Causal Mechanisms Correlation Mahoney

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CAUSAL MECHANISMS, CORRELATIONS,

AND A POWER THEORY OF SOCIETY

James MahoneyDepartment of Sociology

Maxcy Hall, Box 1916

Brown UniversityProvidence, RI 02912

[email protected]


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ABSTRACT

This paper explores how theories of causal mechanisms can help empirical analysts

improve the quality of their research. Drawing on philosophical realism, the paper conceives

causal mechanisms as unobserved entities and processes that act as ultimate causal forces in

the world. Defined as such, causal mechanisms can help empirical researchers better explain

particular outcomes, derive new testable hypotheses, and integrate existing correlational

knowledge. To illustrate these applications, the paper examines the two social science traditions

with the most developed causal mechanisms: functionalist theory and rational choice theory. In

addition, the paper discusses the applications of a new causal mechanism associated with a

power theory of society. This power mechanism suggests that many important social

outcomes are ultimately generated by the physical properties (i.e., mass, force, and direction) of

actors. The paper concludes by considering the normative entailments of treating functionalist,

rational choice, and power mechanisms as ultimate causes in the social world.


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CAUSAL MECHANISMS, CORRELATIONS,

AND A POWER THEORY OF SOCIETY

Recently, several analysts have called for new theories of causal mechanisms in the

social sciences (e.g., Elster 1989, 1998; Goldthorpe 2000; Hedstrm and Swedberg 1998;

Stinchcombe 1993; Tilly forthcoming). Despite differences in their understanding of causal

mechanism, these scholars all argue that the identification of such mechanisms can substantially

improve empirical work. For some, this empirical payoff entails using theories of causal

mechanisms to explain why certain variables are regularly conjoined with one another; that is,

identifying the processes through which one variable affects another variable. For others, the

payoff involves using causal mechanisms to explain diverse outcomes from a common

theoretical approach; that is, subsuming multiple outcomes under one basic theory.

Although this agenda promises a great deal, its contribution depends on analysts actually

using theories of causal mechanisms in empirical work. To this point, however, writings about

causal mechanisms do not appear to have had much of an impact. Part of the problem may be

that, for empirical researchers, the concept of causal mechanism is still too unclear and its

specific application too vague to be readily employed in analysis. Indeed, a bewildering number

of definitions of causal mechanism can be found in the literature. Likewise, the actual steps

involved in using causal mechanisms for empirical research are rarely specified in the literature.

As a result, despite pleas for incorporating theories of causal mechanisms in substantive

research, this strand of social theory remains largely divorced from empirical analysis.

This paper attempts to build a bridge between social theory focused on causal mechanisms

and analyses centered around the testing of empirical hypotheses. It does so by introducing and

defending a particular conceptualization of causal mechanism grounded in philosophical realism,


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and showing how this conceptualization can inform substantive research. Causal mechanisms

are defined here as unobserved entities and processes that act as ultimate causal forces in the

social world. By assuming the existence of such ultimate causes and employing certain bridging

assumptions, analysts can enhance empirical research in several specific ways. First, causal

mechanisms can help scholars address questions about the black box linking independent

variables with dependent variables. Second, by subsuming independent variables under a

common theoretical framework, causal mechanisms can help scholars understand why diverse

explanatory factors are all empirically associated with a particular dependent variable. Finally,

scholars can use causal mechanisms to logically deduce new and perhaps counterintuitive

hypotheses that can subsequently be tested in empirical research.

Currently, the two most developed causal mechanisms in the social sciences are found in

the core assumptions of rational choice theory and functionalist theory. The basic mechanism of

rational choice theory entails the claim that individuals will select the option that best enables

them to realize their private goals when faced with a range of behavioral alternatives.1 For

functionalist theory, this mechanism entails the claim that enduring features of a social system

exist because they are functional for that system.2 Below I explore how these mechanisms can

help empirical researchers subsume explanations under a single theoretical framework and better

understand why certain variables are associated with other variables. Yet, I am aware that

rational choice theory has come under attack for its limited empirical application (e.g., Green and

Shapiro 1994; Gould 2000), and that functionalism is currently a stagnant theoretical tradition

(e.g., Turner and Maryanski 1979). In response, I argue that these traditions could be revitalized

by recognizing that their core claims are not empirical hypotheses to be evaluated as either true


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or false, but rather causal mechanisms to be evaluated according to their usefulness for empirical

work.

In addition to defending the causal mechanisms used in rational choice and functionalist

theories, I seek to show that a good deal of social science analysis assumes the existence of an

alternative mechanism. This mechanism is built around the notion that social outcomes are the

product of the physical properties (i.e., mass, force, and direction) of actors. I see this

mechanism as constituting the baseline assumptions of what can be called a power theory of

society, and I argue that such a theory can make substantial contributions to empirical research. I

develop this argument by considering how the mechanism underpins deductive theorizing in the

field of comparative-historical analysis, a research area that is sometimes considered purely

inductive.

LINKING SOCIAL THEORY AND EMPIRICAL RESEARCH

Unlike some areas of social theory, recent work on causal mechanisms has been concerned

with making links to empirical analyses, including quantitative sociology. This agenda is

especially important now because social theory and empirical sociology are increasingly distant

from one another.3 In this section, I seek to illustrate how this division is harmful to the advance

of social research. I argue that the findings of empirical researchers are incomplete and not fully

intelligible without theories of causal mechanisms; by contrast, theories of causal mechanisms

are entirely speculative until their usefulness is revealed through empirical correlations. A

complete science must therefore strive to identify both empirical regularities and causal

mechanisms (see Dessler 1991; Jasso 1989).


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Moving Beyond Correlations

The concern with causal mechanisms grows out of a distinction sometimes made in the

philosophy of science between correlational analysis and causal analysis. Whereas correlational

analysis involves identifying antecedents regularly conjoined with outcomes, causal analysis

consists of specifying the mechanism that underlies and generates empirical regularities and

outcomes. In contrast to correlational research, the challenge of causal analysis involves

postulating entities, properties, processes, relations . . . that are held to be causally responsible

for the empirical regularities to be explained (McMullin 1984a, p. 210; see also Blalock 1961,

pp. 11-13; Keat and Urry 1982, chap. 2). These mechanisms explain why social regularities

exist in the first place; knowledge of their operation allows researchers to go beyond

correlations.

At the base of this argument is a rejection of the Humean model of constant conjunction for

understanding causation. According to Hume (1748/1988), we infer causation when we

repeatedly observe putative causes followed effects; that is, two events are constantly

conjoined in our experience. However, we can never actually confirm the existence of

causation because the imagined necessary connections linking the two events cannot

themselves be observed or known. In contrast to this Humean model, scholars concerned with

causal mechanisms argue that exploration of the black box connecting independent and

dependent variables is essential to good causal research. On this view, we are not satisfied with

merely establishing systematic covariation between variables or events; a satisfactory

explanation requires that we are also able to specify the social cogs and wheels that have

brought the relationship into existence (Hedstrm and Swedberg 1998, p. 7; see also Elster

1989, p. 3; Glennan 1996; Harr 1970, p. 230; Harr 1972, p. 170).


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On a substantive level, the identification of causal mechanisms can help empirical analysts

overcome two basic problems associated with their research. First, correlational findings are

almost inevitably vulnerable to charges of selectivity and omitted variable bias (see Lieberson

1985). As a consequence, scholars are often left uncertain whether a given association reflects

causation, or whether the association is simply the spurious product of an unknown antecedent

variable. Second, empirical researchers typically discover that a highly heterogeneous group of

independent variables are associated with an outcome, and they lack tools for understanding why

such diverse factors are related to the phenomenon of interest. The result can be an absence of

theoretical integration, which in turn contributes to the fragmentation of knowledge that

currently characterizes the social sciences (Dessler 1991; Srensen 1998).

The identification of causal mechanisms can help researchers overcome these problems by

providing the theoretical basis for understanding why an association between empirical variables

exists (Hedstrm and Swedberg 1998, pp. 8-9). For example, by logically deriving a correlation

from assumptions about a causal mechanism, analysts can explain why the independent variable

of the correlation affects the dependent variable. Likewise, by showing how diverse independent

variables all affect a given dependent variable by virtue of a common causal mechanism,

scholars can integrate existing correlational findings and put social science analysis on a better

foundation for accumulating knowledge.

Defining Causal Mechanisms

Unfortunately, a good deal of confusion currently surrounds the precise meaning of causal

mechanism. This terminological confusion is one reason why theoretical work on causal

mechanisms has not attracted wide attention among empirical analysts. Here I attempt to make


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sense of existing definitions by arranging them into four groups, each of which contributes

insight toward a synthetic conception of the term (see Table 1).

---------------------------

Table 1 about here----------------------------

First, the simplest definitions treat causal mechanisms as synonymous with independent

variables or causal factors that help explain outcomes (see Table 1). For example, Boudon

defines a mechanism as the well-articulated set of causes responsible for a given social

phenomenon (1998, p. 172). This definition usefully suggests that causal mechanisms are, in

fact, causes, and that they help produce outcomes. However, it does not distinguish the idea of

mechanism from the standard notion of an independent variable, raising the question of what the

study of mechanisms adds to correlational research.

Second, other definitions see mechanisms as interveningvariables, events, or processes that

explain how one variable influences another (see Table 1). These definitions are useful in that

knowledge of intervening processes can help researchers understand whya given independent

variable exerts a causal effect on a given dependent variable. In addition, knowledge of

intervening processes can increase ones confidence that a statistical association is not spurious.

However, this kind of definition leaves open the possibility that causal mechanisms must be

identified and analyzed as correlations. That is, to locate a causal mechanism, the analyst must

show how intervening processes and events are themselves correlated with both independent and

dependent variables.4 In this sense, the analyst explains a correlation by appealing to another

correlation, begging the question of why the new association exists (King, Keohane, and Verba

1994, p. 86; McMullin 1984a, p. 206). To explain the new correlation, the analyst must identify

an additional mechanism, which itself will contain a black box and require explanation. Under


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this definition, in short, the distinction between an independent variable and a mechanism

becomes somewhat arbitrary, and the analyst may be forced into an infinite regress in search of

deeper and deeper mechanisms.

Third, other scholars view causal mechanisms as underspecified causal propositions that

can be applied to a fairly wide range of cases (see Table 1). For example, according to Elsters

(1998) influential definition, mechanisms are frequently occurring and easily recognizable

causal patterns that are triggered under generally unknown conditions or with indeterminate

consequences (p. 45). This definition assumes that a mechanism identifies a cause-effect

relationship that is applicable in many social situations. Such a cause-effect relationship differs

from empirical work on correlations in that it is underspecified; that is, it makes reference to

analytical constructs that are not actually observed (Rueschemeyer 2001). For example, one of

Elsters mechanisms is the spillover effect, defined as follows: if a person follows a certain

pattern of behaviorPin one sphere of life,X, he will also tend to followPin sphere Y (p. 54).

This mechanism identifies a probabilistic relationship between an underspecified independent

variable (a type of behavior in one sphere) and an underspecified dependent variable (a type of

behavior in another sphere).

Although these underspecified causal propositions do not have empirical content, they can

be used to derive empirical hypotheses. For example, the spillover effect could be used to

hypothesize that alienation at work will produce alienation at home, or that greater participation

at work will produce greater participation in politics (Elster 1998, pp. 54-55). While clearly

useful, this definition of causal mechanism nonetheless fails to explain whythe underspecified

cause-effect relationship embodied in mechanism itself obtains. For example, with respect to

Elsters spillover effect, the mechanism itself does not explain the process through which a given


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type of behavior might spillover into a new sphere; that is, it does not explain why the

underspecified independent variable affects the underspecified dependent variable. As a result,

scholars have no way of knowing when the spillover effect will be in operation as opposed to

some other mechanism, including a mechanism that predicts exactly the opposite of the spillover

effect, such as the crowding-out effect (i.e., if a person follows a certain pattern of behaviorP

in one sphere of life,X, she will nottend to followPin sphere Y). In short, this definition of

mechanism itself contains a black box, not identifying the reasons why the underspecified

association holds and thereby posing problems for empirical research.

Scholars associated with the realist school in the philosophy of science offer a fourth

definition that, I believe, synthesizes insights from the above definitions while avoiding their

shortcomings (see Table 1). This synthetic definition understands a causal mechanism as an

unobserved entity, process, or structure that acts as an ultimate force in generating outcomes. An

ultimate force is a final cause that itself requires no further explanation (Harr 1970, pp. 101-4;

see also Jasso 1998, pp. 5-6; Steinmetz 1998, pp. 24-25). When causal mechanisms are

understood to be ultimate forces, they cannot be studied through direct empirical means.

However, by appealing to their existence, scholars can explain diverse empirical outcomes from

a single theoretical perspective and offer an account of why empirical correlations exist in the

first place.

In the natural sciences, it is common for researchers to posit causal mechanisms in this

sense of unobserved ultimate forces. For example, scientists assume the existence of strings in

superstring theory, quarks in particle theory, and gravitons in gravitational theory (Hacking

1983; McMullin 1984b; Musgrave 1985). The utility of these entities is not as independent

variables that explain variation on an outcome; rather, they are believed to be the real


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mechanisms that physically constitute or otherwise generate phenomena (including empirical

correlations) in the natural world.

When a scholar asserts that a mechanism causes some outcome, the scholar is typically

presenting a claim that is, by itself, tautological or otherwise not directly testable. For example,

it is tautological to argue that gravity causes objects to fall, given that gravity is defined as a

hypothetical force that will have this effect. Likewise, in rational choice theory, it is tautological

to argue that actors carry out particular actions because these actions are the most appropriate for

achieving their goals, given that one cannot falsify the proposition with empirical evidence. The

nonfalsifiable nature of explanations based on causal mechanisms is closely related to their status

as ultimate causes. Once an explanation based on a mechanism has been proposed, it is no

longer relevant to ask whythe mechanism produces the outcome in question, because the

mechanism stands in a necessary relationship to the outcome. If the mechanism really exists and

is operating, it will produce the outcome of interest. In this sense, explanations that appeal to

causal mechanisms are explanations without black boxes.

Using Causal Mechanisms in Empirical Research

Because they lead to tautological propositions, causal mechanisms have little value unless

they are linked to correlational research through bridging assumptions (see Kelle and

Ludemann 1998; Morton 1999). The process of formulating bridging assumptions is in fact one

of the main activities that practicing theorists carry out when building their models. Yet, it is

surprising how little has been said about the use of such assumptions in the social sciences. This

silence has stood in the way of better linking social theory and empirical research. As a remedy,

I discuss three ways in which bridging assumptions can be used in conjunction with causal

mechanisms to generate empirical propositions (see Table 2).


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---------------------------

Table 2 about here

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First, researchers can use bridging assumptions to test whether specific outcomes are

produced by a given causal mechanism. With this strategy, the researcher begins by asserting

that a specific outcome of interest is the product of a particular mechanism. For example, a

biologist asserts that a given trait of a species (e.g., bipedalism among humans) exists because it

is (or was) conducive to survival and reproduction, thereby using the natural

selection/functionalist mechanism of biology. Next, the analyst introduces a set of assumptions

from which the initial assertion is logically derived. For example, the biologist makes certain

assumptions about the ancestral environment in which bipedalism first developed (e.g.,

availability of food, type of predators) to logically deduce the assertion that bipedalism conferred

reproductive advantages. In this sense, bridging assumptions are used as premises and the

proposition that a natural selection mechanism causes bipedalism is treated as a conclusion that

necessarily follows from these premises. The analyst then empirically assesses the premises of

the argument by formulating them as testable hypotheses. For the biologist studying bipedalism,

this entails using the fossil record to support assumptions about the nature of the ancestral

environment. Testing premises is relevant because if one establishes that they are likely true,

one has reason to believe the conclusion is also likely true, given that the conclusion follows

logically from the premises.

In a second strategy, the analyst derives testable hypotheses through a logical proof that

combines a causal mechanism with other bridging assumptions. Here the goal is to work

deductively from a set of premises that include both a causal mechanism and additional bridging

assumptions to new propositions. For instance, the natural selection mechanism of biology can


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be combined with assumptions about parental investment to arrive at the prediction that the

relative degree to which females versus males of different species are eager to initiate courtship

and mating will covary inversely with the amount of resources and time they must invest to

produce surviving offspring. Propositions derived in this fashion are of particular relevance to

empirical researchers because they contain no black boxes; that is, the logical connection

between the independent variables and the dependent variables of the propositions is explained

by the theoretical proof itself. Unlike the first strategy, the premises used to derive conclusions

in this strategy are nottested themselves (Cohen 1989; Jasso 1988; Stinchcombe 1993;

Kanazawa 1998). Rather, it is the propositions logically derived from the premises that represent

the hypotheses to be empirically evaluated. As a general rule, scholars strive to use as few

bridging assumptions as possible to generate as many testable hypotheses as possible.5

A final strategy involves working backward from one or more existing correlations to a

causal mechanism. With this strategy, the analyst attempts to show how already established

correlations can be logically derived from a set of premises that includes a causal mechanism

plus additional bridging assumptions. For example, by assuming that natural selection drives

evolution and that the ancestral environment featured individuals living in small villages near

close kin, sociobiologists can offer a theoretically grounded explanation for the association

between todays large, impersonal urban settings and crime and uncivil behavior. This strategy

parallels the second strategy above, except that the analyst begins with correlational findings that

have already been supported and works back to the premises from which one can logically

deduce these correlations.6

The analyst does not actually test any correlations, since this work

has already been completed. Instead, the analyst integrates existing knowledge by showing how

established associations can all be explained in terms of a single mechanism.


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social system (e.g., survival). However, a claim like this cannot be directly tested and is of little

value in itself. To be of use, the claim must be logically derived from a set of testable premises

or used with bridging assumptions to derive new propositions.8 Lacking a strong connection to

empirical research, functionalist theory eventually degenerated into abstract exercises in

conceptual classification, exemplified by the later writings of Talcott Parsons.9

If it is to be revived, functionalism must be understood as embracing a causal mechanism

not a set of directly testable hypotheses that can be applied to select research questions in

sociology. The basic functionalist mechanism treats social aggregates as systems with internal

parts that exist because they serve (or previously served) systemic functions; it assumes that

societal structures would not exist in their present form if they did not currently or previously

serve a function for the social system within which they are embedded. Beyond this claim,

functionalist theory says little about the workings of societies.

As many observers have noted, studies that draw on the functionalist mechanism make

teleological claims. That is, they assume outcomes occur because they help realize some future

end or goal; the future end or goal acts as a cause of the outcome. However, not all teleological

explanations are illegitimate (Stinchcombe 1968, pp. 80-101; Turner and Maryanski 1979, p.

118-19; Elster 1982, p. 455). They are illegitimate only when the analyst fails to specify the

reasons why a future end or goal leads to the occurrence of a given outcome. Traditionally,

functionalists tried to identify such reasons by specifying the needs of society that must be met

to ensure its survival or stability (e.g., Aberle et al. 1950; Levy 1952, p. 151). These functional

requisites, however, were often stated so vaguely that it was impossible to decide empirically

when a given need was being met. For example, it was difficult to know when a systems need

for normative regulation or socialization was adequately met.


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Insights about natural selection in Darwinian theory can help functionalists better specify

system needs. Darwinian theory assumes that biological traits are initially introduced into a

population through an essentially random process (i.e., mutation),10

and that the ability of a trait

to persist in the population depends on the reproductive success of the organisms who carry it. If

a trait confers a reproductive advantage to its carriers in a given environment, the trait will

proliferate. In many cases, in fact, the trait will continue to exist even when, in a subsequent

environment, it no longer confers a reproductive advantage.

Functionalists could fruitfully extend these ideas to explain the existence of social

structures found widely across societies (e.g., social stratification, religion, or government) (see

Merton 1949, p. 50). Like biologists, functionalists need not explain why a given social structure

was first introduced into a society; its initial appearance can be treated as exogenous to the

theory.11

However, once the trait appears in at least one member of the population, functionalists

must offer an account of the winnowing process through which it spreads to other members.

Here is where the natural selection argument of biology can be put to good work.

Many of the societal traits that interest functionalists developed in an early environment

when human society was organized as hunter-gatherer bands or primitive agricultural

settlements. These early societies were vulnerable to elimination through two primary sources:

warfare and/or starvation. For example, there is strong evidence that warfare was extremely

common in early agricultural societies, and that societies that were better adapted to warfare

extended their control of territory at the expense of societies that failed to meet the exigencies of

warfare (e.g., Mann 1986). Hence, traits that favored success in warfare should have been

selected for in this early environment (Wendt 1999, pp. 321-23).12

Likewise, given that

starvation could and did eliminate hunter-gatherer societies, one would expect hunter-gatherer


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bands that developed features that enabled them to avoid this outcome to proliferate at the

expense of those that did not. Especially given the possibility of cultural selection, in which

units acquire traits through imitation, it is hardly unrealistic to believe that societies would

eventually converge on certain social structures that provided an early adaptive advantage. Once

these traits were institutionalized, they could continue to exist through a path-dependent process,

even long after they had outlived their original useful purpose (Mahoney 2000).

Although some observers might see this call for employing ideas about natural selection as

an unwelcome return to previous failed efforts to use Darwinian theory, I believe that

contemporary sociologists can profitability revisit some of the classic works carried out by

functionalists in the 1950s and 1960s. Of these works, perhaps the best known and most

controversial is Davis and Moores (1945) analysis of social stratification. These authors argued

that stratification along the lines of status and income exists because it is functional for society.

Table 3 summarizes four premises that constitute a modified version of this argument. The first

premise asserts that individuals inherently desire status and/or income. The second premise adds

the assumption that individuals, ceteris paribus, do not desire social positions that require

training. A third premise explains why individuals sometimes fill social positions that require

training: status and/or income compensate for the costs of training. A final premise then paves

the way for the authors conclusion about the functionality of stratification: in order for society

to survive, it is necessary that individuals occupy certain social positions that require training.

---------------------------Table 3 about here

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To assess this argument, the three strategies discussed above are available to researchers.

First, the premises of the Davis-Moore argument can be directly tested. In the past, critics


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argued that certain of these premises are not true, especially the notion that training is inherently

undesirable (Tumin 1953). In addition, some observers wondered why training is or was

necessary for societal survival.13

Nevertheless, the most rigorous tests of these hypotheses were

generally supportive (e.g., Cullen and Novick 1979).

Second, the argument could be assessed by asking about additional hypotheses that should

be true if we assume the premises of the argument are true. To derive such hypotheses, it would

be necessary to decide whether selection pressures in favor of social stratification exist in

contemporary societies, or whether these pressures operated only in the past. Davis and Moore,

for their part, believe that selection pressures are still at work in the contemporary world and that

failure to conform to such pressures produces social instability. Assuming this is true,14

testable

hypotheses about todays societies can be formed. For example, the Davis-Moore argument

suggests that the level of social stability in society should be, in part, a product of the degree to

which there is a positive linear relationship between occupational training and occupational

income/status. Likewise, the argument suggests that governments that attempt to eliminate the

status differentials across occupations marked by different levels of training should not be able to

survive over the long run. These hypotheses can, in principle, be tested with empirical measures

of social stability, occupational training, and occupational income and status.

Third, the Davis-Moore argument could be used to make sense of existing correlations that

already have received significant empirical support. One such correlation concerns the

relationship between the level of economic development of a society and its level of economic

inequality. Substantial research on nation-states has confirmed the Kuznets inverted-U

hypothesis, which holds that inequality first increases during the process of economic growth,

but subsequently decreases as economic growth continues (see Lindert and Williamson 1985;


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Nielson and Alderson 1995). Despite agreement concerning this association, scholars have not

reached any consensus regarding whythe association exists. The Davis-Moore theory could be

used to explain the correlation through the following argument. During the early phases of

economic growth, new occupations emerge that require some individuals to receive dramatically

more training and thus income and status than others. This change in occupational structure

therefore increases the level of income inequality. However, with sustained economic

development, the pronounced differences in the occupational training required for individuals

begin to decline as more and more individuals are trained to work in a modern economy. Income

and status inequality follow suit, leveling off and then declining in the course of economic

development.

To fully assess the validity of a functionalist explanation of social stratification, one would

have carry out empirical tests, which is not my purpose here. Nevertheless, the example does

illustrate how functionalism could be revived in sociology. It is remarkable that virtually no

active sociologist identifies himself or herself as a functionalist, given that the theory seems

appropriate for understanding how certain social institutions (e.g., governmental, educational,

religious, health, familial institutions) proliferated to the point that they are now found across

most or all modern societies. In fact, while I have focused on early selection pressures as a

means of reviving functionalism, the theory could be used to study how certain modern social

institutions (e.g., the family) may persist over time because conscious learning and design have

led actors to believe that these institutions are functional for society. The potential for research

along all of these lines is there, but it is up to scholars to actually seize the opportunities.


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Rational Choice Theory

In the 1950s, it was common for analysts to assert that functionalism was the only viable

theory for social analysis. Fifty years later, one hears remarkably similar claims about rational

choice theory (e.g., Geddes 1991; Kiser and Hechter 1991, 1999; Wallerstein 2001). Despite

these claims, there are good reasons to believe that rational choice theory, at least in sociology,

could meet a fate similar to functionalism in the not too distant future. This is true because the

very developments that led to the swift abandonment of functionalism increasingly apply to

rational choice theory.

First, one hears mounting criticisms that rational choice theory is abstract, tautological, and

circular (e.g., Almond 1991; Green and Shapiro 1994). Again, these concerns stem from the

tendency of critics to evaluate the program from the standpoint of correlational analysis rather

than a theory of a causal mechanism. Second, rational choice theorists have been slow to link

their work to correlational research. Like functionalists before them, these theorists often spend

too much time specifying elaborate models at the expense of generating testable propositions

(Levi 1999, pp. 154-55; Munck 2001, p. 200). Finally, the universalistic aspirations of some

rational choice theorists have become a focal point of criticism. Skeptics and even pragmatic

rational choice theorists themselves contend that it is unrealistic to think that rational choice

theory can be applied to all domains of social analysis (e.g., Green and Shapiro 1994, pp. 27-28;

Munck 2001, p. 188; Tsebelis 1990, pp. 42-43).

As others have noted, the core of rational choice theory has no empirical content (e.g.,

Denzin 1990; Kelle and Ludemann 1998, p. 112). This is true because the theory is nothing

more than the basic mechanism discussed above: given a set of alternatives, individuals will

choose the option that best enables them to realize their goals. To be of use, this mechanism


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must be empirically specified through bridging assumptions. In rational choice theory, bridging

assumptions typically identify actors, preferences, available choices, opportunity costs, and the

means through which individual choices combine to produce social outcomes (Friedman and

Hecther 1988). In conjunction with such assumptions, rational choice theorists can pursue the

three strategies discussed above and thereby build a bridge to correlational analysis.

The first strategy involves using testable assumptions to logically derive the conclusion that

the rational choice mechanism produces an outcome of interest. For example, to explain why a

collection of individuals fails to produce a public good from which all would benefit, rational

choice theorists draw on bridging assumptions about egoism, non-excludable goods, and the

relative contribution of a single individuals participation to show how this seemingly illogical

outcome is the result of rational individual choices (see Olson 1965). Theoretical proofs that

take this form are internally coherent in the sense that, if the premises are true, the conclusion

about the causal role of the mechanism in producing the outcome necessarily follows from these

premises. With this strategy of explaining particular outcomes, the challenge is to substantively

demonstrate the validity of the premises. For example, when using Olsons theory of collective

action to explain the absence of some specific public good, theorists must be held accountable

for premises about egoism and the non-excludability of the good in question.

With the second strategy, new testable propositions are derived from an initial set of

premises that include the rational choice mechanism and other bridging assumptions. For

example, by assuming that individuals make rational choices and that the premises of the theory

of collective action are true, analysts have logically arrived at the prediction that broad-based

cartels will not exist unless these organizations offer selective incentives to their members. This

is true because non-participators can free-ride and still enjoy the benefits of the cartel. Unlike


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pre-existing correlational findings by interpreting them in light of overarching assumptions about

rational choice theory (e.g., Geddes 1998 on findings about democracy). The contribution of

these researchers is to integrate existing knowledge into a single theoretical model.

Despite the proliferation of literature on the methodology of rational choice theory, these

three strategies have not been previously identified. As a result, rational choice theorists have

lacked a solid foundation for discussing the relationship between their theoretical models and

empirical analysis. In distinguishing between outcome explainers, proposition derivers, and

theoretical integrators, I hope to provide a stimulus for more empirical work from this

perspective.

Other Causal Mechanisms?

The quest for ultimate causes that generate empirical associations and outcomes animates

much scientific research. Following the realist literature, I have used the expression causal

mechanism to designate such ultimate causes. In the social sciences, however, there are very

few examples of causal mechanisms beyond the functionalist notion that system needs

ultimately drive important outcomes and the rational choice notion that the utilitarian cost-

benefit assessments of individuals are the final movers of social happenings. Given this limited

range of causal mechanisms, social theorists might consider building new theories of such

mechanisms.16

Formulating theories of causal mechanisms requires abstract thinking and imagination, but

the process need not be purely deductive. Rather, it is possible to work inductively from existing

studies, asking what implicit mechanism informs a given work. In many cases, one will find that

social science arguments are simply not grounded in any clear mechanism. Yet, for other


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studies, especially studies that group together to form a coherent body of literature, one may be

able to identify an underlying mechanism.

The field of comparative-historical sociology is a particularly fruitful area for formulating

ideas about new causal mechanisms. Although work in this field has been characterized as

antitheoretical (e.g., Kiser and Hechter 1991), its proponents maintain that they draw

extensively on general theoretical principles (e.g., Quadagno and Knapp 1992). If this is true, it

should be possible to identify the causal mechanism that guides research in this field. In what

follows, I attempt to do this for those comparative-historical works that emphasize power as an

unobserved but ultimate causal force.

POWER THEORY:

BASIC COMPONENTS AND APPLICATIONS

Much comparative-historical research assumes that the abstract power of actors ultimately

generates outcomes in the social world. Taking this assumption as a starting point, a new power

theory of society can be introduced to complement functionalist and rational choice theories.

Power theory makes an analogy between force and movement in the physical world and events

and processes in the social world. In this sense, it builds on both materialistic approaches that

assume causal effects in the social world may exist independent of consciousness (see Mann

1979; Porpora 1993) and conflict theories that view struggles between competing actors as major

determinants of social happenings (e.g., Dahrendorf 1958; Lenski 1966; Collins 1975).

Building Blocks of Power Theory

Like rational choice theory, power theory assumes that actors (not necessarily defined as

individuals) have certain essential properties that ultimately generate social outcomes. Two


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characteristics in particular define actors: power and direction. Taken in isolation, both of these

characteristics are abstract and incomplete. Yet, by assuming their existence, one can build a

theory of society in which the basic causal imagery parallels the study of motion in physics,

including the idea of vectors moving through space.

Power itself can be usefully disaggregated into two sub-dimensions: mass and force.17

Massrefers to the total latent capability of an actor to produce some end, acting as the equivalent

of weight in the physical sciences. Typically, the mass of an actor is defined in materialistic

terms and relative to other actors. For example, with respect to the end of winning an interstate

war, the United States has considerable mass, because it possesses the latent capacity to defeat

nearly any other individual country. Force refers to the extent to which the total latent capacity

of an actor is actually directed toward an outcome, acting like speed or acceleration in

physics. Actors with extensive mass have the potential to push aside less massive actors, but

they may lack sufficient force to do so. For example, Vietnam may be able to prevail over the

United States in a war if the latter country does not move with adequate force. In this theory, the

product of the mass and the force of an actor define its power; that is, mass xforce = power.

The second component of actors is their direction, or the particular outcome toward which

they move. Actors may be directed at outcomes in a conscious or unconscious fashion. For

example, the Swedish government may consciously direct power toward cleaning up the

environment. By contrast, the citizens of the Sweden may unconsciously use power to damage

the environment, simply by virtue of driving automobiles. In power theory, it is highly possible

that final outcomes will not reflect the conscious preferences of any actor, given that outcomes

are often the result of actors moving in very different directions. Hence, the theory (like rational


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choice theory) provides a basis for understanding the unintended nature of many social

occurrences.

To be linked to empirical analysis, the basic components of power theory must be specified

through bridging assumptions. This process is equivalent to adding empirical content to the

empty cores of functionalist theory and rational choice theory. Among the key assumptions that

often must be made in power theory are the definition of all relevant actors, the empirical

specification of the mass and force of these actors, and the identification of the outcomes at

which these actors are directed. Although formulating such assumptions may seem a large task,

it is no more demanding than the process of specifying bridging assumptions in functionalist and

rational choice theories.

Power theory leads to a particular conception of causation in the social world, one that

contrasts with both the natural selection imagery of functionalism and the utilitarian imagery of

rational choice theory. With power theory, social outcomes are the product of objects moving

with mass and force that may collide with one another; the vision here is one of billiard balls in

motion. Given this conception, vector analysis from physics (Durrant 1996) can help analysts

formally diagram their arguments. A vector refers to both the direction of an actor, which is

diagramed using an arrow, and the power (mass xforce) of an actor, which is specified by the

length of the arrow. If the power and direction of all actors are known, standard rules of vector

addition can be used to calculate final outcomes. For example, if space is conceptualized as a

one-dimensional line containing two or more vectors, the lengths of each vector can be summed

together to determine a final location. If space is conceived as having two dimensions (anxand

yaxis), the addition rules of vector mathematics can also be easily applied to determine final

locations.


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Application to a Balance of Power Argument

If only implicitly, power theory has already underpinned empirical research in the field of

comparative-historical sociology. To illustrate this point, I consider in this section

Rueschemeyer, Stephens, and Stephens Capitalist Development and Democracy(1992),

arguably the most important recent work in the field. I show how the authors implicitly use a

power mechanism to carry out three basic tasks: explaining particular outcomes, deducing new

propositions, and subsuming existing correlational knowledge under a single theoretical account.

---------------------------

Figure 1 about here---------------------------

Rueschemeyer, Stephens, and Stephens summarize the central argument of their study as

follows: power relations . . . determine whether democracy can emerge, stabilize, and then

maintain itself (p. 5). To develop this argument, the authors offer a balance of power model in

which the occurrence and stabilization of democracy depends on the relative power and direction

of different actors. Through bridging assumptions, the authors identify their main actors as

different social classes: workers, peasants, middle classes, the bourgeoisie, and landed elites (see

Figure 1). The authors also make assumptions about the directionof these actors (pro-

democracy or anti-democracy) based on whether they are likely to gain or lose from the

establishment of democracy. They assume that workers will be a consistently pro-democratic

force, while landlords will generally be an anti-democratic force. The other social classes

peasants, middle classes, the bourgeoisie could move in either direction depending on

historical contingencies. Hence, in the model, democracy hinges significantly on the relative

power of workers and landlords, and on the direction of the other class actors.


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----------------------------

Figure 2 about here

----------------------------

Much of Capitalist Development and Democracyuses this model to explain particular

outcomes, especially the onset of democratic or authoritarian regimes in specific countries. In

offering these explanations, the authors implicitly introduce assumptions about the power and

direction of the various class actors. They then draw on historical narrative to justify these

assumptions. While the authors do not develop their model by assigning numerical values to

different actors, their qualitative assessments can be reconstructed in this fashion. For example,

Figure 2 offers a formal summary of the authors argument about Sweden and Italy (pp. 92-4,

103-6). The basic outcome to be explained is the development of democracy in Sweden versus

the onset of authoritarianism in Italy during the interwar period. The key difference between the

countries involves landed elites, the bourgeoisie, and middle classes. In both cases, landed elites

exerted full force in a negative direction, but their massdiffered considerably: the Swedish

landed classes were tiny and had almost no mass (thus a power score of 1), while the Italian

landed elites were well developed and had considerable mass (thus a score of 5). As for middle

classes and the bourgeoisie, the difference concerned not their mass, but rather their direction;

that is, middle classes and the bourgeoisie were an anti-democratic force in Italy (-3 each) and a

neutral force in Sweden (0 each). All of these assumptions about the power and direction of

actors are backed up by considerable qualitative evidence.

By offering historical narratives that implicitly describe the relative mass, force, and

direction of class actors, Rueschemeyer, Stephens, and Stephens explain nearly forty cases of

democracy and authoritarianism. In this sense, even though the authors do not formally specify


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the power of their actors with numerical values, their book illustrates how power theory can be

used to explain particular outcomes.

The authors also derive a number of testable propositions from the abstract model

presented in Figure 1 above. For example, they hypothesize that the size of the working class

will be positively correlated with democracy, while the size of landlords will be negatively

correlated with democracy. This hypothesis is built around the assumption that size is an

empirical indicator of the relative mass of workers and landlords. Likewise, because working-

class power will usually not surpass that of all other class groups combined, the authors

hypothesize that the working class will not be able to create democracy by itself, but rather will

require help from other classes. This reasoning leads to the bold prediction that pro-democratic

class coalitions that include the working class should be an almost necessary condition for full

democracy.

Several other variables should be correlated with democracy because they affect the power

of the working class or landed elites, or because they move other class actors in a particular

direction. For example, the presence of a powerful church that is strongly linked to landed elites

will be negatively correlated with democracy because it tends to weaken working class demands

for democracy and move other class actors in a negative direction. On the other hand, a state

apparatus that is autonomous from landed elites should be positively associated with democracy

because it tends to increase the force of working classes by enabling them to realize and act on

their interests.

In a similar vein, several variables should notbe correlated with democracy because they

do not consistently alter power relations in one direction or another. For example, in contrast to

the expectations of much writing on democracy, the authors do not believe increases in the size


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or strength of the middle classes will be positively associated with democracy. This belief is

grounded in the theoretical assumption that middle classes will not consistently gain or lose from

democracy. Likewise, the authors assume that war, political dependence, and economic

dependence will not consistently move social classes in one direction or the other, and thus will

not be correlated with democratization.18

Finally, the authors use the power mechanism to integrate existing correlational knowledge.

Most importantly, they use their model to explain theoretically the longstanding statistical

correlation between economic development and democracy. They do so by suggesting that this

correlation exists because economic development alters the power of different class actors. In

particular, economic development generally increases the power of the working class and

weakens the power of landed elites, thereby activating the underlying process that generates and

sustains democracy. Although the association between development and democracy is but a

single correlation, it is probably the most important finding to emerge from the quantitative

literature on democracy, a finding that previously had been without a well-developed theoretical

explanation.

This discussion suggests that comparative-historical works are neither inductive nor

antitheoretical. Rather, these analyses implicitly follow an abstract theoretical program that is

not unlike rational choice theory in basic form. The problem is that comparative-historical

analysts, for all their many substantive contributions, have not said enough about how they use

abstract principles to guide their research. As a result, they have made it more difficult than

necessary to understand and evaluate the implicit power theory driving many of their arguments.


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Future Applications of Power Theory

Power theory opens up exciting new possibilities for comparative-historical sociology and

other fields in which power can be meaningfully treated as an ultimate causal force. For one

thing, scholars can attempt to elucidate the general theoretical principles underlying classic

works in this area, such as Moores Social Origins of Dictatorship and Democracy(1966) or

Skocpols States and Social Revolutions (1979). Studies such as these have been criticized

precisely for their silence about ultimate theoretical principles (Kiser and Hechter 1991). By

locating these theoretical principles, scholars will not only better clarify the logic of the

arguments offered in these analyses, but they will help identify new models that could be used to

study other substantive topics. In this sense, a key agenda of power theorists should be to

accumulate a set of models equivalent to those that already exist in rational choice theory. The

advantage of working backward from existing studies is that the potential for empirical

application of the model is already addressed; that is, the analyst knows that the model can

actually be used in empirical investigation.

Reconstructing the implicit models used to generate empirical findings for entire bodies of

literature focused on particular outcomes is also a fruitful area of research. To take one possible

example, consider the literature on the origins of social revolutions (see Foran 1993; Goldstone

1980; Skocpol 1994 for reviews). Scholars working in this field have identified several

conditions that make social revolution more likely, such as the presence of a personalistic

regime, divisions among elites, and sustained fiscal crisis. Likewise, research has pointed to

conditions that make successful social revolution nearly impossible, including the presence of an

industrial economy or a regime type such as democracy, populism, or bureaucratic-

authoritarianism. To explain these correlations, one may hypothesize that social revolution is


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ultimately driven by the power and direction of three basic actors: the state, dominant classes,

and subordinate classes. Countries with personalistic regimes are vulnerable to revolution

precisely because that kind of regime is associated with a weak state, the actor most committed

to preventing a revolution. Furthermore, personalistic regimes may disillusion dominant classes

and lead them to defend the existing order with only marginal force or perhaps even move in the

direction of promoting social revolution. By contrast, countries with industrialized economies or

military regimes are nearly invulnerable to revolution because of the overwhelming mass of the

state in comparison to subordinate classes who might lead revolutionary movements. Divisions

among elites are correlated with social revolutions precisely because these divisions diminish the

mass and force with which dominant classes might move to prevent revolution; and fiscal crises

have the obvious effect of weakening the mass of the state, thereby also encouraging revolution.

In short, power theory can be used to uncover the general theoretical principles that guide

empirical explanation in this field.

Power theory could make contributions to other literatures as well. For example, it might

help put Marxist analysis and feminist sociology on a stronger theoretical foundation. Although

Marxist scholars use both functionalist and rational choice mechanisms in their work (e.g.,

Cohen 1982; Elster 1982; Roemer 1982), they often do so reluctantly because these mechanisms

do not seem to fully capture the explanatory thrust of much of Marxs original writings. By

turning to power theory, analysts could revive the Marxian concern with actor capability and

force as the ultimate cause of societal development, an orientation that seems to provide a much

better fit with important parts of Marxs original program than either functionalism or rational

choice theory. Likewise, feminist work in sociology routinely conceptualizes social outcomes as

the product of aggregate group actors defined significantly by their relative power. If feminist


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scholars explicitly draw on power theory, they might better connect their research to the social

science concern with assessing empirically testable hypotheses, and thereby more actively

influence the mainstream of sociology.

Literatures that seek to identify the sources of actors capabilities also lend themselves to

empirical work using power theory. For example, scholars working in the area of resource

mobilization theory have long hypothesized about the effects of organizational, coercive, and

other resources on the ability of social movements to move with force and mass toward certain

outcomes (see McAdam, McCarthy, and Zald 1996 for a review). These ideas have been

usefully supplemented by scholars who emphasize the role of non-material resources such as

cognitive orientations and cultural framings in the generation of social movement power. The

effort of scholars in the field of network analysis to uncover the relational sources of behavior is

also consistent with power theory (see Emirbayer and Goodwin 1994 for a review). In this case,

the power of an actor is conceived not as a categorical attribute, but rather as a product of the

actors social ties and position within a social network. These and other literatures can help

scholars develop better empirical measures for studying the force and mass of real actors ranging

from countries to movements to individuals.

Other extensions of power theory entail new modeling strategies. In some cases, for

example, it may be useful to model actors in a two-dimensional space that contains both anxand

yaxis. With such a model, the outcomes of interest would be specified along two separate

dimensions, and actors would move according to the settings on a compass (e.g., 45 degrees west

of north). For example, scholars who work on the politics of social provision are often interested

in explaining both the publicfundingof welfare and the public deliveryof welfare (e.g., Huber

and Stephens 2000). Yet, actors may not move in a consistent direction and with the same level


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of power toward each of these outcomes. As a result, to model the mechanism generating

welfare outcomes, one must sketch a diagram with two axes and trace how the vectors for all

actors radiate from the center of the space along both dimensions according to their relative

power and direction. The final outcome will correspond to the point in space representing the

linear combination of all vectors.19

It bears emphasis that scholars interested in using power theory in these or other ways can

learn from the experiences of functionalists and rational choice theorists. Most basically, they

must recognize that power theory does not contain any fully specified proposition, and it need

not be held up as a theory of everything (Wallerstein 2001). At the core of power theory is a

simple causal mechanism, one that asserts that the power and direction of actors ultimately

generate social outcomes. This causal mechanism is empirically empty and, by itself, embodies

no testable propositions. As a result, power theory is useful only insofar as it can be combined

with bridging assumptions to help inform empirical research. Rather than asserting the universal

application of power theory, therefore, analysts would be better off trying to identify a specific

class of social outcomes appropriate for the theory.

CONCLUSION

Major social theorists argue that the study of causal mechanisms can substantially improve

empirical work. Unfortunately, scholars have not clearly defined the concept of causal

mechanism in way that unambiguously differentiates the study of such mechanisms from

standard correlational research. In this paper, I have argued that causal mechanisms are

unobserved entities or processes that act as ultimate causal forces in the social world. By

themselves, causal mechanisms are inherently underspecified; to be of use, they must be given


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empirical content though bridging assumptions. In conjunction with such bridging assumptions,

causal mechanisms can help scholars explain particular outcomes of interest, derive testable

hypotheses, and integrate existing correlational findings.

The mechanisms associated with three theoretical traditions were explored: functionalist

theory, rational choice theory, and power theory. Each of these mechanisms identifies a

particular entity and causal process that is understood to generate occurrences in the social

world. Functionalist theory sees underspecified social systems as the crucial entity of the social

world, and it holds that the needs of these systems ultimately produce major social outcomes. By

contrast, rational choice theory suggests that individuals are the essential entity of the social

world, and that the rational utilitarian behavior of these individuals is the social worlds ultimate

causal force. Finally, power theory argues that social actors are the most appropriate unit of

analysis, and that the power capabilities of these actors ultimately produce social outcomes.

I believe there is ample room for the use of multiple causal mechanisms in the social

sciences; one need not necessarily see functionalist theory, rational choice theory, and power

theory as direct competitors. Rather, each mechanism appears to be especially relevant for

certain substantive questions. With regard to functionalist theory, ideas about natural selection

are especially helpful in explaining social structures that are found widely across societies and

that have persisted over long periods of time. For example, natural selection at the level of social

systems may explain the nearly universal existence of nation-states with an economic division of

labor, government, private property, and organized religion. By contrast, rational choice theory

seems most appropriate for explaining outcomes in institutional settings where the rules of the

game are fixed and transparent. Within these settings such as legislatures, formal economic

markets, and bureaucracies one can more readily model the ways in which individuals make


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Table 1. Glossary of Definitions of Mechanism

I. Mechanism as a cause of an outcome

Boudon (1998, p. 172): A SM [social mechanism] is, in other words, the well-articulated set of causes responsible for a given

social phenomenon. With the exception of typical simple ones, SMs tend to be idiosyncratic and singular.

Cowen (1998, p. 125): I interpret social mechanisms (defined in greater detail below) as rational-choice accounts of how aspecified combination of preferences and constraints can give rise to more complex social outcomes.

Elster (1989, p. 3): nots and bolts, cogs and wheels that can be used to explain quite complex social phenomena.

Tilly (forthcoming): Mechanisms are events that alter relations among some specified set of elements.

II. Mechanism as an intervening process, event, or variable

Bennett and George (1997, p. 1): the processes and intervening variables through which causal or explanatory variables producecausal effects.

Goldthorpe (2000, p. 149): some process existing in time and space, even if not perhaps directly observable, that actually

generates the causal effect ofXon Yand, in so doing, produces the statistical relationship that is empirically in evidence.

Hedstrm and Swedberg (1998, p. 11): Mechanism-based explanations usually invoke some form of causal agent that isassumed to have generated the relationship between the entities observed.

Hedstrm and Swedberg (1998, p. 13): Mechanisms . . . are analytical constructs that provide hypothetical links betweenobservable events.

Keat and Urry (1982, p. 30): causal explanations require the discovery both of regular relations between phenomena, and ofsome kind of mechanism that links them. . . . In describing these mechanisms and structures we will often, in effect, becharacterizing the nature, essense, or inner constitution of various types of entity.

King, Keohane, and Verba (1994, p. 85): Some scholars argue the central idea of causality is that of a set of causal

mechanisms posited to exist between cause and effect. This view makes intuitive sense: any coherent account of causality needsto specify how the effects are exerted.

Kiser and Hechter (1991, p. 5): A complete explanation also must specify a mechanism that describes the process by which onevariable influences the other, in other words, how it is thatXproduces Y.

Koslowski (1996, p. 6): A causal mechanism is the process by which a cause brings about an effect. A mechanism is a theoryor an explanation, and what it explains is how one event causes another.

Little (1991, p. 15): A causal mechanism, then, is a series of events governed by lawlike regularities that lead from theexplanans to the explanandum.

Mahoney (2000, p. 531): Causal mechanisms are the intervening processes through which one variable exerts a causal effect on

another variable.

Somers (1998, p. 726, citing Coleman [1986, p. 1328]): meaningful connection between events as the basic tool of description

and analysis.

Srensen (1998, p. 240): My definition of mechanism is simple: It is an account of how change in some variable is broughtabout a conceptualization of what goes into a process.


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III. Mechanism as an underspecified causal process

Elster (1998, 45, his emphasis): Roughly speaking, mechanisms arefrequently occurring and easily recognizable causal

patterns that are triggered under generally unknown conditions or with indeterminate consequences.

Gambetta (1998, p. 102): I take mechanisms to be hypothetical causal models that make sense of individualbehavior. Theyhave the form, Given certain conditionsK, an agent will doxbecause ofMwith probabilityp. Mrefers either to forms of

reasoning governing decision making (of which rational choice models are a subset) or to subintentional processes that affectaction both directly (as impulsiveness) or by shaping preferences or beliefs.

Schelling (1998, pp. 32-33): I propose . . . that a social mechanism is a plausible hypothesis, or set of plausible hypotheses, that

could be the explanation of some social phenomenon, the explanation being in terms of interactions between individuals andother individuals, or between individuals and some social aggregate.

Stinchcombe (1998, p. 267): I have defined mechanisms before as bits of sometimes true theory or model that represent a

causal process, that have some actual or possible empirical support separate from the larger theory in which it is a mechanism,and that generate increased precision, power, or elegance in the large-scale theories.

Rueschemeyer (2001, p. 31): Incomplete theoretical propositions . . . a causal hypotheses, but one whose conditions are

insufficiently specified.

IV. Mechanism as an unobserved entity that necessarily generates outcome

Bhaskar (1979, p. 15): the construction of an explanation for . . . some identified phenomenon will invol

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