Month of birth & SES

Liam's post below points to differing patterns in the seasonality of birth by family background. Using PISA 2000, I plot month of birth according to the data's measure of SES - using a median split.
There does appear to be differences although they don't look dramatic to me. Around Autumn/Fall they seem fairly similar and this is usually when the cut-off dates for school entry are.

Season of Birth as an Instrument

The use of season of birth as an instrument for education is something that has been debated in Economics for the last 20 years or so, with the basic idea being that season of birth is randomly distributed but can effect school attainment through timing of compulsory school and so on (see the original Angrist and Krueger paper and recent work referenced in the paper below for a much more eloquent summary). A number of papers, particularly Bound and Jaeger (1995) argue the relationship between season of birth and schooling is too weak for it to be used as an instrument. A recent NBER paper by Buckles and Hungerman provides a new argument as to why this instrumentation strategy may not be valid, namely that people with different socioeconomic characteristics time their conceptions differently.

How not to do Instrumental variables

Instrumental Variable estimation, Generalized Method of Moments and related techniques are part of the standard toolkit for applied economists. They are also increasingly used in other fields such as health.
What everyone knows, or should know, is that while one can think of these models as a two stage process this is not actually how you do it. But this paper which looks at how systolic blood pressure depends on anti-hypertensive drugs in Japan, published in the Bulletin of the World Health Organization 2008, gets it badly wrong. As they note, a simple regression of blood pressure on medication is likely to get a positive slope so you need to instrument or do something.
They estimate a logit and then stick the predicted values into an OLS model. Aside from the identifying assumption (which isn't discussed & looks pretty dodgy to me), this is not IV as usually defined and it is not clear that the estimate is consistent or that the standard errors are correct. The model also includes controls for exercise but these are also likely to be endogenous but this is ignored.

On a lighter note

Stata resources for treatment effects

There are a large number of resources within Stata for the estimation of treatment effects. Some are part of official Stata and others are user written that can be easily downloaded.

To estimate regression discontinuity models, there is a download rd due to Austin Nichols. Further details at Nichols, Austin. 2007. "Causal Inference with Observational Data." Prepublication draft available at http://pped.org/stata/ciwod.pdf. It is published in the Stata Journal now I think.

To estimate IV models there are several options in Stata.
ivregress is the main program. A download ivreg2 due to Baum, Schaffer & Stillman is very useful - I recommend it. Make sure you get the latest version. Their paper should be used in conjunction with it: http://ideas.repec.org/a/tsj/stataj/v7y2007i4p465-506.html. xtivreg2 is the equivalent program for panel data.

ivtobit and ivprobit do what their names suggest. If using them, you need to satisfy yourself that they are consistent estimators. Caution is appropriate where the instrumented variable is binary. In the latter case biprobit may be better.

cmp (due to David Roodman) allows you to estimate using MLE a wide range of simultaneous models with combinations of linear and non-linear equations provide they satisfy a recursive structure.

treatreg allows the estimation of what Stata calls "treatment effects models". This is something of a misnomer since it only for a very specific model: a linear regression with an endogenous dummy.

condivreg estimates IV models with a single endogenous variable and provides an exact confidence interval for the slope as opposed to the usual asymptotic one. It is particularly useful if weak instruments are a concern.

For estimating Treatment effects using Propensity Score matching there are several downloads including: psmatch2 (Leuven & Sianesi) which does a wide range of matching estimators and nnmatch which does nearest neighbour matching. psbalance allows you to test covariate balance after matching - something that is recomended.