What are the challenges faced by state and local governments in revenue management?
What will be an ideal response?
An ideal response would be:
Challenges for state and local government are great, especially in a time of financial hardship and economic change. Finding the revenues needed in a changing economy and in the aftershocks of a deep recession is a major challenge. Pensions for state employees are projected to run an increasing percentage of state revenues for the foreseeable future. Educating children and young adults, providing health care for the poor, and protecting society against crime, fire, natural disaster, and both domestic and international terrorism are all funded by state and local governments. Cutting funding in any of these policy areas is at best politically difficult and at worst impossible.
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Plaintiff sued a city college that terminated his employment without affording him a hearing on the grounds for his dismissal. The defendant college argued to the trial judge that as the plaintiff was an at-will employee without tenure, without a contract of employment, and without any promise of continued employment, he had no property interest in his continued employment and was not entitled to
due process prior to dismissal. Before trial, the defendant college asked the judge to rule as a matter of law that it should prevail, as the fact of his at-will employment status was undisputed. If the trial judge believes that the plaintiff indeed had no reasonable expectation of continued employment, hence no property interest and no entitlement to constitutional due process, what should he do? a. Grant the defendant's motion for summary judgment. b. Grant summary judgment in favor of the plaintiff. c. Grant the defendant's motion for a directed verdict. d. Grant plaintiff's motion to JNOV.
The magnitude of random sampling error in a study depends upon the __________ of a sample and the amount of ____________ in the population characteristic being measured.
a. Error/Bias b. Size/Variation c. Variation/Error d. Bias/Variation