young and save later in the life cycle (consistent with the LCH), or a generational change, in which the savings behavior of younger and older cohorts is fundamentally different, with older cohorts acting more frugally than younger.<sup>12</sup>

# _Alternative specifications_

The baseline model offers a number of results regarding Armenian household saving behavior which are examined further using alternative model specifications. Appendix 5 presents results of pooled OLS using data from all survey years (excluding 2006), a binary dependent variable model predicting saving behavior (column 5), along with quintile regressions at the first, second, and third quartile of the distribution (columns 2, 3 and 4). The binary dependent variable model (estimated using a probit regression) allows for the comparison of household level decisions regarding whether or not to save with decisions regarding the magnitude of saving presented in table 2 (along with column 1 of Appendix 5). The inclusion of quintile regression results seems appropriate for two reasons: as a manner to examine if the determinants of savings are constant along the savings distribution, and to estimate specifications using minimum absolute deviations in order to reduce the sensitivity to outliers in the savings model.<sup>13</sup> Results from the probit model predicting whether a household makes any savings in the survey month are generally in agreement with results from the pooled OLS. As per the results discussed previously, savings behavior responds to income in a positive and significant manner. In the probit specification all results are reported as marginal effects at the mean, and at these values the importance of household income in determining whether to save exceeds the importance of income in determining the quantity to save.

Point estimates on household characteristics agree with those from Appendix 4 as well as with the pooled OLS results in column 1 of Appendix 5. This suggests that the mechanism underlying a household’s decision of optimal savings rate responds to similar factors in a similar way as their decision of whether or not to save. The age gradient result is more pronounced in pooled OLS than in the probit specification, suggesting that the life-cycle or cohort specific savings preferences owe more to older age groups’ preference to save a higher proportion of their income, rather than a greater preference to make any savings.

# _Robustness check_

Finally, a series of quantile regressions are run in order to examine the robustness of the OLS regressions at various points of the savings distribution. Columns 2-4 of Appendix 5 present estimations at the 25<sup>th</sup> , 50<sup>th</sup> and 75<sup>th</sup> percentile, and suggest that savings decisions are quite homogenous, particularly when considering certain determinants such as income. Whilst income is a significant predictor of savings at all savings rates, this seems to be particularly true at lower rates. Appendix 6 (figure 6.a.) presents the coefficient on income at each percentile of the savings distribution. Dotted lines represent the OLS estimations and confidence intervals (from column 1), whereas the solid line and shaded region presents the

> 12 Such a hypothesis could be tested with savings data over a longer time period, but falls outside the scope of the present paper. It may also be that this age profile behavior responds to the extension of the national pension system from nonexistent, to a PAYG system, and, from 2014, to a fully funded system (Yeritsyan, 2011).

> 13 Standard errors are calculated in the quartile regression framework using bootstrapping with 100 replications. All regression specifications in Appendix 5 include the full set of control variables listed in Appendix 2, although non-informative coefficients have been removed from the output for the sake of simplicity.

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