Demographics and FDI: lessons from China’s one-child policy

Donaldson, John B.; KOULOVATIANOS, Christos; LI, Jian; MEHRA, Rajnish

doi:10.1017/S1365100524000166

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Demographics and FDI: lessons from China’s one-child policy

Donaldson, John B.; KOULOVATIANOS, Christos; LI, Jian et al.

2024 • In Macroeconomic Dynamics, 29

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10.1017/S1365100524000166

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Keywords :

capital-labor ratio; FDI intensity; model; one-child policy; open economy; overlapping generations; population aging; Economics and Econometrics

Abstract :

[en] Following the introduction of the one-child policy in China, the capital-labor ratio of China increased relative to that of India, while FDI/GDP inflows to China versus India simultaneously declined. These observations are explained in the context of a simple neoclassical overlapping generations paradigm. The adjustment mechanism works as follows: the reduction in the growth rate of the (urban) labor force due to the one-child policy increases the capital per worker inherited from the previous generation. The resulting increase in China’s domestic capital-labor ratio thus "crowds out" the need for foreign direct investment (FDI) in China relative to India. Our paper is a contribution to the nascent literature exploring demographic transitions and their effects on FDI flows.

Disciplines :

International economics
Macroeconomics & monetary economics
Finance

Author, co-author :

Donaldson, John B.; Columbia Business School, Columbia University, New York, United States

KOULOVATIANOS, Christos ; University of Luxembourg > Faculty of Law, Economics and Finance (FDEF) > Department of Finance (DF)

LI, Jian ; University of Luxembourg > Faculty of Law, Economics and Finance > Department of Economics and Management ; Institute for Advanced Economic Research, Dongbei University of Finance and Economics, Dalian, China

MEHRA, Rajnish ; University of Luxembourg > Faculty of Law, Economics and Finance > Department of Finance ; Department of Economics, Arizona State University, Tempe, United States ; NBER, Cambridge, United States ; NCAER, New Delhi, India

External co-authors :

yes

Language :

English

Title :

Demographics and FDI: lessons from China’s one-child policy

Publication date :

02 May 2024

Journal title :

Macroeconomic Dynamics

ISSN :

1365-1005

eISSN :

1469-8056

Publisher :

Cambridge University Press

Volume :

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S1365100524000166

Funding text :

We are grateful to Costas Arkolakis, Costas Azariadis, Espen Henriksen, and Gustavo Ventura, as well as participants at the Econometric Society meetings in Cotonou, for their valuable feedback. We pay special tribute to the late Robert Lucas and the late Edward Prescott for their insightful comments. Special thanks are also due to the late Thomas Cooley for his detailed feedback and suggestions. We appreciate the constructive critiques and guidance from three anonymous referees and co-editor Lee Ohanian, which have significantly enhanced this paper. Li acknowledges financial support from the Natural Science Foundation of Zhejiang Province, China (Contract LQ20G030006), and the China Scholarship Council.

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Bibliography

A recent study examining the impact of population aging on economic performance through savings channels is Eggertsson et al. (2019).
Various papers offer different explanations for this savings increase, all of a social nature. See Appendix G for a full discussion.
From the empirical side, the increase in life expectancy in India and China during the period under study was essentially the same. Furthermore, there is little evidence that the bequest motive is a dominant social force in China (see Horioka, 2014). These observations suggest that neither phenomenon has been a significant determinant of relative FDI flows into China versus India.
In the three most recent papers of this literature, capital per worker is a crucial determinant of FDI flows, but exogenous variations in the growth rate of workers across countries and over time (for the same country), are not possible, due to the tight perfect foresight equilibrium conditions of infinitely lived agents. What we add to this neoclassical production FDI literature is the overlapping generations analysis that allows us to vary the growth rate of workers over time and across countries. Backus et al. (2014) and Cooley and Henriksen (2018) are two additional citations with a demographic emphasis. In the former, the authors directly explore the implications of differing population dynamics (life expectancies, population age distributions) for capital flows between countries. In the latter the focus is on the implications of population dynamics for economic growth rates within countries, particularly Japan and the USA. The mechanism we have emphasized, however, is not showcased in either of these papers.
Both data and population projection scenarios portrayed in Figure 1 are obtained from the United Nations Population division. Computations are done using an open-source package described in Raftery et al. (2012) and Gerland et al. (2014).
This delayed reaction is also due to a gradual increase in policy effectiveness and the gradual elimination of rural exemptions. The model to be proposed captures this decline as occurring in a single 25 year period, which is an artifact of the model’s parsimony and the choice of a time interval equivalent to 25 years.
In early 2023, the total population of India exceeded that of China.
The recently introduced (2017) two-child policy in China may alter the anticipated population dynamics in China, depicted in the left panel of Figure 1, after 2030. Nevertheless, population dynamics 15 years ahead will not be affected: see the time interval bracketed by the vertical dashed lines. As of January 2020, there has been no uptick in Chinese fertility. See also Zhang (2017).
Of course, China and India differ with respect to other dimensions that also affect FDI as well, such as capital market institutions and information flows (Froot and Stein, 1991) and the intellectual property environment (Holmes, et al. 2015). We thank an anonymous referee for pointing this out.
For the calculations in Table 1 and the subsequent theoretical analysis, we employ a Cobb–Douglas production function given by Yt = Ktα (AtLt)1−α, where Y is GDP, K is capital, L is labor, and A is labor productivity (K is measured as the value of the capital stock and L as total hours worked). The two columns of Table 1 under “gA” labor productivity growth, have been calculated using the formula gA = (gY − αgK) / (1 − α) − gL, where we have assumed that the capital intensity parameter, α = 1/3 for both China and India.
The similarities in productivity differences between China and India are also supported by Hsieh and Klenow (2009), and Bollard, et al. (2013).
The data underlying Figure 3 is found in Table H.1 of Appendix H, available online.
In Appendix H we document the data used in Figure 3 and offer a robustness check focusing on K/L trends of the non-agricultural workforce in both countries (see Figure H.6 and Table H.2 in Appendix H). It is important to note that FDI in China and India during the period examined did not represent the purchase of existing domestic capital by foreign entities; observed FDI data predominantly describes the formation of new capital. This experience contrasts with that of the USA where the vast majority of FDI is for the purchase of claims to already existing capital stock.
Our production structure is a simplified version of the one in McGrattan and Prescott (2009, 2010) and Holmes et al. (2015).
While labor productivity may be firm-specific, we lack any data on productivity growth in foreign-owned versus domestically owned firms for either China or India, hence, the simplifying assumption. Assuming Ai,t ≠ Air,t leads to the same conclusions as the present formulation.
These growth rates need not be identical across countries as our notation allows. Indeed, the one-child policy will manifest itself as a structural change in the constant population growth rate in one country.
See Online Appendix A for the derivation of expression (6).
Note that the capital return in both countries is fixed by the world interest rate r∗ plus the common depreciation rate. This is a consequence of the competitive assumption and the free flow of capital internationally. Returns, particularly in China, have historically shown wide variation, however. See Appendix G for a discussion of the related literature.
A quick way to numerically verify the sign of the term m (r∗) /m (r∗) is to compute the difference in ln [m (r∗)] for small changes in r∗
For details, see our calibration section below and our robustness checks in Online Appendix F.
We do not explicitly match the dynamics of capital. Capital stocks are not easy to measure, especially at the sectoral level, that is, the distinction between the stock of capital invested in domestic production, versus capital stock connected to FDI activities. Future research may focus on these measurement issues, and an extension of our model could focus on matching these sectoral-capital-stock dynamics.
Here, we assume that working life started at 15 years old in the early agrarian regime years, with T = 50 and TR = 40 implying 65 years of life expectancy and retirement at 55 years.
Backus, D., T. Cooley, E. Henriksen (2014) Demography and low-frequency capital flows. Journal of International Economics 92, S94–S102.
Bai, C.-E., C.-T. Hsieh and Y. Qian (2006) The return to capital in China. Brookings Papers on Economic Activity 2006(2), 61–101.
Bollard, A., P. J. Klenow, G. Sharma (2013) India’s mysterious manufacturing miracle. Review of Economic Dynamics 16(1), 59–85.
Chamon, M. D. and E. S. Prasad (2010) Why are saving rates of urban households in China rising? American Economic Journal: Macroeconomics 2(1), 93–130.
Chen, K. and Y. Wen (2017) The great housing boom of China. American Economic Journal: Macroeconomics 9(2), 73–114.
Choukhmane, T., N. Coeurdacier and K. Jin (2020) The one-child policy and household saving, mimeo. Journal of the European Economic Association 21(3), 987–1032.
Cooley, T. and E. Henriksen (2018) The demographic deficit. Journal of Monetary Economics 93, 45–62.
Curtis, C. C., S. Lugauer and N. C. Mark (2015) Demographic patterns and household saving in China. American Economic Journal: Macroeconomics 7(2), 58–94.
Diamond, P. A. (1965) National debt in a neoclassical growth model. American Economic Review 55, 1126–1150.
Eggertsson, G. B., M. Lancastre, L. H. Summers (2019) Aging, output per capita, and secular stagnation. American Economic Review: Insights 1(3), 325–342.
Froot, K. A. and J. C. Stein (1991) Exchange rates and foreign direct investment: an imperfect capital markets approach. The Quarterly Journal of Economics 106(4), 1191–1217.
Gerland, P., A. E. Raftery, H. Ševčíková, N. Li, D. Gu, T. Spoorenberg, L. Alkema, B. K. Fosdick, J. Chunn, N. Lalic, G. Bay, T. Buettner, G. K. Heilig, J. Wilmoth (2014) World population stabilization unlikely this century. Science 346(6206), 234–237.
Holmes, T. J., E. R. McGrattan and E. C. Prescott (2015) Quid pro quo: technology capital transfers for market access in China. The Review of Economic Studies 82(3), 1154–1193.
Holston, K., T. Laubach and J. C. Williams (2017) Measuring the natural rate of interest: international trends and determinants. Journal of International Economics 108, S59–S75.
Horioka, C. Y. (2014) Are Americans and Indians more altruistic than the Japanese and Chinese? Evidence from a new international survey of bequest plans. Review of Economics of the Household 12(3), 411–437.
Hsieh, C.-T., P. J. Klenow (2009) Misallocation and manufacturing TFP in China and India. Quarterly Journal of Economics 124(4), 1403–1448.
Imrohoroğlu, A. and K. Zhao (2018) The chinese saving rate: long-term care risks, family insurance, and demographics. Journal of Monetary Economics 96, 33–52.
Lucas, R. E., Jr (1990) Why doesn’t capital flow from rich to poor countries? American Economic Review, Papers and Proceedings 80, 92–96.
McGrattan, E. R. and E. C. Prescott (2009) Openness, technology capital, and development. Journal of Economic Theory 144(6), 2454–2476.
McGrattan, E. R. and E. C. Prescott (2010) Technology capital and the US current account. American Economic Review 100(4), 1493–1522.
Raftery, A. E., N. Li, H. Ševčíková, P. Gerland and G. K. Heilig (2012) Bayesian probabilistic population projections for all countries. Proceedings of The National Academy of Sciences of The United States of America 109(35), 13915–13921.
Song, Z., K. Storesletten and F. Zilibotti (2011) Growing like China. American Economic Review 101(1), 196–233.
Wei, S.-J. and X. Zhang (2011) The competitive saving motive: evidence from rising sex ratios and savings rates in China. Journal of Political Economy 119(3), 511–564.
Yang, D. T., J. Zhang and S. Zhou (2013) Why are saving rates so high in China?. In: Yang, D. T., J. Zhang and S. Zhou. (eds.), Capitalizing China, pp. 249–278. Cambridge, MA: National Bureau of Economic Research.
Zhang, J. (2017) The evolution of China’s one-child policy and its effects on family outcomes. Journal of Economic Perspectives 31(1), 141–160.