Expert answer:Public Policy
Solved by verified expert:Week 5 Discussion 1″Approaches to Forecasting Policy Outcomes” Please respond to the following:Describe a real or hypothetical situation that requires someone to make a policy decision. Then, select one of the three approaches to forecasting to apply: extrapolative, theoretical, or judgmental. Provide at least two reasons for your selection of the forecasting approach. Week 5 Discussion 2″Types of Forecasting” Please respond to the following:Debate It: Take a position for or against this statement: It is better to be approximately right than exactly wrong. Provide at least two reasons and one hypothetical example to support your position. Week 5 Assignment 2 SubmissionClick the link above to submit your assignment.Students, please view the “Submit a Clickable Rubric Assignment” in the Student Center. Instructors, training on how to grade is within the Instructor Center. Assignment 2: Stakeholder Analysis Due Week 5 and worth 150 pointsWrite a five to six (5-6) page paper in which you:(Note: Refer to Review Question 8 located at the end of Chapter 3 for criteria 1-3. Select two (2) editorials / essays / columns (by staff or freelance writers) on a current issue of public policy from two (2) different publications (large metropolitan or national newspaper such as Washington Post or the New York Times or national magazines such as Newsweek, Time, and The New Republic.)Apply the procedures for argumentation analysis (located in Chapter 8) to display contending positions and underlying assumptions for the content of Review Question 8.Rate the assumptions and plot them according to their plausibility and importance. (Refer to Figure 3.16, “Distribution of warrant by plausibility and importance.”)Determine which arguments are the most plausible. Provide a rationale for your views.(Note: Refer to Demonstration Exercise 1 located at the end of Chapter 3 for criteria 4-6. Examine Box 3.0 – Conducting a Stakeholder Analysis. Choose one of the following policy issues in the U.S. gun control, illegal drugs, medical insurance fraud, and environmental protection of waterways, job creation, affordable health care, or Medicare.)Apply the procedures for stakeholder analysis presented in Box 3.0 “Conducting a Stakeholder Analysis” to generate a list of at least five to ten (5-10) stakeholders who affect or are affected by problems in the issue area chosen for analysis. (Note: Refer to page 111 of the textbook for a step-by-step process on stakeholder analysis.)After creating a cumulative frequency distribution from the list, discuss new ideas generated by each stakeholder. (Note: The ideas may be objectives, alternatives, outcomes causes, etc.; ideas should not be duplicates.)Write an analysis of the results of the frequency distribution that answers the following questions: (a) Does the line graph flatten out? (b) If so, after how many stakeholders? (c) What conclusions can be drawn about the policy problems in the issue area? (Note: Compare your work with Case Study 3.1 at the end of the chapter.)Include at least two (2) peer-reviewed references (no more than five [5] years old) from material outside the textbook to support your views. Note: Appropriate peer-reviewed references include scholarly articles and governmental Websites. Do not use open source Websites such as Wikipedia, Sparknotes.com, Ask.com, and similar Websites are not acceptable resources.Your assignment must follow these formatting requirements:Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides; citations and references must follow APA or school-specific format. Check with your professor for any additional instructions.Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required assignment page length.
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FIGURE 8.2
Argument map—privatizing transportation
BOX 8.1
Mapping a Policy Argument
Policy arguments have seven elements: information, claim, qualifier, warrant, backing, objection,
and rebuttal. The following guidelines are useful in identifying and arranging these elements:
1.If possible, identify arguments by performing a stakeholder analysis (see Chapter 3).
Stakeholders are the main source of policy arguments.
2.Start by locating the claim, which is the endpoint or output of the argument. A claim is always
more general than the information on which it is based. Claims involve an “inferential leap”
beyond information.
3.Look for language that indicates the degree of credibility the arguer attaches to the claim—this
is the qualifier.
4.Look for the information that supports the claim. The information answers two questions:
What does the arguer have to go on? Is it relevant to the case at hand?
5.Look for the warrant, which in conjunction with the information supports the claim. The
warrant answers the question: Why is the arguer justified in making the claim on the basis of the
information?
6.Repeat the same procedure with the backing. If there is a question whether a statement is a
backing or a warrant, look for the one that is more general. This is the backing.
7.Remember that a warrant or backing may be implicit and unstated—do not expect arguments
to be entirely transparent.
8.Look to the arguments of other stakeholders to find objections and rebuttals. If possible, obtain
objections and rebuttals from someone who actually believes them.
9.Remember that elements may contain rules, principles, or entire arguments.
10.An uncontested argument is static; argumentation, which involves at least two parties, is
dynamic and usually contested.
11.The initial qualifier usually changes when objections and rebuttals are advanced to challenge
the claim.
12.Most qualifiers become weaker, although some stay the same. Some can grow stronger (a
fortiori) by withstanding challenges.
13.Argumentation produces “trees” and “chains” involving dynamic processes of argumentation
that change over time.
MODES OF POLICY ARGUMENTATION
Distinct modes of argumentation are used to justify policy claims. Modes of argumentation,
which are specific patterns of reasoning include reasoning from authority, method,
generalization, classification, cause, sign, motivation, intuition, analogy, parallel case, and
ethics.6 Each of these modes of argumentation and its characteristic reasoning pattern is
described in Table 8.1. Note that more than one mode may be used in a policy argument.
TABLE 8.1
Modes of Policy Argumentation with Reasoning Patterns
Mode
Reasoning Pattern
Authority
Reasoning from authority is based on warrants having to do with the achieved or
ascribed statuses of producers of policy-relevant information, for example, experts,
insiders, scientists, specialists, gurus, power brokers. Footnotes and references are
disguised authoritative arguments.
Method
Reasoning from method is based on warrants about the approved status of methods or
techniques used to produce information. The focus is on the achieved or ascribed
status or “power” of procedures. Examples include approved statistical, econometric,
qualitative, ethnographic, and hermeneutic methods.
Reasoning from generalization is based on similarities between samples and
populations from which samples are selected. Although samples can be random,
generalizations can also be based on qualitative comparisons. In either case, the
Generalization assumption is that what is true of members of a sample is also true of members of the
population not included in the sample. For example, random samples of n ⩾ 30 are
taken to be representative of the (unobserved and often unobservable) population of
elements from which the sample is drawn.
Classification
Reasoning from classification has to do with membership in a defined class. The
reasoning is that what is true of the class of persons or events described in the warrant
is also true of individuals or groups described in the information. An example is the
untenable ideological argument that because a country has a socialist economy it must
be undemocratic, because all socialist systems are undemocratic.
Cause
Reasoning from cause is about generative powers (“causes”) and their consequences
(“effects”). A claim may be made based on general propositions, or laws, that state
invariant relations between cause and effect for example, the law of diminishing utility
of money. Other kinds of causal claims are based on observing the effects of some
policy intervention on one or more policy outcomes. Almost all argumentation in the
social and natural sciences is based on reasoning from cause.
Sign
Reasoning from sign is based on signs, or indicators, and their referents. The presence
of a sign or indicator is believed to justify the expectation that some other sign or
indicator will occur as well. Examples are indicators of institutional performance such
as “organizational report cards” and “benchmarks” or indicators of economic
performance such as “leading economic indicators.” Signs are not causes, because
causality must satisfy temporal precedence and other requirements not expected of
signs.
Motivation
Reasoning from motivation is based on the motivating power of goals, values, and
intentions in shaping individual and collective behavior. For example, a claim that
citizens will support the strict enforcement of pollution standards might be based on
reasoning that since citizens are motivated by the desire to achieve the goal of clean air
and water, they will support strict enforcement.
Intuition
Reasoning from intuition is based on the conscious or preconscious cognitive,
emotional, or spiritual states of producers of policy-relevant information. For example,
the belief that an advisor has some special insight, feeling, or “tacit knowledge” may
serve as a reason to accept his or her judgment.
Analogy
Reasoning from analogies is based on similarities between relations found in a given
case and relations characteristic of a metaphor or analogy. For example, the claim that
government should “quarantine” a country by interdicting illegal drugs — with the
illegal drugs seen as an “infectious disease” — is based on reasoning that since
quarantine has been effective in cases of infectious diseases, interdiction will be
effective in the case of illegal drugs.
Parallel Case
Reasoning from parallel case is based on similarities among two or more cases of
policy making. For example, the claim that a local government will be successful in
enforcing pollution standards is based on information that a parallel policy was
successfully implemented in a similar local government elsewhere.
Ethics
Reasoning from ethics is based on judgments about the rightness or wrongness,
goodness or badness, of policies or their consequences. For example, policy claims are
frequently based on moral principles stating the conditions of a “just” or “good”
society, or on ethical norms prohibiting lying in public life. Moral principles and
ethical norms go beyond the values and norms of particular individuals or groups. In
public policy, many arguments about economic benefits and costs involve unstated or
implicit moral and ethical reasoning.
FIGURE 3.16
Distribution of warrant by plausibility and importance
On the basis of reasoned arguments and evidence provided by these two sources—along with
information about the reasons provided by stakeholders along with statistics on accident rates by age
group—we would conclude that this particular warrant has low plausibility. However, because the
warrant has high importance it is relevant to the conclusions of the argument and must be examined.
DEMONSTRATION EXERCISE
1.Choose a policy issue area such as crime control, national security, environmental protection,
or economic development. Use the procedures for stakeholder analysis presented in Procedural
Guide 3 to generate a list of stakeholders who affect or are affected by problems in the issue area
you have chosen for analysis.
After generating the list, create a cumulative frequency distribution. Place stakeholders on the
horizontal axis, numbering them from 1 … n. On the vertical axis, place the number of new
(nonduplicate) ideas generated by each stakeholder (the ideas can be objectives, alternatives,
outcomes, causes, etc.). Connect the total new ideas of each stakeholder with a line graph.
■Does the line graph flatten out?
■If so, after how many stakeholders?
■What conclusions can you draw about the policy problem(s) in the issue area?
Compare your work with Case Study 3.1 at the end of the chapter.
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 policymaking 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. ■
CASE 3.1 STRUCTURING PROBLEMS OF RISK IN MINING AND TRANSPORTATION
Complex 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 wellstructured (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 yconfirm 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, enumerative induction 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 “welldefended” 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 Limit
A 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 uniquerival 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.
FIGURE C3.1
Pareto chart—cumulative frequency of rival causes of traffic fatalities
Evaluating Research on Risk in Mine Safety and Health
In 1997, a branch of the U.S. Office of Mine Safety and Health Research began a process of
strategic planning. The aim of the process was to …
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