workers have the same choice, however, they are allowed to switch back and forth between the PAYGO and the individual accounts every three years. In Uruguay, rather than separate pillars, the system of retirement accounts is stacked onto the PAYGO in a system of "tiers" where participation is determined by income level - that is poorer workers are only allowed to contribute to the public system, while workers earning above a threshold contribute to both systems. The active, parallel systems in many countries make it difficult to keep track of where workers are contributing, posing the danger of double counting contributors and overstating the dependent variable in a given year.<sup>8</sup> Further, in Chile and Peru there are only poor records on contribution to the PAYGO systems prior to reform, challenging attempts to assess the impact of the new regimes in each country. This said, the data in the panel are supported by Holzmann (1997) and Arenas de Mesa (2000). We return to these points and how they may effect our estimates in the next section.

The variation in the sample comes from differences across countries and over time. I control for common, country-specific, unobservable effects that remain constant over time and that may affect both the dependent and the independent variables, by employing fixed effects in the regressions reported below. Regressions employing random effects are also reported, however, these estimates may be biased since the control variables (such as per capita income or share of women in the labor force) are likely to be correlated with country error terms. For this reason I estimate the basic model using OLS, random effects and fixed effects, and test the suitability of the different estimators.

# V. **Results**

Regression results are presented in Table 3. The point estimates on the independent variables vary between the pooled OLS, random effects and fixed effects regressions. The estimated coefficients on all of the control variables except the share of women in the labor force, are significant in all three estimates.<sup>9</sup> The unexpected positive sign on the coefficient of the social security tax variable indicates that all but the most general specification - fixed effects - may be generating biased estimates.'<sup>0</sup> An F test rejects the OLS estimation at the 1% significance level in favor of country fixed effects. However, a Hausman specification (X2) test failed to reject the restrictions on the model imposed by the random effects estimation.

Turning attention to the random and fixed effects estimators (in the last two columns of to Table 3) we see that the coefficients on the control variables for level of development (log of per-capita income, lgnppc, life expectancy at birth, lifeexpf) and changes in the

> **8** I am grateful to Hermann Von Gersdorff at World Bank for pointing out this danger, particularly in the data from Colombia.

> 9 Women's participation in the labor force is positive and significant in the OLS estimation.

> **10** From theory we would expect the social security tax, sstax, variable to be negative, however, in the OLS estimation the coefficient is positive and significant at 1%. Since they do not control for country effects, the OLS coefficients on sstax are most likely capturing the relatively higher number of contributing workers in countries where the population is relatively "older", and where social security systems have been in place longer or matured, requiring higher rates of contribution (Argentina, Uruguay, Colombia, Brazil) (James, 1997).

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