(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)

José Iparraguirre writes:

I’ve read a recent paper by the International Monetary Fund on the effects of fiscal consolidation measures on income inequality (Fiscal Monitor October 2012, Appendix 1).

They run a panel regression with 48 countries and 30 years (annual data) of a measure of income inequality (Gini coefficient) on a number of covariates, including a measure of fiscal consolidation.

Footnote 39 (page 51) informs that they’ve employed seemingly unrelated regression and panel-corrected standard errors, and that to double-check they’ve also run ordinary least squares and fixed-effects panel regressions—all with similar results.

So far, so good. However, the footnote goes on to explain that “Some of the results (e.g. the causal relationship between consolidation and inequality) may be subject to endogeneity and should be interpreted with caution”. (Italics are mine).

Therefore, it seems that the crux of the exercise—i.e. estimating the relationship between fiscal consolidation on inequality—should be interpreted with caution because the direction of causality might go the other way round and they haven’t controlled for or tested this possibility.

This is not a peer-reviewed paper, but it is very influential nonetheless in policy circles (if not more so than the average academic paper). After such huge caveat, in general and without delving into this particular publication, how valid would you say any of the reported results and conclusions drawn from them would be?

My reply: I’ll retreat to the statistical view of causality, in which all effects are defined in terms of potential interventions. In this case, “fiscal consolidation” sounds like a reasonably clearly-defined treatment, so the comparison problem seems pretty clear: you’d want to use matching and regression to control for as many pre-treatment variables as possible.

**Please comment on the article here:** **Statistical Modeling, Causal Inference, and Social Science**