Tax is essentially a contribution that the state requires from its citizens. The efficiency of inter-state taxes is evaluated using Data Envelopment Analysis (DEA). To apply DEA, we need information about both outputs and inputs. In this case, we’ve chosen the tax-to-GSDP ratio as the output, while per-capita GSDP and the inverse of the agricultural share in GSDP serve as inputs. Since inputs should positively influence the output, and the agricultural share has a negative impact, we use its inverse as an input. DEA assesses each state's tax efficiency by creating an efficient frontier. States that fall on this frontier are considered 100% efficient in generating tax revenue, while those below the frontier are deemed inefficient, with their level of inefficiency measured by how far they stray from the efficient frontier. There are various versions of DEA, but the one used in this study is input-oriented and operates under constant returns to scale (CRS) assumptions. This input-oriented model aims to minimize the number of physical inputs needed to produce a specific level of output. Conversely, the output-oriented DEA model focuses on maximizing output given a set number of inputs. The CRS-based DEA model assumes that the size of the state doesn’t affect tax efficiency, treating all states as if they operate at a similar scale. In contrast, the variable returns to scale (VRS) model takes state size into account when estimating tax efficiency.
No takes yet. Share an insight, caveat, or question.
Bhardwaj et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: