Encyclopedia of SociologyVol._4

SOCIAL EXCHANGE THEORY

Other applications of exchange theory include broader efforts to investigate the balance of power in the health care industry, the strategic role of insurance companies in an era of managed care, and the response of physicians to the loss of power and autonomy. Several researchers have attempted to analyze the nature of physician referrals in network exchange terms and to characterize the nature of physician–patient interaction as an exchange relation in which power is asymmetrical (or imbalanced) and trust plays a key role in ‘‘balancing’’ that power differential. The patient must place his or her fate in the hands of a more competent, more informed actor and trust that the physician will do no harm. Future applications of the exchange model of interaction and of network exchange in other domains will help clarify and extend the underlying theoretical framework.

REFERENCES

Blau, P. M. 1964 Exchange and Power in Social Life. New York: Wiley, 2d printing, 1986. New Brunswick: N.J.: Transaction.

———1987 ‘‘Microprocess and Macrostructure.’’ In K. S. Cook, ed., Social Exchange Theory. Newbury Park, Calif.: Sage

Bienenstock, Elisa I., and Phillip Bonacich 1992 ‘‘The Core as a Solution to Negatively Connected Exchange Networks.’’ Social Networks 14:231–243.

——— 1997 ‘‘Network Exchange as a Cooperative Game.’’ Rationality and Society 9:937–965.

Bonacich, P. 1986 ‘‘Power and Centrality: A Family of Measures.’’ American Journal of Sociology 92:1170–1182.

Coleman, J. S. 1972 ‘‘Systems of Social Exchange.’’

Journal of Mathematical Sociology 2:145–163.

——— 1990 The Foundations of Social Theory. Cambridge, Mass.: Harvard University Press

Cook, Gillmore and Yamaguchi 1986 ‘‘Point and line vulnerability as bases for predicting the distribution of power in exchange networks: Reply to Willer.’’

American Journal of Sociology 92:445–448.

Cook, K. S., ed. 1987 Social Exchange Theory. Newbury Park, Calif.: Sage.

———, and R. M. Emerson 1978 ‘‘Power, Equity, and Commitment in Exchange Networks.’’ American Sociological Review 43:721–739.

———, ———, M. R. Gillmore, and T. Yamagishi 1983 ‘‘The Distribution of Power in Exchange Networks: Theory and Experimental Results.’’ American Journal of Sociology 89:275–305.

Cook and Whitmeyer 1992 ‘‘Two Approaches to Social Structure: Exchange Theory and Network Analysis.’’

Annual Review of Sociology. 18:109–127

Deutsch, M. 1964 ‘‘Homans in the Skinner Box.’’ Sociological Inquiry 34:156–165.

Ekeh, P. P. 1974 Social Exchange Theory: The Two Traditions. Cambridge, Mass.: Harvard University Press.

Emerson, R. M. 1962 ‘‘Power-Dependence Relations.’’

American Sociological Review 27:31–40.

———1972a ‘‘Exchange Theory, Part I: A Psychological Basis for Social Exchange.’’ In J. Berger, M. Zelditch, and B. Anderson, eds., Sociological Theories in Progress, vol. 2. Boston: Houghton Mifflin.

———1972b. ‘‘Exchange Theory, Part II: Exchange Relations and Networks.’’ In J. Berger, M. Zelditch, and B. Anderson, eds., Sociological Theories in Progress, vol. 2. Boston: Houghton Mifflin.

Friedkin, Noah E. 1992 ‘‘An Expected Value Model of Social Power: Predictions for Selected Exchange Networks.’’ Social Networks 14:213–229.

Heath, A. 1976. Rational Choice and Social Exchange: A Critique of Exchange Theory. Cambridge, Mass.: Cambridge University Press.

Homans, G. C. 1958 ‘‘Social Behavior as Exchange.’’

American Journal of Sociology 62:597–606.

———1961 Social Behavior: Its Elementary Forms. New York: Harcourt, Brace, and World.

———1974 Social Behavior: Its Elementary Forms, 2nd ed. New York: Harcourt, Brace, and World.

Kelley, H. H., and J. Thibaut 1978 Interpersonal Relations: A Theory of Interdependence. New York: Wiley.

Lawler, Edward J., and Jeongkoo Yoon 1996 ‘‘Commitment in Exchange Relations: A Test of a Theory of Relational Cohesion.’’ American Sociological Review

61:89–108.

Levi-Strauss, C. 1949 Les Structures Elementaires de la Parents. Paris: Presses Universitaires de France.

——— 1969 The Elementary Structures of Kinship. Boston: Beacon.

Malinowski, B. 1922 Argonauts of the Western Pacific. London: Routledge and Kegan Paul.

Markovsky, B., D. Willer, and T. Patton 1988 ‘‘Power Relations in Exchange Networks.’’ American Sociological Review 53:220–236.

Mauss, M. 1925 Essai sur le don in Sociologie et Anthropologie. Paris: Presses Universitaires de France. Translated into English by Ian Cunnison as The Gift. New York: Free Press, 1954.

Miller, N. E. and J. Dollard 1941 Social Learning and Imitation. New Haven, Conn.: Yale University Press.

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Molm, L. D. 1981. ‘‘The Conversion of Power Imbalance to Power Use.’’ Social Psychology Quarterly

44:151–163.

———1987 ‘‘Power-Dependence Theory: Power Processes and Negative Outcomes.’’ In E. J. Lawler and B. Markovsky, eds., Advances in Group Processes, vol. 4. Greenwich, Conn.: JAI Press.

———1989 ‘‘Punishment Power: A Balancing Process in Power-Dependence Relations.’’ American Journal of Sociology 94 (6):1392–1418.

Molm, Linda D. 1997 Coercive Power in Social Exchange. Cambridge, UK: Cambridge University Press.

Pfeffer, Jeffrey, and Gerald R. Salancik 1978 The External Control of Organizations: A Resource Dependence Perspective. New York: Harper and Row.

Schneider, H. K. 1974 Economic Man: The Anthropology of Economics. New York: Free Press.

Thibaut, J., and H. H. Kelley 1959 The Social Psychology of Groups. New York: Wiley.

Turner, J. H. 1986 The Structure of Sociological Theory, 4th ed. Chicago: Dorsey Press.

Willer, David 1987 Theory and Experimental Investigation of Social Structures. New York: Bordon and Breach.

Willer and Anderson 1981 Willer, David and Bo Anderson, eds. 1981. Networks, Exchange and Coercion. New York: Elsevier/Greenwood

Yamaguchi, K. 1996 ‘‘Power in Networks of Substitutable and Complementary Exchange Relations: A Ration- al-Choice Model and an Analysis of Power Centralization.’’ American Sociological Review 61:308–322.

KAREN S. COOK

SOCIAL FORECASTING

Forecasting has been important in sociological thought. Early European sociologists argued that societies progress through inevitable historical stages; those theories helped sociologists predict all societies’ futures. Early American sociologists adopted the pragmatists’ rule that a science proves it ‘‘works’’ by predicting future events (Schuessler 1971). Sociologists, however, have only recently adopted methods appropriate for those early goals. The review in this article of the delayed development of social forecasting includes (1) three sociologists’ conceptual uses of forecasting and some reasons their suggestions were not followed, (2) qualitative and quantitative methods of forecast-

ing, and (3) recent indications of increased interest in forecasting.

FORECASTING TRADITIONS

Sociologists have contributed several social forecasting concepts that were historically significant enough to become traditional orientations in the analysis of the future. William F. Ogburn ‘‘held that in the modern world technological inventions commonly come first and social effects later. By reason of this lag, it is possible, he argued, to anticipate the future and plan for its eventualities’’ (Schuessler 1971, p. 309). For example, new possibilities came into conflict with family values when the invention of effective birth control gave women new choices. Ogburn’s contribution was to suggest that cultural lags are inevitable but that the period of disruption they cause can be shortened (Reiss 1986).

Merton (1949) challenged Ogburn’s idea that the effects of inventions can be easily anticipated. Each invention has an apparent goal, or manifest function, that it is hoped it will perform in society. Each change, however, also contains the possibility of performing a number of latent functions. These are unanticipated side effects that often are not desired and sometimes are dangerous. The institutions of society are closely intertwined, and an invention in one area can cause shocks throughout the system. The automobile is an example. Its manifest function of changing transportation has been fulfilled, but at the cost of serious ecological and sociological changes.

Merton’s (1949) second warning was that social forecasting is unique because it tries to predict the behavior of humans, who change their minds. The self-fulfilling prophecy is a forecast that makes people aware of real or imagined new opportunities or dangers to be avoided. Merton demonstrated that false forecasts can have powerful effects if they gain public acceptance. For example, a sound bank can be destroyed by a run on its funds caused by a prediction of failure. Henshel’s more inclusive concept—the self-altering prediction— shows that forecasts can be self-defeating as well as self-fulfilling. W. I. Thomas’s theorem, ‘‘If men define situations as real, they are real in their consequences,’’ applies particularly to the definitions societies make of the future (Henshel 1978, p. 100).

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Moore challenged sociologists to go beyond safe prophecy based on orderly trends and attack the difficult problem of ‘‘how to handle sharp changes in the magnitude of change, and sharp (or at least clear) changes in direction’’ (Moore 1964, p. 332). There are four types of discontinuous societal change: (1) Some societies are changed drastically by an exogenous variable, an idea or value from another society. Modern Japan is an example. (2) A society’s rate of development can increase spontaneously, creating an abundance of new ideas. This is an exponential acceleration, a change in the rate of change. (3) Moore attributes changes in the direction of change to the existence of a dialectic of values in each society’s apparent trend. For example, a society may appear to be profit-oriented and ecologically exploitative, but there also exists a counterset of values that stress harmony with each other and with nature. If a shift in such basic value emphases could be predicted, many other associated forecasts could be made. (4) Finally, Moore recognizes that there are pure emergents, inventions such as money and writing, that cannot be thought of as parts of trends.

Moore drew a methodological moral from these complexities: ‘‘One must somehow move from discrete necessary conditions to cumulative and sufficient ones’’ (Moore 1964, p. 334). That is, the search for the one trend or causal variable that drives societal change should be abandoned. The summation and particularly the interaction of many component developments create events.

In 1966 Moore asked sociologists to put aside value-free scientific rules and attempt to construct preferable futures that might help ‘‘mankind survive for the next twenty years’’ (Moore 1966, p. 270). Moore was confronting what he felt to be the main reason why forecasting was done so infrequently. It is professionally permissible for sociologists to examine social change both currently and retrospectively, but making a forecast leaves one liable to being labeled a utopian (Winthrop 1968, p. 136). Utopian thinking is in disrepute because past advocates allowed their values to cloud their constructions. However, images of the future provide goals and determine how people plan and therefore how they behave in the present. Moore sought utopias that would perform a necessary social planning function by constructing alternative directions for human purpose.

WHY SOCIAL FORECASTING HAS

DEVELOPED SLOWLY

Sociologists’ basic methodological orientations preclude an interest in forecasting. Sociologists analyze society’s static interconnections and concentrate on the social structures that persist. They have not developed skill in isolating the sequences of dynamic social behavior (Moore 1966). They are better at categorizing and typing people than at predicting how individuals might change from one type to another.

Many sociologists feel that not enough is known to predict future events. They point to economists and demographers and ask, If they are failing with their more quantifiable data, how can complex social changes be anticipated? One school of thought sees sociology as a qualitative art form that will never be a statistically modeled science. Critical sociologists object on moral grounds. They feel that society requires essential restructuring before positive change can be effected. Since most forecasting is based on models of the current structure, they feel that it sanctions unjust social arrangements (Henshel 1982).

JUDGMENTAL AND QUALITATIVE FORECASTING METHODS

The futurists (Bell 1997; Kurian and Molitor 1996) see ‘‘the challenge being not just to forecast what the future will be, but to make it what it ought to be’’ (Enzer 1984, p. 202). The actual future is too complex to be predefined, but possible futures can be constructed that can be instructive. In addition, secondary forecasts can be made that estimate the effects of policy actions on the original course of development (Colquhoun 1996). The pace of change is considered too rapid to be captured by traditional methods reliance on a careful quantitative reconstruction of the past. This justifies the use of experts’ opinions, and futurists’ methods are ways of systematizing those judgments (Allen 1978, p. 79).

A discontinuous social change usually is preceded by a ‘‘substantial restructuring of basic tenets and beliefs’’ (Holroyd 1978, p. 37). Such paradigm shifts are revolutionary, such as the rejection of the earth as the center of the universe. They appear in fields of knowledge in which one system

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of thought seems to be in control but is unable to solve important problems. Holroyd, for example, predicts a paradigm shift in economics because its current theories are unable to deal with essential problems such as scarcity of natural resources. Futurists anticipate shifts by compiling lists of crucial issues in the institutions of society. When the gap between current and desired conditions is large, that area is monitored closely for discontinuous change (Holroyd 1978, p. 38).

Cross-impact matrices are constructed by listing all possible future events in the problem area under study (Allen 1978, pp. 132–145). Each event is recorded as a row and a column in a square matrix. This allows the explicit examination of every intersection of events when one asks: What is the probability that the first will occur if it has been preceded by the other? The probabilities of occurrence can be derived from available data but are often judgments. Cross-impact analysis is a systematic way of heeding Merton’s warning about not overlooking possibly damaging latent consequences. It is a tool for spotting crucial turning points or originating novel viewpoints by examining the intersections of change at which experts’ judgments conflict.

Delphi surveys constitute an ingenious method for allowing the interaction of expert judgments while avoiding the contamination of social status or damage to reputations because of radical or mistaken pronouncements (Henshel 1982). In a series of survey rounds, everyone sees the distribution of others’ responses without knowing the proponents’ identities. A composite forecast emerges as anonymous modifications are made at each round.

After a review of forecasting methods, Ascher (1978) chose scenarios as one of only two methods he could recommend. A scenario is ‘‘a hypothetical sequence of events constructed for the purpose of focusing attention on causal processes and decision points’’ (Herman Kahn, quoted in Wilson 1978, p. 225). It is a story, but a complex one based on all available data and usually constructed after a cross-impact analysis has isolated possible turning points. Usually, two or three related scenarios are constructed to illustrate alternative futures that could be determined by particular decisions.

It is not surprising that an expert’s decision process can be made explicit. What is surprising is

that in many studies the systematic model of an expert often forecasts better than the person does (Armstrong 1978). In bootstrapping, the forecaster’s individualized decision procedures become the ‘‘bootstraps’’ by which a systematized procedure is ‘‘lifted’’ into an orderly routine. Such a model can be made deductively through interviews that isolate and formalize the decision rules or inductively by starting with a series of past forecasts and attempting to infer the rules that accounted for the differences between them.

Metaforecasting (Makridakis 1988) represents an essential summary of these considerations and a bridge to more quantitative methods. It combines judgmental and statistical estimates. It attempts to include historical and social information to overcome the tendency to ignore or overreact to changes in established patterns or relationships.

SOCIAL DEMOGRAPHY

Demography is the most established form of social forecasting, and its methods and record can be found elsewhere (Henshel 1982). This article will discuss only two elements from its continuing development: a method that has had wide influence and what can be learned from its frequent failures to predict future population sizes.

A cohort is an aggregate of individuals of similar age who therefore experience events during the same time period (Reiss 1986, p. 47). Cohort analysis was first used by Norman Ryder to study the changing fertility behaviors of women born during the same five-year periods. Since that time, cohorts have been used in the study of many areas of social change to differentiate the changes that are result from individuals maturing through the stages of life from those caused by powerful societal events or value shifts.

Demographers failed to anticipate the postwar baby boom and the onset of its decline. These errors were due to assumption drag, ‘‘the continued use of assumptions long after their validity has been contradicted by the data’’ (Ascher 1978, p. 53). Henshel (1982) says that demographers probably ignored these turning points because they simply talked to each other too much. They reassured each other that their assumptions and their extrapolations from past trends would soon reas-

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sert themselves in the data. Recognition of this error of developing an isolated club of forecasters has helped economists and will help sociologists avoid a similar regimentation of estimates.

The mix of assumptions and actual data varies widely in simulation models. The most useful models test a set of explicit assumptions so that no interactions between variables are overlooked. Models have contributed the idea of the feedback loop as an important caution against unidirectional thinking. This common system characteristic occurs when an effect reaches a sensitive level and begins a reaction that modifies its own cause (Simmons 1973, p. 195). Often, however, the mix of assumptions and facts in simulations leans too heavily toward judgments. So-called black-box modeling (McLean 1978), in which equations are hidden, can produce output that is plausible and provocative but also unrealistic. The creator of the Limits to Growth study admitted that ‘‘in World Dynamics . . . there is no attempt to incorporate formal data. . . . All relationships are intuitive’’ (Simmons 1973, p. 208). That study extrapolated what have come to be seen as extreme assumptions of geometric growth unchecked by social adaptation. Its dramatic predictions of imminent shortages had a wide but unwarranted impact (Cole et al. 1973). A comment on those failed predictions and their popularity at the time of their publication sets the context in which all ‘‘modeled’’ forecasts should be received: ‘‘The apparent detached neutrality of a computer model is as illusory as it is persuasive. Any model of a social system necessarily involves assumptions about the workings of that system, and these assumptions are necessarily colored by the attitudes and values of the individuals or groups concerned. . . .

[C]omputer models should be regarded as an integral part of political debate. . . . The model is the message’’ (Freeman 1973, p. 7).

PRAGMATIC STATISTICAL ANALYSIS OF

TIME SERIES

Attention has shifted to techniques that are less concerned with demonstrating the effects of assumed patterns. Time series are records of observations through time. Traditional time series analysis projects ‘‘future values of a variable based entirely on the past and present observations of that vari-

able’’ (Levine et al. 1999). It involves isolating the trend inside the many ‘‘noisy’’ or seasonal factors that may obscure it. The techniques have been well developed, are taught in undergraduate management statistics courses, and have been adapted for spreadsheet software available on most computers. The problem, however, is how much faith one can put in the idea that ‘‘people do what they usually do.’’ Time series projections are essential first steps in discovering patterns of behavior of aggregates of people over time. Such patterns often persist, but some shock (invention, immigration, social redefinition such ‘‘the sixties,’’ or adjustment of tradition such as decreasing sexism) may cause disruption. In recognition of these sociological disruptions, time series are being explored from the viewpoint that any variable may be uniquely complex and subject to sudden change.

Time series regressions uncover structural relationships involved in the history of two or more variables. Before the relationship can be assessed, sources of error must be isolated and controlled. The most important of these errors are (1) the overall trend of change that would obscure any specific interrelationship and (2) the autocorrelation effect of internal dependence of an observation on previous observations. If a relationship seems to explain the data series’ movements, it is tested with ex-post forecasts that can be verified within the range of available data. If these succeed, ‘‘ex-ante-forecasts can be used to provide educated guesses about the path of the variables into the blind future’’ (Ostrom 1990, p. 77).

Autoregressive moving average (ARIMA) models predict a variable’s current status by using a combination of its previous observations and mathematically approximated random shocks. The goal is to find a pattern that fits the immediate data, not to understand relationships. ARIMA models are useful in interrupted time series analysis, in which the impact of a policy or another intervention can be examined by seeing how different the variable’s patterns are before and after the intervention (McDowall et al. 1980). Autoregressive models have a limitation important for social forecasting, in which historical data are relatively scarce. ‘‘Because ARIMA models must be identified from the data to be modeled, relatively long time series are required’’ (McCleary and Hay 1980, p. 20). Fifty observations are recommended.

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Exponential smoothing is widely used and is as reliable as more complicated methods (Gardner 1985). In its simplest form, the next period’s forecast is based on the current forecast plus a portion of the error it made. That is, the difference between the current time period’s forecast and the actual value is weighted and used to adjust the next period’s expected value. The higher the value of the weight used is, the more the error adjustment contributes and the more quickly the model will respond to changes. Exponential smoothing is used in early detection of curvilinear changes, when the rate of change speeds or slows (Gardner 1987).

FUTURE TRENDS

Forecasting is being done. It is central in business and government planning. Even though many of these forecasts’ essential variables are social or are found in social contexts (such as family decisions to move, build, and purchase or the development of social problems), economists have become society’s designated forecasters (Henshel 1982; Stimson and Stimson 1976). Sociologists will not change this imbalance easily, but there are some indications that forecasting may finally become part of everyday sociological work.

Assumptions that a particular cycle or curve is the natural or underlying process of all change have been abandoned, and pragmatic methods are now widespread. It is also accepted that a forecast is developed only to be monitored for possible discontinuities. Trend extrapolations rarely are done without accompanying methods for describing the expected deviations.

Two forecasting methods are particularly promising because they allow sociologists to build on traditional skills. Componential or segmentation forecasting (Armstrong 1978) recognizes that an aggregate forecast can be improved by combining forecasts made on the population’s component social groups. Sociologists are best able to distinguish the groups that should be treated separately. Pooled time series analysis (Sayrs 1989) combines cross-sectional descriptions such as one-time surveys. Sociologists are expert at describing interconnections in the structures of organizations or societies, and now they have the opportunity to study these social arrangements over time.

Society has recognized the wisdom of the early concern about anticipating the latent effects of social and technological inventions. Progress no longer seems inevitable. The popular question now is, Can someone assure us that a new element will not be as destructive as past changes?

Sociologists seem to be uniquely suited to help forecasting become more plausible because their working assumptions counter the weaknesses of current methods. The idea that technological innovation or economic cycles drive social change has produced today’s mechanistic, ultrarational, antiindividualistic models that assume that the population is homogeneous (Dublin 1992). All these weaknesses are naturally contradicted when sociologists expand their vision of a population to include the cultural diversity of the social contexts that produce, accept or reject, and always modify the effects of technological and economic circumstances.

The future acceptance of forecasting also depends on sociologists’ ability to improve the preparation and presentation of forecasts by using their traditional strengths. Forecasts will be accepted by policymakers and the public only when the quasitheories they hold about the future are specifically addressed and proved false. Sociologists know this better than other social scientists do; they often are called on to dispel labels and popular theories that are so entrenched that they make any new attempt at explanation seem a ‘‘fool’s experiment’’ to the forecaster’s audience They also are used to the idea of various and multiple causes acting in a situation and therefore are skilled at isolating ‘‘unanticipated consequences.’’

Forecasts will improve and become more plausible when they place less importance on traditional scientific formulations. A forecast is not a hypothesis. Hypotheses must be made in advance of the behavior they are meant to predict to assure a full and objective test of the theories that produced them. Forecasts demand monitoring of predictions and adaptation of forecasts to circumstances. A forecast is as good as its ability to anticipate and allow the inclusion of changing social forces. That is, its main function is not to make an accurate prediction of future events but to isolate and interrelate the many factors in the current situation that may be causally powerful. Understanding

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the current social situation’s complexity is the most important factor.

REFERENCES

Allen, T. Harrell 1978 New Methods in Social Science Research: Policy Sciences and Futures Research. New York: Praeger.

Armstrong, J. Scott 1978 Long-Range Forecasting: From Crystal Ball To Computer. New York: Wiley.

Ascher, William 1978 Forecasting: An Appraisal for Policymakers and Planners. Baltimore: Johns Hopkins University Press.

Bell, Wendell 1997 Foundations of Futures Studies, vols. I and II. New Brunswick, N.J.: Transaction.

Cole, H. S. D., Christopher Freeman, Marie Jahoda, and K. L. R. Pavitt, eds. 1973 Models of Doom: A Critique of the Limits to Growth. New York: Universe.

Colquhoun, Robert 1996 ‘‘The Art of Social Conjecture: Remembering Bertrand de Jouvenel.’’ History of the Human Sciences 9(1): 27–42

Dublin, Max 1992 FutureHype: The Tyranny of Prophecy. New York: Plume.

Enzer, Selwyn 1984 ‘‘Anticipating the Unpredictable.’’

Technological Forecasting and Social Change 26:201–204.

Freeman, Christopher 1973 ‘‘Malthus with a Computer’’ In H. S. D. Cole, Christopher Freeman, Marie Jahoda, and K. L. R. Pavitt eds., Models of Doom: A Critique of the Limits to Growth. New York: Universe.

Gardner, Everette S., Jr. 1985 ‘‘Exponential Smoothing: The State of the Art.’’ Journal of Forecasting 4:1–28.

——— 1987 ‘‘Short-Range Forecasting.’’ LOTUS 3:54–58.

Henshel, Richard L. 1978 ‘‘Self-Altering Predictions.’’ In Jib Fowles, ed., Handbook of Futures Research. Westport, Conn.: Greenwood Press.

——— 1982 ‘‘Sociology and Social Forecasting.’’ In Ralph H. Turner and James F. Short, eds., Annual Review of Sociology, vol. 8. Palo Alto, Calif.: Annual Reviews.

Holroyd, P. 1978 ‘‘Change and Discontinuity: Forecasting for the 1980s.’’ Futures 10:31–43.

Kurian, George T., and Graham Molitor 1996 Encyclopedia of the Future, vols. I and II. New York: Macmillan.

Levine, David M., Mark L. Berenson, and David Stephan 1999 Statistics for Managers Using Microsoft Excel. Upper Saddle River N.J.: Prentice-Hall

Makridakis, Spyros 1988 ‘‘Metaforecasting: Ways of Improving Accuracy and Usefulness.’’ International Journal of Forecasting 4(3):467–91.

McCleary, Richard, and Richard A. Hay, Jr. 1980 Applied Time Series Analysis for the Social Sciences. Beverly Hills, Calif.: Sage.

McDowall, David, Richard McCleary, Errol E. Meidinger, and Richard A. Hay, Jr. 1980 Interrupted Time. Newbury Park, Calif.: Sage.

McLean, J. Michael 1978 ‘‘Simulation Modeling.’’ In Jib Fowles, ed., Handbook of Futures Research. Westport, Conn.: Greenwood Press.

Merton, Robert K. 1949 Social Theory and Social Structure, rev. ed. Glencoe, Ill.: Free Press.

Moore, Wilbert E. 1964 ‘‘Predicting Discontinuities in Social Change.’’ American Sociological Review 29:331–338.

——— 1966 ‘‘The Utility of Utopias.’’ American Sociological Review 31:756–772.

Ostrom, Charles W., Jr. 1990 Time Series Analysis. Regression Techniques, 2nd ed. Newbury Park, Calif.: Sage.

Reiss, Albert J., Jr. 1986 ‘‘Measuring Social Change.’’ In Neil J. Smelser and Dean R. Gerstein, eds., Behavioral and Social Science: Fifty Years of Discovery. Washington, D.C.: National Academy Press.

Sayrs, Lois W. 1989 Pooled Time Series Analysis. Beverly Hills, Calif.: Sage.

Schuessler, Karl 1971 ‘‘Continuities in Social Prediction. ‘‘In H. L. Costner, ed., Sociological Methodology, 1971. San Francisco: Jossey-Bass.

Simmons, Harvey 1973 ‘‘System Dynamics and Technocracy.’’ In H. S. D. Cole, Christopher Freeman, Marie Jahoda, and K. L. R. Pavitt eds., Models of Doom: A Critique of the Limits to Growth. New York: Universe.

Stimson, John, and Ardyth Stimson 1976 ‘‘Sociologists Should Be Put to Work as Forecasters.’’ American Sociologist 11:49–56.

Wilson, Ian H. 1978 ‘‘Scenarios.’’ In B. Fowles, ed.

Handbook of Futures Research. Westport, Conn.: Greenwood Press.

Winthrop, Henry 1968 ‘‘The Sociologist and the Study of the Future.’’ American Sociologist 3:136–145.

JOHN STIMSON

SOCIAL GERONTOLOGY

See Aging and the Life Course; Cohort Perspectives; Filial Responsibility; Intergenerational Relations; Intergenerational Resource Transfers; Long-Term Care, Long Term Care Facilities; Retirement; Widowhood.

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SOCIAL IMITATION

See Behaviorism; Socialization; Social

Psychology.

SOCIAL INDICATORS

Social indicators are statistical time series that are ‘‘used to monitor the social system, helping to identify changes and to guide intervention to alter the course of social change’’ (Ferriss 1988, p. 601). Examples are unemployment rates, crime rates, estimates of life expectancy, health status indices such as the average number of ‘‘healthy’’ days (or days without activity limitations) in the past month for a specific population, school enrollment rates, average achievement scores on a standardized test, rates of voting in elections, and measures of subjective well-being such as satisfaction with life as a whole.

HISTORICAL DEVELOPMENTS

Social Indicators in the 1960s. The term ‘‘social indicators’’ was given its initial meaning in an attempt by the American Academy of Arts and Sciences for the National Aeronautics and Space Administration in the early 1960s to detect and anticipate the nature and magnitude of the sec- ond-order consequences of the space program for American society (Land 1983, p. 2; Noll and Zapf 1994, p. 1). Frustrated by the lack of sufficient data to detect such effects and the absence of a systematic conceptual framework and methodology for analysis, some in the project attempted to develop a system of social indicators—statistics, statistical series, and other forms of evidence—with which to detect and anticipate social change and to evaluate specific programs and determine their impact. The results of this part of the project were published in a volume (Bauer 1966) called Social Indicators.

The appearance of this volume was not an isolated event. Several other influential publications commented on the lack of a system for charting social change and advocated that the U.S. government establish a ‘‘system of social accounts’’ that would facilitate a cost-benefit analysis of more than the market-related aspects of society already

indexed by the National Income and Product Accounts (National Commission on Technology, Automation and Economic Progress 1966; Sheldon and Moore 1968). The need for social indicators also was emphasized by the publication of Toward a Social Report on the last day of the Johnson administration in 1969. The report was conceived of as a prototypical counterpart to the annual economic reports of the president, and each of its chapters addressed major issues in an area of social concern (health and illness; social mobility; the physical environment; income and poverty; public order and safety; learning, science, and art; and participation and alienation) and provided an assessment of the current conditions. In addition, the document firmly linked social indicators to the idea of systematic reporting on social issues for the purpose of public enlightenment.

Generally speaking, the sharp interest in social indicators in the 1960s grew out of the movement toward the collection and organization of national social, economic, and demographic data that began in Western societies in the seventeenth and eighteenth centuries and accelerated in the twentieth century (Carley 1981, pp. 14–15). The work of the sociologist William F. Ogburn and his collaborators at the University of Chicago in the 1930s and 1940s on the theory and measurement of social change is more proximate and sociologically germane (Land 1975). As chairman of President Herbert Hoover’s Research Committee on Social Trends, Ogburn supervised the production of the two-volume Recent Social Trends (1933), a pathbreaking contribution to social reporting. Ogburn’s ideas about the measurement of social change influenced several of his students—nota- bly Albert D. Biderman, Otis Dudley Duncan, Albert J. Reiss, Jr., and Eleanor Bernert Sheldon— who played major roles in the emergence and development of the field of social indicators in the 1960s and 1970s.

Social Indicators in the 1970s and 1980s. At the end of the 1960s, the enthusiasm for social indicators was sufficiently strong and broad-based for Duncan (1969, p. 1) to write of the existence of a social indicators movement. In the early 1970s, this led to, among other things, the establishment in 1972, with National Science Foundation support, of the Social Science Research Council Center for Coordination of Research on Social Indica-

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tors in Washington, D.C.; the publication of several major efforts to define and develop a methodology for the measurement of indicators of subjective well-being (Campbell and Converse 1972; Andrews and Withey 1976; Campbell et al. 1976); the commencement of a federal government series of comprehensive social indicators books of charts, tables, and limited analyses (U.S. Department of Commerce 1974, 1978, 1980); the initiation of several continuing data series based on periodic sample surveys of the national population (such as the annual National Opinion Research Center’s [NORC] General Social Survey and the annual National Crime Survey of the Bureau of Justice Statistics); the publication in 1974 of the first volume of the international journal Social Indicators Research; and the spread of social indicators and/or social reporting to numerous other nations and international agencies, such as the United Nations and the Organization for Economic Cooperation and Development.

Social indicators activities slowed in the 1980s as funding cuts and nonrenewals led to the closing of the Center for Coordination of Research on Social Indicators; the discontinuation of related work at several international agencies; the termination of government-sponsored social indicators reports in some countries, including the United States; and the reduction of statistical efforts to monitor various aspects of society. Several explanations have been cited for this slowdown (Andrews 1989; Bulmer 1989; Innes 1989; Johnston 1989; Rockwell 1987). Certainly, politics and the state of national economies in the early 1980s are among the most salient proximate causes. Administrations that came to power in the United States and elsewhere based decisions more on a ‘‘conservative ideology’’ and less on current social data than had been the case earlier. Also, faltering economies that produced large government budget deficits provided an incentive to make funding cuts. In addition to these immediate factors, there was a perceived lack of demonstrated usefulness of social indicators in public policymaking that was due in part to an overly simplistic view of how and under what conditions knowledge influences policy; this topic is treated more fully below in the discussion of current uses of social indicators. Before that, a more detailed discussion of types of indicators and their measurement and organization into accounting systems is necessary.

THREE TYPES OF SOCIAL INDICATORS

Criterion Indicators. On the basis of the premise that social indicators should relate directly to social policymaking considerations, an early definition by the economist Mancur Olson, the principal author of Toward a Social Report, characterized a social indicator as a ‘‘statistic of direct normative interest which facilitates concise, comprehensive and balanced judgments about the condition of major aspects of a society’’ (U.S. Department of Health, Education, and Welfare 1969, p. 97). Olson stated that such an indicator is in all cases a direct measure of welfare and is subject to the interpretation that if it changes in the ‘‘right’’ direction while other things remain equal, things have gotten better or people are better off. Accordingly, by this definition, statistics on the number of doctors or police officers could not be social indicators, whereas figures on health or crime rates could be.

In the language of policy analysis (Fox 1974, pp. 120–123), social indicators are ‘‘target’’ or ‘‘output’’ or ‘‘outcome’’ or ‘‘end-value’’ variables toward changes in which a public policy (program or project) is directed. This use of social indicators requires (Land 1983, p. 4) that (1) society agree about what needs improving, (2) it be possible to decide unambiguously what ‘‘getting better’’ means, and (3) it be meaningful to aggregate the indicators to the level of aggregation at which the policy is defined.

In recognition of the fact that other meanings have been attached to the term ‘‘social indicators,’’ the tendency among recent authors is to use a somewhat different terminology for the class of indicators identified by Olson. For instance, Land (1983, p. 4) termed this the class of ‘‘normative welfare indicators.’’ Building on the Olson approach, MacRae (1985, p. 5) defined ‘‘policy indicators’’ as ‘‘measures of those variables that are to be included in a broadly policy-relevant system of public statistics.’’ With a meaning similar to that of MacRae, Ferriss (1989, p. 416) used the felicitous term ‘‘criterion indicators.’’

Life Satisfaction and/or Happiness Indicators. Another class of social indicators has its roots in the work of Campbell and Converse in the early 1970s. In The Human Meaning of Social Change

(1972), they argued that the direct monitoring of key social-psychological states (attitudes, expectations, feelings, aspirations, and values) in the popula-

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tion is necessary for an understanding of social change and the quality of life. In this approach, social indicators are used to measure psychological satisfaction, happiness, and life fulfillment by employing survey research instruments that ascertain the subjective reality in which people live. The result may be termed ‘‘life satisfaction,’’ ‘‘subjective well-being,’’ or ‘‘happiness indicators.’’

The Campbell-Converse approach led to two major methodological studies in the 1970s (Andrews and Withey 1976; Campbell et al. 1976) and a subsequent edited volume (Andrews 1986) exploring the utility of various survey and analytic techniques for mapping individuals’ feelings of satisfaction with numerous aspects (‘‘domains’’) of their experiences. These studies examine domains ranging from the highly specific (house, family, etc.) to the global (life as a whole). A large number of other studies and applications of these concepts and techniques have appeared over the past three decades (for reviews, see Diener 1994; Diener et al. Smith1999; and Veenhoven 1996) and continue to appear; one or more studies of subjective wellbeing indicators can be found in almost every issue of Social Indicators Research. Research on the related concept of happiness as an index of wellbeing was surveyed by Veenhoven (1984).

The principle that the link between objective conditions and subjective well-being (defined in terms of responses to sample survey or interview questions about happiness or satisfaction with life as a whole) is sometimes paradoxical; therefore, the idea that subjective as well objective states should be monitored is well established in the social indicators literature. However, numerous studies of the measurement and psychodynamics of subjective well-being over the last three decades have led to a better understanding of this construct (Cummins 1995, 1998). While research continues and the debates have not been settled, it appears that this construct may have both traitlike (i.e., a durable psychological condition that differs among individuals and contributes to stability over time and consistency across situations) and statelike (i.e., a condition that is reactive to situational differences) properties (Stones et al. 1995; Veenhoven 1994, 1998).

With respect to the statelike properties of subjective well-being, Davis (1984) used an accumulated sample from several years of NORC Gen-

eral Social Surveys to document the responsiveness of happiness with life as a whole to (1) ‘‘new money’’ (recent changes in the respondents’ financial status compared with the current income level),

(2) ‘‘an old man/lady’’ (being married or having an intimate living partner), and (3) ‘‘two’s company’’ (a household size of two compared to living alone or in a family of three or more). Many other studies have found additional factors that are more or less strongly associated with variations in subjective well-being, but the relevance of intimate living conditions and/or family status almost always is replicated. The connection of subjective well-being to income levels has been an intriguing problem for social indicators researchers since Easterlin’s (1973) finding that income differences between nations predict national differences in happiness but that the association of happiness with income within countries is much weaker (for a review of this research literature, see Ahuvia and Friedman 1998). However, Davis’s finding of a positive relationship between ‘‘new money’’ or recent income changes and happiness has been replicated by Saris (1998), using data from a panel study conducted in Russia in the period 1993–1995.

Descriptive Social Indicators. Building on Ogburn’s legacy of research on social trends, a third approach to social indicators focuses on social measurements and analyses designed to improve the understanding of what the main features of society are, how they interrelate, and how these features and their relationships change (Sheldon and Parke 1975, p. 696). This produces descriptive social indictors—indices of the state of society and the changes taking place within it. Although descriptive social indicators may be more or less directly (causally) related to the well-being goals of public policies or programs and thus include policy or criterion indicators, they are not limited to such uses. For instance, in the area of health, descriptive indicators may include preventive indicators such as the percentage of the population that does not smoke cigarettes as well as criterion indicators such as the number of days of activity limitations in the past month and an index of selfreported satisfaction with health. Ferriss (1990) published a compilation of descriptive indicators for the United States at the end of the 1980s; regularly published national social indicator compilations for other nations also contain numerous examples.

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