This paper investigates the relationships among controlled language (CL), machine translation (MT) quality, and post-editing (PE). Previous research has shown that the use of CL improves the quality of MT. By extension, it is assumed that the use of CL will lead to greater productivity or reduced PE effort. The paper examines whether this three-way relationship among CL, MT quality, and PE holds. Beginning with a set of CL rules, the authors determine what types of CL rules have the greatest cross-linguistic impact on MT quality. They create two sets of English data, one which violates the CL rules and the other which conforms to them. Both sets of sentences are translated into four typologically different languages (Dutch, Chinese, Arabic, and French) using MSR-MT, a statistical machine translation system developed at Microsoft. The authors then measure the degree of impact of CL rules on MT quality based on the difference in human evaluation as well as BLEU scores between the two sets of MT output. Finally, they examine whether the use of CL improves productivity in terms of reduced PE effort, using character-based edit-distance.