Changes-leading-to-changes makes language difficult,
because we are used to talk about ‘imaginary changes in X leading to imaginary
changes in Y’ to describe Y(X) relations from cross-sectional/timeless regressions X->Y, whereas in reality
there are no actual changes to talk about in the same-time Y-on-X regression, only potential ones.

You can still talk about all of these, with some help from peeking at where the averages for change-in-X & change-in-Y are (see the big green dot), whether they are positive or negative.

A simple way of describing these graphs where the changesX->changesY slope is >0 , is to say that if one increases in X, then we expect their Y to also increase, even though the 2 graphs show that the Y actual mean shows an average Y increase, then an average Y decrease: the only difference is that the slope was 'pushed down' by the 2 means, as the slope naturally crosses the (MeanX, MeanY) dot.

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