3.4-percentage-point decrease in the unemployment rate in 2012, the same policymaker would have to explain an increase of 3.0 percentage points in 2003. To a much greater degree, the same story unfolds for Argentina with a whopping 15.3-percentage-point increase in 2002 and 15.3-percentagepoint decrease in 2012. It is worth noting that while the importance of cyclical fluctuations in the unemployment rate hinders the ability to diagnose trend-driven gains in both countries, this problem is much more severe in Argentina. In fact, and following the previous univariate-based variance decomposition analysis, the cyclical component explains 62.3 and 95.2 percent of the variance of the unemployment rate in Chile and Argentina, respectively. We will explore the root of these differences in the next section.

In the same vein, for the case of monetary poverty, a policymaker evaluating social welfare gains in Argentina in 2002 would reach the opposite conclusions than one evaluating the situation in 2012. These large differences make it almost impossible to understand the long-term evolution of social conditions in this country. On the other hand, a policymaker using the same indicator in Chile would be much less exposed to sample-dependent variation in her conclusions. In other words, since the monetary poverty indicator in Chile is dominated by its trend component, we can approximate the long-run gains in the underlying indicator with relative short-time samples. In fact, and following the previous univariate-based variance decomposition analysis, the cyclical component explains 84.8 percent of the variance of monetary poverty in Argentina (this figure is larger than the world-sample average associated with the unemployment rate), but only 2.3 percent in Chile (a figure very close to the world-sample average associated with the HDI).

In sharp contrast, a policymaker using the HDI indicator would not fall into misleading sampledependent conclusions regardless of the country under study. Indeed, the cyclical component explains 0.5 and 2.7 percent of the variance of the HDI rate in Chile and Argentina, respectively.

Finally, Figure 3, Panels B, D, and F translate the previous results (i.e., Panels A, C, and E) into density plots for the 10-year differences in the unemployment rate, monetary poverty, and the inverse of the HDI, respectively. The relatively high dispersion in the distributions of the 10-year differences in the unemployment rate displayed in Figure 3, Panel B shows that both Argentina and Chile are highly exposed to myopic views of the long-term improvements in the unemployment rate. While the case of Argentina is more extreme, both countries display relatively high standard deviations for the 10year difference observations. For monetary poverty, Figure 3, Panel D, tells us that, again, a policymaker from Argentina is quite exposed to large errors assessing long-term improvements. In this case though, given the dominance of the trend component of monetary poverty in Chile, the Chilean policymaker would be correct in her assessment of long-term gains no matter the year she starts the study or the length of available data. Finally, using the HDI, the latter situation would apply to both countries. The HDI not only lowers the chances of sample-dependent errors but also eliminates the differences across countries, as shown in Figure 3, Panel F.<sup>14</sup>

> 14 Appendix C also illustrates that not all social indicators are created equal in Indonesia and Thailand, two other emerging economies where long-time series are available.

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