Category Mostly Harmless Econometrics: An Empiricist’s Companion

1.4.1 Weighting Regression

Few things are as confusing to applied researchers as the role of sample weights. Even now, 20 years post – Ph. D., we read the section of the Stata manual on weighting with some dismay. Weights can be used in a number of ways, and how they are used may well matter for your results. Regrettably, however, the case for or against weighting is often less than clear-cut, as are the specifics of how the weights should be programmed. A detailed discussion of weighting pros and cons is beyond the scope of this book. See Pfefferman (1993) and Deaton (1997) for two perspectives. In this brief subsection, we provide a few guidelines and a rationale for our approach to weighting.

A simple rule of thumb for weighting regression is use weights when they make it more likely th...

But when you start exercising those rules, all sorts of processes start to happen and you start to find out all sorts of stuff about people… Its just a way of thinking about a problem, which lets the shape of the problem begin to emerge. The more rules, the tinier the rules, the more arbitrary they are, the better.

Douglas Adams, Mostly Harmless (1995)

Regression discontinuity (RD) research designs exploit precise knowledge of the rules determining treat­ment. RD identification is based on the idea that in a highly rule-based world, some rules are arbitrary and therefore provide good experiments. RD comes in two styles, fuzzy and sharp. The sharp design can be seen as a selection-on-observables story. The fuzzy design leads to an instrumental-variables-type setup.

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^XlYl = (X ‘X )-1X ‘y,

i where X is the NXK matrix with rows Xi and y is the N x 1 vector of Yi’s. We saw in Section 3.1.3 that /3 has an asymptotically Normal distribution. We can write:

VN(3 – /) – n(0, n)

where П is the asymptotic covariance matrix. Repeating (3.1.7), the formula for П in this case is

Пг = E [XiXi]-1^ [XiXief] E [XiXi]-1, (8.1.1)

where ei = Yi—Xi/. When residuals are homoskedastic, П simplifies to fic = a2E[XiXi]~1 where a2 = E[e2].

We are concerned here with the bias of robust standard errors in independent samples (i. e., no clustering or serial correlation). To simplify the derivation of bias, we assume that the regressor vector can be treated as fixed in repeated samples, as it would be if we sampled stratifying on Xi...

A vast literature in social science is concerned with peer effects. Loosely speaking, this means the causal effect of group characteristics on individual outcomes. Sometimes regression is used in an attempt to uncover these effects. In practice, the use of regression models to estimate peer effects is fraught with peril. Although this is not really an IV issue per se, the language and algebra of 2SLS helps us understand why peer effects are hard to identify.

Broadly speaking, there are two types of peer effects. The first concerns the effect of group characteristics such as the average schooling in a state or city on individually-measured outcome variable. This peer effect links the average of one variable to individual outcomes as described by another variable...

Many empirical studies involve variables that take on only a limited number of values. An example is the Angrist and Evans (1998) investigation of the effect of childbearing on female labor supply, discussed in

Section 3.4.2 in this chapter and in the chapter on instrumental variables, below. This study is concerned with the causal effects of childbearing on parents’ work and earnings. Because childbearing is likely to be correlated with potential earnings, the study reports instrumental variables estimates based on sibling – sex composition and multiple births, as well as OLS estimates. Almost every outcome in this study is either binary (like employment status) or non-negative (like hours worked, weeks worked, and earnings)...