Normalisation of citation impact in economics is a research paper published in Scientometrics (2019). On theSindex it has a DataRank of 0.578. It has been cited 46 times.
Abstract This study is intended to facilitate fair research evaluations in economics. Field- and time-normalisation of citation impact is the standard method in bibliometrics. Since citation rates for journal papers differ substantially across publication years and Journal of Economic Literature classification codes, citation rates should be normalised for the comparison of papers across different time periods and economic subfields. Without normalisation, both factors that are independent of research quality might lead to misleading results of citation analyses. We apply two normalised indicators in economics, which are the most important indicators in bibliometrics: (1) the mean normalised citation score (MNCS) compares the citation impact of a focal paper with the mean impact of similar papers published in the same economic subfield and publication year. (2) PPtop 10 % is the share of papers that belong to the 10% most cited papers in a certain subfield and time period. Since the MNCS is based on arithmetic averages despite skewed citation distributions, we recommend using PPtop 10 % for fair comparisons of entities in economics. In this study, we apply the normalisation methods to 294 journals (including normalised scores for 192, 524 papers). We used the PPtop 10 % results for assigning the journals to four citation impact classes. Seventeen journals have been identified as outstandingly cited. Two journals, Quarterly Journal of Economics and Journal of Economic Literature, perform statistically significantly better than all other journals. Thus, only two journals can be clearly separated from the rest in economics.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.578
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.