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Expenditure visibility and voter memory: a compositional approach to the political budget cycle in Indian states, 1959–2012
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Expenditure visibility and voter memory: a compositional approach to the political budget cycle in Indian states, 1959–2012

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  • J. Stephen Ferris

    (Carleton University)

  • Bharatee Bhusana Dash

    (Xavier University)

Abstract

In this paper we argue that the political budget cycle test for opportunistic spending is weakened by the absence of a strong reason for why spending increases should be restricted to the time period leading into the next election. One would expect that a targeted benefit should elicit the same degree of voter support whenever it is received. Here we argue that while the political need to fulfill a list of pre-election promises serves to constrain excessive spending, the characteristic that some expenditure items better demonstrate the contributions of the current government to voters (with depreciating memories) leads to a predictable reallocation of the composition of budgetary spending across the life of a government. Our test for a predictable timing pattern to subcomponents of the budget uses capital expenditures as the budgetary item with greater visibility spillovers and a data set of 14 Indian states over 54 years (1959/60–2012/13). The predictions that capital expenditures relative to both total government expenditure and government consumption should rise across the governing interval are found to be consistent with the data and provide a better fit with the data than more traditional political budget cycle models that use aggregate spending/deficits in the pre-election period.

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  • J. Stephen Ferris & Bharatee Bhusana Dash, 2019. "Expenditure visibility and voter memory: a compositional approach to the political budget cycle in Indian states, 1959–2012," Economics of Governance, Springer, vol. 20(2), pages 129-157, June.
    • Handle: RePEc:spr:ecogov:v:20:y:2019:i:2:d:10.1007_s10101-018-0216-1
    DOI: 10.1007/s10101-018-0216-1
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    Cited by:

    1. Stanley L. Winer & J. Stephen Ferris & Bharatee Bhusana Dash & Pinaki Chakraborty, 2021. "Political competitiveness and the private–public structure of public expenditure: a model and empirics for the Indian States," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 28(6), pages 1430-1471, December.
    2. Batabyal, Amitrajeet & Beladi, Hamid, 2020. "A Political Economy Model of the Ganges Pollution Cleanup Problem," MPRA Paper 102790, University Library of Munich, Germany, revised 28 May 2020.
    3. Batabyal, Amitrajeet A. & Beladi, Hamid, 2022. "A political-economy perspective on mayoral elections and urban crime," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).
    4. Potrafke, Niklas, 2020. "General or central government? Empirical evidence on political cycles in budget composition using new data for OECD countries," European Journal of Political Economy, Elsevier, vol. 63(C).
    5. Bharatee Bhusana Dash & J. Stephen Ferris, 2018. "Economic Performance and Electoral Volatility: Testing the Economic Voting Hypothesis on Indian States, 1957–2013," Carleton Economic Papers 18-07, Carleton University, Department of Economics.
    6. Sacchidananda Mukherjee & Shivani Badola, 2021. "Public Financing of Human Development in India: A Review," Indian Journal of Human Development, , vol. 15(1), pages 62-81, April.

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    More about this item

    Keywords

    Political business or budget cycle; The time varying composition of Indian state government spending; Visibility of capital expenditures; Panel data; ARDL modeling;
    All these keywords.

    JEL classification:

    • H5 - Public Economics - - National Government Expenditures and Related Policies
    • H72 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Budget and Expenditures
    • O53 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Asia including Middle East
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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