Suppose You've Run a Regression Relating Log(Output) to Log(Worker Hours)

Suppose you've run a regression relating log(Output) to log(Worker Hours) in Excel. You are willing to make the necessary assumptions to deduce causality and run hypothesis tests. Your results are as follows: $$\begin{array} { l c c c c } & \text { Coefficients } & \text { Standard Error } & t \text { Stat } & P \text {-value } \\\hline \text { Irtercept } & 17.4583044 & 28.47075584 & 0.613201297 & 0.541240031 \\\text { Log(Worker Hours) } & 2.699483287 & 0.711729096 & 3.792852225 & 0.000264681\end{array}$$ How should you interpret the coefficient on Log(Worker Hours) of 2.69?

A) A 1% increase in Worker Hours leads to a 2.69% increase in Output.
B) A 1 unit increase in Worker Hours leads to a 2.69% increase in Output.
C) A 1% increase in Worker Hours leads to a .0269% increase in Output.
D) A 2.69% increase in Worker Hours leads to a 1% increase in Output.