Abstract
In 2010, Strunk and Reardon introduced a potentially transformative method for analyzing teacher collective bargaining agreements (CBAs). We extend Strunk and Reardon’s work by assessing whether the Partial Independence Item Response (PIIR) approach can be applied to subsets of provisions from CBAs, data that may be more feasible for researchers to collect. Utilizing a new data set derived from all provisions in all active CBAs in Washington state, we find that estimates calculated from a subset of high-profile provisions are moderately highly correlated with estimates calculated from the full range of provisions, as are estimates calculated from several categories of provisions. This suggests that researchers can still draw important conclusions by applying the PIIR method to readily available data on teacher CBAs.
Highlights
A large literature on bargaining in the private sector suggests that competition between firms in a given industry limits private-sector unions from demanding inflated benefits and wages (Clark, Delaney, & Frost, 2002)
How restrictive are the 270 teacher contracts in the state of Washington, and does the Partial Independence Item Response (PIIR) estimate produced from all provisions correlate with PIIR estimates that rely on a reduced set of provisions, particular subsets of provisions, or particular cherry-picked provisions utilized in prior research? we present restrictiveness estimates for every collective bargaining agreements (CBAs) in our data set and discuss the relationship between measures of restrictiveness relying on various data subsets
Our results suggest that while the PIIR method is an important development in the analysis of collective bargaining outcomes, researchers do not necessarily need to code every provision in CBAs to utilize this methodology and draw meaningful conclusions from these agreements
Summary
A large literature on bargaining in the private sector suggests that competition between firms in a given industry limits private-sector unions from demanding inflated benefits and wages (Clark, Delaney, & Frost, 2002). Once we have this “risk matrix,” we can limit the binary response matrix to only those observations that correspond to items in the risk set for a particular CBA. Many researchers only have access to data on high-profile provisions—for example, the National Council on Teacher Quality (2009) maintained a publicly available database of high-profile provisions for 150 large districts across the country—and the PIIR method can still generate an objective measure of CBA restrictiveness given the subset of provisions considered. We use our item response data to obtain a “restrictiveness” measure for each contract and each provision in all 270 of Washington’s CBAs. Restrictiveness estimates obtained via fixed effects logit PIIR are presented in Column 2 of Table 10. Aberdeen Adna Almira Anacortes Arlington Asotin-Anatone Auburn Bainbridge Island Battleground Bellevue Bellingham Bethel Blaine Boistfort Bremerton Brewster Bridgeport Brinnon Burlington-Edison Camas Cape Flattery Cascade Cashmere Castle Rock Centerville Central Kitsap Central Valley Centralia Chehalis Cheney Chewelah Chimacum Clarkston Cle Elum-Roslyn Clover Park Colfax College Place Colton Columbia (Stev) Columbia (Walla) Colville Concrete Conway Cosmopolis Coulee-Hartline Coupeville Crescent Creston Curlew Cusick Darrington Davenport Dayton Deer Park Dieringer East Valley (Spk) East Valley (Yak) Eastmont Easton Eatonville Edmonds Ellensburg Elma
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