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Central Fiscal Transfers and States’ Own-Revenue Efforts in India

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  • Prasant Kumar Panda

    (Prasant Kumar Panda is with the P.G. Department of Economics, Dr S.R.K. Government Arts College (affiliated to Pondicherry University), Yanam 533464, India; e-mail: pkp.pondyedu@gmail.com)

Abstract

Using the fixed and random effect panel regression models the paper tries to empirically examine the incentive effects of federal transfers on states’ own-revenue. Per capita resource transfers from the centre are found to be significant and negatively associated with states’ own-revenue, own-tax revenue and own-non-tax revenue in per capita terms, irrespective of choices of models or specification. This possibly indicates that central transfers have a dampening effect on states’ revenue efforts. Further, the incentive criterion for tax effort as used in the Finance Commission devolutions and in the Gadgil formula used by the Planning Commission is not reflected in the system, and it has failed to induce the desired, positive revenue pattern in states. The paper calls for assigning a higher weight to tax effort in the devolution formula and for more effective co-ordination among different channels in designing criteria and incentives.

Suggested Citation

  • Prasant Kumar Panda, 2009. "Central Fiscal Transfers and States’ Own-Revenue Efforts in India," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 3(3), pages 223-242, July.
    • Handle: RePEc:sae:mareco:v:3:y:2009:i:3:p:223-242
    DOI: 10.1177/097380100900300302
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    References listed on IDEAS

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    Cited by:

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    3. Prasant Kumar Panda & Velan Nirmala, 2013. "Central Fiscal Transfers and States' Spending In India: An Analysis of Incentive Effect," Economics Bulletin, AccessEcon, vol. 33(2), pages 1229-1246.
    4. K. R. Shanmugam & K. Shanmugam, 2022. "Designing Transfers Policy with Normatively Determined Revenues and Expenditures of State Governments in India," Working Papers 2022-237, Madras School of Economics,Chennai,India.
    5. Garg, Sandya & Ashima Goyal & Rupayan Pal, 2014. "Why tax effort falls short of capacity in Indian states: A Stochastic frontier approach," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2014-032, Indira Gandhi Institute of Development Research, Mumbai, India.
    6. Jaime Bonet Moron & Fabio Rueda Devivero, 2013. "Esfuerzo fiscal municipal en Guatemala," Revista Economía y Región, Universidad Tecnológica de Bolívar, vol. 7(2), pages 5-41, December.
    7. Yongzheng Liu & Jianmei Zhao, 2011. "Intergovernmental fiscal transfers and local tax efforts: evidence from provinces in China," Journal of Economic Policy Reform, Taylor & Francis Journals, vol. 14(4), pages 295-300, December.
    8. Mrinal Kanti Dutta & Parag Dutta, 2015. "Revenue Mobilization Effort of Special Category States: The Case of Assam in North-east India," South Asian Journal of Macroeconomics and Public Finance, , vol. 4(2), pages 178-204, December.
    9. Avipsa Mohanty & Dinabandhu Sethi & Asit Ranjan Mohanty, 2020. "Central Transfer a Curse or Blessing? Evidence from the Relative Revenue Effort of Indian States," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 53(2), pages 214-227, June.
    10. Jhorland Ayala‐García & Sandy Dall'erba, 2021. "The natural resource curse: Evidence from the Colombian municipalities," Papers in Regional Science, Wiley Blackwell, vol. 100(2), pages 581-602, April.

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    More about this item

    Keywords

    Federal Transfers; Revenue Effort; Panel Data Models; Incentive Effects; JEL Classification: H70; JEL Classification: H71; JEL Classification: C23;
    All these keywords.

    JEL classification:

    • H70 - Public Economics - - State and Local Government; Intergovernmental Relations - - - General
    • H71 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Taxation, Subsidies, and Revenue
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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