Abstract
This paper constructs a small open two-sector (health care and non-health care) overlapping generations model and investigates how changes in the demand for health care induced by population aging influence the economy’s employment structure and per capita income growth rate. We show that population aging induces a shift in labor from the non-health care sector to the health care sector and lowers the per capita income growth rate. This paper also investigates public policy for child care and demonstrates the existence of an intergenerational conflict between current and future generations concerning public policy on child care.
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Notes
Source: The World Population Prospects: The 2004 Revision Population Database. The more developed regions include all countries in Europe plus North America, Australia, New Zealand, and Japan.
Source: OECD Health data 2006 and World Bank Development Indicators 2006.
The World Bank Development Indicators 2006 are used to calculate the old-age dependency ratio and the per capita output growth rate. We use data for 108 countries over the period 1975–2004.
This description of agents’ health care service demand is influenced by Bednarek and Pecchenino (2002), while they explicitly consider the aspect of health care service as “investment goods.”
Provided that agents demand health care in both youth and old age purely for consumption, the implications of the model remain mostly unchanged.
The relationship between the mortality rate at different ages and fertility remains poorly understood. Therefore, there is an ongoing empirical debate on this relationship. Contrary to Lorentzen et al. (2008), Acemoglu and Johnson (2006) find the positive effect of life expectancy on fertility. Moreover, Hazan and Zoabi (2006) provide theoretical insight into this question and show under which assumptions a standard model a là Barro–Becker yields each of the observed empirical cases.
Empirical work by Lorentzen et al. (2008) concludes that the impact of adult mortality on the investment in human capital is inconclusive. Therefore, this paper is partly consistent with their empirical results.
Because the level of \(A_{t}^{N}\) is irreversible, the relation \(G_{t}^{N} \ge 1\) holds. Thus, the incomplete specialization assumptions \(l_{t}^{H} \in (0,1)\) are summarized as the following parameter restrictions: \(l_{1}^{H}= \frac{(1-\gamma)R}{n_{0}}\frac{1+\phi+p}{1+p}\frac{V_{1}}{\bar{w}A_{1}h_{1}} \le 1\) and \(l^{H}(1,p)=\frac{(1-\gamma)R}{n_{t-1}(p)}\frac{p}{1+p} \le 1\).
See, e.g., Atoh and Akachi (2003).
Empirical studies by Winegarden and Bracy (1995) and Averett and Whittington (2001) conclude that the fertility effect of maternity leave is positive. On the other hand, Gauthier and Hatzius (1997) show that maternity leave has no significant effect on fertility, while family allowances have a positive and significant effect.
In the numerical simulation, we specify the production function of the non-health care sector as \(Y_{t}^{N}=K_{t}^{\alpha }(A_{t}^{N}L_{t}^{N})^{1-\alpha}\). In addition, the law of motion of the sector-specific labor productivity index \(A_{t}^{i}\) is given by \( A_{t+1}^{i}=[1+\beta^{i}(h_{t})^{\lambda ^{i}}(l_{t}^{N})^{\xi ^{i}}]A_{t}^{i}\). Then, in order to consider the case where the labor productivity growth rate of the non-health care sector is higher than that of the health care sector, we assume β N > β H. Rigorous explanations for the parameter values of our numerical simulation are available from the authors upon request.
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Acknowledgements
We wish to thank two anonymous referees, Noritsugu Nakanishi, Yoshiyasu Ono, Koji Shimomura, and participants of IEFS Japan Conference 2005 held at Kobe University for helpful comments and suggestions. All remaining errors are our own. We also acknowledge the financial support from the Ministry of Education, Culture, Sports, Science and Technology of Japan under the Grant-in-Aid for Young Scientists (B): (No. 20730132) and (No. 20730217).
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Appendix
Appendix
The derivation of \(l_{t}^{H}\)
By substituting Eq. 3, \(w_{t}=\bar{w}A_{t}^{N}\), h t = h into Eq. 22, we obtain
In addition, by substituting Eqs. 7, 10, and 21 and h t = h into L t = h t η t N t , we obtain
Moreover, from substituting Eqs. 7, 10, and 21 into Eq. 19, we have
Using N t = n t − 1(θ)N t − 1, \(z_{t}=\frac{(1-\gamma)R}{1+\phi+p} \frac{(1-\tau_{t-1})}{G_{t-1}^{N}}A_{t}^{H}h\), \(N_{t}h=\frac{1+\phi+p}{1+p- \frac{\theta}{1-\theta}\phi}L_{t}\), and \(\tau_{t}=\frac{\phi}{1+p}\frac{\theta }{1-\theta}\), Eq. 12 is written as
Here, it corresponds to Eq. 23 when θ > 0 and Eq. 15 when θ = 0.
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Hashimoto, Ki., Tabata, K. Population aging, health care, and growth. J Popul Econ 23, 571–593 (2010). https://doi.org/10.1007/s00148-008-0216-5
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DOI: https://doi.org/10.1007/s00148-008-0216-5