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Four Essays on University Economics - KOPS - Universität Konstanz

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Research Productivity in Business Ec<strong>on</strong>omics<br />

induce shifts in rankings mainly at the top of the lists. This interpretati<strong>on</strong> is c<strong>on</strong>firmed by<br />

the rank correlati<strong>on</strong> between our preferred ranking and the ranking computed with unitary<br />

quality weights. Again, the rank correlati<strong>on</strong> is higher for low-productivity departments.<br />

Thus, high productivity and high quality are correlated.<br />

Table 2.2 also displays rank-order correlati<strong>on</strong>s vis-à-vis productivity rankings based<br />

<strong>on</strong> the journal weighting schemes by Combes and Linnemer (2003) and the Tinbergen<br />

Research Institute at the Erasmus <strong>University</strong>, Rotterdam. Both classificati<strong>on</strong>s focus <strong>on</strong><br />

journals in ‘proper’ ec<strong>on</strong>omics (Ec<strong>on</strong>Lit). Hence, they do not account for most business<br />

journals that we include in our ranking. The correlati<strong>on</strong>s between our preferred ranking<br />

and these two rankings are - not surprisingly - significantly lower than the correlati<strong>on</strong>s<br />

discussed above. This finding indicates that publicati<strong>on</strong>s in WISO journals that are not<br />

listed in Ec<strong>on</strong>Lit cannot be neglected in a well-balanced ranking for the business ec<strong>on</strong>omics<br />

professi<strong>on</strong>. Although Ec<strong>on</strong>Lit covers the most important and influential ec<strong>on</strong>omics jour-<br />

nals, business ec<strong>on</strong>omists very often choose other publicati<strong>on</strong> outlets. Only 21% of the<br />

publicati<strong>on</strong>s in our dataset are recorded by Ec<strong>on</strong>Lit. Restricting the analysis to these<br />

journals would thus seriously distort the evaluati<strong>on</strong> of research in business ec<strong>on</strong>omics.<br />

Rauber and Ursprung (2008a) propose to c<strong>on</strong>trol for cohort effects if evaluating de-<br />

partments with different age structures. Following their method, we therefore define an<br />

individual’s cohort by the group of peers who received their doctor’s degree up to two<br />

years before or after the reference individual. We then order the peers in each cohort<br />

according to research productivity and assign the appropriate quantile to each individual.<br />

In a last step each department’s score is calculated as the mean of the quantile values of<br />

its individual members.<br />

Our cohort ranking based <strong>on</strong> the sample including junior staff is presented in Table 2.7<br />

(c). The leading department according to this ranking is at the <strong>University</strong> of K<strong>on</strong>stanz<br />

followed by the departments of the Technical <strong>University</strong> of Braunschweig and the Ludwig-<br />

Maximilians-<strong>University</strong> München. The rank correlati<strong>on</strong> coefficient between the produc-<br />

tivity and the cohort ranking is 0.7983. However, cohort rankings do not use informati<strong>on</strong><br />

<strong>on</strong> the absolute differences of productivities within cohorts. Furthermore, not every ad-<br />

diti<strong>on</strong>al publicati<strong>on</strong> increases the score. Thus, performance measurement using cohort<br />

rankings may provide somewhat weaker incentives to publish.<br />

26

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