Many applied economists are concerned with average effects of  a policy intervention or changed economic conditions on economic subjects. Often linear regression is their tool of choice for conducting impact analysis. However, focusing on the mean might mask some interesting effects along the distribution of the outcome variable.

Suppose you were interested in evaluating a labour market reform that possibly has an impact on the wage distribution. Then, calculating the average effects on wages could be useless – imagine the politically sensitive case when mean wages remain constant but increase in the upper quantile while decrease in the lower quantile. That is, focusing on the mean masks the fact that the reform resulted in higher inequality. In the case of applied economics, the use of quantile regression has therefore become quite popular to get insights into the effects that occur below or above the mean.

For the same reason we have to consider the effects along the whole wage distribution in applied labour economics, we have to look in the tails when considering the economic impacts of climate change: We just might miss the relevant stuff! (more…)

Most of us know that we need to reduce how much energy we use – but did you know that the timing of our energy use will also make a difference to the fight against climate change? (more…)