|Percent of population th|at is<br>-0.15|0.25|-0.22|0.27|-0.18|0.29|
|---|---|---|---|---|---|---|
|65 and older|(0.38)|(0.13)|(0.18)|(0.094)|(0.27)|(0.073)|
|Observations|243|243|258|258|258|258|
|R-squared|0.359|0.235|0.335|0.221|0.336|0.225|
|Number of countries|46|46|47|47|47|47|

Note: All specifications are ordinary least squares, controlling for country fixed effects. Clustered, robust p-values in parentheses. _MarketP_ incomes are market incomes including pensions. _Sharegain_ is the change in income share of the respective quintile after comparing the _MarketP_ income share of the quintile with its disposable or gross income share.

The final two columns examine whether the party age variables may be significant because of the omission of the tenure of the country executive. Parties might be older because the party (and country) leader has been in power longer and it is because the long-surviving leader has a longer horizon (as argued in Clague, et al. 1996), not because of the party horizon, that the party age effects emerge. The DPI includes a variable for the years in office of the executive (of the country). Our results are robust to controlling for leader tenure using this variable. Leader years in office is itself insignificant in the regressions in columns (5) and (6). However, the signs are consistent with the argument that long-surviving leaders have longer horizons: the longer the years in office of the leader, the lower is the _sharegain_ of the poor and the greater is the _sharegain_ of the rich.

# **7. Robustness:  Other robustness tests**

Table 4 investigates whether party effects are driven by other political features of countries that might be correlated with party characteristics. In this section, we show that the effects are also not driven by possible peculiarities of the sample of countries and years in our database. Our focus is on the first four columns of Table 2, those using market incomes that include pension payments and those that focus on market incomes that exclude pension payments. These results are summarized in Table 5, which reports the coefficients on the left-wing party and age of left-wing party variables from the corresponding specifications.

The first results in Table 5 confirm that the results are not dependent on the particular constellation of control variables that we use. Removing all of the non-political control variables does not disturb the basic findings from Table 2. The second set of results confirms that the results are robust to allowing between-country variation to identify effects. We control for country fixed effects to account for unobserved, fixed country characteristics that might lead to a spurious correlation between party characteristics and income distribution. However, most of the variation in party variables and redistribution is between countries. The second set of results in Table 5 reestimates the Table 2 specifications using random effects. Random effects allow results to be identified based on both cross-country and within-country variation. While these effects are not as well-identified as those in Table 2, it is reassuring that the party variables can also explain the additional variation between countries that enters into the random effects specification. We also, in the third set of results, ensure that the results are not driven by countries with only a few observations on income distribution, excluding all those countries from the analysis with fewer than 5 observations. Results are robust to this change.

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