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Demography and cross-country differences in savings rates: a new approach and evidence

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Abstract

This paper includes both dependency thesis and pension motive for savings to explain the large differences in cross-country savings rates. The two demographic factors are incorporated into an overlapping generation model, and the steady-state savings rates for a sample of 109 countries are computed. Both demographic factors can explain up to 68% of the dispersion in the cross-country savings rates. Furthermore, if the expenditure burden is sufficiently high, fertility has a greater impact on cross-country savings rate differences than longevity does. This study also satisfactorily explains the large gap in savings rates between the high- and low-income countries.

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Notes

  1. By replicating Mankiw et al. (1992) and using more recent data, we find that savings rates and labor growth rate explain about 48% of the variation in international income. Using Klenow and Rodriguez-Clare’s (1997) variance decomposition, savings rates account for 23% of international income differences. Working along the lines of Prescott (1998), we find little evidence that savings rates explain international income differences. Given the results of these three approaches, we are inclined to conclude that the effect of savings rates on income levels is moderately important.

  2. Some countries have low savings rates because of institutions and policies that discourage savings and investments such as high effective tax rates on capital income (McKinnon 1973; Easterly and Rebelo 1993). The observed low/high savings might also be due to subsistence consumption needs (Gersovitz 1988).

  3. While previous studies have investigated the effect of lifetime uncertainty on savings (Yaari 1965; Davies 1981; Hurd 1989), their main focus is to study the effect of lifetime uncertainty on the dissaving of the elderly to reconcile the empirical findings which indicate that the elderly do not dissave as fast as the simple life-cycle model predicts (Mirer 1979; Menchick and David 1983). Instead, this paper focuses on the lifetime uncertainty of the working-age population.

  4. The presence of public pension system such as social security may mitigate the effect of longevity on saving. In the absence of democratic factors such as longevity and fertility, Zhang and Zhang (2004) find an insignificant effect of social security on savings. In this paper, we rule out the effect of public pension system.

  5. Bloom et al. (2003) also add longevity to a standard model of life-cycle saving, and through their regression empirical tests, they found that the inclusion of life expectancy into savings regressions not only improves the fit but also can explain the remarkable savings boom in East Asia, which is difficult to explain using standard life-cycle model. Unlike their work, we use survival rates, instead of life expectancy, of the population aged 20–40 and 40–60 and calibrate the steady-state savings rates for the countries in this study’s dataset.

  6. The expenditure burden of children can change households’ consumption behavior which may be amplified when their decision is also affected by their life expectancy. All these effects influence the factor prices, such as wages and rental rate of capital. Thus, the advantage of our approach over the empirical estimation is that it takes into account the endogeneity of factor prices and their effects on the household saving behavior.

  7. I assume a closed economy. Theoretically, in an open economy where capital is internationally mobile, domestic savings rates would not necessarily be correlated with domestic investment rates. However, the correlation between national savings and investment rates across OECD countries, where capital is relatively free to move, is one of the best-established facts in international economics. Empirical evidence suggests that the high correlation is robust to changes in time period. This indicates that, on average and over long periods of time, changes in capital accumulation respond mostly to changes in domestic savings (see, for example, Feldstein and Bacchetta 1992). For less developed countries which are less open, the correlation between investment and savings rates is expected to be higher. Therefore, the closed-economy assumption is not unreasonable.

  8. For instance, higher fertility rates may lower saving as parents expect to receive old-age “pensions” from their children. It can also tighten the households budget left for saving or lower future discount rate.

  9. Endogenizing fertility decision is left for future work.

  10. Alternatively, I can assume that there is a perfect annuities market whereby all savings are intermediated through mutual funds which invests the savings and guarantees a gross return R t + j to the surviving agents of t − 1 at period t + j (j = 1, 2) as in Blanchard (1985) and Yaari (1965). In both cases, the gross return on capital is given by R t + j /p j , and the results will be similar to the scheme introduced in this paper.

  11. This is the steady-state savings rate for any value of σ. However, the steady-state savings rate relations are based on the effects of demographic factors on capital accumulation which is derived under the assumption of log-utility (σ = 1).

  12. Controlling for the mean of the model predictions is important because although one can generate predictions on savings rates such that its standard deviation is close to that of data, its mean can be remotely distant from the data mean which is not the objective of this study.

  13. See, for example, Bank Negara Malaysia (2003), “Improving the Allocation of Domestic Savings for Economic Development: Case Study for Malaysia.”

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Acknowledgements

We gratefully acknowledge helpful comments from B. Ravikumar, Raymond Riezman, and Matthew Mitchell. We also would like to thank the Editor Junsen Zhang and an anonymous referee for their valuable suggestions. All errors are our own.

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  1. School of Business and Management, Azusa Pacific University, 901 E. Alosta Ave., PO Box 7000, Azusa, CA, 91702-7000, USA

    Elwin Tobing

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Correspondence to Elwin Tobing.

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Responsible editor: Junsen Zhang

Appendix

Appendix

1.1 Data description

The savings data used are gross domestic savings rates from the World Development Indicators and private savings rates from the World Saving Database, both published by the World Bank. Attention is restricted to countries where time series data on savings rates are available for at least 10 years during the period 1980–2000. Excluded as well are countries with labor force less than 100,000 during the period. The 1980–2000 period provides a relatively stable series on savings rates:

  1. 1.

    Gross domestic savings rates

    $$ {\rm GDSR} = \frac{{\rm GDS}}{{\rm GDP}} = \frac{{\rm GDP} - C - G}{{\rm GDP}}, $$

    where GDS is gross domestic savings, GDP is gross domestic products, C is aggregate private consumption, and G is aggregate public consumption.

  2. 2.

    Private savings rates, PSR

    $$ {\rm PSR} = \frac{{\rm GDS} + ({\rm NFI} - {\rm CGS})}{{\rm GDP} + {\rm NFI}}, $$

    NFI is net factor income from abroad plus international transfers and CGS is central government savings. Notice that private savings rates are not always less than gross domestic savings rates. PSR = GDSR if NFI = CGS = 0, PSR < GDSR if NFI < CGS and PSR > GDSR if CGS < 0.

  3. 3. Survival rates

    Survival rates are obtained from WHO’s lifetable. The table provides the death rate in 5-year intervals for the year of 2000. To construct the survival rates for the age groups of 20–40 and 40–60, I do the following: First, I obtain the mortality rate for each age group. Denote the mortality as \(M_{x}^{n}\) which means the mortality rate of the population living in the interval (x, x + n). Next is to obtain the probability of death of the population living in the interval (x, x + n), which is denoted as \(q_{x}^{n}\) and given by

    $$ q_{x}^{n} = \frac{n M_{x}^{n}}{1 + \left[n - a_{x}^{n}\right]M_{x}^{n}}, $$

    where \(a_{x}^{n}\) is the separation factor which, in this case, is 10. The survival rate, \(p_{x}^{n}\), is \(1 -q_{x}^{n}\).

    For p 1, we first calculate survival rate for age groups 0–1, 1–4, and 5–19 or \(p_{0}^{1}\), \(p_{1}^{4}\), and \(p_{5}^{19}\). We use separation factors of 0.05 for age group 0–1 and 1.524 for age group 1–4. Then p 1 is given by \(p_{0}^{19} = p_{0}^{1} \times p_{1}^{4} \times p_{5}^{19}\).

  4. 4. Fertility rates

    The data are taken from the World Development Indicators, 2004 (World Bank). Total fertility rate represents the number of children that would be born to a woman if she was to live to the end of her childbearing years and bear children in accordance with prevailing age-specific fertility rates. For the computation of steady-state savings rates, we divide fertility by 2 because young agents in our model are either males or females.

  5. 5. Income per worker

    The data are taken from the Penn World Table version 6.0.

  6. 6. Labor force growth rate

    The source is the World Development Indicators. We calculate the geometric growth rate between the year of 1980 and 2000.

1.2 Sample

Albania; Algeria; Angola; Argentina; Australia; Austria; Bahrain; Bangladesh; Barbados; Belgium; Belize; Benin; Bolivia; Botswana; Brazil; Bulgaria; Burkina Faso; Cameroon; Canada; Central African Republic; Chile; China; Colombia; Congo, Democratic Republic; Congo, Republic; Costa Rica; Cote d’Ivoire; Cyprus; Denmark; Dominican Republic; Ecuador; Egypt; El Salvador; Ethiopia; Fiji; Finland; France; Gabon; Gambia; Germany; Ghana; Greece; Guatemala; Guinea; Guyana; Hungary; Iceland; India; Indonesia; Iran; Ireland; Israel; Italy; Jamaica; Japan; Kenya; Korea Republic; Madagascar; Malawi; Malaysia; Mali; Malta; Mauritania; Mauritius; Mexico; Mongolia; Morocco; Namibia; Nepal; the Netherlands; New Zealand; Niger; Nigeria; Norway; Oman; Pakistan; Panama; Papua New Guinea; Paraguay; Peru; Philippines; Poland; Portugal; Romania; Saudi Arabia; Senegal; Sierra Leone; Singapore; South Africa; Spain; Sri Lanka; Sudan; Swaziland; Sweden; Switzerland; Syrian Arab Republic; Thailand; Togo; Trinidad and Tobago; Tunisia; Turkey; Uganda; UK; USA; Uruguay; Venezuela; Yemen Republic; Zambia; Zimbabwe.

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Tobing, E. Demography and cross-country differences in savings rates: a new approach and evidence. J Popul Econ 25, 963–987 (2012). https://doi.org/10.1007/s00148-011-0386-4

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