Shiv Shankar
Department of Statistics and Information Management, Reserve Bank of India, Mumbai, India

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Government fiscal spending and crowd-out of private investment: An empirical evidence for India Shiv Shankar; Pushpa Trivedi
Economic Journal of Emerging Markets Volume 13 Issue 1, 2021
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/ejem.vol13.iss1.art8

Abstract

Purpose - The paper evaluates the crowding-in or crowding-out relationship between public and private investment in India, controlling fiscal and monetary variables.Methods - In a flexible accelerator theoretical framework, the paper estimates long and short-run investment dynamics, employing Autoregressive Distributed Lag (ARDL) cointegration approach. We use a back series of national account statistics that incorporates enhanced coverage of the organized corporate sector.   Findings - Our results suggest investment complementarity between the public and private sector at an aggregate and sectoral level over the period 1981-2019. Barring short-run crowding-out in construction and financial services at industry level, public investment stimulates private counterparts, both in the long and short-run. However, fiscal deficit, inflation expectation, and sovereign vulnerability influence private investment adversely. Moreover, the long-run crowding-out bearing of fiscal imbalance is quantitatively higher when the public sector invests in mining and manufacturing and insignificant with infrastructure.Implication - Sizable infrastructure investment as a proportion of government finances would moderate the adverse impact of the deficit on private investment. Further, quality fiscal adjustments and containing inflation would enhance private investment activities.Originality - Besides aggregate and sectoral levels, the study also evaluates the impact of industry-level public investment on private capital expenditure.  This paper also incorporates derived variables in the regression framework using statistical filters and the principal component technique.