The Black-White Achievement Gap Amongst Struggling Readers: A Case Study of Early Intervention Outcomes in Massachusetts

A number of analyses of large data sets have suggested that the reading achievement gap between African American and White U.S. is negligible or small at school entry, but widens substantially during the school years because African American students show slower rates of growth in elementary and secondary school. Identifying when and why gaps occur, therefore, is a an important research endeavor. In addition, being able to predict which African American children are most likely to fall behind can contribute to efforts to close the achievement gap. This paper analyzes first grade and third grade data on African American and White children in Massachusetts who all were identified in first grade as struggling readers and enrolled in Reading Recovery—an individualized intervention. All the children were low-income and attending urban schools. Using Observation Survey data from first grade, and MCAS Reading data from 3rd grade, we found that the African American and White students made equal average progress while in first grade, but by the end of third grade showed a large gap in MCAS proficiency rates. We discuss the results in terms of school quality, reading development, dialect issues, testing formats, and the need to provide long-term support to vulnerable learners.

Forecasting and Analyzing Economic Activity with Coincident and Leading Indexes: The Case of Connecticut

We develop coincident and leading employment indexes for the Connecticut economy. Four employment-related variables enter the coincident index while five employment-related variables enter the leading index. The peaks and troughs in the leading index lead the peaks and troughs in the coincident index by an average of 3 and 9 months. Finally, we use the leading index in vector-autoregressive (VAR) and Bayesian vector-autoregressive (BVAR) models to forecast the coincident index, nonfarm employment, and the unemployment rate.

Emissions Control and the Regulation of Product Markets: The Case of Automobiles

The paper investigates alternative policies to regulate emissions from polluting product markets, specifically considering the case of the automobiles market. The two policies we consider are: a quota that limits the quantity produced of the polluting model and a more flexible average efficiency standard that requires a minimum energy efficiency across all models produced by a firm, similar to the US Corporate Average Fuel Economy (CAFE) standards. We use a duopoly model of vertical differentiation where firms produce both an economy (i.e., low polluting) version and a luxury (i.e., high polluting) version of a given product. We show that while a quota can raise firm profit over a certain range, CAFE always reduces firm profit relative to the pre-regulation. We also show that while the quota reduces emissions, it is possible that emissions increase under CAFE. The optimal policy choice will depend on the magnitude of unit damages. We show that when unit damages are sufficiently high, the quota policy is more efficient than the average efficiency standard. This suggests that instead of tightening CAFE to limit damages from emissions, policy makers can shift to a quota policy which is both welfare enhancing and more profitable for firms.