This paper studies the impact of differences in citation practices at the sub-field, or Web of Science
subject category level using the model introduced in Crespo et al. (2012) according to which the number of
citations received by an article depends on its underlying scientific influence and the field to which it belongs.
We use the same Thomson Reuters dataset of about 4.4 million articles published in 1998-2003 with a fiveyear
citation window used in Crespo et al. (2013) to analyze a classification system consisting of 22 broad
fields. The main results are the following four. Firstly, as expected, when the classification system goes from
22 broad fields to 219 sub-fields the effect on citation inequality of differences in citation practices increases
from approximately 14% at the field level to 18% at the sub-field level. Secondly, we estimate a set of
exchange rates (ERs) to express the citation counts of articles in a wide quantile interval into the equivalent
counts in the all-sciences case. For example, in the fractional case we find that in 187 out of 219 sub-fields the
ERs are reliable in the sense that the coefficient of variation is smaller than or equal to 0.10. ERs are
estimated over the [660, 978] interval that, on average, covers about 62% of all citations. Thirdly, in the
fractional case the normalization of the raw data using the ERs (or sub-field mean citations) as normalization
factors reduces the importance of the differences in citation practices from 18% to 3.8% (3.4%) of overall
citation inequality. Fourthly, the results in the fractional case are essentially replicated when we adopt the
multiplicative approac
