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Revisiting Indicators of Public Debt Sustainability: Capital Expenditure, Growth and Public Debt in India

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  • Bhatt, Antra

Abstract

The paper tests whether productive expenditures share a long run re- lationship with debt to GDP ratio by using a multivariate time series framework. The theoretical model is based on dynamic optimization of utility and productive expenditure with respect to capital and debt. Literature on growth theory has suggested that all less productive expenditures can have a negative effect on the growth rate of real GDP per capita until the optimal level of productive expenditure is reached. This would indeed lead to higher level of debt as growth rate will be reduced. Aggregate yearly data for India covering the period 1980-2009 have been used. The CAPRATIO and Debt to GDP ratio are cointegrated. VAR modeling with error correction reveals that the model can be used for forecasts. The regression coecient between the two variables is negative, signifying the inverse relationship. Having proved the hypothesis of an inverse long run relationship between the two variables, a new indicator based on the Government Inter-temporal budget constraint is suggested, revolving around capital expenditure.

Suggested Citation

  • Bhatt, Antra, 2010. "Revisiting Indicators of Public Debt Sustainability: Capital Expenditure, Growth and Public Debt in India," MPRA Paper 27422, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:27422
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    References listed on IDEAS

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    Cited by:

    1. Kaur, Balbir & Mukherjee, Atri & Ekka, Anand Prakash, 2017. "Debt Sustainability of States in India: An Assessment," MPRA Paper 81929, University Library of Munich, Germany.

    More about this item

    Keywords

    Public Debt sustainability indicators; Capital Expenditure; Growth.;

    JEL classification:

    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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