The vocabulary of economics and academic English

Abstract

Identifying the features of a text is useful in order to determine the most important aspects to teach for English for Specific Purposes. One possible way of doing this is by looking at the vocabulary needed by learners when reading a text. This research specifically examines the vocabulary used in an Economics text used by first year university students and compares it with another corpus of similar length. These two corpora have different features. The Economics text is by one writer, and is on one general topic - Economics, and was written so that it forms a coherent continuous text. The general academic English corpus was made up of 160 two thousand word texts by many different writers on diverse topics, across a wide range of academic disciplines. The most striking difference between the two texts was the number of word forms and word families each contained. The Economics text consisted of 9,469 different words forming 5,438 word families. The academic English corpus, although roughly the same length as the Economics corpus, consisted of over twice as many as word forms and word families namely 21,399 different word forms and 12,744 word families. In the Economics text a few content words were extremely frequent. In the most frequent 100 words, there were 34 words like price, cost, demand, firm, supply, etc., which occurred with at least seven times the frequency of their occurrence in general academic English. The total difference in their frequency of occurrence accounted for 32,214 tokens which equalled 10.91% of the tokens in the Economics text. This 10.91% coverage easily accounted for the coverage of the extra word types and families in the general academic corpus. It was also found that 2,124 of the 5,438 word families in the Economics text did not occur in the general academic English corpus. Only 5.4% of the non-overlapping words were technical words occurring in a dictionary of Economics. This suggests that learners should move to an ESP course as soon as possible after becoming familiar with the general service and high frequency general academic vocabulary, rather than continuing with a general academic focus. It was possible to use frequency and range of occurrence as a statistical means of isolating technical words, but there were still problems with such an approach, particularly for low frequency technical words.