We estimate state-level tax efficiency in India using an error correction framework, making a distinction between a long-term cointegrating relationship and a short-term dynamics around it. We use a stochastic frontier approach in a panel data framework considering 17 medium and large (ML) states for the period 2004–2005 to 2019–2020. We find that the FC14’s initiative to sharply increase the states’ share in the divisible pool of central taxes had an adverse impact on states’ own tax revenues. The short-term relationship converges to the long-term relationship in 2.6 years. In terms of relative tax effort, the most efficient state was Tamil Nadu, while the least efficient was Bihar. Results from this study would be useful in averting the problem of adverse incentives, while determining the intergovernmental transfers. In the post-GST scenario, operating at their tax efficiency frontier would be critical for states especially in the light of discontinuation of the GST compensation cess.
AignerD., LovellC. K., & SchmidtP. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21–37.
2.
BahlR. W.Jr. (1971). A regression approach to tax effort and tax ratio analysis. IMF Staff Papers, 18(3), 570–612.
3.
BahlR. W.Jr. (1972). A representative tax system approach to measuring tax effort in developing countries. IMF Staff Papers, 19(15), 87–124
4.
BatieseG. E. (1992). Frontier production functions and technical efficiency: A survey of empirical applications in agricultural economics. Agricultural Economics, 7(3–4), 185–208.
5.
BatteseG. E., & CoelliT. J. (1992). Frontier production functions, technical efficiency and panel data: With application to paddy farmers in India. Journal of Productivity Analysis, 3, 153–169.
6.
BatteseG. E., & CoelliT. J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics, 20(2), 325–332.
7.
BrunJ. F., ChambasG., & MansourM. (2014). Tax effort of developing countries: An alternative measure. In BoussichasM. & GuillaumontP. (Eds), Financing sustainable development by addressing vulnerabilities (pp. 205–216). Ferdi.
8.
ChelliahR. J. (1971). Trends in taxation in developing countries. IMF Staff Papers, 18(2), 254–331.
9.
CyanM., Martinez-VazquezJ., & VulovicV. (2013). Measuring tax effort: Does the estimation approach matter and should effort be linked to expenditure goals? [GSU Paper No. 1308]. International Center for Public Policy, Andrew Young School of Policy Studies, Georgia State University.
10.
EltonyM. N. (2002). The determinants of tax effort in Arab countries [API-Working Paper Series]. Arab Planning Institute.
11.
GuptaA. S. (2007). Determinants of tax revenue efforts in developing countries. IMF, 2007(184), 1–39. https://doi.org/10.5089/9781451867480.001
12.
JhaR., MohantyM. S., ChatterjeeS., & ChitkaraP. (1999). Tax efficiency in selected Indian states. Empirical Economics, 24, 641–654.
13.
KawadiaG., & SuryawanshiA. K. (2021). Tax effort of the Indian states from 2001–2002 to 2016–2017: A stochastic frontier approach. Millennial Asia. https://doi.org/10.1177/09763996211027053
14.
LotzJ. R., & MorssE. R. (1967). Measuring tax effort in developing countries. IMF Staff Papers, 14, 478–499.
15.
Martinez-VazquezJ. (2001). Mexico: An evaluation of the main features of the tax system [International Studies Programme Working Paper 01–12]. Andrew Young School of Policy Studies.
16.
MukherjeeS. (2020). Goods and services tax efficiency across Indian states: Panel stochastic frontier analysis. Indian Economic Review, 55(2), 225–251.
17.
NayakD. K., & HazarikaB. (2022). Linkage between income and government expenditure at Indian sub-nationals: A second generation panel co-integration techniques [NIPFP Working Paper No. 22/374]. National Institute of Public Finance and Policy.
18.
OommenM. A. (1987). Relative tax effort of states. Economic & Political Weekly, 22(11), 466–470.
19.
PalR., GargS., & GoyalA. (2014). Why tax effort falls short of capacity in Indian states: A stochastic frontier approach [Working Paper No. 32]. Indira Gandhi Institute of Development Research.
20.
PiancastelliM. (2001). Measuring the tax effort of developed and developing countries: Cross country panel data analysis—1985/95 [IPEA Working Paper No. 818]. https://ssrn.com/abstract=283758;https://dx-doi-org.web.bisu.edu.cn/10.2139/ssrn.283758
21.
PittM. M., & LeeL. F. (1981). The measurement and sources of technical inefficiency in the Indonesian weaving industry. Journal of Development Economics, 9(1), 43–64.
22.
PurohitM. C. (2006). Tax efforts and taxable capacity of central and state governments. Economic & Political Weekly, 41(8), 747–751, 753–755.
23.
RaoH. (1993). Taxable capacity tax-efforts and forecasts of tax-yield of Indian states. Institute for Social and Economic Change, Bangalore, 165. https://www.nipfp.org.in/media/medialibrary/2014/10/TAXABLE_CAPACITY_AND_TAX_EFFORT_OF_STATES_IN_INDIA.pdf
24.
SainiS., SharmaS., & SinghR. (2015). Better utilization of correlation between metrics using Principal Component Analysis (PCA). In 2015 Annual IEEE India Conference (INDICON) (pp. 1–6). IEEE.
25.
ShanmugamK. R., & SrivastavaD. K. (2009). Designing fiscal transfers: A normative approach [MSE Working Paper, 39/2009]. https://www.mse.ac.in/wp-content/uploads/2021/05/Working-paper-39.pdf
