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The authors thank Kenneth B. Bercuson, Peter Clark, and SEA department seminar participants for useful comments or discussions. Any remaining errors and the views expressed are those of the authors.
Specifically, they examine the long-run impact of the dependency ratio and the public debt ratio (relative to the remaining G7 countries) on the net foreign asset position of the United States, Germany, and Japan. For a broad study on saving trends in OECD countries see Dean et al. (1990) and the references cited therein.
Lahiri's estimations of saving behavior are conducted country by country, rather than with a pooled dataset for all the countries in his sample.
Several cross-sectional studies have found that higher dependency ratios are associated with lower saving rates. See for example Graham (1987), Masson and Tyron (1990).
Empirical studies, however, generally find that real interest rate effects on saving are quite small. For example, Ogaki, Ostry, and Reinhart (1995) find that the interest sensitivity of saving rises with income, but from a very low level.
To the extent that the wealth as a ratio to income--e.g., capital-output ratio--is roughly constant over time, wealth may only affect the level of saving.
Leaving aside the issue of foreign firm ownership, this measure of saving may be appropriate under the presumption that households see through the “corporate veil” and account for corporate saving in their own saving decisions.
Which excludes provident fund contributions in the case of Singapore and Malaysia.
Data on consumption, income, money, and quasi-money are obtained from International Financial Statistics. Data on tax revenue for the period 1972-92 are obtained from Government Financial Statistics, and figures for the early part of the sample are calculated based on the average tax revenue to GNP ratios during 1972-74. Demographic data are taken from the Demographic Yearbook and country national accounts publications. Figures for missing observations are calculated using period average growth rates of working age population and total population. Data for provident fund saving are based on figures contained in the annual reports of the CPF and the EPF.
Moreover, the number of independent cointegrating vectors r must be such that 0 < r < N. If there were exactly N such linearly independent combinations, then the set of variables must all be stationary (I(0)). If no combinations exist (r = 0), the series are independent difference-stationary (I(1)) variables.
Saving and provident fund saving are defined as ratios to private disposable income. The demographic variable is the working age population ratio, financial deepening is the ratio of money plus quasi-money to private disposable income, and growth is the percentage change in real per capita private disposable income.
It should be noted, however, that the sample period for each country is small and the ADF test is low-powered, too often accepting a false null hypothesis (unit root)--i.e., prone to making type II errors.
The corresponding normalized cointegrating vector is given by (1,-β), representing the stationary linear combination of the I(1) variables, S/Y and X.
In general, since the long-run coefficients of cointegration are based on the co-movements between the series over the entire sample period, substituting observed values of the fundamentals directly into the estimated long-run relationship neglects the effects of stationary short-run noise in the explanatory variables. To compensate, filtered estimates of the fundamentals are subsequently used where appropriate. Specifically, the permanent (trend) component is smoothed using a three-year centered moving average, including one lead and one lag, in the case of Singapore largely due to the transitory noise in the PFS variable.
In general, with N difference-stationary time series, there can exist up to N-1 possible cointegrating vectors. See Cuthbertson, Hall, and Taylor (1992) for a discussion.
Initially, PFS and DEM, without the inclusion of a time trend, proved insufficient for cointegration with saving. Subsequently, the time trend was replaced with the financial deepening variable and virtually the same parameter estimates and R2, DW, and ADF statistics were obtained (reported in Table 3). At that stage, further tests suggest that cointegration obtains even after dropping PFS from the regression.
The findings are consistent with the results of the study conducted by the Monetary Authority of Singapore (1991). See also Chandavarkar (1993). In contrast, Husain (1994) finds that CPF saving had a statistically insignificant impact on total private saving in Singapore.
The inclusion of the financial deepening variable results in a coefficient estimate with a negative sign, due to the strong co-movement in the trends of demographics and financial deepening.
The Dickey-Fuller test statistic, while quite high for the entire panel in this case, is not readily comparable to available critical values. Hence, country-by-country ADF test statistics were also calculated using corresponding subsets of the pooled residuals from the panel regression. These tests indicate that the residuals for Singapore and Thailand exhibit nonstationarity (evidence against cointegration) when only demographics is included.
In terms of the panel estimation, coefficients for these variables for Indonesia, Malaysia, and Thailand were constrained to be zero as warranted. For example, in the case of Malaysia, constraining the coefficient on DEM to equal that of Singapore and Thailand and near the point estimate obtained from the country estimates confirms that the long-run impact of EPF savings on total savings is zero.
Included in the last component in equation (2) is present and past innovations in X reflecting transitory fluctuations in the long-run determinants of saving, which may affect short-run changes in saving.
In fact, the Granger representation theorem shows that for any set of I(1) variables that cointegrate, there exists a valid error correction representation of the data. See Engle and Granger (1987).
Including contemporaneous innovations in the explanatory variables in the error correction representation gives the model more structural interpretation with regard to short-run dynamics. However, this specification also raises the risk of simultaneity bias. Fortunately, based on the Lagrange Multiplier (LM) tests for serial correlation reported in Table 6, simultaneity does not appear to be a serious problem.
To the extent that consumption displays some habit persistence or is subject to adjustment costs, consumption rates and, hence, saving rates may display some inertia and depend on past innovations. This effect appears weak, based on the error correction estimates.
The error correction mechanism (ECM) term in Table 6 captures the effect of equilibrium errors on the short-run adjustment of saving to its long-run value while the short-term factors capture the effects of disequilibrium disturbances. The speed of adjustment to the long-run trend rate of saving can be calculated as follows: the number of periods T required for all but x percent of a shock to remain is (1−α)T = x.