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New Approach to Estimating the Productivity of Public Capital : Evidence from 22 OECD Countries

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  • MORITA, Hiroshi

Abstract

Investigating the productivity of public capital is a long-standing issue in one strand of macroeconomic literature. This study develops a new approach to estimate the output elasticity of public capital using a vector autoregressive (VAR) model with identification restrictions derived from a theoretical model. Our empirical analysis of 22 OECD countries for the period 1960–2019 reveals that public capital accumulation has a positive effect on GDP in both the short- and longrun horizons in all countries, supporting both demand-stimulating and growthenhancing effects. Furthermore, the estimated output elasticity of public capital lies within a reasonable range, between 0 and 0.5, and, as in the literature, shows substantial differences across countries. Therefore, the proposed methodology is valid for studying public capital productivity.

Suggested Citation

  • MORITA, Hiroshi, 2024. "New Approach to Estimating the Productivity of Public Capital : Evidence from 22 OECD Countries," Discussion paper series HIAS-E-141, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    • Handle: RePEc:hit:hiasdp:hias-e-141
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    More about this item

    Keywords

    Public capital; Hierarchical panel VAR model; Max share identification;
    All these keywords.

    JEL classification:

    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy; Modern Monetary Theory
    • H54 - Public Economics - - National Government Expenditures and Related Policies - - - Infrastructures
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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