Handbook on Contemporary Austrian Economics

The facts of the social sciences are what people believe and think 35
has spun." Like the social stock of knowledge, a people's culture contains
interpretive schemes, relevance systems, skills, useful knowledge and
recipes that members of the society can use to define and master situations.
As Geertz writes (ibid., p. 89), by culture we mean "an historically
transmitted pattern of meanings ... a system of inherited conceptions ... by
means of which men communicate, perpetuate, and develop their knowledge
about and attitudes toward life." Culture is a frame of reference, a
backdrop, a way of seeing the world and an ethical system in which certain
beliefs, actions, outcomes are possible and permissible and others are not.
To say that the facts of the social sciences are what people believe and
think, is to concede that the social sciences must be preoccupied with
culture. Although we cannot gain direct access to people's inner worlds,
we can gain access to their cultural systems. As such, empirical work in the
social sciences must resemble ethnographies and/or employ archival and
oral history methods if social scientists are to learn the relevant facts.
3.3 Learning the facts: ethnography and thick descriptions
What applied methods should complement a science of meaning If the
facts of the social sciences are what people think and believe, then how
are social scientists to go about learning the facts Austrian economists
have been reluctant to embrace quantitative empirical methods because
they have doubts about the potential of statistical methods (alone) to
accomplish this task.
Admittedly, there are some questions that can only be adequately
explored by using quantitative measures and employing statistical
methods. For instance, looking at whether or not there is a causal relationship
between literacy and economic prosperity is a question that begs
for a quantitative examination. If, say, literacy rates and indicators of
economic well-being are correlated, then there is some reason, albeit not a
definitive reason, to believe that a relationship does exist. If, on the other
hand, these measures are not correlated, then there is some reason, again
not a definitive reason, to believe that a relationship does not exist. The
same rationale holds for utilizing more advanced statistical techniques like
regression analysis, which reveals the relationship between one variable
and the other variables that are believed to "explain" it.
There are several reasons why discovering a quantitative relationship
of this sort can never (by itself) allow us to be certain of a relationship
between two phenomena. Continuing with the example above, it is possible
that the measures that we used for literacy and economic well-being
are poor measures of the actual phenomena. If our measures are imperfect,
then the meaning of any relationship between them is suspect. Moreover,
even if our measures are perfect, the statistical relationship that we find