The study of eigenvalue distributions in random matrix theory is often conducted by analyzing the resolvent matrix $ \mathbf{G}_{\mathbf{M}}^N(z) = (z \mathbf{1} - \mathbf{M})^{-1} $. The normalized trace of the resolvent, known as the Stieltjes transform $ \mathfrak{g}_{\mathbf{M}}^N(z) $, converges to a limit $ \mathfrak{g}_{\mathbf{M}}(z) $ as the matrix dimension $ N $ grows, which provides the eigenvalue density $ ρ_{\mathbf{M}} $ in the large-$ N $ limit. In the Hermitian case, the distribution of $ \mathfrak{g}_{\mathbf{M}}^N(z) $, now regarded as a random variable, is explicitly known when $ z $ lies within the limiting spectrum, and it coincides with the distribution of any diagonal entry of $ \mathbf{G}_{\mathbf{M}}^N(z) $. In this paper, we investigate what becomes of these results when $ \mathbf{M} $ is non-Hermitian. Our main result is the exact computation of the diagonal elements of $ \mathbf{G}_{\mathbf{M}}^N(z) $ when $ \mathbf{M} $ is a Ginibre matrix of size $ N $, as well as the high-dimensional limit for different regimes of $ z $, revealing a tail behavior connected to the statistics of the left and right eigenvectors. Interestingly, the limit distribution is stable under inversion, a property previously observed in the symmetric case. We then propose two general conjectures regarding the distribution of the diagonal elements of the resolvent and its normalized trace in the non-Hermitian case, both of which reveal a symmetry under inversion.