Public Policy and Statistics: Case Studies from RAND

Edited by Sally C. Morton and John E. Rolph. Published by Springer-Verlag, New York, April 2000.

The casebook describes the varied analytical techniques and substantive applications that typify how statistical thinking has been applied at RAND by members of the RAND Statistics Group and colleagues over the past two decades. The philosophy is to be critical yet constructive.

Case studies of public policy problems are useful for teaching because they are familar. Almost everyone knows something about health insurance, global warming, and capital punishment to name but a few of the applications covered in this casebook. Each case study has a common format that described the policy questions, the statistical questions, and the successsful and unsuccessful analytic strategies. This book is designed for statistics courses in areas ranging from economics to health policy to the law at both the advanced undergraduate and graduate levels. Empirical researchers and policy-makers should also find this casebook informative.

For most of the book chapters, datasets accompany the exercises. In general, we provide the data in ASCII column or space delimited format, an ASCII file describing the dataset, and a simple SAS program that reads the ASCII datafile in and produces a SAS dataset. The SAS code assumes that the associated dataset (*.dat) is saved in your SAS root directory. We follow the data presentation style of Case Studies in Biometry (eds. Lange, Billard, Conquest, Ryan, Brillinger, and Greenhouse; Wiley 1994).

Chapter 1. School-Based Drug Prevention: Challenges in Designing and Analyzing Social Experiments
by Robert M. Bell and Phyllis L. Ellickson
datafile in ASCII (column-delimited)
documentation in ASCII
SAS program

Chapter 2. The Health Insurance Experiment: Design Using the Finite Selection Model
by Carl N. Morris and Jennifer L. Hill
This chapter's exercises do not require a dataset.

Chapter 3. Counting the Homeless: Sampling Difficult Populations
by Allan F. Abrahamse
This chapter's exercises do not require a dataset. Exercises 4 and 6 use data from Tables 3.1 and 3.2 respectively. We provide those tables as Excel (5.0/95) files.
Table 3.1 in Excel.
Table 3.2 in Excel.

Chapter 4. Periodicity in the Global Mean Temperature Series?
by John L. Adams, James K. Hammitt, and James S. Hodges
The data for this chapter are available on the web. See the chapter for details.

Chapter 5. Racial Bias in Death Sentencing: Assessing the Statistical Evidence
by Sally C. Morton and John E. Rolph
datafile in ASCII (column-delimited)
documentation in ASCII
SAS program

Chapter 6. Malpractice and the Impaired Physician: An Application of Matching
by Kimberly A. McGuigan and John E. Rolph
datafile in ASCII (space-delimited)
documentation in ASCII
SAS program

Chapter 7. Supply Delays for F-14 Jet Engine Repair Parts: Developing and Applying Effective Data Graphics
by Lionel A. Galway
datafile in ASCII (space-delimited)
documentation in ASCII
SAS program

Chapter 8. Hospital Mortality Rates: Comparing with Adjustments for Case Mix and Sample Size
by Neal Thomas and John E. Rolph
datafile in ASCII (space-delimited)
documentation in ASCII
SAS program

Chapter 9. Eye-Care Supply and Need: Confronting Uncertainty
by Daniel A. Relles, Catherine A. Jackson, and Paul P. Lee
work datafile in ASCII
prev datafile in ASCII
xwk datafile in ASCII
prov datafile in ASCII
hours datafile in ASCII
documentation in ASCII
SAS program

Chapter 10. Modeling Block Grant Formulas for Substance Abuse Treatment
by Daniel F. McCaffrey and John L. Adams
datafile in ASCII (zipped; space-delimited)
documentation in ASCII
SAS program