We provide two datasets of census block group-level mode choice parameters for New York City and New York State. The parameters are estimated by GLAM logit model using Replica's synthetic population datasets (For details of the GLAM logit model, please refer to this paper https://doi.org/10.1016/j.trb.2025.103220). Each row contains a set of mode choice parameters for each block-group OD pair and one of the four population segments (low-income, not low-income, students, and senior population). Six trip modes are considered: private auto, public transit (such as buses, light rail, and subways), on demand auto (taxi or TNC services such as Uber or Lyft), biking (including e-bike), walking, and carpool. Parameters of twelve mode attributes are estimated, including, auto travel time, transit in-vehicle time, transit access time, transit egress time, number of transit transfers, non-vehicle travel time, trip cost, and five alternative specific constants (setting carpool as the reference level). Travel cost endogeneity is addressed using the control variable approach. The empirical distribution of agent-level parameters is neither Gumbel nor Gaussian, which reveals a regional divergence of the value of time and mode preference, indicating potential inequity issues in the transportation system. This is infeasible for conventional discrete choice models (DCMs) to capture.