market value when they decide to issue new shares.<sup>60</sup> Thus, we use both variables in order to get a rough idea of such price effects. Finally, the behavior of contractual savings institutions may significantly depend on their investments. For instance, they will have greater incentive to be active investors in the stock market when they hold a large share of their assets in stocks; conversely, explanations favoring corporate governance issues are less likely to be relevant in countries in which contractual savings hardly invest in the stock market. In order to account for such effects, we define the following variable: CSSHFA is defined as the proportion of shares in the portfolio of contractual savings institutions; It is likely that the incentive for contractual savings institutions to actively monitor on the stock market is positively correlated to CSSHFA. Therefore, this variable aims at capturing cross-country and time-series differences in the behavior of these institutions.<sup>61</sup>

### _(4) Empirical Strategy_

We start with simple descriptions of corporate financing choices and contractual savings characteristics across countries. We look at the evolution of contractual savings size and investments, and we compute simple correlations between the variables. These simple statistics show strong correlations between the capital structure variables and the contractual savings characteristics. Next, we control for other variables in pooled and panel estimates that confirm the initial intuition. Given that we use macroeconomic variables in our estimations, firm level data are not appropriate. However, we still want to keep some heterogeneity within countries. For this reason, our analysis is conducted at two different levels. First, at the country level, by taking the average values of firms’ characteristics by country, and for each year. This gives us 229 observations. We use this country-level data set to illustrate our results. Second, we confirm the robustness of the results by doing the same analysis at the 2 digit (SIC code) industry level by taking the average values of firms’ characteristics by country, industry, and per year. Therefore, we obtain a panel data set (of approximately 6000 observations) in which the unit is industry-country-year.

# **5. Empirical Results**

## **_5.1 Descriptive Statistics_**

Table 1 presents the sample of countries that are included in our contractual savings database, and the total number of firms available for each country. We have data for 35 countries, including 13 emerging economies (Argentina, Brazil, Chile, Hungary, India, Korea, Malaysia, Mexico, Singapore, South Africa, Sri Lanka, Thailand and

> 60 Pagano, Panetta and Zingales (1998) show for instance that IPOs  are partly motivated by stock overvaluation in the industry in which the firm operates.

> 61 In the final set of estimations, we also report  the results obtained with the variable CSSHCAP (contractual savings’ equity investments, as a % of stock market capitalization). This variable allows instead investigating whether the _size_ of the contractual savings’ stock holdings, relative to stock market capitalization, is an important factor.

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