Recent whole genome polymerase binding assays in the Drosophila embryo have shown that a large proportion of unexpressed genes have pre-assembled RNA pol II transcription initiation complex stably bound to their promoters. These constitute a subset of promoter proximally paused genes which are regulated at transcription elongation rather than at initiation, and it has been proposed that this difference allows these genes to both express faster and achieve more synchronous expression across populations of cells, thus overcoming the molecular "noise" arising from low copy number factors. Promoter-proximal pausing is observed mainly in metazoans, in accord with its posited role in synchrony. Regulating gene expression by controlling release from a promoter paused state instead of by regulating access of the polymerase to the promoter DNA can be described as a rearrangement of the regulatory topology so that it controls transcriptional elongation rather than transcriptional initiation. It has been established experimentally that genes which are regulated at elongation tend to express faster and more synchronously; however, it has not been shown directly whether or not it is the change in the regulated step per se that causes this increase in speed and synchrony. We investigate this question by proposing and analyzing a continuous-time Markov chain model of polymerase complex assembly regulated at one of two steps: initial polymerase association with DNA, or release from a paused, transcribing state. Our analysis demonstrates that, over a wide range of physical parameters, increased speed and synchrony are functional consequences of elongation control. Further, we make new predictions about the effect of elongation regulation on the consistent control of total transcript number between cells, and identify which elements in the transcription induction pathway are most sensitive to molecular noise and thus may be most evolutionarily constrained. Our methods produce symbolic expressions for quantities of interest with reasonable computational effort and can be used to explore the interplay between interaction topology and molecular noise in a broader class of biochemical networks. We provide general-purpose code implementing these methods.