The amount of income that the household receives in contributory pensions also has a strong positive effect (significant at 1% level) on the contribution density of men, but no significant effect on that of women. Retirement benefits received by the elderly in the household may have a positive learning impact on the contribution behavior of workingage men, demonstrating the benefits of formal saving and pooling. That the coefficient on the same variable for women is not significant, comes as little surprise. Benefits received by resident elderly women are likely to have been earned through the contributions of a deceased.husband, and thus, less likely to have a "leaming" impact on the contribution behavior of younger women.<sup>48</sup> If the positive sign on LJUB is capturing a demonstration effect, it would be reasonable to expect a negative and significant coefficient on the amount of _non-contributory_ old age benefits received by elderly the household. However, the coefficient on log of household income from social assistance pensions (LPASIS) is not significantly different from zero. This may indicate that social assistance benefits for old age in Chile are efficiently targeted to the elderly poor, or set sufficiently low as to prevent moral hazard among those of working age.

# _(vi. b) The Probability of Contribution at a Point in Time_

In the set of regressions discussed in this sub-section, I have excluded inactive men and women - those without work who were not looking for employment - as well as the unemployed.<sup>4 9</sup> Many of the variables included in the regressions presented in Tables 9 and 10, are carried over from the probits on contribution to social security, presented in Packard, et al (2001). These variables control for factors that might limit individuals' _access_ to the pension system (labor market insertion; firm size; hours worked; industry of employment). The dummy controlling for access to credit (CREDCON) is also included as a control for factors determining access. Including these variables in the probits helps ensure that the variables constructed from the PRIESO data capture _demand-side_ factors only - individuals' portfolio decisions with respect to their expectations and available options to pool and save outside the system.

To variables used in the tobits discussed above, I have added a larger set of household composition variables (SHYKIDS, SHKIDS, SHOLDM, SHOLDW, NYFEM, RESFAM) to better capture preferences for informal pooling;<sup>50</sup> whether the respondent significant to contribution density. A related variable, the difference between the objective life expectancy as predicted by Chile's life table and respondents' expectations, was similarly positive and significant. However, the simple subjective life-expectancy SLIFEX is preferred to avoid suspected biases discussed in an earlier footnote, and because it is less correlated with age and lowers the risk of biases from multicollinearity.

48 If anything, one might expect a significantly negative coefficient on this variable **for** women of working age.

49 Including inactive men and women in the sample, along with a binary variable to capture their choice to stay out of the labor market, does not change the sign of the significant coefficients in either regression. However, as might be expected, the significance of several coefficients is increased from leaving inactives out of the sample. Since it has been demonstrated elsewhere that inactive respondents are significantly different from those who entered the labor market, I prefer to exclude them.

50 These household composition variables were not included in the tobits out of concem for possible endogeneity.

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