sumptions. The fundamental difficulty of empirical research is to de-
cide what assumptions to maintain.

Given that strong conclusions are desirable, why not maintain strong
assumptions? There is a tension between the strength of assumptions
and their credibility. I have called this (Manski 2003, p. l):

The Law of Decreasing Credibility: The credibility of inference decreases
with the strength of the assumptions maintained.

This ‘law" implies that analysts face a dilemma as they decide what as-
sumptions to maintain: Stronger assumptions yield conclusions that
are more powerful but less credible.

I will use the word credibility throughout this book, but 1 will have
to take it as a primitive concept that defies deep definition. The second
edition of the Oxford English Dictionary (OED) defines credibility as ”the
quality of being credible." The OED defines credible as “capable of being
believed; believable." It defines believable as ”able to be believed; credible.”
And so we come full circle.

Whatever credibility may be, it is a subjective concept. Each person
assesses credibility on his or her own terms. When researchers largely
agree on the credibility of certain assumptions or conclusions, they may
refer to this agreement as 'scientific consensus." Persons sometimes push
the envelope and refer to a scientific consensus as a ”fact" or a 'scientific
truth." This is overreach. Consensus does not imply truth. Premature
scientific consensus sometimes inhibits researchers from exploring fruit-
ful ideas.

Disagreements occur often. Indeed, they may persist without reso-
lution. Persistent disagreements are particularly common when as-
sumptions are nonrefutable—that is, when alternative assumptions are
consistent with the available data. As a matter of logic alone, disregard-
ing credibility, an analyst can pose a nonrefutable assumption and ad-
here to it forever in the absence of disproof. Indeed, he can displace the
burden of proof, stating 'I will maintain this assumption until it is
proved wrong." Analysts often do just this. An observer may question
the credibility of a nonrefutable assumption, but not the logic of hold.-
ing on to it.

To illustrate, American society has long debated the deterrent ef-
fect of the death penalty as a punishment for murder. Disagreement

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persists in part because empirical research based on available data has
not been able to settle the question. With this background, persons find
it tempting to pose their personal beliefs as a hypothesis, observe that
this hypothesis cannot be rejected empirically, and conclude that soci-
ety should act as if their personal belief is correct. Thus, a person who
believes that there is no deterrent effect may state that, in the absence
of credible evidence for deterrence, society should act as if there is no
deterrence. Contrariwise, someone who believes that the death pen-
alty does deter may state that, in the absence of credible evidence for no
deterrence, society should act as if capital punishment does deter. I will
discuss deterrence and the death penalty further in Chapter 2.

1.2. Incentives for Certitude

A researcher can illuminate the tension between the credibility and
power of assumptions by posing alternative assumptions of varying
credibility and determining the conclusions that follow in each case. In
practice, policy analysis tends to sacrifice credibility in return for strong
conclusions. Why so?

A proximate answer is that analysts respond to incentives. I have
earlier put it this way (Manski 2007a, 7—8):

The scientific community rewards those who produce strong novel find-
ings. The public, impatient for solutions to its pressing concerns, rewards
those who offer simple analyses leading to unequivocal policy recom-
mendations. These incentives make it tempting for researchers to main-
tain assumptions far stronger than they can persuasively defend, in
order to draw strong conclusions.

The pressure to produce an answer, without qualifications, seems
particularly intense in the environs of Washington, D.C. A perhaps apoc-
ryphal, but quite believable, story circulates about an economist's attempt
to describe his uncertainty about a forecast to President Lyndon B. John-
son. The economist presented his forecast as a likely range of values for
the quantity under discussion. Johnson is said to have replied, ‘Ranges
are for cattle. Give me a number.’

When a president as forceful as Johnson seeks a numerical prediction
with no expression of uncertainty, it is understandable that his advisers
feel compelled to comply.

 

Jerry Hausman, a longtime econometrics colleague, stated the in-
centive argument this way at a conference in 1988, when I presented
in public my initial findings on policy analysis with credible assump-
tions: ”You can’t give the client a bound. The client needs a point." (A
bound is synonymous with a range or an interval. A point is an exact
prediction.)

Hausman's comment reflects a perception that I have found to be
common among economic consultants. They contend that policy mak-
ers are either psychologically unwilling or cognitively unable to cope
with uncertainty. Hence, they argue that pragmatism dictates provi-
sion of point predictions, even though these predictions may not be
credible.

This psychological-cognitive argument for certitude begins from the
reasonable premise that policy makers, like other humans, have limited
willingness and ability to embrace the unknown. However, I think it too
strong to draw the general conclusion that ”the client needs a point." It
may be that some persons think in purely deterministic terms. However,
a considerable body of research measuring expectations shows that most
make sensible probabilistic predictions when asked to do so; see Chap-
ter 3 for further discussion and references. I see no reason to expect that
policy makers are less capable than ordinary people.

Support for Certitude in Philosophy of Science

The view that analysts should offer point predictions is not confined to
us. presidents and economic consultants. It has a long history in the
philosophy of science.

Over fifty years ago, Milton Friedman expressed this perspective
in an influential methodological essay. Friedman (1953) placed predic-
tion as the central objective of science, writing (p. S): ”The ultimate
goal of a positive science is the development of a ’theory' or ’hypothe-
sis' that yields valid and meaningful (i.e. not truistic) predictions about
phenomena not yet observed." He went on to say (p. 10):

The choice among alternative hypotheses equally consistent with the
available evidence must to some extent be arbitrary, though there is
general agreement that relevant considerations are suggested by the
criteria ‘simplicity' and ‘fruitfulness,’ themselves notions that defy
completely objective specification.

 

Thus, Friedman counseled scientists to choose one hypothesis (that is,
make a strong assumption), even though this may require the use of
”to some extent . . . arbitrary" criteria. He did not explain why scien-
tists should choose a single hypothesis out of many. He did not enter-
tain the idea that scientists might offer predictions under the range of
plausible hypotheses that are consistent with the available evidence.
The idea that a scientist should choose one hypothesis among
those consistent with the data is not peculiar to Friedman. Researchers
wanting to justify adherence to a particular hypothesis sometime refer
to Ockham's Razor, the medieval philosophical declaration that “plural-
ity should not be posited without necessity." The Encyclopaedia Britan-
nica Online (2010) gives the usual modern interpretation of this cryptic
statement, remarking that “the principle gives precedence to simplicity;
of two competing theories, the simplest explanation of an entity is to be
preferred." The philosopher Richard Swinburne writes (1997, l):

I seek . . . to show that—other things being equal—the simplest hy-
pothesis proposed as an explanation of phenomena is more likely to be
the true one than is any other available hypothesis, that its predictions
are more likely to be true than those of any other available hypothesis,
and that it is an ultimate a priori epistemic principle that simplicity is
evidence for truth.

The choice criterion offered here is as imprecise as the one given by
Friedman. What do Britannica and Swinburne mean by 'simplicity"?
However one may operationalize the various philosophical dicta for
choosing a single hypothesis, the relevance of philosophical thinking to
policy analysis is not evident. In policy analysis, knowledge is instrumen-
tal to the objective of making good decisions. When philosophers discuss
the logical foundations and human construction of knowledge, they do so
without posing this or another explicit objective. Does use of criteria such
as “simplicity" to choose one hypothesis among those consistent with
the data promote good policy making? This is the relevant question for
policy analysis. As far as I am aware, philosophers have not addressed it.

1.3. Conventional Certitudes

John Kenneth Galbraith popularized the term conventional wisdom,
writing (1958, chap. 2): ”It will be convenient to have a name for the