估计有很多人做regression Discontinuity Design的。这个 guide 总结了theory 和 implication. 非常有用
This paper provides an introduction and "user guide" to Regression Discontinuity (RD) designs for
empirical researchers. It presents the basic theory behind the research design, details when RD is likely
to be valid or invalid given economic incentives, explains why it is considered a "quasi-experimental"
design, and summarizes different ways (with their advantages and disadvantages) of estimating RD
designs and the limitations of interpreting these estimates. Concepts are discussed using examples
drawn from the growing body of empirical research using RD.


英文资料 111 页

Regression Discontinuity (RD) designs were first introduced by Thistlethwaite and Campbell (1960) as a
way of estimating treatment effects in a non-experimental setting where treatment is determined by whether
an observed “assignment” variable (also referred to in the literature as the “forcing” variable or the “running”
variable) exceeds a known cutoff point. In their initial application of RD designs, Thistlethwaite and
Campbell (1960) analyzed the impact of merit awards on future academic outcomes, using the fact that the
allocation of these awards was based on an observed test score. The main idea behind the research design
was that individuals with scores just below the cutoff (who did not receive the award) were good comparisons
to those just above the cutoff (who did receive the award). Although this evaluation strategy has been
around for almost fifty years, it did not attract much attention in economics until relatively recently.
Since the late 1990s, a growing number of studies have relied on RD designs to estimate program effects
in a wide variety of economic contexts. Like Thistlethwaite and Campbell (1960), early studies by Van der
Klaauw (2002) and Angrist and Lavy (1999) exploited threshold rules often used by educational institutions
to estimate the effect of financial aid and class size, respectively, on educational outcomes. Black (1999)
exploited the presence of discontinuities at the geographical level (school district boundaries) to estimate
the willingness to pay for good schools. Following these early papers in the area of education, the past five
years have seen a rapidly growing literature using RD designs to examine a range of questions. Examples
include: the labor supply effect of welfare, unemployment insurance, and disability programs; the effects of
Medicaid on health outcomes; the effect of remedial education programs on educational achievement; the
empirical relevance of median voter models; and the effects of unionization on wages and employment.
One important impetus behind this recent flurry of research is a recognition, formalized by Hahn et
al. (2001), that RD designs require seemingly mild assumptions compared to those needed for other nonexperimental
approaches. Another reason for the recent wave of research is the belief that the RD design
is not “just another” evaluation strategy, and that causal inferences from RD designs are potentially more
credible than those from typical “natural experiment” strategies (e.g. difference-in-differences or instrumental
variables), which have been heavily employed in applied research in recent decades. This notion
has a theoretical justification: Lee (2008) formally shows that one need not assume。。。。