# **2.2. Are Structural Indicators such as the HDI Structural Enough?**

The above sections emphasized the potential bias implicit in social indicators largely driven by cyclical fluctuations when trying to evaluate long-lasting improvements in social conditions. A simple solution would be to extract the trend component of each indicator and use it in the evaluation of long-run gains in poverty reduction. A practical problem with this approach is the risk of sample-dependent bias explained above due to, precisely, the short-sample nature of available data.<sup>15</sup> An alternative solution comes from the type of indicator used. Throughout the paper, we have seen that social indicators based on structural factors such as the HDI are much less influenced by cyclical fluctuations. The large trend component of this type of indicators implies that we can better proxy long-run improvements in social conditions with relatively short periods of available data.

Having said that, indicators such as the HDI are also subject, to some extent, to the same type of conceptual critique. Why? Because the HDI is an index that also includes a criterion based upon income. Figure 2 shows the relative importance of the cyclical component in the total variance of each underlying indicator of the HDI. While this share is relatively low for all components, this simple exercise clearly shows that while structural components such as education and life expectancy have a cyclical share that explains little of their total variance, the measure of income included in the HDI is, as expected, much more important in terms of cyclical fluctuations. Again, including such cyclicallydriven components in the construction of structural measures like the HDI should affect some countries more than others depending on the underlying volatility of the business cycle.

# **3. What Drives the Cyclical Fluctuations of Social Indicators?**

Whereas the large differences in the time series behavior of various social indicators uncovered in our previous section may be striking at first sight, they should hardly come as a surprise given the importance of business cycle fluctuations in some of these social indicators.

While the drivers of the trend component of social indicators may be diverse – and to some degree independent from long-term income growth (for example, drivers could be related to long-term trends in income redistribution and technological diffusion) – the cyclical components of the social indicators used in this study should be, in principle, tightly related to business cycle fluctuations. This is particularly the case of social indicators such as the unemployment rate and monetary poverty. The reason lies in the fundamental link between these social indicators and income. For example, in the case of the unemployment rate, any Keynesian model with price or wage rigidities would predict a strong correlation between the unemployment rate and the business cycle. Specifically, negative real or monetary shocks should lead to short-term rises in the unemployment rate. Changes in monetary poverty should also depend, at least partially, on the evolution of income per capita. In contrast, by

> 15 Section 4 will discuss how to overcome (when necessary) some of this micro-based survey data limitations.

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