public-policy-4

Week 4 Discussion 1

“Structuring the Problem” Please respond to the following:

• Examine Box 3.0 – Conducting a Stakeholder Analysis. Choose one (1) policy issue in the U.S. and generate a list of at least five (5) stakeholders who affect or are affected by problems in the issue area. Next, apply the procedures for a stakeholder analysis. Note: Refer to page 111 in the text for a step-by-step process.
• From the e-Activity, provide at least two examples from the article and from your own experience of ways that worldviews, ideologies, and popular myths may have shaped the formulation of a specific problem or issue. Provide the source(s) of the paper or report you selected.

BOX 3.0 – CONDUCTING A STAKEHOLDER ANALYSIS

Definition

A stakeholder is a person who speaks for or represents a group that is affected by or affects a policy. Stakeholders include the president of a legislative assembly or parliament, a chairperson of a legislative committee, or an executive director or members of an organized interest or advocacy group such as the National Rifle Association, the Sierra Club, or Human Rights Watch. Policy analysts and their employers are stakeholders, as are clients who commission a policy analysis. Persons or groups who do not have a stake in a policy (e.g., an uninvolved college professor) are not stakeholders.

Assumptions

•Stakeholders are best identified by policy issue area. A policy issue area is a domain in which stakeholders disagree or quarrel about policies Housing, welfare, education, and international securiy are policy issue areas.

•Stakeholders have specific names and titles—for example, State Senator Xanadi; Mr. Young, chairperson of the House Finance Committee; or Ms. Ziegler, a spokesperson for the National Organization of Women (NOW).

•A sociometric or “snowball” sample such as that described next is an effective way to estimate the “population” of stakeholders.

STEP 1:	Using Google or a reference book such as The Encyclopedia of Associations, identify and list about ten stakeholders who have taken a public position on a policy. Make the initial list as heterogeneous as possible by sampling opponents as well as supporters.
STEP 2:	For each stakeholder, obtain a policy document (e.g., a report, news article, e-mail, or telephone interview) that describes the position of each stakeholder.
STEP 3:	Beginning with the first statement of the first stakeholder, list other stakeholders mentioned as opponents or proponents of the policy.
STEP 4:	For each remaining statement, list the new stakeholders mentioned. Do not repeat.
STEP 5:	Draw a graph that displays statements 1, 2, … n on the horizontal axis. On the vertical axis, display the cumulative frequency of new stakeholders mentioned in the statements. The graph will gradually flatten out, with no new stakeholders mentioned. If this does not occur before reaching the last stakeholder on the initial list, repeat steps 2 to 4. Add to the graph the new statements and the new stakeholders.
STEP 6:	Add to the estimate stakeholders who should be included because of their formal positions (organization charts show such positions) or because they are involved in one or more policy-making activities: agenda setting, policy formulation, policy adoption, policy implementation, policy evaluation, and policy adaptation, succession or termination.

Retain the full list for further analysis. You now have an estimate of the “population” of key stakeholders who are affected by and affect the policy, along with a description of their positions on an issue. This is a good basis for structuring the problem. ■

• Week 4 eActivity

  • Go to one or more of the following Websites and review at least two policy papers or reports published by a government agency or a think tank that address a problem or an issue of interest: Public Agenda, located at www.publicagenda.org; RAND Corporation located at www.rand.org; and / or American Enterprise Institute, located at www.aei.org. Be prepared to discuss.

  Week 4 Discussion 2

  “Models and Structuring” Please respond to the following:

  • Review Question 6 and select one of the ill-structured problems taken from the journal Policy Analysis (now the Journal of Policy Analysis and Management Under the title “Department of Unintended Consequences”. Analyze the problem; then, provide an example on how classification analysis, hierarchy analysis, and synectics might be used to structure the problem you selected. Identify the problem you selected in your discussion with one of the following key phrases: (a) Egyptian agriculture, (b) ecologists and field mice, (c) San Francisco’s North Beach parking.
  • From the case study, Case 3.1, analyze the problem; then, provide two key differences in data collection represented by the process of group interviewing and content analysis. Take a position on which data collection method is better. Provide at least two reasons for your position.

  Question 6
  6.QThe ill-structured problems that follow are taken from illustrations published in the journal
  Policy Analysis (now the
  Journal of Policy Analysis and Management) under the title “Department of Unintended Consequences.”For several thousand years, Egyptian agriculture depended on the fertilizing sediment deposited by the flood of the Nile. No longer, however. Due to expensive modern technology intended to improve the age-old lot of the peasant, Egypt’s fields must be artificially fertilized. John Gall, writing in the
  New York Times Magazine (December 26, 1976), reports that the Nile sediment is now deposited in the Aswan Dam’s Lake Nasser. Much of the dam’s electrical output is used to supply enormous amounts of electricity to new fertilizer plants made necessary by the construction of the dam.University of Illinois ecologists can explain how certain harmful field mice spread from their native regions into areas where they had never before been found. They are using the new, limited-access, cross-country highways, which turn out to be easy escape routes with few barriers. Older highways and roads, as well as railroad rights-of-way, run into towns and villages every few miles and effectively deter mice migration. The Illinois group found that before interstate highways ran through central Illinois, one type of mouse was limited to a single county. But in six years of superhighways the four-inch-long creatures have spread sixty miles south through the center of the state. The ecologists are concerned lest the mice, a species that loves to chew on trees, become a threat in central and southern
  counties where apple orchards abound (
  Wall Street Journal, December 1, 1977).Edward J. Moody … argues persuasively that worship of Satan has the effect of normalizing abnormal people. Thus, to “keep secret” from ordinary people their satanic power and existence, such persons are urged to behave as straight as possible. The effect, of course, is more effective social relations—the goal for which Satan’s name has been invoked in the first place! (P. E. Hammond, “Review of Religious Movements in Contemporary America,”
  Science, May 2, 1975, p. 442).Residents of San Francisco’s North Beach areas must now pay $10 for the privilege of parking in their own neighborhood. A residential parking plan was recently implemented to prevent commuters from using the area as a daytime parking lot. But according to a story in the
  San Francisco Bay Guardian (March 14, 1978), the plan has in no way improved the residential parking situation. Numbers of commuters from outlying districts of the city have simply been changing their car registrations to North Beach addresses. A North Beach resident—now $10 poorer—still spends a lot of time driving around the block.
  
  CASE 3.1Complex problems must be structured before they can be solved. The process of structuring a policy problem is the search for and specification of problem elements and how they are the elements are

  Policy stakeholders. Which stakeholders affect or are affected by a problem?
  
  Policy alternatives. What alternative courses of action may be taken to solve the problem?
  
  Policy actions. Which of these alternatives should be acted on to solve the problem?
  
  Policy outcomes. What are the probable outcomes of action and are they part of the solution to the problem?
  
  Policy values (utilities). Are some outcomes more valuable than others in solving the problem?
  

  Most policy problems are messy or ill-structured. For this reason, one or more problem elements can be incorrectly omitted from the definition of a problem. Even when problem elements are correctly specified, relations among the elements may be unknown or obscure. This makes it difficult or impossible to determine the strength and significance, practical as well as statistical, of causal relations. For example, many causal processes that are believed to govern relations among atmospheric pollution, global warming, and climate change are obscure. The obscuriy of these processes stems not only from the complexity of “nature” but also from the conflicting beliefs of stakeholders who disagree, often intensely, about the definition of problems and their potential solutions. For this reason, the possible combinations and permutations of problem elements—that is, stakeholders, alternatives, actions, outcomes, values—
  appear to be unmanageably huge.Under these conditions, standard methods of decision theory (e.g., risk-benefit analysis), applied economics (e.g., benefit-cost analysis), and political science (e.g., policy implementation analysis) are oflimited value until the problem has been satisfactorily defined. This is so because an adequate definition of the problem must be constructed before the problem can be solved with these and other standard methods. Standard methods are useful in solving relatively well-structured (deterministic) problems involving certainty, for example, problems represented as fixed quantities in a spreadsheet. Standard methods are also useful in solving moderately structured (probabilistic) problems involving uncertainty, for example, problems represented as policy outcomes with different probabilities. However, ill-structured problems are of a different order. Estimates of uncertainty, or risk, cannot be made because we do not even know the outcomes to which we might attach probabilities. Here, the analyst is much like an architect who has been commissioned to design a
  custom building for which there is no
  standard plan.
  ⁷⁹ The adoption of a standard plan, if such existed, would almost certainly result in a type III error: solving the wrong problem.Public policies are deliberate attempts to change complex systems. The process of making and implementing policies occurs in social systems in which many contingencies lie beyond the control of policy makers. It is these unmanageable contingencies that are usually responsible for the success and failure of policies in achieving their objectives. The contingencies are rival hypotheses that can challenge claims that a policy (the presumed cause) produced one or more policy outcomes (the presumed effects). In such cases, it is usually desirable to test, and when possible eliminate, these rival hypotheses through a process of
  eliminative induction. Eliminative induction takes this general form: “Repeated observations of policy xand outcome
  y confirm that
  x is causally relevant to the occurrence of
  y. However, additiona observations of
  x,
  z, and
  y confirm that unmanageable contingency
  z and not policy
  x is responsible for the
  occurrence of
  y.” By contrast,
  enumerativeinduction takes this general form: “Repeated observations confirm that the policy
  x is causally relevant to the occurrence of policy outcome
  y.”Eliminative induction permits a critical examination of contingencies that are beyond the control of policy makers. Because the number of these contingencies is potentially unlimited, the process of identifying and testing rival explanations is never complete. Yet, precisely for this reason, it seems impossible to identify and test an unmanageably huge number of potential rival hypotheses. How is this to be done?One answer is creativity and imagination. But creativity and imagination are impossible to teach, because there are no rules governing the replication of creative or imaginative solutions. Another answer is an appeal to well-established theories. However, the bulk of theories in the social sciences are disputed and controversial. “Well-established” theories are typically “well-defended” theories, and rival hypotheses are rarely considered seriously, let alone tested.A more appropriate alternative is the use of boundary analysis and estimation to structure problems involving a large number of rival hypotheses. Boundary analysis and estimation look for rival hypotheses in the naturally occurring policy quarrels that take place among stakeholders. In addition to the policy analyst, these stakeholders include scientists, policy makers, and organized citizen groups. The aim of boundary estimation is to obtain a relatively complete set of rival hypotheses in a given policy context. Although boundary estimation strives to be comprehensive, it does not attempt the hopeless task of identifying and testing
  all plausible rival hypotheses. Although the range of rival hypotheses is never complete, it is possible to
  estimate the probable limit of this range.
  Assessing the Impact of National Maximum Speed LimitA boundary analysis was conducted with documents prepared by thirty-eight state officials responsible for reporting on the effects of the original 55 mph and later 65 mph speed limits in their states. As expected, there were sharp disagreements among many of the thirty-eight stakeholders. For example, some states were tenaciously committed to the hypothesis that speed limits are causally related to fatalities (e.g., Pennsylvania and New Jersey). Others were just as firmly opposed (e.g., Illinois, Washington, Idaho). Of direct importance to boundary estimation is that 718 plausible rival hypotheses were used by thirty-eight stakeholders to affirm or dispute the effectiveness of the 55 mph speed limit in saving lives. Of this total, 109 hypotheses were unique, in that they did not duplicate hypotheses advanced by any other stakeholder.Here, it is important to note that from the standpoint of communications theory and language, the information content of a hypothesis is inversely related to its relative frequency or probability of occurrence. Hypotheses that are mentioned more frequently—those on which there is greater consensus—have less probative value than rarely mentioned hypotheses, because highly probable or predictable hypotheses do not challenge accepted knowledge claims.The rival hypotheses were analyzed according to the cumulative frequency of
  unique(nonduplicate) causal hypotheses. As
  Figure C3.1 shows, the cumulative frequency curve of unique rival hypotheses flattens out after the twenty-second stakeholder. Although the
  total number of rival hypotheses continues to increase without
  apparent limit, the boundary of
  unique rival hypotheses is reached within a small and affordable number of observations. This indicates that a satisfactory definition of the problem has probably been achieved. Indeed, of the 109 unique rival hypotheses, several variables related to the state of the economy—unemployment, the international price of oil, industrial production—explain the rise and fall of traffic fatalities better than average highway speeds and the 55 mph speed limit.
  
  
  

 

Do you need a similar assignment done for you from scratch? We have qualified writers to help you. We assure you an A+ quality paper that is free from plagiarism. Order now for an Amazing Discount!
Use Discount Code "Newclient" for a 15% Discount!

NB: We do not resell papers. Upon ordering, we do an original paper exclusively for you.