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We are grateful to Fei Han, Yitae Kim, Jerry Schiff, Alison Stuart, Chikahisa Sumi, and participants at the IMF Asia and Pacific Department seminar and the joint IMF/HKMA seminar for helpful comments. All remaining errors are our own responsibility.
A higher ranking indicates a lower score out of the sample countries.
Previous empirical analyses find an important role for demographic variables on saving rates based on the life cycle hypothesis, such as Modigliani (1970), Feldstein (1980), Chinn and Prasad (2003), Park and Shin (2009), and Hung and Qian (2010), and Chinn and Ito (2008). Higher portion of elderly in the population is often associated with lower saving rates given the elderly typically finance the living expenses by drawing down savings. Similarly, higher young dependency in the population will typically consume without earning income, posing a negative impact on the overall domestic saving rates. The elderly also sees less need for precautionary savings as the youth would likely provide more care and finance assistance. This would imply higher elderly and youth dependency in the population would generally be associated with lower saving rate.
The regression uses lagged terms on the explanatory variables to mitigate the potential endogeneity problems. An alternative will be using the non-overlapping periods for saving rate and the explanatory variables as in Chinn and Ito (2006) and Ayadi et al. (2013).
In this exercise the working-age population is defined as age 20-64.
Although the elderly dependency ratios in China are projected to more than double by 2030, its youth dependency ratios are expected to decline sharply. As a result, its working-age population ratios will only decline from 65% in 2010 to 63% in 2030.
We assume a 1 percent increase in working age population ratio will increase savings rate by 0.3 percent in emerging Asia and 0.5 percent in industrialized Asia.
The scenario of a 2 percent increase in investment as a share of GDP is for illustrative purposes. Although it is widely recognized that there is a need to increase infrastructure investment in emerging Asia, partly through public investment, there is no consensus on the optimal level of infrastructure investment, the financing scheme or the efficiency of the investment.
In the GIMF model the central bank uses an inflation-forecast-based interest rate rule. The central bank varies the gap between the actual policy rate and the long-term equilibrium rate to achieve a stable target rate of inflation over time. Under this framework, monetary policy stance tends to tighten when there is a positive output gap or inflation gap and vise versa.
Typically, a risk-sharing compares how growth in marginal utility of consumption differs across countries, which is indicative of how much risk is shared.
Meanwhile, risk sharing should not be expected to contain the most extreme of shocks.
Long-term syndicated bank lending to Asia from outside the region has also been affected by the global financial crisis and continued deleveraging of European banks, although a number of Asian banks have stepped up cross border lending, particularly Australian, Singaporean and Japanese banks.
Probably unknown to many, the share of stock market capitalization as a percentage of GDP in most Asian countries is comparable to their total banking sector assets, with debt securities markets coming a distant third. It contrasts with developments in many advanced countries, where the banking sector continues to dominate financial intermediation
The revenue stream from infrastructure tends to be less sensitive to the economic cycle and generally inflation-protected too.