Analyzing the Determinants of the Matching Public School Teachers to Jobs: Estimating Compensating Differentials in Imperfect Labor Markets

Although there is growing recognition of the contribution of teachers to students' educational outcomes, there are large gaps in our understanding of how teacher labor markets function. Most research on teacher labor markets use models developed for the private sector. However, markets for public school teachers differ in fundamental ways from those in the private sector. Collective bargaining and public decision making processes set teacher salaries. Thus it is unlikely that wages adjust quickly to equilibrate the supply and demand for worker and job attributes. The objective of this paper is to develop and estimate a model that more accurately characterizes the institutional features of teacher labor markets. The approach is based on a game-theoretic two-sided matching model and the estimation strategy employs the method of simulated moments. With this combination, we are able to estimate how factors affect the choices of individual teachers and hiring authorities, as well as how these choices interact to determine the equilibrium allocation of teachers across jobs. Even though this paper focuses on worker-job match within teacher labor markets, many of the issues raised and the empirical framework employed are relevant in other settings where wages are set administratively or, more generally, do not clear the pertinent markets for job and worker attributes.

Unsupervised String Transformation Learning for Entity Consolidation

Data integration has been a long-standing challenge in data management with many applications. A key step in data integration is entity consolidation. It takes a collection of clusters of duplicate records as input and produces a single "golden record" for each cluster, which contains the canonical value for each attribute. Truth discovery and data fusion methods, as well as Master Data Management (MDM) systems, can be used for entity consolidation. However, to achieve better results, the variant values (i.e., values that are logically the same with different formats) in the clusters need to be consolidated before applying these methods. For this purpose, we propose a data-driven method to standardize the variant values based on two observations: (1) the variant values usually can be transformed to the same representation (e.g., "Mary Lee" and "Lee, Mary") and (2) the same transformation often appears repeatedly across different clusters (e.g., transpose the first and last name). Our approach first uses an unsupervised method to generate groups of value pairs that can be transformed in the same way (i.e., they share a transformation). Then the groups are presented to a human for verification and the approved ones are used to standardize the data. In a real-world dataset with 17,497 records, our method achieved 75% recall and 99.5% precision in standardizing variant values by asking a human 100 yes/no questions, which completely outperformed a state of the art data wrangling tool