

any

68.

0024

814

.88

317

1.33

390

2.48

015

020

1.25

159

226

185

Vic

tori

aC

anad

a15

11.0

019

727

.86

196

0.73

321

01.

857

205

202.

0017

220

8

186

EU V

iad

rin

aG

erm

any

119.

0022

323

.40

221

0.81

818

62.

127

182

203.

0017

021

418

7B

ryn

Maw

rU

SA5

6.33

306

16.2

029

61.

266

105

3.24

010

620

3.25

157

228

188

Flo

ren

ceIt

aly

3021

.00

119

36.3

715

50.

700

225

1.21

231

720

4.00

172

202

189

CEU

Hu

ng

ary

3118

.17

136

43.9

114

00.

586

269

1.41

627

320

4.50

173

205

190

Erla

ng

en N

urn

ber

gG

erm

any

24.

5038

911

.52

381

2.25

017

5.76

036

205.

7515

923

1

191

Staf

ford

shir

eU

K8

7.83

257

18.9

726

10.

979

151

2.37

115

520

6.00

165

228

192

Fern

of

Hag

enG

erm

any

108.

0024

822

.79

224

0.80

018

92.

279

165

206.

5017

222

019

3C

on

nec

ticu

tU

SA33

17.5

013

947

.33

128

0.53

029

11.

434

269

206.

7517

720

819

4Yo

rkU

K20

14.0

017

030

.57

180

0.70

022

51.

529

253

207.

0017

221

119

5B

erg

enN

orw

ay16

11.9

218

626

.11

207

0.74

520

61.

632

235

208.

5017

521

5

196=

McM

aste

rC

anad

a19

12.8

317

929

.49

185

0.67

523

11.

552

245

210.

0017

721

719

6=So

uth

ern

Met

ho

dis

tU

SA15

10.5

020

227

.21

202

0.70

022

51.

814

211

210.

0017

521

519

8=C

arle

ton

Can

ada

3519

.90

126

43.7

014

20.

569

280

1.24

930

821

4.00

179

211

198=

Juan

Mar

chSp

ain

45.

5034

013

.77

337

1.37

581

3.44

398

214.

0016

525

120

0U

lste

rU

K9

8.17

244

18.5

026

90.

908

163

2.05

618

821

6.00

172

240

310 SIMON HIX

Table 3: Correlations between the Ranks of the Top 200 PoliticalScience Institutions, 1998–2003

Rank 1 Rank 2 Rank 3 Rank 4

Rank 1 (Quantity) –Rank 2 (Impact) 0.962 –Rank 3 (Quantity/Faculty Size) 0.429 0.405 –Rank 4 (Impact/Faculty Size) 0.507 0.583 0.896 –Overall Rank 0.862 0.879 0.759 0.832

Method: Spearman’s rank-order correlation.

However, English is the international language for the publication and citationof research in political science, as in other social sciences and the natural sci-ences. Because of the ease of reading, publishing in and teaching from theseinternational journals, scholars in English-speaking universities are inevitablymore closely integrated into the global discipline than scholars outside theEnglish-speaking world. As a result, a ranking of departments using researchpublished in the ‘top’ international journals in a field is inevitably not a fairrepresentation of the quality of departments outside the English-speakingworld.

One possible solution would be to include more non-English-language journalsin the analysis. However, given the low number of citations to research published in non-English-language journals, it is hard to make a case for includ-ing some non-English journals while omitting others, or even for including non-English-language journals with low citations while excluding some journalswith higher citations.

A second problem is that book publications are more common and importantin political science than in economics. As discussed, if one assumes that a gooddepartment would produce a lot of articles as well as books, then only mea-suring journal publications may not make a difference to the ranking of insti-tutions at the departmental level. Nevertheless, this hypothesis can only bechecked if a similar ranking could be established using book publications, andthe results of the two rankings are compared and perhaps integrated.

Despite these shortcomings, two major advantages of the method proposedhere are that it would be (i) simple to mechanize and (ii) easy to add otherjournals or books to the dataset. If ‘the discipline’, perhaps via a committee ofthe International Political Science Association, could agree a set of English andnon-English-language journals and book publishers that are the main vehiclesfor research output in the global discipline, it would not be too difficult tomodify this method and establish a mechanized system for entry and updatingof the dataset and for calculating new rankings every year. Ideally, each insti-

POLITICAL SCIENCE DEPARTMENTS 311

Table 4: The Rolling Global Top Fifty, 1997–2002

1993–1997 1994–1998 1995–1999 1996–2000 1997–2001 1998–2002

1 Stanford Stanford Stanford Stanford Columbia Columbia2 Harvard Harvard Harvard Harvard Stanford Harvard3 Columbia Columbia = Columbia Columbia = Harvard Stanford4 Indiana Indiana Essex EUI EUI Ohio State5 = UC Berkeley UC Berkeley EUI UC Berkeley UC Berkeley EUI6 ANU EUI Indiana UCSD Ohio State UCSD7 Essex Houston UC Berkeley Essex UCSD UC Irvine8 Houston Essex UCSD = Indiana Indiana Indiana9 Iowa UCSD Ohio State Ohio State Princeton Princeton

10 UCSD Princeton Yale Yale Yale Yale11 EUI Yale UCLA Princeton Michigan State UC Berkeley12 Princeton UCLA Princeton Michigan State = Chicago Michigan State13 Arizona Ohio State Oxford Birmingham MIT Chicago14 Warwick Warwick Michigan State UC Irvine = UCLA UCLA15 Chicago ANU Vanderbilt UCLA UC Irvine LSE16 Georgia Birmingham American Chicago Essex Essex17 Yale Iowa = UC Davis Vanderbilt Birmingham = Georgetown18 = Oxford Chicago UW Madison = Washington U Vanderbilt MIT19 UW Madison UC Davis Texas A&M UW Madison Johns Hopkins Oxford20 Johns Hopkins = Michigan = Houston Oxford Cambridge = SUNY

Binghamton21 Pittsburgh = Oxford Johns Hopkins UC Davis SUNY = ANU

Binghamton22 = UCLA Arizona Washington U Cambridge Oxford Birmingham23 Ohio State Washington U Chicago Johns Hopkins Georgetown Cambridge24 UC Davis Georgia Birmingham Texas A&M American Florida State25 Birmingham UCol Boulder UC Irvine Bristol = LSE Sheffield26 MIT = UC Irvine ANU Sheffield Florida State = Washington U27 Michigan UW Madison Warwick = Georgetown UW Madison Michigan28 Washington U Michigan State UCol Boulder MIT Texas A&M Johns Hopkins29 UCol Boulder Vanderbilt Georgia ANU Penn State = Texas A&M30 Michigan State Johns Hopkins Michigan Florida State = Emory Emory31 American American Bristol American = ANU UCol Boulder32 Florida State MIT Cambridge = Warwick UC Davis American33 Texas A&M Cambridge Georgetown SUNY Michigan Penn State

Binghamton34 Pennsylvania Texas A&M Arizona GWU Washington U Bristol35 SUNY Stony Glasgow = GWU Houston Sheffield UNC Chapel

Brook Hill36 UC Irvine Leiden Iowa Georgia Bristol GWU37 Strathclyde SUNY Stony LSE LSE GWU Cardiff

Brook38 Cambridge Pennsylvania Sheffield Cal Tech Rice = UW Madison39 Leiden LSE Emory SUNY Stony SUNY Stony Aberystwyth

Brook Brook40 Glasgow Florida State MIT Michigan Aberystwyth Trinity (Dublin)41 = LSE GWU Leiden Rice Trinity (Dublin) = Vanderbilt42 Cal Tech Cal Tech SUNY Stony Penn State = Arizona Cornell

Brook43 UW Milwaukee Pittsburgh Florida State = Emory Georgia Geneva44 Arizona State = South UNC Chapel Iowa Cardiff Illinois

Carolina Hill45 South Carolina Strathclyde Rice Hebrew = Cornell Rice46 Rice Emory New Mexico Pennsylvania UNC Chapel UCL (London)

Hill47 GWU = Georgetown Pennsylvania Arizona Claremont SUNY Stony

Brook48 Maryland UC Riverside Hull Maryland Geneva = UC Davis49 Vanderbilt Sheffield Maryland UCol Boulder Houston Arizona50 Georgetown = Hull Glasgow Claremont = UCol Boulder = Virginia

Note: = means that an institution is in the same position as the institution listed immediately before it.

312 SIMON HIX

tution that wanted to be included in the rankings could be asked to provideaccurate and up-to-date information about the size of their faculty.

(Accepted: 7 May 2004)

About the AuthorSimon Hix, Department of Government, London School of Economics, Houghton Street,London, WC2A 2AE, UK; email: s.hix@lse.ac.uk; website: http://personal.lse.ac.uk/hix

NotesResearch for this paper was partially funded by The Nuffield Foundation (Grant No: SGS/00889/G).I would like to thank Gabriele Birnberg, Ryan Barrett and Bjorn Hoyland for data collection; NancyBeyers at the Institute for Scientific Information for help with purchase of the Social Science Cita-tion Index data; and Daniele Archibugi, Rodney Barker, Kenneth Benoit, Jeff Checkel, Tom Coupé,Philip Cowley, Pepper Culpepper, Nicole Deitelhoff, Vedran Dzihic, Matthew Gabel, Fabrizio Gilardi,Nils Petter Gleditsch, Robert Goodin, Justin Greenwood, Metin Heper, Karl Magnus Johansson, PeterKurrild-Klitgaard, Iain McLean, Martin Lodge, Peter Mair, Paul Mitchell, Andrew Moravcsik, CasMudde, Michael Munger, Yannis Papadopoulos, Thomas Pluemper, Ben Reilly, Christian Reus-Smit,Gerald Schneider, David Shambaugh, Gunnar Sivertsen, Ulf Sverdrup, Alec Sweet, Jon Tonge, ErikVoeten, Albert Weale and the three Political Studies Review referees for comments on the researchand previous versions of this paper.

1 See ‘The ISI® Database: The Journal Selection Process’: <http://www.isinet.com/isi/hot/essays/selectionofmaterialforcoverage/199701.html>.

2 I considered adding journals of other national political science associations (such as the journalsof the Belgian, Swiss, Austrian, Irish and Japanese associations) and a number of other politicalscience journals (such as Aussenwirtschaft). However, none of these journals met the thresholdof at least 100 citations per year.

3 The adjusted R2 for the model is 0.781.

4 Part of the difference between these scores and the SSCI scores is explained by the fact that myindex is an average impact across several years, whereas the scores I have compared them againstare only for the impact of a journal in 2002.

5 More detailed information about how this was calculated for each university can be obtainedfrom the author.

6 Tables showing the top 400 in each five year period between 1993 and 2002 can be found onmy website: <http://personal.lse.ac.uk/hix>.

ReferencesBallard, M. J. and Mitchell, N. J. (1998) ‘The Good, the Better, and the Best in Political Science’, PS:

Political Science and Politics, 31 (4), 826–8.

Coupé, T. (2003) ‘Revealed Preferences: Worldwide Rankings of Economists and Economics Departments, 1969–2000’, Journal of the European Economic Association, 1 (4),<http://student.ulb.ac.be/~tcoupe/ranking.html>.

Dusansky, R. and Vernon, C. J. (1998) ‘Rankings of U.S. Economics Departments’, Journal of EconomicPerspectives, 12 (1), 157–70.

Garand, J. C. and Graddy, K. L. (1999) ‘Ranking Political Science Departments: Do PublicationsMatter?’, PS: Political Science and Politics, 32 (1), 113–16.

Goodson, L. P., Dillman, B. and Hira, A. (1999) ‘Ranking the Presses: Political Scientists’ Evaluationsof Publisher Quality’, PS: Political Science and Politics, 32 (2), 257–62.

Jackman, R. W. and Siverson, R. M. (1996) ‘Rating the Rating: An Analysis of the National ResearchCouncil’s Appraisal of Political Science Ph.D. Programs’, PS: Political Science and Politics, 29 (2),155–60.

POLITICAL SCIENCE DEPARTMENTS 313

Katz, R. and Eagles, M. (1996) ‘Ranking Political Science Departments: A View from the Lower Half’,PS: Political Science and Politics, 29 (2), 149–54.

Lowry, R. C. and Silver, B. D. (1996) ‘A Rising Tide Lifts All Boats: Political Science Department Reputation and Reputation of the University’, PS: Political Science and Politics, 29 (2), 161–7.

McCormick, J. M. and Rice, T. W. (2001) ‘Graduate Training and Research Productivity in the 1990s:A Look at Who Publishes’, PS: Political Science and Politics, 34 (3), 675–80.

Miller, A. H., Tien, C. and Peebler, A. A. (1996) ‘Department Rankings: An Alternative Approach’, PS:Political Science and Politics, 29 (4), 704–17.

PS: Political Science and Politics (1996a) ‘National Research Council Relative Rankings for Research-Doctorate Programs in Political Science’, PS: Political Science and Politics, 29 (2), 144–7.

PS: Political Science and Politics (1996b) ‘U.S. News and World Report Ranking of Graduate PoliticalScience Departments’, PS: Political Science and Politics, 29 (2), 148.

Rice, T. W., McCormick, J. M. and Bergmann, B. D. (2002) ‘Graduate Training, Current Affiliation andPublishing Books in Political Science’, PS: Political Science and Politics, 35 (4), 751–5.

Schmitter, P. (2002) ‘Seven (Disputable) Theses Concerning the Future of “Transatlanticised” or“Globalised” Political Science’, European Political Science, 1 (2), 23–40.

Scott, L. C. and Mitias, P. M. (1996) ‘Trends in Rankings of Economics Departments in the U.S.: AnUpdate’, Economic Inquiry, 34, 378–400.

Teske, P. (1996) ‘Rankings of Political Science Departments Based on Publications in the APSR, JOP,and AJPS, 1986–1995’, State University of New York, Stony Brook, manuscript.

Welch, S. and Hibbing, J. R. (1983) ‘What Do the New Ratings of Political Science DepartmentsMeasure?’, PS: Political Science and Politics, 16 (3), 532–40.