Correlated fluctuations of neural activity, termed noise correlations (NCs), are widespread in cortex and affected by sensory and non-sensory variables. Past work suggests that divisive normalization (DN), a cortical computation responsible for coordinating neural activity, could explain those modulations of neuronal covariability. However, quantifying the relation between DN and NCs remains challenging, as most successful descriptive models of neural variability do not account for DN. We propose a pairwise stochastic DN model that accounts for the effect of DN on covariability. In the model, the numerators (representing excitatory input drive) and denominators (normalization signals) of two neurons are correlated random variables. Therefore, the model partitions NCs into two main sources of correlated fluctuations. We show analytically and with simulations that the effects of DN on NCs depends qualitatively on whether normalization signals are correlated, highlighting the importance of understanding the sharing of normalization between neurons to fully understand DN’s influences on covariability. By fitting the model to simulated pairwise responses, we study the regimes of parameter identifiability: importantly, the denominators’ correlation parameter is best recovered when the numerators contribution to NCs is small. Furthermore, we find that the pairwise model can improve both predictive power and inference of single-trial normalization strength, relative to independent stochastic DN. Lastly, we demonstrate that the model fits accurately Ca2+ imaging data in mouse primary visual cortex (V1) and captures the modulation of NCs by contrast. Our descriptive model can be used to characterize NCs in neural data and quantitatively measure the modulation of NCs by stimulus and state variables. This will enable researchers to study mechanisms underlying DN, test predictions made by normative models about how the structure of population variability affects information coding, and quantify the effects of DN and neural variability on behavior.