Knowledge that Transforms

A Dream Deferred or Realized: The Impact of Public Policy on Fostering Black Homeownership in New York City Throughout the 1990's by Lance Freeman and Darrick Hamilton. Published in volume 92, issue 2, pages 320-324 of American Economic Review, May 2002

Did Community Rating Induce an Adverse Selection Death Spiral? Evidence from New York, Pennsylvania, and Connecticut by Thomas Buchmueller and John Dinardo. Published in volume 92, issue 1, pages 280-294 of American Economic Review, March 2002

Time limits are among the most fundamental of recent welfare reforms. Whereas welfare benefits were an entitlement under the old Aid to Families with Dependent Children (AFDC) program, the Personal Responsibility and Work Opportunity Reconciliation Act of 1996 (PRWORA), which replaced AFDC with the Temporary Aid to Needy Families (TANF) program, restricts most families to 60 months of federally funded benefits. Although the states are free to extend benefits further using their own funds, almost all have incorporated some sort of time limit into their TANF programs. Time limits may affect welfare receipt in two distinct ways. The most obvious effect is mechanical, reducing welfare use once recipients exhaust their benefits. However, time limits may also reduce welfare receipt before recipients reach the limit. If families are forwardlooking, they may reduce their current welfare use in order to preserve their benefits for the future. The purpose of this paper is to provide evidence on such anticipatory, or behavioral, effects of time limits. I use data from the Survey of Income and Program Participation (SIPP) to replicate my earlier estimates based on the Current Population Survey (CPS). I also exploit the SIPP to relax some of the restrictive assumptions that were implicit in my previous work.

The proliferation of economics courses offered partly or completely online (Arnold Katz and William E. Becker, 1999) raises important questions about the effects of the new technologies on student learning. Do students enrolled in online courses learn more or less than students taught face-to-face? Can we identify any student characteristics, such as gender, race, ACT scores, or grade averages, that are associated with better outcomes in one technology or another? How would the online (or face-to-face) students fare if they had taken the course using the alternative technology? This paper addresses these questions using student data from our Principles of Microeconomics courses at Michigan State University.