Legitimacy,
Controversies and Translation in Public Statistics: The Experience of the
Brazilian Institute for Geography and Statistics
Simon Schwartzman
Published in Science, Technology and Society,
1999, 4, 1, Jan-June, 1-34. A first version of this paper was presented
for the joint meeting of the Society for the Social Studies of Science and
the European Association for the Study of Science and Technology, Bielefeld,
Germany, October 10-13, 1996, session on "The Sociology of Public Statistics",
organized by the Research Committee on the Sociology of Science and Technology
of the International Sociological Association. I am grateful for comments
from Antônio Botelho, Aant Elsinga, René Padieu, Peter Wagner and this journal's
anonymous readers.
Summary
Introduction
The
two traditions of public statistics and the Brazilian Institute
Shifting Roles
Responses from the profession
Chains and networks
The
strive for stability and legitimacy: the role of credibility.
Strong and weak translation
chains
Translation
I - from empirical research to legal entitlements
Translation
II - from social concerns to statistical research
Conclusion:
the sociology of science and the future of public statistics
References
Notes
Introduction
"Public statistics," or "official statistics," refer
to the statistical information produced by government statistical agencies
- census offices, statistical bureaus and similar institutions. They are
of special interest to sociologists of science because they are done in
institutions which are, simultaneously, research centers - pertaining, therefore,
to the realm of scientific and technological values, perspectives and approaches
typical to their fields of enquiry - and public, or official institutions,
bound by the rules, values and constraints of the public Their products
- figures related to income, national product, urbanization, employment,
human fertility, and many others - are published in the press, used to support
government policies and evaluate its outcomes, and can lead to legal and
financial entitlements to specific groups. This plurality of roles, contexts
and perspectives associated with public statistics is at the very origin
of the field.
This article draws on the author's familiarity with the daily activities
on the Brazilian Institute for Geography and Statistics (IBGE) and hopes
to raise some general questions about the way knowledge about different
aspects of society is built at the frontier where different actors and institutions
- statisticians, social scientists, interest groups, computer specialists,
the legal system, public opinion - interact. None of these questions are
completely new for those acquainted with the ways scientific and technological
knowledge are built and consolidated in society, nor for those in touch
with the daily workings of public statistics institutions. But it may throw
some light on specific theoretical and empirical issues, that may have broader
significance. In the following, I will discuss how public statistics developed
as an institutionalized research activity, bringing together different professional
groups; how it struggles for legitimacy and stability in terms of its concepts
and procedures; how different stakeholders, with different interests and
understanding about the nature and characteristics of statistical information,
eventually get together; the conflicting tendencies of uniformity, standardization
and stability and the role of controversies; and the possible trends for
future development of this type of activity, giving the current institutional,
societal and technological trends.
The
two traditions of public statistics and the Brazilian Institute.
Alain Desrosières, who has written extensively on the subject,
shows how modern statistics emerged from at least two major traditions,
one from Germany, another from England. The German tradition is essentially
descriptive, taxonomic, and concerned with providing the government with
the necessary information to run its state. The association between these
two terms, "state" and "statistics," is not fortuitous.
The birth of German statistics is thus summarized by Desrosières:
Elle propose au Prince ou au fonctionnaire responsable un cadre
d'organisation de savoirs multiformes disponibles sur un État, c'est à-dire
une nomenclature dotée d'une logique d'inspiration aristotélicienne. Cette
forme a été codifiée, vers 1660, par Cornring (1606-1681). Elle a été
transmise ensuite, tout au long du XVIIIe siècle, par l'université
de Gottingen et son "école statistique", notamment par Achenwall
(1719-1772), réputé comme le créateur du mot "statistique",
puis par son successeur à la chaire de statistique, Schlözer (1935-1809).
Celui-ci, auteur d'un "Traité de Statistique" traduit en français
en 1804 par Donnant (ce qui fera connaître ce mode de pensée allemand
dans la France du début du XIXe siècle) a été le premier de
ce courant à recommander l'usage de chiffres précis plutôt que d'indications
exprimés en termes littéraires, sans pour autant le faire beaucoup lui
même. Une formule de Schlözer est significative de la tournure plutôt
structuraliste et synchronique de la statistique allemande: "La statistique
est de l'histoire immobile, l'histoire est de la statistique en marche"
(Desrosières, 1993, p. 30).
Desrosières links the British tradition, known in the past as "political
arithmetic," to the relatively smaller place of the State in that country,
regarding other social groups and institutions. These groups and institutions
needed precise indicators for specific goals, and developed methods for
sampling and indirect estimations, bringing statistics close to mathematics.
The English statisticians, he says,
"Ce ne sont pas des universitaires théoriciens qui édifient
une description globale et logique de l'État en général, mais des gens
d'origines diverses qui ont forgé des savoirs pratiques dans leurs activités
y que les proposent au "gouvernement." [. . . ] Ainsi s'esquisse
un rôle social nouveau: l'expert à la compétence précise qui propose de
techniques aux gouvernants, en essayant de les convaincre que, pour réaliser
leurs desseins, ils doivent en passer par lui. Ils offrent un langage
précisément articulé, alors que les statisticiens allemands, s'identifiant
a l'État, proposent un langage général englobant. (p. 30).(1)
The Brazilian Institute for Geography and Statistics - IBGE - was created
in the 1930s as one element in an ambitious attempt to organize a modern
and authoritarian state which could know and rule upon a vast and unknown
territory and a scattered population. The ideologies of the time assumed
that the central government should draw its strength from the country's
grassroots, the municipalities, bypassing the traditional state oligarchies.
At the beginning, the goal was to coordinate the statistical work carried
on by the municipalities throughout the country, and the German inspiration
was explicitly acknowledged by one of its forebears, José Bulhões de Carvalho
(Carvalho, 1924a, 1924b and 1930). A National Council of Statistics (Conselho
Nacional de Estatística) was formally established in 1936, followed by a
National Council of Geography (Conselho Nacional de Geografia) in 1937.
In 1942, as Brazil joined the Allies in World War II, a very tight system
of economic and administrative centralization was established under U. S.
inspiration, and the statistical and geographical institutions followed
suit - the local statistical and geographical entities were abolished, and
absorbed into a national bureaucracy which remained for the decades to come(2).
Geography was probably more important, in the early years, than statistics
itself, for the fulfillment of this task. The more direct influence came
from French geographers, which had a strong presence in the establishment
of Brazil's first universities at the time(3), but again, the geopolitical thinking derived
from German authors was strong. The introductory volume of the 1940 census,
the first done by the Institute, was a lengthy and ambitious book called
The Brazilian Culture, written by Fernando de Azevedo, a sociologist of
education which was influential in the creation of the Universidade de São
Paulo, and who edited, some years later, the first comprehensive survey
of Brazil's scientific institutions and groups (Azevedo, 1971 and 1955).
It was the geographer's task to depict the land, define its borders and
identify the available resources for the construction of a powerful nation
state; it was the task of the sociologist and educator to identify the cultural
elements that were turning the country into a modern, Western-type society.
In the sixties and seventies economics took precedence over geography. The
Institute was placed under a new Ministry of Planning, which included also
the National Research Council and the National Development Bank, and its
main task was redefined as to be the provider of basic information for the
country's economic development plans (Fishlow, no date). Besides the usual
demographic information and mapping, the institute became responsible for
organizing the national accounts, and its centerpiece was to be an ambitious
input-output matrix allowing for the identification of bottlenecks and evaluation
of the potential impact of investments in energy, transportation, steel
production, petrochemicals and other inputs in the country's economic fabric.
France, again, may have provided the intellectual and organizational model
- not the geographers any longer, but the economists at INSEE, perhaps in
combination with technical assistance coming from the United Nations (and
particularly from the Latin American Statistical Commission - ECLAC). A
whole new generation of economists was recruited and charged with redrawing
the Institute's research strategy, under the assumption that all information
should fit together in a comprehensive economic model.(4)
Writing in 1972, IBGE' s President Isaac Kerstenetzky presented his view
of how the country's planning system had to be organized, and the role the
statistical office was supposed to play in this grand scheme (Kerstenetzky,
1972):
The policy theory implicit in the synoptic, or decisional model,
follows a sequence which is the inverse of the one used by conventional
economic analysis. First, we identify some goals we consider desirable;
second, we look for what should be done in order to manipulate the instruments
we have at our disposal to reach our goals (5).
And later:
The set of activities in the field of statistics and socioeconomic
research would bring together and organize data and carry on studies needed
to construct models with the more salient aspects of the country's socioeconomic
structure. These models would allow for the identification of alternative
development paths. The political sector, based on an evaluation of the
main social objectives, would establish a plan according to the chosen
path (underlines in the original)(6).
The association between geography, statistics and economic planning was
not difficult to justify, at least in principle(7):
planning was not to be done by simple manipulation of macroeconomic variables,
but by direct intervention in the country's physical and economic landscape.
Less easy was to link this whole project with the awareness that Brazil's
modernization project was leaving a large part of its population at its
margins, and was affecting society in unpredictable ways. Neither geography
nor economics provided good answers to these questions, and a group of social
anthropologists was brought in to develop a system of social indicators
which would, hopefully, be integrated with the global model for economic
planning, rendering it more humanitarian and socially aware(8).
In practice, the Brazilian economy was never run through the Ministry of
Planning(9), and it is doubtful that the data produced by
the Statistical Office were ever used systematically by governments for
their long range planning, except in very general terms. But the planning
imagery had deep consequences for the internal organization of the office.
Now each research line could be said to have a definite place in a coherent
picture, and could not be easily challenged or changed. As long as the planning
imagery retained its appeal, the office's legitimacy would remain intact.
It helps if one could argue that one discipline is central, and responsible
for keeping the coherence and integrity of the whole. The Brazilian experience,
in this regard, is similar to that of France, where the introduction of
national accounts and the elaboration of input-output matrices l offices
gave economists a preeminent role, and appeared to offer a rationale for
the whole system, linking it to another important imagery, that of economic
planning (Fourquet, 1980). As the strength of the planning imagery faded,
this argument lost much of its strength, being replaced by the search of
another disciplinary framework, that of statistics itself as a comprehensive
and all encompassing discipline. When, years later, the Brazilian statistical
office went through a difficult period of lack of resources and loss of
prestige, the usual interpretation for the crisis among its technicians
was that it was a consequence of the governments' loss of its planning capabilities.
Today, the office's organization and research agenda is still very much
that of those times, and it is difficult to reconcile it with the current
skepticism about government planning and comprehensive modeling.
Shifting roles
This apparent loss of function and relevance of public statistics was more
than compensated by an opposite trend, through which statistical information
increased its importance and relevance in modern societies.
The use of statistics is expanding in society in different ways(10).
None of these tendencies is new, but, when brought together, they point
to a potentially new situation. First, it is expanding to other scientific
disciplines. Today, statistics is a central component of the education and
daily works not only of demographers, sociologists and economists, but also
of psychologists, epidemiologists, biomedical researchers, climatologists,
and in many other disciplines. Each of these disciplines developed its own
statistical traditions, giving preference to procedures and "rules
of proof" which are not necessarily familiar or equally accepted by
others.
Secondly, statistical information leads to conclusions and decisions which
can affect the lives of millions. This leads to efforts from the lay public
to understand, question and influence the way data is collected and interpreted.
In Brazil, population figures for municipalities affect their share of the
country's tax income, and special credits they can get for education, health
and poverty relief. Population figures, however, are not taken in isolation:
they should be combined with data on regional income and the number of people
living below the poverty line. Different estimations of the National Product
can lead to different conclusions about a country's tax burden, and justify
changes in tax. Unemployment statistics can affect the government's image,
and lead to specific policies related to unemployment benefits, employment
incentives or "flexibilization" of the job market.
A third, more recent trend is the growing power of personal computers and
the development of commercial computer software packages to perform statistical
which were previously restricted to statistical offices endowed with large
hardware, computer specialists and accomplished statisticians. Complex tabulations
involving thousands of calculations and files with several hundred thousand
megabytes can be done in a matter of minutes. Today, the user of such processing
power has still to be able to choose among a wide array of strategies and
to interpret the results. Progressively, however, artificial intelligence
techniques may automate the procedures for selecting of statistical tools
and interpretating the results. The consequence of this development is the
growing demand for access to the micro-data produced by statistical offices,
and the proliferation of summary data and results which would not necessarily
receive the stamp of approval either from well-trained professional statisticians
or from the statistical agencies responsible for the original data gathering.
There are other, less obvious aspects to this expansion and tansformation.
One has the clear impression that numbers are more important today in the
press, and in shaping public opinion, than in the past. Rates related to
economic growth, unemployment, inflation, income distribution, crime and
deprivation produce headlines, affect the prestige of public figures, and
can make and unmake ministers. Most of these rates did not exist before
the current systems of official statistics were put in place. Some authors
have speculated that this high profile of numbers is part of a more general
trend to surround data with an aura of scientificity and objectivity, part
of a broader trend to gain the trust and confidence of the population regarding
scientists, researchers and their organizations (Porter, 1995). Statistical
reasoning is starting to be introduced as elements of proof in legal proceedings,
and statisticians are being called to perform roles similar to that of traditional
forensic medicine in courts (Van-Matre and Clark, 1976; Gastwirth, 1988).
Responses from the
profession
There are two standard types of response to this situation coming from the
profession, neither quite adequate. The first is to argue that statistics
is a kind of "science of the sciences," and should therefore incorporate
and condition the ways other fields of knowledge are organized and behave.
This is the well-known stand taken by Leslie Kish in his 1977 Presidential
address to the American Statistical Association (Kish, 1977), as well as
by D.W. Marquardt a few years earlier in the same context (Marquardt, 1987).
Closely associated is the notion that society should somehow restrict the
use of statistical instruments and concepts by people without the proper
training. The implicit goal is to control the use of statistical concepts
and data the same way the medical profession controls the use of medical
diagnostics and the consumption of prescription drugs.
The first ambition of statistics, for being the "primo inter pares"
among sciences, can be dismissed without much trouble. Most fields of knowledge,
from physics to sociology, share the same ambition. The trend, however,
does not seem to be toward scientific convergence and unification, but toward
increasing diversity and multiplicity of working paradigms. The efforts
to control and limit the use of technical objects and concepts by laymen,
best exemplified by the professional monopolies of medical doctors and lawyers,
require extremely complex legal and institutional apparatus, and is probably
on the wane even for medicine, challenged in the courts (Jasanoff, 1995),
through to the spreading of different forms of alternative medicine and
deep changes in the traditional doctor-patient relationships, with consumer
awareness replacing the usual relationships of trust and confidence. It
is clear that, in this process, the medical profession is often on retreat,
while the public is exposed to different forms of quackery; but it is not
obvious that the current situation is worse than when the powers of the
medical profession went unchallenged. It is possible that the professional
monopoly of the legal profession will be the one to last longer, and not
necessarily because of the scientific content of Law as an intellectual
discipline.
Statisticians may aspire to the role of oracles, providing society with
unquestioned interpretations and predictions of things to come, but such
role is not easily granted. "In the absence of a culturally accepted
mythology of deites to provide oracular status, statisticians can only gain
status and recognition by providing value to their consumers. The statistician
must assess what is wanted and needed by the consumer and find ways to provide
both" (Boroto and Zahn, 1989). This stand creates an immediate reaction,
which the authors are ready to acknowledge: "the idea of addressing
what the consumer wants (particularly if it conflicts with what the statistician
thinks the consumer needs) could appear to be a form of prostitution."
The answer, argue the authors, is not to provide the public with what it
does not want, but to engage in active dialogue with the public - "the
master statistician relies on dialogue"; an effective statistician
is essentially a skillful translator, the consumer should never have the
experience of being lost in a foreign land." A year later, another
article at The American Statistician took a similar position, arguing however
for a much more active role for the statistician to perform. "As providers
of information we can no longer complain that the users of our information
do not know how to tell us what they need and, therefore, it is not our
fault that if relevant, timely, easy-to-see and cost-effective information
is not available for decision makers." The question, then, is not just
to do what the consumer wants, but to answer, to ourselves, "how can
we govern, by our own actions, the future environments in which statisticians
will work?" (Barabba, 1990).
Barabba's stand is a far cry from the technocratic arrogance of trying to
shove one's preferred medicine down everyone else's throats, but it is nevertheless
one step back from Boroto and Zahn's position. He is willing to make statistics
more palatable, but he is the one who knows, for instance, that "we
are entering a period in which the tolerable margin of error that both the
governmental and private sectors will be allowed in the conduct of everyday
affairs is greatly narrowed," and is willing to fulfill this need.
Statisticians should be like good doctors: aware of the needs, concerns
and limitations of their patients, but not to the point of letting them
decide what afflicts them, and what medicine they should take.
Brazil, statistics itself, as a discipline, does not seem to have ever been
the central intellectual component of the Institute's technical and professional
make up(11). In an attempt to follow the
French tradition of government controlled "grandes écoles," the
Brazilian census office created its own National School of Statistical Sciences
(ENCE), which was supposed to become its main source for professional recruitment.
Although the school still exists, it never fulfilled this role, for several
reasons. The Institute never succeeded in assuring employment to ENCE's
graduates(12); and, as an isolated establishment,
it was not able to keep abreast with the scientific and intellectual developments
in the field, and lost place to other courses and degree programs in universities.
Most important was the fact that statisticians, regardless of their place
of training, did not have the knowledge, skills and profile associated with
the prevailing economic planning imagery.
As other subject matters entered the agenda of statistical offices - issues
like employment, education, health, agriculture, environment conditions,
social and political participation, race, language, social discrimination
- the professional profile of statisticians also changes. Other professional
identities - that of the economists, of course, but also sociologists, educators,
environment and health specialists - may be stronger in many statistical
agencies than that of the statistician. This proposition should be verified
empirically, since users of data are not necessarily trained and interested
in the chores of data gathering, processing and validation, which are typical
of the daily work of statistical agencies. But, if true, it would be related
to the fact that statistics today, as an academic subject, is essentially
a specialized branch of mathematics, while statistical skills are an increasing
component of the education in all social and economically related fields,
and greatly simplified by ready-made software. In many countries, these
separate specialties are associated with the multiplication of statistical
institutions - the United States is probably the extreme, but not the only
case. When statistical offices are unified - as in Brazil or Mexico, which
include also geography under their umbrella - the consequence may be the
development of internally differentiated technical cultures, more related
to each academic field outside than with the other sectors within the institution.
How can all these different activities, endeavours and interests be held
together?
Chains and networks
One of the more interesting insights of the sociology of science is that
what is usually known and understood by "science" and "technology"
are just segments of much larger networks of people, institutions, instruments,
hardware and nature itself. A personal computer (one of the examples elaborated
by Latour, 1987) links academic physicists and mathematicians, engineers,
hardware and software producers, patent offices, standards committees, marketing
agencies, shops, technical assistant networks and users of all kinds; and
it depends on the physical properties and availability of semiconductors
and a wide array of raw materials. People at one end of the chain usually
do not understand what others are doing at the other, which means that there
is a constant work of translation going on between adjacent actors. Application
producers have to understand the possibilities and limitations of operational
systems, which depend on hardware, which depends on the physical properties
of the materials that can be delivered by industry. In the other direction,
users have to understand the language of programmers (who, in turn, strive
to translate their devices in terms of natural languages), and vendors have
to anticipate the needs of buyers. Once in place, these chains affect the
way work is organized in offices and companies, influence the curricula
of schools, and introduce changes in the labor market. These chains are
never created linearly, either from top down (a conceptual theory leading
to an experimental model, leading to a tested product, leading to further
development and marketing, and so forth) or from bottom up (a consumer demand
leading to a product, leading to research, leading to new concepts and theory).
Innovation may take place in all links anytime, and dead ends and brilliant
failures are common throughout (David, 1992; Latour, 1993; Gibbons and others,
1994) . At the end, to paraphrase Bruno Latour, it is not necessarily the
best product, theory or technology that gets established; rather, it is
the product, theory or technology that gets established that becomes the
best, not only because it is the "winner," but because it will
benefit from increasing investments from all parts concerned. One of the
most striking features of modern society is the establishment of such networks,
which is not necessarily a peaceful and harmless procedure, as witnessed
by the expansion of western science and technology to the rest of the world.
Nevertheless, once established, these networks lead to increasing benefits
to all participants, forging alliances that seem to grow without limits
and barriers.
A similar picture of networks, translations and alliances can be used to
describe a well established statistical procedure carried on by a public
statistical agency. Take the cost of living indexes, used almost everywhere
to measure inflation, to set income policies and to evaluate the prospects
of a given economy. For the economist, prices are linked to a series of
concepts such as investment, consumption, saving patterns, exchange rates,
productivity, interest rates. Several of these concepts are used by governments
in their efforts to control and direct the economy, and for private actors
to make decisions about investment, consumption and employment. Trade unions
use cost of living indexes to set targets for their negotiations, and political
parties use them to mount campaigns in favor or against governments. For
the press, cost of living indexes can be a hot topic for their readers,
particularly if they can be easily interpreted in terms of their private
expectations and the image of the performance of public authorities.
Going in the opposite direction of the chain, the economist's concepts are
translated by statisticians in a series of procedures to measure variations
in the index. They include the identification of items and sectors that
are to be monitored (consumption goods, durable goods, capital goods, services);
their relative weight, based on consumption patterns of specific groups
(workers, middle class, poorer segments); and their distribution in the
geographic space. Samples of informers, regions and products are to be established,
accepted limits of error are defined, and permanent mechanisms for data
collection and processing are put in place. These two last tasks go beyond
the realm of the statistician's work, and include other actors in the process.
Data can be collected by specialized firms, temporary workers or permanent
staff, which establish their own routines for getting into the field and
bringing in the data. Information processing is handled by computer specialists
that make decisions about the equipment to be used, the appropriated software
and the timing for data processing and delivery.
Similar descriptions could be made about other types of indicators, such
as employment, poverty levels, crop forecast, industrial production, international
trade, migration patterns, population growth, national income and income
distribution(13). To keep the analogy with
the personal computer, all actors would have problems if they had to contend
with different and incompatible products - IBM PCs, Macintosh and Amiga
- or three different indexes of employment and inflation and two different
values of per-capita income. Whenever a technological chain reaches the
size of mass consumption markets, the tendency is for one product or industrial
standard to prevail, while the others either disappear or find special niches
of users and applications.
One would expect from this confluence of interests that public statistics
would naturally evolve toward unification along well established standards,
leaving little space for controversies and disputes. The logic of standardization
explains the uneasiness created whenever competing figures or information
are presented to describe or quantify presumably identical "realities."
International agencies, such as the United Nations Statistical Offices,
the United Nations regional and specialized offices, Eurostat, the World
Bank, the International Labor Organization and similar institutions play
a very important role of setting the agenda, establishing standards for
comparability and providing statistical offices throughout the world with
technical training. National statistical agencies want to have their data
accepted within their countries and in the international community, and
react whenever competing figures or indicators are presented by other national
institutions or international organizations. Newspapers complain and talk
about "confusion" whenever different figures appear. Governments,
of course, are not happy when the figures they use to set their targets
and evaluate their achievements are placed against competing and diverging
information. Conceptual and empirical standardization is always a very complicated,
costly and uncertain process. The irony of it is that, at the end of the
day, all parts involved are committed to the notion that they are talking
about the same "reality" which was already there from the beginning,
making it very hard to explain why it cost so much to get there.
There is, however, an opposite trend, towards fragmentation. The agenda
of public statistical offices is set by a combination of government requests,
social demands, concepts developed by economists, demographers and social
scientists, and methodologies developed and tested by statisticians. In
spite of this constant pressure toward standardization, a survey of current
practices would probably show a wide range of variation in the way statistical
offices respond to the demands of their different clients and professional
communities(14). This trend towards fragmentation
can be conceived as part of a broader tendency for research centers and
institutions to become much more pragmatic and goal-oriented than in the
past (Gibbons and others, 1994). There is growing skepticism about comprehensive
systems of social analysis and interpretation, which are associated with
the demise of comprehensive planning as a tool of government policy; and
the expansion of applied, goal-oriented and product-oriented research. An
important element of this change is the breaking down of disciplinary barriers
and the development of all kinds of interdisciplinary and interinstitutional
cooperation and networking in all knowledge fields These trends are related,
in turn, to the growing pressures upon universities and research institutes
to link more closely with industry and to relate to many other social groups
besides the conventional students - to leave the ivory tower and to respond
more pragmatically to short-term demands. For the statistical offices, this
trend suggests a pressure to move from comprehensive statistics to service-oriented
work, not only in terms of how data is to be distributed and disseminated,
but even in terms of what data should be collected and processed.
This pressure towards what Gibbons and others would call the "mode
2" of knowledge production, defined among other things by its fragmentation,
in contrast with the traditional "mode 1" of scientific work along
well defined, self-centered disciplinary lines, is not a peculiarity of
the Brazilian Institute, but a trait which can be found in many other parts
of the world. In fact, the main characteristics of knowledge production
today in most areas is not just a transition from the so-called "mode
1" to "mode 2", but precisely this tension between the trend
towards the creation of increasingly larger and coherent chains of what
Bruno Latour calls "technoscience" (Latour, 1987), of which coordinating
statistical institutions like Eurostat and the United Nations Statistical
Commission are good examples, and the centrifugal pressures for fragmentation,
deriving from scattered demands and heterogeous realities.
The
strive for stability and legitimacy: the role of credibility
Statistical concepts and technical devices play important roles in this
process of stabilization of social interaction, a "moral role"
which is not apparent from their deceptively simple technicalities(15)
There are many reasons for resisting fragmentation. On its simplest form,
it is just a matter of who will get the resources or the contracts to do
the job. If the figures produced by one institution are adopted by everybody,
this institution will get the resources and support to continue its work,
while others will wither away. But the consequences can be much wider, since,
for instance, different estimations of income distribution could lead to
different policies of investment and resource allocation from governments.
The reasons why such conflicts do not linger forever are the same that explain
why other social conflicts eventually get settled: on the long run, the
collective gains of stabilized systems tend to be higher than the private
benefits gained through protracted conflicts.
As could be expected, statistical offices strive to keep their information
stable, non-controversial and technically well grounded. One simple expedient
is the use of figures. In modern society, if you can express yourself in
numbers, your credibility increases (Porter, 1995). The trouble is when
the numbers are unstable, or conflicting. Ivan Fellegi, Canada's Chief Statistician
and a leading personality in the field, insists in a recent paper that "the
core values of effective statistical systems are legitimacy and credibility"
(Fellegi, 1996). Authoritarian governments can define what the official
figures should be, but the question is whether anyone would believe them
(one is reminded of the 99% of votes always obtained by official candidates
in elections in the Soviet Union). Credibility, thus, is an essential component
for the acceptance and adoption of uniform standards and procedures. But
what are the origins of credibility, from where does it come?
A credible information is one that comes out from a credible institution,
and may not be rigged in favor of a specific interest group or ideology
(Fellegi refers, in the above mentioned paper, to "non-political objectivity").
Institutional credibility is very much a matter of political culture. Public
institutions in Germany or France are supposed to be credible, while similar
institutions in the United States(16) or
Brazil can never take their credibility for granted.
Another source of credibility is technical and scientific. Information is
accepted as credible if they are provided by people or institutions with
a strong scientific and technical profile. This is a curious paradox, since
empirical sciences are dominated by provisional, tentative, probabilistic
and even contradictory findings and controversies, rather than by hard-rock
logic, evidence and demonstration(17). Matters are made still more complicated by
the fact that the production of public statistics is not limited to a single
discipline, that of the statistician. Statistical offices are staffed by
economists, social scientists, computer analysts, statisticians and mathematicians,
each with their own professional culture, biases and preferences. Besides
their differences in origin, these different professional groups keep ties
with their professional communities, and disputes of turf and professional
precedence are likely to occur.
Another source of credibility is stability and consistency. Figures produced
always according to the same procedures are easier to be accepted than figures
that vary depending on shifting methodologies, concepts and procedures(18). Institutions organized to defend the interests
of specific groups are less credible than those supposed to be independent,
at least for other sectors of society. A research center financed by the
tobacco industry will have difficulties gaining acceptance for their findings
showing that secondary smoking is harmless. Research institutions associated
with trade unions will have a hard time convincing others that their figures
for unemployment and cost of living are the best. To gain credibility, they
should try to disentangle themselves from their supporting sectors, and
raise their scientific and technical credentials.
Other factors, related more specifically to the nature of the data, may
influence the credibility of public statistics. Whenever the data affect
specific interests (like the consumer prices indexes, when used to correct
salaries or pensions for inflation, or population figures affecting the
distribution of tax revenues, subsidies and electoral apportionments), they
are likely to be challenged; if the affected sector is narrow, the challenge
is probably less threatening than when it affects the whole society. One-of-a-kind
surveys are more likely to be challenged than the results of permanent,
on-going statistical practices; data on "hidden" or illegal practices,
such as tax evasion, gambling and "informal" economic transactions
are also prone to disbelief. Sometimes the mistrust goes to the informer,
sometimes to the real independence of the statistical office, sometimes
to its technical competence(19).
Strong and weak
translation chains
We have argued that there is a tendency in the field of public statistics
to look for unified and consistent figures and concepts which would be accepted
by a wide range of actors, including social scientists of different disciplines,
professional statisticians, bureaucrats in statistical offices, governments,
legislators, interest groups, the press, political parties and international
organizations. For each actor, the advantage of unified concepts and figures
is that they can always argue that they are not just defending their own
interest, but are relying on "objective" and "scientific"
data that are beyond dispute. At the same time, they would prefer to get
the figures and concepts that are more convenient for their particular interests
and professional cultures. In other words, the tendency toward unification
is not without its tensions, since what is at stake is who, or what, will
set the standards and control the process of data production and evaluation.
Our further contention is that these tensions find their outlets and feeding
grounds in the fissures and inconsistencies that take place in the translation
process between different actors and milieux.
Given the plurality of actors and interests which participate or can be
affected by the work of statistical offices, stable alliances supporting
them have to be constructed. Michel Callon deals with this issue in very
broad terms, suggesting a sequence of steps in the constitution strongly
integrated networks of interests, which he seems to consider as typical
of how these science and technology networks are built. (Callon 1986, 196-233).
The first step in his scheme is "problematization, or how to get indispensable."
An essential element in this first stage is the "definition of obligatory
passage points." If I want to develop a new survey on technological
innovation, for instance, all interested parties should be convinced that,
if they want to incorporate modern technology in their activities, they
would have to get the proper data to measure and evaluate it, and my institution
or research group is the best one for doing this work. The second step is
"interessement," a very complicated and unpredictable process
of convincing all potential actors that they have a common interest, to
be fostered in this specific way. To use Callon's words, "for all groups
involved, the interessement helps to corner the entities to be enrolled.
In addition, it attempts to interrupt all potential competing associations
and to construct a system of alliances. Social structures comprising both
social and natural entities are shaped and consolidated." (Callon,
1986, p. 211). The third step is "enrolment," how to get all actors
to behave in compatible ways. In our example, I must convince the companies
to respond to the questionnaires, to convince the government to provide
de necessary resources, and the policy makers to consider this information
in their future investment decisions. The fourth step is "the mobilization
of allies." All actors must agree that the research institute is their
spokesman, that the data produced expresses the common realities and interests
of all parts involved.
Michel Callon seems to imply that the process of translation is not just
a way of linking different actors and interests in a chain of meanings,
but of bringing them together in very rigid networks. "To translate
is to displace". "Translation is the mechanism by which the social
and natural wolds progressively take form. The result is a situation in
which certain entities control others" (Callon, p. 223-224). It is
possible to have a weaker, more pluralistic view of the translation process,
more to the sense adopted by Glifford Geertz (1983): to translate is to
be able to communicate, however imperfectly, among cultures and languages
that remain different. What is natural and proper in the academy may be
questionable and problematic in other contexts. Once published, public statistics
gain a life on their own, which is usually not fully compatible with the
way they are constructed. Sometimes they are translated into legally binding
decisions; sometimes they are taken by the press, and translated to the
general pubic in simplified terms. Sometimes they are taken by political
parties and non-government organizations, which use them to justify their
actions. Whenever concepts and expectations from one sector are used to
evaluate what takes place in the other, tensions and misunderstandings are
likely to occur. Still, since there is a tendency for each sector to look
for its legitimation elsewhere, keeping them apart is impossible.
Translation
I - from empirical research to legal entitlements
A very frequent type of translation in the field of public statistics takes
place between empirical research and the establishment of legal entitlements
of different kinds.(20)
In Brazil salaries, rents and other prices have often been pegged to cost
of living indexes, which cannot be defined in probabilistic terms. The way
it is done, the president of the statistical office signs an official act
each month announcing what the official inflation figure is. What differentiates
this act from an arbitrary decree is the assumption that this figure was
obtained through valid scientific procedures, open to anyone to inspect
and replicate. The practice is complicated. Although the general statistical
procedures, weights and sampling techniques are public, details are kept
confidential to protect the privacy of informers, and to protect the index
from actual or presumed manipulation from outside, including the government
itself. Statistical errors are usually not published, and the systems of
data collection and processing, including the weights attributed to the
different items in the consumers' basket, are kept stable for long periods.
This situation is further complicated by the existence of several inflation
indexes produced by different institutions and yielding slightly different
results. These differences are not difficult to explain on technical grounds,
but, particularly in times of high inflation, they are almost impossible
to explain to the public. For the specialist, prices clearly do not rise
and fall at the same time in the whole economy, and the existence of different
institutions producing independent estimates of similar data can be seen
as a positive trait of an open and democratic society. Government, however,
needs just one figure to establish its policy, and can come under suspicion
if he can choose, among several indicators, the one that suits him best.
Another example is related to population estimates. Yearly population figures
provided by the statistical office are used to distribute part of the federal
tax resources among municipalities (the so-called "Fundo de Partipação
dos Municípios"). Because of this legislation, the number of municipalities
in Brazil increased about 50% in a few years, reaching a figure close to
six thousand, and in each case the statistical office was asked to inform
the population and the boundaries of the new jurisdiction. The reliability
of the figures provided, however, depends on the quality of the previous
population census (the last one in Brazil was in 1991) and on assumptions
on migration patterns, fecundity and mortality rates derived from different
studies. Statistical errors are unavoidable, and are likely to get bigger
the smaller is the population group to which the projections refer. Besides,
one of the main findings of Brazil's 1991 census was a dramatic reduction
on fecundity rates in the previous decade, leading to a much smaller population,
and lower projections for the 1990s, than was generally expected. Thousands
of complaints and requests from municipalities for revision of population
estimates flooded the statistical office. The Federal Accounting Office
(Tribunal de Contas da União) decided to keep using the population estimates
of the 1980's instead of those based on the 1991 census for the distribution
of resources. In 1996 the statistical office obtained government support
to do a mid-decade population enumeration to adjust the country's population
estimates. The population specialists at the Institute believed that this
was necessary on technical grounds, and the budgetary request got ample
support in Congress, largely because of the municipalities' complaints.
There is no assurance, however, that the new population estimates would
be any more favorable to the municipalities than before.
Other examples could be taken from the institute's cartographic and geographic
activities. Boundaries between countries, states and municipalities depend
on detailed and precise maps, but, before that, on legally binding decisions,
based on agreements, negotiations, litigation and even warfare. If a conflict
cannot be decided by force or negotiation, would it not be possible to get
a "technical" solution to the problem, coming from the country's
geographical institute? If one knew how to divide the open seas between
the states of Paraná and Santa Catarina, facing each other in the Atlantic,
one would know how much each should get in royalties from the oil being
produced by Brazil's oil company, Pretrobrás, in this area. Since there
is no single technical solution to the problem (straight lines can be drawn
based on different kinds of assumptions about an irregular shoreline), the
Institute is under constant accusation from one part of being partial to
the other, while it is impossible for the parts to get a binding decision
from the Supreme Court.
The pattern in all these examples is similar. There are interests in conflict,
and the statistical office is required to provide a technical solution.
It is a request for arbitration, which is usually better for all parts involved
than a protracted conflict. But arbiters are bound to decide in favor of
one of the litigants, and therefore may have its authority challenged by
the loser. To play this role, the arbiter has to convince the litigants
that his moral, legal and technical virtues are beyond doubt and criticism.
There is a constant process of translation going on - conflicts of interest
being translated into technical questions, and technical and scientific
processes being translated into legally binding decisions. As in any translation,
communication between different languages and cultures is possible, but
something is also lost in the process.
Translation
II - from social concerns to statistical research
In the examples above, one might conceive of a chain of events going from
the scientific and technical realm to the world of interests; the examples
below point to an opposite sequence, from social concerns to technical concepts
and constructs. Professional sociologists and economists would expect that
concepts, categorizations and procedures used in their research would come
from social and economic theories in their fields. In practice, society
places demands on the statistical office which are not only not derived
from existing theoretical and conceptual models, but are often extremely
difficult to conceptualize and measure in technically acceptable ways. Three
outstanding examples are race, poverty and employment.
Should Brazilian statistics include figures on race? Brazil is a multiracial
country (native Indians, Portuguese and Dutch colonizers, black African
slaves, German, Italian, Central European, Jewish. Arab and Japanese immigrants
in this century) with a large mixed-blood population. Racial discrimination
is a criminal offence, but there is evidence that race (or color of the
skin) is strongly related with all indicators of social mobility and well
being. Social discrimination, even if not explicit, is common. Differently
from the United States, however, the dividing line between whites and blacks
is blurred. In the United States one are "black" if one of his
parents (or even grand parents) is black; in Brazil different shades of
blackness bring different social definitions, and it is very easy to "pass"
from one race to another if one can associate a fair skin with some education
and a reasonable income. The prevailing interpretation is that Brazil does
not have a "racial question," but a large social question, and
a high correlation between poverty and the color of the skin, explained
by the fairly recent history of black slavery. For some time, race was kept
outside the census and the official statistics. First, because it would
be impossible to have an "objective" racial classification for
the population, given the high levels of miscegenation; and second, because
the collection of figures on race could lead to the development of race
cleavages that did not exist.
When the question about race was finally introduced in the 1980 census questionnaire,
it was phrased in terms of "color of the skin," and the answers
were classified into back (preto), white (branco), brown (pardo) and yellow
(amarelo), the last one combining Japanese and Chinese descendants with
Indian natives(21). Since it was a self-classification,
it could only be interpreted culturally. The data confirmed that race or
color of the skin had an independent effect on social conditions, but did
not challenge the dominant view that race (or color) was not a criterion
to be used for social policy. More recently, however, there has been a demand
from black militant groups to introduce policies of affirmative action similar
to those adopted in the United States, a demand associated with the request
to introduce race questions in all kinds of pubic documents, including the
public registry for birth, marriage and deaths. The expectation is that,
through these means, a racial classification will be introduced in Brazilian
society, creating entitlements for social and economic benefits. The argument
is that this classification already exists, and is just not well portrayed
by existing statistics; the opposite view is that the collection of these
data would sharpen and shift the current social issues to other arenas,
converting the current shifting racial self-classifications into sharply
defined categories. On the long run, people may freeze their identities
according to officially defined classification, race identification may
be required in identity cards and even in arm bands, and sharp and scary
race cleavages, which do not exist today, may materialize, in a rather scary
self-fulfilling prophecy.
Poverty and employment, or unemployment, are similar concepts in popular
perception, but very different issues both from historical and from the
official statistics' point of view. Desrosières links the first statistical
studies on poverty with 19th century England, and the emergence of unemployment
statistics with the New Deal in the United States almost a century later
(Desrosières, 1993) Poverty has been a constant presence in man's history,
but its meaning has changed through time (Castel, 1995). Most people in
traditional societies were poor, and this was accepted as natural and unavoidable.
Pauperism becomes an issue when the poor are displaced from their usual
environment and life patterns and move out of their regions looking for
food, shelter or work. Poverty was a constant source of concern and debate
in England since the inception of the Industrial Revolution, most of the
discussion being on whether the poor should be treated as victims, and therefore
entitled to protection and support, or morally inept, to be left to their
own fate. The second view was to prevail, not only among hard-core liberal
economists, but for Marx himself, with his well-known contempt for the lumpenproletariat.
Poverty became a moral issue, a question of character and good will, not
something related to the way society was organized.
If you did not work, but wanted to, you were not poor, but unemployed. Economic
fluctuations created unemployment, and the 1929 crisis produced millions
of unemployed in the United States and Europe. Different from poverty, unemployment
was understood to be a cyclical by-product of modern industrial economy,
and mechanisms had to be devised to reduce it, or compensate for its consequences.
Everybody, in principle, should have a stable work, and action was needed
when it did not happen. Anti-cyclic policies, on one hand, and unemployment
compensation, on the other, were landmarks of the post 1929, Welfare State
capitalism. Unemployment had to be measured, and proper statistics were
needed, but it should not be confounded with poverty. To be unemployed was
an attribute of industrial workers, not of people outside the productive
system - housewives, old people, beggars, the lumpenproletariat. The current
standard statistical definition of unemployment, adopted and implemented
by the International Labor Organization, measures precisely that. Unemployed
are those who are without jobs, but are actively looking for one, or living
from unemployment benefits. If you are not looking for a job, if you live
from welfare, if you live from handouts from your family, if you beg in
the streets, you are not unemployed, but simply outside the economically
active population. Unemployment statistics became an excellent instrument
for measuring the short-term fluctuations of economic activity, and the
widespread use of similar methodologies allowed for meaningful international
comparisons.
The assumption that everybody should have a stable job, however, is being
questioned in industrialized countries, and never really existed in developing
and underdeveloped societies. The concern that economic development was
leaving large segments of the population at its margins led to the emergence
of marginality, first (Germani, 1973), and poverty, more recently, as objects
of social research and, gradually, to the establishment of regular statistical
procedures in statistical offices. Statistics on poverty and on unemployment
developed independently, and today in Brazil they are subject of two, quite
separate controversies.
The unemployment controversy is centered on the existence of two regular,
independent unemployment surveys in Brazil. One, PME (Pesquisa Mensal de
Emprego), is done by IBGE, and the other, PED (Pesquisa de Emprego e Desemprego),
is carried on by the statistics office of the State of São Paulo, Fundação
SEADE, in association with a research center maintained by the trade unions,
DIEESE. The most evident aspect of the controversy is that PED figures are
consistently higher than those of PME. Part of the difference is well explained
on technical grounds: PME is centered on the concept of "open unemployment,"
while PED includes also "hidden unemployment." But even when this
difference is eliminated in the analysis and comparisons are made for the
same period of reference, there are still discrepancies, which may be attributed
to the sequence in which the questions are presented to the respondent during
field work, duration of the interviews and other technicalities. The technical
differences between the two surveys do not seem unsurmountable, although
this statement in itself may be controversial. Besides the final single
figures, both surveys measure different types of unemployment (those looking
for jobs in the last week, or in the last month, for instance). Both include
information on the quality of the jobs held, distinguishing among stable
employment (which in Brazil requires a formal contract and the payment of
several taxes on social security) and different types of precarious work.
The Brazilian Ministry of Labor, which provides funds for the SEADE-DIEESE
survey, established an expert group to analyze and try to reconcile the
two surveys, with the expectation that a methodological unification could
lead to economies of scale and an extension of the unemployment surveys
to other parts of the country. The reason why a technical solution is not
readily worked out to reconcile the two surveys is that there are many other
layers in this controversy besides the technical one. Part of the discussion
is precisely on whether the differences between the two surveys are just
technical or have an underlying ideological or political content. The arena
for the controversy changes completely if one accepts one or the other interpretation;
or, conversely, one may wish to displace the controversy to the arena where
one feels stronger. The fact that one survey is carried on by the Federal
Government and the other by an institution linked to the trade unions may
be used on both sides as an argument for the political hypothesis. And a
unified methodology would lead to questions about who will receive or stop
receiving resources for doing the survey, processing the information and
publishing the results.
The controversy on poverty hinges around the question of how many paupers
and indigents there are in Brazil: the figures may vary from eight to 64
million, for a total population of 157 million. A similar controversy exists
about the number of destitute children living in the countries' streets,
with figures varying from a few thousand to several million. Contrary to
the unemployment controversy, all data used on the poverty controversy comes
from a single source, IBGE.
This issue has an obvious public opinion appeal, and figures on absolute
numbers of paupers, indigents and destitute children are eagerly sought
after by the Brazilian national and international press. Marginality and
poverty are morally charged issues, raised by religious groups, charitable
foundations and, more recently, non-governmental and mission-oriented international
organizations, which build their reputation on the strength of their condemnation
of social ills. High poverty figures mean an overall condemnation of the
society that produces them, and from this perspective the issues of employment
and unemployment are minor or irrelevant. From another point of view, however,
it would seem obvious that meaningful social policies of poverty alleviation
would require detailed and well-differentiated information on the needs
and conditions of specific groups, for which specific policies could be
devised.
The translation of the poverty issue into internationally comparable statistical
procedures, required by international agencies that have raised the social
questions to the top of their agenda, has led to an almost impossible quest
for an "objective" definition of absolute poverty (United Nations,
996; The World Bank, 1993; Rocha, 1992; Barros and others, 1994). Declared
income in a national survey or census is obviously inadequate, not only
because of under-reporting, but because of unsurmountable problems of exchange
rates and the different weights of non-monetary earnings in different regions
and cultures. Nutrition and health conditions of the population are possible
alternatives, but systematic information on these issues is difficult to
get, and there are no consensual definitions of their meaning except for
extreme conditions. Another possibility is to try to define a minimum basket
of products considered essential for survival, and to use the access to
this basket as the dividing line. Shifting consumption habits, shifting
availability of staple products and, for international comparisons, fluctuating
exchange rates makes these evaluations extremely unreliable and unstable.
These difficulties do not mean, of course, that the issues of poverty should
be left aside. It is possible, and necessary, to measure and compare indicators
of social inequality, and to develop instruments to evaluate how different
population groups are facing the problems of social deprivation, and the
policy alternatives that could be devised to support them. Overall figures
mean very little, because they vary widely depending on shifting assumptions,
and in any case encompass many different situations and social conditions.
From the public opinion perspective, however, as reflected in the printed
press and militant groups involved on the poverty issues, different figures
are unacceptable demonstration of "statistical confusion," "lack
of clarity" or technocratic obfuscation.
Conclusion:
the sociology of science and the future of public statistics
Sociology of science can do for public statistics the same service it can
provide for science and technology in general: to show how knowledge production
is organized in a particular field, the different actors that take part
in their production, the complex translations, shifts of meaning, interpretation
and responsibilities that take place, and the shifting alliances and conflicts
that accompany this whole process. Whenever one makes translations and shifts
responsibility from the political, legal and public opinion to the technical
sphere, one starts to reveal the uncertainties that exist also in the technical
area. The first and typical reaction of the statistical office to this invasion
of its technical realm is to stiffen its stand: "this is the correct
figure, we do it scientifically, we are legally empowered to do it, we stand
by our reputation and tradition, our technical procedures are too complex
(or confidential) for you to see and understand." This reaction can
be successful on the short run, since it reduces ambiguity; but it may limit
the office's ability to improve its methodology and remain open for criticism,
innovations and new approaches. The opposite reaction is to be more candid,
to recognize the limitations and implicit choices present in all kinds of
statistical and cartographic procedures, and to insist that it is impossible
to provide technical solutions to conflicts of interest which cannot be
accommodated. This kind of reaction is more in tune with the ethos of academic
research and the usual patterns of intellectual honesty, but runs the risk
of not being well received, and may be translated, simply, in the idea that
the institute lacks competence to provide proper and unquestionable information
on economic realities and social needs.
There is no coming back, however, from the second alternative. The dividing
lines between producers and users of knowledge are breaking up almost everywhere,
not in the sense that "science" is becoming accessible to everybody
(which it is not), but in two other important senses. First, producers of
knowledge are being evaluated more closely by the worthiness of the products
they provide, and have to strive to get their products at the consumer's
hands. It is not enough to produce complex statistics to be published in
lengthy volumes full of tables or interpreted in esoteric journals; it is
necessary for the knowledge producers to travel through the whole chain
of translations from data production to product dissemination, making sure
that the translations are reliable and credible. Second, thanks in large
part to the new computational and information facilities available to the
informed user, he is much more able to revise and reorganize the information
he receives for his personal use than in the past. To respond to this demand,
the statistical offices must be able to travel also in the opposite direction
in the translation process, from products to production, making more open
and explicit the technical and methodological choices that are part of the
daily life of any research institution. Combined, these two tendencies can
make the life of the public statistics institutions more difficult than
in the past, but perhaps also more challenging and interesting.
When Barite and Zhan argue that the statistician should be a skillful translator,
they mean that they should be able to explain to the laymen the meaning
of statistical concepts, and translate in statistical terms the laymen's
requests for measurements and estimations. The assumption is that there
is just one reality, expressed in different languages, the role of the translator
being to make this unified reality explicit. This is not, however, what
translators do in real life. Languages are carriers of cultures, and cultures
are never as close and exclusive as to exclude the possibility of translation
of their meanings, but never so open and flat that everything can be transposed
to other contexts without significant losses of meanings and content. This
is true of people in different societies speaking different languages, and
is also true of people occupying different positions in the same society
- consumers, employees, statisticians, researchers, teachers, newspapermen,
businessmen, politicians. What does "unemployment"mean, for instance,
for each of these people? Although they may share the same word, and understand
it in ways which overlap, each thinks on unemployment in a way that is coherent
with other aspects of their professional and social context. The role of
the translator is not to make sure that everybody uses the term in the same
way the statistician does, but to build bridges, and help each to understand
the way the word is used by others.
Hopefully, this work of multiple translation can provide the basis for understandings
and alliances that bring different sectors of society together, increase
their mutual awareness, and brings benefits to all. Even if the meanings
of "unemployment" are not the same to everybody, society can benefit
from a stable, reliable and trustworthy source of unemployment statistics.
It is the role of the statistician, as someone with a vested interest in
such coalitions of interest, to go out of his way to understand the meanings
and uses of statistical data by different groups in society, and build bridges
between these needs and what the statistician can provide, thanks to his
special skills and his knowledge of other, similar uses and needs. This
requires a very active stand, different both from the passive response to
the user's needs and from technocratic self-sufficiency. It requires to
maintain one's knowledge, culture and approaches, and to develop the willingness
to understand and accept the others' point of views.
The bottom line seems to be that statistics will neither be redefined in
a more inclusive way, from which it could to regain the control over the
uses society makes of its concepts and figures, nor recover its role of
the certerpiece in a grandiose scheme of comprehensive planning. But its
pervasiveness will continue to increase, and the statisticians will have
important roles to play, not only as translators and brokers, but also as
creators of new meanings and providers of new instruments by which society
can look and understand itself better.
Notes
1. The literature on the social construction of statistics,
probability and even mathematics is ample. See, among others, Hacking, 1975,
and the recent text by MacKenzie, 1993. I believe these studies to be convincing,
and to demonstrate that it is possible to study the development and consolidation
of formal and empirical knowledge from a social point of view without falling
into the traps of untrammeled relativism. My approach, here, is much more
modest, since I am not discussing the emergence of basic statistical concepts,
but their adoption, although the boundary between "basic" and
applied knowledge, in this instance as in others, is always blurred.
2. Penha, 1993.See the bibliographical references for
sources on IBGE's history, available at the Institute's library in Rio de
Janeiro.
3. The influence of French geography was probably indirect,
through members of the French academic missions which came to Brazil in
the thirties (Pettitjean, 1996), to work at the universities in São Paulo
and Rio de Janeiro. The best known were Pierre Monbeig (Queiróz, 1996) and
Pierre Deffontaines.
4. A similar situation occurred in India in the fifties,
when the ideas of comprehensive planning were dominant, leading to the creation
of the Indian Planning Commission, under the influence of the renowned statistician
and mathematician P. C. Mahalanobis.. (I am grateful to the anonymous reader
of this paper for this comment, drawing attention to the parallel between
the Brazilian and the Indian experience. For the work and role of Prasanta
Chandra Mahalanobis, see Rudra, 1996.)
5. "A teoria da política, contida nos modelos de
tipo sinótico ou de decisão, apresenta (...) uma seqüência inversa à da
análise econômica convencional. (...) Identificamos, em primeiro lugar,
alguns objetivos que consideramos desejáveis e indagamos, em seguida, o
que deve ser feito de modo a manipular os vários meios (instrumentos) à
nossa disposição no sentido de alcançar os objetivos desejados"
6. "O conjunto de atividades da área de estatística
e pesquisa sócio-econômica reuniria e sistematizaria dados e realizaria
estudos capazes de permitir a construção de modelos com os aspectos
mais salientes da estrutura sócio-econômica do país. Estes modelos permitiriam
a identificação de trajetórias alternativas de desenvolvimento. A esfera
política, em função da avaliação dos grandes objetivos sociais, estabeleceria
um plano de ação segundo a trajetória escolhida".
7. In practice, there were problems, sometimes severe,
as in the early fifties, when the whole statistical system organized by
Teixeira de Freitas came under threat by a newly designated Institute' s
president, a military man associated with the field of cartography (Freitas,
1952).
8. In recent years there has been an effort to include
environment issues in this grand scheme. The idea, put forward by international
organizations and already being tried in several countries, is to develop
national systems of "environment accounts", which could be linked
with the national accounts, hopefully, with associated measurements of "human
welfare", or human development.
9. Still today, the Ministry of Planning is responsible
for the budgetary process, investments and long term, general planning,
while the Ministry of Economics, through the Central Bank, handles the main
economic variables, such as the exchange and interest rates and the control
of government expenditures.
10. The following paragraphs are based on "Expansion
and Exclusiveness of Statistics, invited paper prepared for session 83,
"Statistics: an inclusive interpretation," of the 51st
Session of the International Statistical Institute, 18-26 August 1997, Istanbul.
11. However, the Italian statistician Giorgio Mortara
provided, for many years, the Institute's main intellectual and professional
orientation in statistical matters (Mortara, 1985).
12. In the Brazilian civil service, only the military
and the Foreign Service can guarantee employment for students of their educational
institutions.
13. A very interesting analysis of the development of
professional classifications in the French, German, British and American
statistical offices can be found in Desrosières, 1990.
14. There is a growing literature on the development
of contemporary statistical practices, but little, it seems, in terms of
systematic comparisons among countries. Extensive bibliographic references
on early and contemporary historical developments are given by Alain Desrosières
in his publications. For a flavor, see Bulmer, Bales, and Kish Sklar, 1991;
Fourquet, 1980; INSEE, 1977 and 1987; Wagner, Wittrock and Whitley, 1991.
15. This observation comes from "Le fardeau moral
d'un porte-clefs", in Latour, 1993b, 47-55,, and other related texts
in that volume.
16. This article is followed by comments by Katherine
K. Wallman, Chief Statistician, U. S. Office of Management and Budget, an
others. It is clear, from Mrs. Wallman's comments, that the statistical
institutions in the United States do not enjoy the same degree of legitimacy
as their Canadian counterpart.
17. The role of credibility, consensus and trust in
the building up of scientific "facts" and theories is a central
issue in the sociology of science, dated usually from the seminal works
of Ludwik Fleck, Michael Polanyi (1958) and Thomas Khun (1962). For a contemporary
view, see the several articles in Pickering, 1992.
18. There is an obvious parallel here with two of Max
Weber's sources of political legitimacy, rationality and tradition. One
could speculate about the possible role of the third one, charisma, in this
context.
19. "All this adds to the following: credibility
ceteris paribus is a function of the degree of threat (acute or diffuse,
widespread or narrowly focused); the element of surprise (notable in one-off
surveys); the gossip value of the statistic; and whether its publication
takes place in a rapidly changing environment. These elements are not exhaustive
but indicative of the kind of analysis that the public reaction to the activities
of a public agency requires" (Jacob Ryten, personal communication).
20. René Padieu provides the following list of contrasts
between legal and statistical concepts from his experience at INSEE, showing
how the issue is general: "legal status of business companies versus
economic nature or organizational feature; officially married versus concubines;
fiscal rules for stocks evaluation and equipment devaluation versus economically
fixed capital consumption, toll and tariff classifications versus technical
or economical ones; town administrative border versus agglomeration limit,
etc" (René Pardieu, personal communication). Peter Wagner has suggested
a more systematic distinction between the two kinds of languages, the statistical
and the legal ones: "In the first case, statistics is, so to say, on
the 'soft' side, collecting data from the diffuse social reality, and it
is another social 'language', the one of law, which makes them 'hard', creates
real boundaries where there have been 'only' statistical classifications.
In the second case, in contrast, the move is from the 'soft' observation
of social problems towards statistics as a 'hardener', a tool to get a grip
on something fixed and identifiable. If you agree with this observation,
it might be useful to reverse the order: to go from, first, the desire to
'hold things together' which turns to statistics as a methodology, to (which
in some cases may really be a second policy step) the case where statistical
classifications are translated into rights and obligations. And one could
try to think of examples where the process is reversed (or threatens to
be reversed): When legal entitlements are abolished, figures lose their
meaning and the social world returns to diffuseness." (Peter Wagner,
personal communication).
21. This classification should be compared with the
usual classification adopted in the United States between "White Anglo-Saxons",
"Black", "American Indians" and "Spanish".
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