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Impact of infrastructure investment on developed and developing economies

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Abstract

This paper uses two longitudinal datasets—one with more limited coverage from the organization for economic co-operation and development and another constructed using general government gross fixed capital formation—to test for the relative effects of infrastructure versus non-infrastructure investment on output per worker, between developed and developing economies. The paper presents evidence that increasing infrastructure per worker has a larger relative impact on developing economies. This also implies that the share of gross capital formation devoted to infrastructure should be higher in developing economies.

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Notes

  1. Even if there is overestimation of China’s expenditure on infrastructure, and with some discounting from the statistics, it would still constitute a very exceptional rate of investment in infrastructure.

  2. The Golden Rule merely states that countries should choose an optimal saving rate and accumulate capital to the point where the marginal return to capital is the same as offsetting factors, namely the depreciation rate of capital plus population growth.

  3. Different from Esfahani and Ramirez (2003), this paper does not seek to model the influences on infrastructure capital accumulation. As will be explained later, we thus avoid directly having to model savings rate or the institutional factors that affect infrastructure accumulation. Secondly, we draw on much larger longitudinal datasets in arriving at our conclusions. This also allows us to partition the data into developing versus developed economies and highlight the key differences.

  4. “Dwellings” includes “Other buildings and structures” for Chile.

  5. Note that for regression purposes, it would matter less if any bias in reporting is steady over time—resulting in no impact in coefficients. If data are recorded with idiosyncratic errors, the subsequent regressions could be biased downward, but can be taken as the lower bound estimate of impact of such infrastructure.

  6. Developed economies in the OECD dataset are Australia, Austria, Belgium, Canada, Switzerland, Germany, Denmark, Spain, Finland, France, United Kingdom, Iceland, Ireland, Israel, Italy, Japan, Luxembourg, Netherlands, Norway, New Zealand, Sweden and the United States. Developing economies in the OECD dataset are Brazil, Chile, Colombia, Costa Rica, Czech Republic, Estonia, Greece, Hungary, Korea, Lithuania, Latvia, Mexico, Poland, Portugal, Slovak Republic, Slovenia and South Africa.

  7. Information on public investment comes from three sources: the OECD Analytical Database (2019 version) for OECD countries, and a combination of the National Accounts of the Penn World Tables (PWT, version 9.1) and the IMF World Economic Outlook (WEO, April 2019 vintage) for non-OECD countries.

  8. In some of the regressions later, we used the shares reported, multiplied these to relevant variables reported in USD, and essentially converted GFCF or infrastructure figures into USD for regressions. This additional regressions in USD are meant to provide robustness checks.

  9. World Bank Private Participation in Infrastructure Database: https://ppi.worldbank.org/en/ppi (Last accessed Oct. 24, 2019). European PPP Expertise Center: https://www.eib.org/epec/ (Last accessed Oct. 24, 2019).

  10. Improving the precision of infrastructure measurement is a nontrivial task and a subject for further research.

  11. Infrastructure spending reported from OECD and GFCF-GG dataset are slightly different. The former is gathered through more detailed components of national accounts reported by OECD countries, while the latter is proxied mostly using general government GFCF.

  12. This is an approximation. Suppose the depreciation rate is small, the GFCF (which is a flow) in each year would be closely matched to the increase in capital stock. Most capital stock series are constructed using rolling annual GFCF figures (net increase after accounting for depreciation).

  13. Note that in the scenario of the negative first difference, capital per worker could still be rising, but it will be rising at a rate that is below steady state.

  14. Can be made available on request.

  15. Small economies have some idiosyncratic characteristics. For example, some recorded very large current account deficits—Maldives, Liberia, Mongolia, Nicaragua, Sao Tome and Principe, Guinea-Bissau, Montenegro, Mozambique, and Congo had all registered current account deficits in excess of 40% of GDP. Furthermore, Mali, Zambia, Equatorial Guinea, Bhutan, Mongolia, and Gabon had in some years recorded government gross capital formation in excess of 50% of GDP. Per capita income growth can also be erratic, with Liberia, Equatorial Guinea, Azerbaijan, Maldives, and others registering more than 20% growth in output per worker (even in PPP terms) in certain years.

  16. Countries below the cut-off of per capita GDP USD1,000 are Niger, Malawi, Madagascar, Central African Republic, Ethiopia, Mozambique, Sierra Leone, Togo, Gambia, Burkina Faso, Liberia, Guinea-Bissau, Uganda, Rwanda, Nepal, Guinea, Haiti, Mali, Benin, Tanzania, Tajikistan, and Chad. There are 49 countries between USD1,000 and USD5,000, including those with sizable populations such as India, Bangladesh, Indonesia, Pakistan, Nigeria, Vietnam, Philippines, and Egypt, giving a fairly representative sample (note that China is above USD5,000 and not included in this subset).

  17. As an aside, we also notice that regressions where output and GFCF are recorded in USD tend to give very large coefficients (see R17 and R18), and also OECD regressions (R6 and R7). We attribute this to the effect of currency affecting both LHS variable (output per worker) and also RHS variable (GFCF) thereby biasing the elasticities upward. To be clear, the upward bias should affect both infrastructure and non-infrastructure coefficients.

  18. Similar pattern holds for regressions with variables recorded in USD: R6 and R7 (21% for developed, 53% for developing); R17 and R18 (35% for developed, 39% for developing). Note that even though regressions with variables in USD may result in upward bias for the coefficients (see footnote 18), the relative impact should not be affected.

  19. Population growth or employment effects were not considered in the Devarajan et al. (1996) study.

  20. Fernald (1999) for example assessed that repeat investment in roads would not give the same productivity boost compared to initial investment. See also (Holtz-Eakin and Schwartz 1995), which found relatively low elasticity of infrastructure capital in the USA.

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Acknowledgements

We are grateful to the discussions and views provided by colleagues at the Bank, OECD Statistical and Data Directorate for answering our queries, as well as comments by the anonymous referee(s). The views expressed in this paper are the author’s and do not necessarily reflect the views and policies of the Bank.

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  1. Economics Unit, Policy and Strategy, Asian Infrastructure Investment Bank, Beijing, China

    Xuehui Han, Jiaqi Su & Jang Ping Thia

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  1. Xuehui Han
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  2. Jiaqi Su
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  3. Jang Ping Thia
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Correspondence to Jang Ping Thia.

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Appendices

Appendix 1

See Table 5.

Table 5 List of variables and estimated values
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Appendix 2

See Table 6.

Table 6 Geographic coverage between OECD and GFCF-GG dataset
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Han, X., Su, J. & Thia, J.P. Impact of infrastructure investment on developed and developing economies. Econ Change Restruct 54, 995–1024 (2021). https://doi.org/10.1007/s10644-020-09287-4

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