YADS

AcronymDefinition
YADSYet Another DOS Shell
YADSYet Another Display Site (writers display site)
YADSYet Another eDonkey Server (eMule)
References in periodicals archive ?
(4) By 2009, more than 30 states had implemented their own variations of the YAD mandates, limiting dependency by age, student status, marital status, and whether or not the young adult has their own dependents.
Research into state-level YAD mandates (left column of Table 1) focuses on the effects on insurance coverage (top panel); care utilization (middle panel) and health outcomes (bottom panel) are much less explored.
This research typically takes a DD approach and considers broader age groups than YAD research as all women of childbearing age may be potentially affected by CMs.
However, because most YAD studies exploit the fact that not all young adults are eligible and so employ a DDD approach, we also estimate two alternative DDD models that rely on different identification strategies.
The effects of a YAD mandate can be estimated in a similar way:
YAD Model : [Y.sub.iast] = [alpha] + [[theta].sub.s] + [[theta].sub.t] + [[theta].sub.a] + [gamma][X.sub.iast] + [pi][Z.sub.st] + [[beta].sub.YA][YAD.sub.st] + [[epsilon].sub.iast],
where all variables are as defined above, and YAD is a dummy variable equal to one if the state has a YAD mandate in that year.
The CM and YAD models commonly estimated often include different controls (i.e., X and Z); as described in Section IV and Table 2, we include a broad set of controls that are representative of both.
Because many young adults in YAD states are not eligible for the mandate by virtue of their age, marital status, or student status, most YAD studies take a DDD approach, where eligible and ineligible young adults in both states are further compared.
To estimate a YAD-DDD model, we must identify a group of young adults who are not eligible for the YAD enacted in their state.
Our "Levine" model continues with the 19-24-year-old samples from the DD approach and defines Eligible as Levine, McKnight, and Heep (2011) do: as a dummy variable equal to one if the individual, i, would be eligible for the YAD enacted (at some point in the sample) in their state, s, on the basis of age a, and marital/student status, m, e; for those living in a state without a YAD, it is equal to one if they are unmarried.
This approach is akin to splitting the sample into eligible ("treated") and ineligible ("control") observations and estimating the difference in the effects of the YAD mandate being implemented.