Monthly Archives: October 2014

Higher word counts, lower rates?

Occasionally, I receive enquiries that request discounts based on job size. Is this justified?

Among my freelancing colleagues, opinions go both ways. Some accept the premise that securing a large job is worth a discount, while others point out that regardless of the size of a job, they still have to process every single word with the same care, and word count therefore has no bearing on their rates.

There are valid arguments for both sides, and to me, the answer boils down to a difference between theory and practice.

In theory, one big job is in many ways better than several smaller ones. You save on various overhead activities like administration, invoicing or marketing. You can plan ahead better than with several small jobs arriving in succession. If the text is homogeneous enough, you might save on research compared to several jobs from different domains.

In practice, however, this is just one aspect of overall pricing, and even in the ideal case when the advantages mentioned above come to bear, their impact is generally dwarfed by more important considerations like subject matter, availability of reference material, quality of writing of the source text and the possibility of approaching the client for clarifications. This is particularly true for well-established freelancers with streamlined internal processes and a regular stream of work. For them, the administrative overhead of individual jobs is minimal, and a number of practical drawbacks often negate any upsides big jobs might have.

Clients’ desired turnaround times usually do not increase proportionally with word count. So you usually have less (sometimes a lot less) time for one big job than for several smaller jobs with an equivalent word count. This can vitally impact a freelancer’s “room for manoeuvre” needed to accommodate jobs coming in at short notice from regular customers.

Big assignments are also often the ones that need to be split up among several translators, incurring significant management and communication overhead to ensure an end result that is consistent both in terminology and style.

In consequence, the simple equation big job = big discount doesn’t really hold up; there are several factors that have a much larger impact on a freelancer’s rate calculations. So next time you receive this request, be sure to take them into consideration before agreeing to a discount you may come to regret – either that same day when another client comes knocking with an urgent project, or later on when you find out that translating 10k words isn’t really that much quicker than translating 5x 2000 words.

And if as a client, budget constraints force you to look for every penny that can be saved, next time maybe you can find a way to offer your trusted translator a few additional days in lead time – they will most certainly appreciate it and may even offer you a discount in exchange.

Excel spellchecking – watch out for false negatives

When doing a spell check in Excel1, you should be aware that it accepts any word as correct as long as it contains a character that is outside the code page of the language checked.

So if you happen to misspell nämlich as namlich in German, Excel correctly flags your typo. However, if you are unlucky enough to spell it as nămlich, Excel happily absolves you. If this example seems contrived to you, think about foreign names. Excel leaves you on your own there, even if the correct form is in your spelling dictionary.

As long as you are aware of the issue, it shouldn’t be a major problem – of course you never rely on spell-checking only – or do you?

While I do use spell-checking as a safety net (a double one in fact by running my output through two spell-checking engines whenever possible), my primary means of living up to my own zero error tolerance is careful proofreading after completion – paced by a text-to-speech program that reads the text out to me and provides two major benefits: it prevents me from reading too fast and it alerts me to particularly surreptitious typos (long words, many consonants) by tripping over their pronunciation.


  1. Checked with Excel 2010. Let me know in the comments if you have seen the issue in later versions.