in the structure of the economy, reduce both saving and investment, but not necessarily the saving-investment balance.

# **IV.2.1. What can we learn from the cross-country data?**

We use this to evaluate what pressures would impinge on saving, investment, and the saving-investment balance in the decades ahead as a result of structural developments in the decades ahead, as opposed to through policy adjustments discussed in section IV.2.2.

Section II.3 discussed the set of variables used by the general literature to explain differences in saving rates across countries and over time, and how this traditional set of variables leaves much of China’s saving unexplained. This is in part because the traditional variables do not capture factors influencing enterprise saving, and—largely being macroeconomic variables—only capture short term developments but not changes in economic structure during development. LSHS add urbanization, a structural variable. But the residual in their analysis is still large.

In light of these observations, we extend the set of variables in our empirical estimation of saving and investment, emphasizing “structural” variables that show trends in the medium and long term. We use data from the World Bank World Development Indicator database and the International Monetary Fund’s World Economic Outlook database on 232 countries, from 1960-2004, expressed in 5 year averages. Missing observations mean that we effectively use data on around 134 countries in an unbalanced panel.

Our model is similar to the existing literature in the following aspects (LSHS discuss the expected effects of variables). We expect gross domestic saving to be positively affected by GDP per capita and growth in GDP per capita and negatively affected by the young age dependency ratio, the old age dependency ratio, and the urbanization rate. We also expect saving to be negatively affected by the credit to GDP ratio. We include the interest rate and inflation without having a prior about the sign. We expect a positive impact of public saving. As an extension vis-à-vis the existing literature, we add public spending on health, education, and the social safety net in GDP, which we expect to have a negative impact on saving. This effect would include a direct effect via the government balance and a possible indirect effect on precautionary household saving. The main innovation with respect to the existing literature is that we want to account for differences across countries in enterprise saving. We think these mainly occur because of differences in economic structure and corporate governance setting. Thus, we expect gross domestic saving to be positively affected by the share of industry in GDP and negatively by corporate governance, although we have not been able to include data on corporate governance with sufficient coverage.

Our regression of gross domestic saving on the above set of variables needs to deal with several econometric issues. First, the choice whether to use original data or 5 year or 10 year averages. The choice of 5 year average is arbitrary. The advantage of using averages is that we do not need to use a dynamic specification to allow for inertia. This avoids one 13