erica

An Interdisciplinary Analysis of the Causes

of Economic Growth

In: Case Studies in Interdisciplinary Research

By: Rick Szostak

Pub. Date: 2013

Access Date: January 31, 2021

Publishing Company: SAGE Publications, Inc.

City: Thousand Oaks

Print ISBN: 9781412982481

Online ISBN: 9781483349541

DOI: https://dx.doi.org/10.4135/9781483349541

Print pages: 159-190

© 2012 SAGE Publications, Inc. All Rights Reserved.

This PDF has been generated from SAGE Research Methods. Please note that the pagination of the

online version will vary from the pagination of the print book.

An Interdisciplinary Analysis of the Causes of Economic Growth

RickSzostak

Introduction

With billions of people still living in poverty in the world, there is perhaps no more important question in human

science than what are the causes of economic growth. Moreover, it is a very complex question, for economic

growth is influenced by interactions among a host of economic, political, social, cultural, and geographical

phenomena. This chapter discusses how a process for interdisciplinary research can usefully be applied to

the study of economic growth. It is thus simultaneously an exploration of how to do interdisciplinary social

science and how to develop a more comprehensive understanding of economic growth. It shows how a

variety of distinct research programs across all social science disciplines can be integrated to enhance our

understanding of the causes of economic growth.

This chapter is organized according to the steps in the interdisciplinary process.1 Although the chapter

will review each of the various steps ideally involved in interdisciplinary analysis, the presentation focuses

primarily on how “common ground” can be achieved among disciplinary insights that conflict, notably with

respect to the role of government, the role of international trade relationships, and the process by which

economic institutions are and should be developed. Lessons are drawn for each step regarding both our

understanding of economic growth and how best to perform interdisciplinary research.

AUTHOR'S NOTE: My chapter summarizes and comments upon my book The Causes of Economic Growth:

Interdisciplinary Perspectives (Szostak, Rick, 2009, Berlin: Springer).

The question addressed in this chapter is both very complicated and very broad. Both characteristics increase

the difficulty of performing any of the steps as completely as one might like (though perhaps especially

the literature survey). The question is complicated because a variety of phenomena combine to influence

economic growth. By asking the question at the general level rather than with respect to a particular time and

place, we broaden the scope of inquiry significantly: We could forget about questions of basic property rights

if we focused on only the rich world, for example. That is, the breadth of the question forces us to engage

its full complexity. We thus need a broader literature survey, and we need to identify, evaluate, and integrate

across a much broader set of disciplinary insights than would normally be the case. One possible exception

may be the first step: It may be just as easy to frame a broad question as a narrow one. Indeed, it may be

easier, for the narrow question requires detailed definition of the boundaries of the question.

Identify an Interdisciplinary Research Question

We have selected our question: What are the causes of economic growth? It is then necessary to ask whether

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this question is suitably interdisciplinary in nature. Can economic growth be understood by relying only on

the insights of one discipline? Or does our understanding increase markedly if insights from many disciplines

are integrated? Economic growth must, in the first instance, involve an increase in the resources devoted

to production—broadly, labor, capital, and natural resources (including land itself)—and/or the productivity

with which these resources are combined to produce output. These four variables—labor, capital, natural

resources, and productivity—are commonly termed the proximate causes of growth, and economists (and

economic historians, who are treated as a separate discipline here) dominate the study of the question

of which of these is most important in driving particular episodes of economic growth. As we shall see,

economists are far from achieving consensus on this basic question. Moreover, economists have long

appreciated that this question then invites a more complicated set of questions, such as the following:

• Why is labor more skilled in some countries than in others?

• Why is there more (saving and) investment in some countries than in others?

• Why do some countries use resources more productively than do others?

• Why is productivity higher in some countries than in others?

These questions tend naturally to invite interdisciplinary speculation. How does a society's culture or social

structure or politics influence its educational attainment, work effort, saving rate, or environmental policy? The

study of these deeper causal influences is pursued across the human sciences (see below).

Special mention should be made here of institutions and technology. The formal rules of a society—its

legal system, economic regulations, firm structure, and so on—have a profound influence on its economic

performance, and yet such institutions arguably (see below) emerge from a historical process involving

political, social, and cultural influences. Likewise, technological innovation is an important source of (at least

modern) economic growth, and again, it seems likely that the rate of innovation in a society may well be

influenced by a host of non-economic factors.

Even the statistical analyses of economists point toward interdisciplinary analysis. Political, institutional, and

social variables are often found to be important in cross-country analyses of postwar growth experience

(Snowdon, 2002, pp. 97–99)—and this despite the twin facts that such variables are often hard to measure

and likely exert their effects over a very long time. Surveying this evidence and the widely divergent growth

experiences of postwar economies more generally, Snowdon concludes:

To understand why some countries have performed so much better than others with respect to

growth it is therefore necessary to go beyond the proximate causes of growth and delve into the

wider fundamental determinants. This implies that we cannot hope to find the magic bullet by

economic analysis alone. (p. 100)

The observation that it is crucial to look beyond proximate causes provides an important insight regarding

interdisciplinary research more generally. Economists have, until recently, been able to view the causes of

economic growth as a strictly disciplinary question by looking only at the interaction of a handful of economic

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variables. Interdisciplinarians need to be sensitive to the precise wording of their focus question (and be

prepared to revise it as they perform later steps), in order to ensure that relevant disciplines are not arbitrarily

excluded from examination. Even though we are striving to explain movements in an economic variable, our

question becomes interdisciplinary once we embrace a wide range of potential causes.

Identifying Relevant Phenomena, Theories, Methods, and Disciplines

The next step or steps must involve the gathering of relevant disciplinary insights. How does the researcher

know where to look? There are two complementary strategies identified in Repko (2008). One is to reflect

on the character of different disciplines and identify those that are likely to have something to say about the

issue of concern. The second—and the one pursued in this chapter—is to ask what phenomena, theories, or

methods are implicated, and then ask which disciplines study each phenomenon identified and/or apply each

theory or method. This approach reduces the risk of favoring the larger and most familiar disciplines (Szostak,

2002). Szostak (2004) develops exhaustive classifications of phenomena, theory types, and methods to

facilitate the latter approach: In the absence of these, it is all too easy to assume that the subset of relevant

theory, method, or phenomena pursued by disciplines is somehow appropriate. Repko (2008) identifies the

defining elements of disciplines in terms of these and other classifications. One challenge the interdisciplinary

researcher will face is that library catalogues are organized by disciplines, and different terminology is used

in different disciplines to refer to the same phenomenon, theory, or method (Szostak [2007, 2008] addresses

how a classification suited to interdisciplinarity might be developed).

In the case of growth, it is embarrassingly easy to identify phenomena that, at least potentially, influence

growth but lie outside the (at least until recently) narrow gaze of economists. These include cultural attitudes,

political institutions, geographic constraints, and ethnic tensions, among others. It is thus straightforward to

implicate (parts of) all social science disciplines as well as the humanities in this study.

As for types of theories, economists rely almost exclusively on methodological individualism: Only individuals

are causal agents. Yet, surely the alternatives of relationship or group agency2 matter for at least some of the

proximate causes of growth. Historians have long since abandoned the idea of the heroic innovator working

in isolation in favor of an appreciation of the networks in which innovators operate; the same logic applies

to entrepreneurship and trade more generally, and surely institutional change cannot be fully appreciated

without recourse to relationships and groups.

Economists also stress rational decision making. Yet, the history of institutional change suggests that

rationality likely plays some role in institutional design—agents consciously design institutional

improvements—but also that tradition does—agents are cautious in moving away from existing institutions.

Historians of both science and technology have long appreciated that a mix of reason and intuition is

involved: The investigator consciously gathers relevant information, but insight comes subconsciously as

novel connections are drawn. Moreover, in a world awash in information, who can doubt that economic

agents often follow decision-making rules (“Buy when the market is rising”) rather than attempt rational

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calculations? Perhaps most important, both investment and innovation decisions must be made under

uncertainty—people simply cannot know the likelihood of particular outcomes—and people cannot fully rely

on rational decision making when faced with uncertainty (as economic theory admits), but necessarily follow

hunches or decision rules or mimic others. Various sorts of nonrational decision making have long been

studied outside economics, especially in sociology. Psychologists have long argued for different types of

decision making; brain imaging shows that different parts of the brain are activated at different times and

make decisions in different ways (Cohen, 2005).

Economists model growth solely in terms of steady-state (constant) growth rates. Yet, growth occurred in the

West much more rapidly in the 19th century than ever before and more rapidly in the first postwar decades

than before or since. In both the 19th and 20th centuries, one can discern multiple periods of a decade

or so in length in which growth was relatively slow (by the standards of those centuries) or negative. The

growth experience of other regions of the world is even more diverse. These diverse experiences suggest

that theories with either cyclical or stochastic elements—allowing growth rates to both rise and fall—should be

important. The fact that growth rates are, at least potentially, much higher than a mere two centuries ago (or

alternatively, common prognostications that growth will soon decline) suggests that theories positing dynamic

change in one direction may also have a role to play.

Various theories more commonly employed outside rather than within economics are thus important in

understanding economic growth (though each of these has its own limitations). Alternative theories worth

exploring include the following:

• Evolutionary theory can potentially embrace all types of agency, decision making, and time path.

• Systems theories (or structuralism/functionalism more generally) can also reflect a variety of types

of agency and decision making. In practice, systems theorists have tended to emphasize system

stability (equilibrium) and thus have had less to say than they might about dynamic processes of

change.

• Social constructionist theories stress the importance of attitudes and beliefs. This provides a useful

complement to the focus of most social science theory on actions, though social constructivists often

see their theories as substitute rather than complement.

• Modernization theories in the early postwar period posited that all countries would move toward

“Western” economic, political, and cultural realizations. These gave way to more pessimistic

dependency and world systems theories (inspired in part by Marxian analysis) that suggested that

poor countries would remain poor. These theories can each be valued for detailing possible links

among economic, political, cultural, and other phenomena without adopting their assumptions about

the inevitable outcome of these systems of interaction.

• Complexity theories can generate all types of time path. They are perhaps particularly valuable

in stressing stochastic outcomes. Complexity theories often emphasize system-level emergent

properties at the expense of careful specification of individual causal links.

• Various theories stress how culture shapes behavior. These theories are often characterized by

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vague terminology and fail to appreciate how cultures evolve.

• Psychological theories support the insight that decision making is not always rational, but these

theories have not often been carefully applied to economic decisions. (The emerging field of

behavioral economics aspires to change this.)

• Literary theory may also be useful. In studying technological diffusion, scholars have long

appreciated that people, not just blueprints, generally have to move from one locale to another.

There is a range of tacit knowledge that is imperfectly captured by the most careful instructions.

This observation is the same as that long made by theories of texts in general: There is always a

divergence between the textual signifier and that which it is presumed to signify.

A similar analysis could be performed with respect to research method. One of the key insights of Szostak

(2004) was that each method is better at investigating some theory types than others, and disciplines

thus choose mutually compatible sets of theory and method (and phenomena). Various case study

methods—observation, textual analysis, interviews—are better suited to the investigation of many of the

theories listed above than are the statistical analyses favored by economists. Moreover, different methods

shed different light on different theories: We should have the greatest confidence in a theory that is supported

by different methods. When examining a complex historical process such as economic growth, which involves

many causal interactions, recourse to multiple methods is particularly important.

There are four criteria for identifying a causal relationship: establishing correlation, establishing temporality

(the cause should generally appear before the effect), ruling out alternative explanations of the result, and

showing how the causal relationship unfolds in practice (including identifying intermediate variables; Singleton

& Strait, 1999). The methods of economists excel with respect to the first two. Their lack of attention to

alternative theories limits the third. With respect to the fourth, economists are often attracted to mathematical

models even when the evidence for particular causal relationships is quite limited. The latest type of economic

growth models—called unified growth models—attempts to model the course of economic performance over

the last millennium. These models posit that small changes (in the first models, changes in population density,

but later models treat other variables) eventually surpass some threshold where they begin to have dynamic

effects on growth. But of course, one can develop mathematical models such that any cause can have any

effect, if one assumes that small changes achieve big results. The creation of these models should not

enhance one's confidence in such a relationship in the absence of careful case study evidence.

Disciplinary perspectives will be treated briefly here. As both Szostak (2003) and Repko (2008) have

stressed, preferences with respect to theory and method and phenomena are critical components of

disciplinary perspective. Ideological, ethical, and epistemological predispositions need also to be appreciated

in evaluating disciplinary insights. In these latter respects, the following can be noted:

• On average, economists believe in markets more than other social scientists do. Although economic

theory suggests a variety of market imperfections that may arise, the average economist may,

nevertheless, tend to downplay the role of governments in the process of economic growth. In turn,

other disciplines may underestimate the role of markets in fostering growth.

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• Economists are generally consequentialist in ethical orientation, while other disciplines place greater

emphasis on tradition, virtue, or intuition. Economists in particular tend to think that economic growth

is good, while scholars in other disciplines are more likely to critique at least some elements of

growth. Although questions regarding the desirability of growth can be distinguished from questions

about causes, thoughts about one naturally influence thoughts about the other.

• Economists tend to be realists and assume that scholars can obtain reasonably accurate

understandings of a fixed external reality. Other disciplines, especially in the humanities, cast

a useful, if often exaggerated, skepticism on the possibility of human understanding. They thus

encourage scholars to be more careful in both their theorizing and policy advice. In particular, these

other disciplines are suspicious of broad generalizations and encourage careful context-dependent

research.

The preceding analysis has illustrated the following points regarding these steps in interdisciplinary analysis:

• Interdisciplinarians should be careful of curtailing the scope of their research unwittingly by following

major currents in the existing literature. In the case of economic growth, which other disciplines have

rarely stressed as a topic, it is particularly likely that scholars may produce valuable insights that are

relatively unheralded within their own discipline.

• Nevertheless, interdisciplinarians can usefully focus on particular disciplinary subfields. The danger

of missing relevant literature will be reduced if they also reflect on what theories and methods might

be relevant to the issue at hand.

• Interdisciplinarians must evaluate disciplinary insights in the context of disciplinary perspective and

with attention to the (complementary) strengths and weaknesses of different theory types and

methods.

Evaluating Disciplinary Insights

The interdisciplinary researcher must next evaluate the disciplinary insights generated by the relevant

theories and methods. This is an exercise in critical thinking. Interdisciplinarians must know how to distinguish

argument from assertion and assumption from evidence. In addition to standard strategies for the critical

analysis of any text, interdisciplinary analysis suggests several important strategies for critique:

• In what ways might a particular disciplinary insight be shaped by the particular “perspective” of that

discipline?

• More concretely, how might the insight be altered if the researcher(s) had examined a wider set of

phenomena?

• Likewise, what are the strengths and weaknesses of the theories and methods used by the discipline,

and how might the insight in question be shaped by these?

• Do the insights of one discipline point to possible weaknesses in the insights of another? Insights

from outside the academy can also be quite useful here.

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The analysis of the causes of economic growth provides examples of each of these evaluative strategies. As

we have seen, economists are likely to stress the role of individuals and rationality and may thus overlook the

actions of groups and/or various types of nonrational behavior. Economists have focused on a narrow set of

economic variables while downplaying the importance of, for example, culture. Economists stress equilibrium

or steady-state outcomes in their modeling exercises (in large part because this makes the math more

tractable, but also because of the emphasis of economists' general equilibrium theory on system stability);

in the real world, of course, growth has never been steady. Economic historians, sociologists, and political

scientists often stress that different countries have differing experiences of economic growth; this provides a

useful counterpoint to the economist tendency to identify central tendencies.

The interdisciplinarian can be heartened by the observation that other disciplines avoid at least some of these

biases in economic analysis. Yet, the interdisciplinarian should never forget that all disciplines have limiting

perspectives:

• The other social sciences have long stressed group or relationship agency (or viewed individuals as

constrained to act in a certain way by culture or institutions), without detailing how these constraints

emerge. In sociology, this approach has, in recent decades, been supplemented by individual-level

analysis, but syntheses of these approaches are rare. The interdisciplinarian must be prepared to

integrate insights that have rarely been juxtaposed in the past.

• Various sorts of nonrational behavior have been investigated in sociology and other social sciences.

As in economics, particular types of decision making are often assumed rather than established

empirically. The interdisciplinarian must thus be prepared to reflect on what sorts of decision making

motivate people in particular situations. This task is made more difficult by the fact that these are

often mixed in practice: An investor may only act if gut instinct, rational calculation, and the actions

of others all point in the same direction. The analyses of many social scientists present a further

challenge: They celebrate the “irrationality” of certain behaviors without carefully identifying what sort

of nonrational decision making is at work.

• The social sciences (including psychology) collectively employ each of the dozen methods utilized

in the scholarly enterprise. Yet, each of these is applied to only a subset of appropriate questions.

The interdisciplinarian, aware of the biases of one method, may look in vain for the application

of alternative methods to particular questions. For the present inquiry, this problem—of difficulty in

identifying work that applies different methods to the same question—is exacerbated by the fact that

scholars in other disciplines have rarely addressed economic growth itself.

• Other social sciences often assume processes involving sustained change in particular directions,

just as economists assume equilibrium outcomes. The important possibility of stochastic

(unpredictable) outcomes is much less commonly explored. The growth process likely entails

elements of each. Investment decisions at times seem to follow herd behavior and at times seem

inexplicable, and yet there is an amazing stability (lack of volatility) in average rates of return over

time. As with types of decision making, the interdisciplinarian generally has recourse to few previous

attempts to integrate insights reflecting these different perspectives.

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• Political scientists and sociologists often assume the superiority of governments over markets. Only

a minority of scholars in any discipline carefully compares the advantages of one or the other

for particular types of decisions (e.g., identifying the reasons why science is publicly funded but

technology is largely left to markets and the potential difficulties with each approach), and only a

minority appreciates that the ideal balance likely varies across time and place.

The lesson for interdisciplinary practice here is that the evaluative step should not be conflated with the next

step of finding common ground (even if, in presentational terms, it proves useful in this chapter to discuss

evaluation while outlining common ground). That is, disciplinary insights should not just be critiqued when

they conflict. The mere fact that different disciplines have asked different questions, and thus often not directly

disagreed with each other, should not render the interdisciplinarian sanguine about these disciplinary insights.

Interdisciplinarians can thus suggest useful clarifications or extensions to theories even in the absence of a

direct contrast among disciplinary insights.

Creating Common Ground

The almost-final step(s) involve finding some “common ground” that integrates (elements of) various

disciplinary insights. The role of creativity, intuition, and inspiration may loom large here. However, certain

straightforward techniques can be applied to find common ground:

• One can first ask to what extent seeming differences in disciplinary perspective are apparent rather

than real: Differences in terminology may mean that different disciplines are not actually talking about

the same causal process even when they appear to be. The interdisciplinarian can often redefine

concepts or extend a concept from one discipline to the subject matter of another.

• When concepts conflict, they can often be placed on a continuum: The tendency of economists to

stress rationality and of sociologists to stress irrationality can be handled by evaluating the degree of

rationality that individuals may display in a certain situation (Newell, 2007).

• One can then ask whether remaining differences can be overcome by small alterations in disciplinary

assumptions.

• The easiest path to creating common ground involves relaxing the assumption made by each

discipline that only its phenomena matter: Economists try to explain growth by focusing on a handful

of economic variables, while sociologists stress matters of culture and social structure (happily,

these attitudes are starting to change). In this simplest situation, the insights of different disciplines

can often be added together: Innovators may respond both to the economic incentives stressed by

economists and the cultural values emphasized by sociologists. Different disciplines are highlighting

different aspects of the question under investigation, but they are assuming other aspects do not

exist. Of course, integration is still required in order to identify how these different causes interact in

different circumstances.

• When different disciplines reach different conclusions regarding the same phenomena, the problem

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is often one of excessive generalization, and it can thus be solved by more carefully expressing the

range of applicability of the theories involved.

Examples of each technique can be readily provided. Economists use the word “investment” quite differently

from business scholars, for example: To economists, it means the actual production of buildings or equipment

that are then used in the production of other goods or services. Business scholars are likely to include any

effort to make a profit, such as speculation in various markets, in the term investment. Economists have

ignored certain things such as the influence of culture, the importance of networks, and the uneven path of

technological innovation. At times, the insights of other disciplines in these areas can simply be added to

the insights of economists—after each has been carefully evaluated in turn. Economists often assume that

a particular institution is optimal, while sociologists and political scientists show that institutions operate in a

complex web of social interactions: The different traditions can together strive to identify how well different

institutions serve economic growth in different contexts.

Rather than organizing this section in terms of the type of integration pursued, it will be more useful to

organize it by topic. Common ground will be found along a variety of different causal links generating

economic growth. Within each topic, though, care should and will be taken to identify the integrative strategies

utilized.

This approach—examining different causal links in turn (but not losing sight of emergent properties of

systems)—is different enough from the standard practice in economics of formally modeling several links

at a time that it deserves some comment. There are difficulties in applying the methodology of models to

open systems—ones which are clearly linked causally (as the economy surely is) to phenomena outside the

model. Although these problems do not destroy the model-building exercise, it is nevertheless true that such

models rely on an unrealistic assumption that the relationships observed in a system can remain fixed through

time. Notably, complexity theory takes a different approach, allowing different causal forces to operate along

different links, and does not assume any particular organizing principle (such as equilibrium) at the outset.

It should also be noted that this emphasis on causal links is not common to all efforts to identify best-practice

processes of interdisciplinary analysis. Interdisciplinarians often stress the importance of emergent properties

(e.g., Bammer, 2005). It is argued here that the two approaches are complementary and thus best pursued in

tandem. This strategy accords well with the general inclination toward integration by interdisciplinarians. As

well, interdisciplinarians have often said vaguely that there are different “facets” to complex problems. The

emphasis here on causal links provides a means of clarifying what might be meant by the vague term “facet.”

Since different causal links tend often to be the focus of different disciplines, the strategy of placing diverse

causal links within an overarching structure can be a powerful technique for interdisciplinary analysis. The

causal link approach helps us identify when different disciplines are, in fact, speaking about the same thing,

and thus it sets the stage for integrating disciplinary insights link by link.3

The Proximate Causes Themselves

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Economists have devoted an enormous amount of effort to “growth accounting” over the past decades. These

empirical exercises attempt to identify the relative importance of the proximate causes of growth: How much is

due to investment as opposed to innovation, for example? These exercises have been valuable: In particular,

economists in the 1960s were shocked by the fact that investment in physical capital accounted for only

about a third of economic growth over the previous century—and they were guided to pay more attention to

education and innovation as a result. Yet, these exercises rely on a rarely voiced assumption: The effect on

growth found for each proximate cause in one study should at least be a central tendency for all economies

at all times. This assumption is dangerous: Easterly (2002), an applied economist, describes how the World

Bank was led to a number of naïve policies over the years as a result, such as calculating the “required

investment” needed for certain target rates of growth and channeling those sums into countries ill-prepared

to utilize them productively.

Historians and economic historians have stressed the particularities of different cases. Economic historians

have long hypothesized that different generations of industrializers faced different challenges, and thus that

they necessarily developed in different ways (The classic argument was that of Gerschenkron [1962]; see

Sylla & Toniolo [1991] for an update). The same concern has been voiced, albeit using quite different theories,

by dependency and world systems theorists in sociology and other social sciences. But these scholars did

not (to my knowledge) directly address the assumptions of growth accounting, and thus it remains for the

interdisciplinarian to make the connection.

The assumption has a corollary: The proximate causes act independently. The main reason that the World

Bank strategy failed was that the return on investment in a country depends on many things: levels

of education, infrastructure, technology, and so on. Complicated relationships among such supposedly

independent variables are hard to capture within standard statistical procedures. That is, it is easy to estimate

how investment affects growth and education affects growth, but it is harder to establish how they combine

to do so. And thus a more nuanced understanding of how proximate causes interact will depend on detailed

case studies.

This adjustment in our understanding of the relationship between investment and growth is entirely in accord

with the strategies for interdisciplinary analysis outlined above: Growth accounting regressions naturally

omit many variables that condition this relationship; the structure of those regressions does not allow for

independent variables to act in concert; the approach reflects a disciplinary tendency to identify supposedly

enduring causal relationships without careful concern for the set of conditions in which these might hold;

and the growth accounting analyses thus represent a widespread tendency in scholarship to assume greater

generality for one's results than they deserve.

Trade and Growth

Solow (2005, p. 4) argues that there has been less modeling of growth in open economies than he would

have expected 50 years ago. Growth models tend to focus entirely on the internal dynamics of the growth

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process. This is unfortunate. Static economic analysis suggests that free trade is good because by pursuing

their comparative advantage, countries will be able to increase the value of their total output. They will export

goods that they are relatively good at producing and import goods that they are relatively poor at producing.

This is one of the most powerful insights in all of economics. But what if productivity (output per labor and

capital input) is advancing fastest in the goods a country imports? It will still benefit by being able to import

more as the international price of these goods falls, but it will not benefit as much as it would if its own

economy was experiencing rapid productivity growth. And thus there is a theoretical possibility that a country

may benefit in the long run by sacrificing some of the short-term gains from importing and exporting in order

to encourage production of goods where productivity advance is more likely.

Empirical research by economists has often suggested that the benefits from openness to trade are much

greater than static theory can explain: They thus point to dynamic benefits such as increased technology

transmission or greater competitive pressures on local firms. Yet, these empirical results have been

questioned even by economists, who note that they are not always obtained when different definitions of

“openness” are used or different time periods investigated. Economic historians, sociologists, and political

scientists have stressed that all successful developed economies have been protectionist early in their

development process. They have theorized that protectionism encouraged growth (see Miller, 2008).

Most economists will not readily accept a suggestion that openness to trade is not necessarily a good

thing (even this author finds it difficult). Yet, we have just seen that economists have proven willing to

accept arguments that the dynamic gains swamp the static comparative advantage gains. If arguments

for huge dynamic advantages are allowed, then arguments for dynamic disadvantages cannot simply be

ignored. Given the limited degree of theorization of dynamic effects, heavy weight must be placed on

the empirical evidence. The statistical analysis is not conclusive. The historical evidence points toward a

role for protectionism. But the historical record is also full of failed efforts to protect, of governments that

allowed protected industries to focus their energies on maintaining government support rather than becoming

internationally competitive. Case study analysis is tricky here: It is much easier to identify the many failed

infant industries—those that received protection from government but failed to grow up—than to observe

industries that developed behind tariff protection and establish that they could only have done so behind such

protection. In the second case, a counterfactual—what would have happened without protection—needs to

be carefully tested.

Although both theoretical and empirical analyses are thus more muddled than we might like on the grand

question of openness, some answers to narrower questions seem clearer:

• If a country protects, it must ensure that firms face clear incentives to enhance productivity.

• Countries with incompetent and/or corrupt bureaucracies should be particularly wary of managed

trade.4

• The inflow of information about technology and institutions should be encouraged, and trade in goods

is one way of doing so.

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• Tariffs are not the only, and perhaps not the best, strategy for supporting industrial development.

These important insights (which emerge from integrating insights from different disciplines) can be thought of

as “common ground” in the debate between advocates of openness and advocates of trade barriers. That is,

although it is difficult at present to sketch a common ground on the larger question of whether free trade is

good for growth, it is at least possible to identify certain circumstances under which this result is more or less

likely. These results, although limited, nevertheless carry important policy implications.

Technology and Growth

As noted above, economists were surprised when early efforts at growth accounting showed that investment

in physical capital could account for at most a third of the growth observed in developed countries.

Economists were guided to look elsewhere and came to stress the importance of education (i.e., investment

in human capital), technology, trade, and institutional changes that might enhance economic efficiency. The

earliest growth models treated technological innovation as exogenous: something that happened outside the

models. The latest generation of endogenous growth models try to bring technological innovation inside the

models; they argue that technological innovation results from another sort of investment—in research and

development—and thus can be explained in terms of economic variables. These models have been valuable

in encouraging economists to devote greater attention to the causes and effects of technological innovation.

Yet, the tendency to assume that the rate (and direction) of innovation can be understood entirely as a

function of economic variables carries the obvious danger that other influences on innovative activity will be

ignored.

Scholars of technology, whether historians of technology or scholars of science and technology studies (STS),

have certainly stressed a wide range of noneconomic influences on both the rate and direction of innovation.

Most obviously, historians of technology have traced how each innovation builds on preceding innovations.

Opportunities for innovation at any point in time (and space) are thus conditioned by the existing body of

knowledge. This insight, long appreciated in economic history, has begun to be voiced in economics itself.

The literature on General Purpose Technologies stresses that occasional “big” innovations set the stage for a

series of minor but cumulatively important follow-up innovations. There was thus more innovative potential in

the decades after the development of internal combustion (or steam) engines than in the decades before. Not

only was there scope for many small improvements to these engines, but they were applied to an increasing

range of uses, from factories to cars and planes.

Economists have shown less interest so far in the variety of other causal links identified by scholars of

technology.5 It is often thought—and not without cause—that STS scholars often assume, rather than identify,

cultural influences. When STS scholars claim that innovation is entirely a cultural product, unconstrained

by whether it reflects how the world works (an argument more often made with respect to science than

technology), skepticism is invited. This should hardly restrain other scholars from respecting more nuanced

arguments, and the evidence for them, that culture interacts with technological potential—and a host of

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other psychological, political, and social influences—to determine especially the direction but also the rate of

innovation.

In the case of technology, different disciplinary insights can largely be integrated by dropping the “only these

things matter” assumption. Different scholars have examined different causal influences on innovation, all

of which likely have a role to play. The popularity of extreme assumptions—that only economic, or cultural,

or technological influences matter—serves as a warning that scholars may assume rather than establish

the importance of different causal links. And thus, the interdisciplinarian attempting to evaluate the relative

importance of different links will need to carefully evaluate the arguments of all scholars. And he or she will

also need to be sensitive to the fact that—as with growth above—the relative importance of these links likely

changes across time and space.

Meeus and Hage (2006) edit an interdisciplinary handbook on innovation precisely because they believe that

management specialists, economists, sociologists, historians, political scientists, and others need to integrate

their efforts. They note that political scientists tend to stress government policies, management scholars look

at firm behavior, economists and sociologists emphasize industry-level analysis, and STS scholars stress

scientific innovation (but that until recently, very few scholars in any discipline have looked at behavior in

research laboratories); they urge a co-evolutionary perspective in which the interactions among different

levels of analysis are appreciated (p. 4).

Institutions

Do the right sort of institutions encourage growth (and if so how), and which sorts of functions are most

necessary for institutions to perform? Because there is abundant evidence that institutions are indeed

important, the question then arises how beneficial types of institutional change can be encouraged.

How important are institutions? Although economists have, in recent years, included many institutional

measures in growth regressions, it has proven difficult to establish any relationship empirically. This outcome

reflects, in part, the facts that different economists emphasize different theoretical arguments and that they

disagree over the precise definition of institution and which particular institutions might be most important.

Measures of social capital and social structure (see below) are often deemed “institutional.” These analyses

emphasize political over economic institutions, yet the former, for the most part, influence economic growth

only indirectly through the latter. Although there may be direct effects of political stability (e.g., on investor

confidence), the primary effect of stability and especially more mundane questions of electoral practices

or executive powers will be to shape the nature of economic institutions. Why not, then, stress economic

institutions in growth equations (with perhaps other equations linking economic and political institutions)?

A further problem is relevant here: It is all too easy for incompetent or corrupt governments to create the

appearance, but not the essence, of good institutions. Just as autocrats often glory in meaningless elections,

so in the economic sphere they can proclaim the protection of property while actively interfering with property

rights. They might expropriate property from citizens through fake legal proceedings or simply fail to enforce

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private contracts. Economists thus stress the quality of institutions (but a very limited set of them). But, of

course, quality is always hard to measure. Finally, the various institutional variables may interact with each

other and reflect feedback effects from growth itself, but simple regressions ignore these possible effects

(see Aron, 2000). Because different variables are strongly correlated, different economists identify different

institutional variables as most important (Lal, 1988).

Moreover, Aron (2000, pp. 100–101) worries that statistical analyses of growth in general face serious

problems of data, methodology, and identification. These are generally more severe than statistical analysis

elsewhere in economics. Although some of the problems identified by Aron can be alleviated by more

careful definitions and theoretical specifications, others cannot. Institutions are not particularly well-suited to

statistical analysis. Institutions are inherently unique. Countries may differ along various dimensions: The

courts may be fair but the police incompetent in one country, while rights may be enforced without much

recourse to courts in another. Such complexities call for comparative case studies and suggest that efforts to

describe a type of institution in terms of one numerical indicator are likely to be fraught with difficulty.

Economic historians have, indeed, investigated the relationship between institutions and growth using

comparative case studies. They have identified certain key institutional functions: protect property, enforce

contracts, facilitate financial intermediation, and so on. Even economic historians are sometimes less careful

than they could be: The right to property is actually a complex of rights (to earn income from, to use, to

change, to sell, to rent, etc.), and financial institutions serve several important functions. The connection

between institutions and growth has been little studied in other disciplines, with the important exception of

the literature on the role of government in economic development. One area in which much more research

is needed across disciplines is identifying the types of institution that serve various institutional functions.

Institutions differ a great deal across successful developed countries, and China has experienced rapid

growth against a backdrop of very peculiar institutions. Yet, it is clear that there are limits to the range of

supportive institutions.

The major insight of the literature on “the developmental state” (primarily in political science, but also

sociology) can be briefly summarized: “Sterile debates about ‘how much’ states intervene have to be replaced

with arguments about different kinds of involvement and their effects” (Evans, 1995, p. 10). That is, we

must move past attempts to identify best institutions to look at how well different institutions are enforced.

Scholars of the developmental state (such as the political scientist Kohli, 2004) tend to argue that effective

governments are necessary for any growth strategy to be effective. This insight has not been ignored

in economics, but in practice it is often neglected: It is generally much easier to identify whether a rule

exists than whether it matters. As noted above, the literature on trade and growth in economics has largely

eschewed the question of whether countries have the capability to manage whatever trade regime they

pursue. And thus, the literature on the developmental state can serve as an important reminder of the

importance of institutional quality.

Although the connection between institutions and growth has received limited attention in most disciplines,

the course of institutional change itself has been investigated across a range of disciplines (interest in

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institutions was reawakened in many disciplines in the 1980s). This wide range of approaches is potentially

complementary, at least once some extreme assumptions are pruned from them:

• Evolutionary theorists might posit that institutional change reflects selection over random institutional

changes, while rational choice theorists and functionalists (and social utilitarianism within sociology)

might assume that institutions represent the farsighted intentions of their creators. Institutions are

likely not the result of either perfect foresight or no foresight at all. So, then, it is an empirical question

to what extent agents know what they are doing and whether the institutions created serve the

purposes intended. Some scholars within all of these traditions relax assumptions of perfect foresight

or no foresight. Game theoretic analyses assume that agents feel their way toward institutional

solutions that work. While this is usually done in an equilibrium framework, this element can be

relaxed so that institutional change is viewed as a never-finished project.

• Whether equilibrium is assumed or not (but especially if it is not), path dependence—such that small

events can have important effects on the results of evolutionary processes—becomes possible and

can be used in explaining a variety of types of suboptimal outcomes of historical processes. Path-

dependent processes are important not just in economic history but in historical institutionalism in

political science, where interactions among a variety of institutions (including influences of political on

economic institutions), and among agents of unequal power, generate path-dependent processes.

The objection to path dependence comes only from rational choice scholars who assume optimal

outcomes.

• Once we move away from assumptions that institutions are purposely designed to serve societal

goals, scope is created for a variety of causal arguments. Most obviously, the relative power

of different agents comes into play. Power is stressed in sociology and political science and by

economists such as Knight or Acemoglu. And economic historians have a long tradition of

appreciating the role of power (e.g., in analyses of the feudal system; Greif, 2006). Political scientists

tend to tell both good stories of purposeful pursuit of beneficial institutional change and bad stories

of the exercise of power; these are likely complementary explanations rather than substitutes.

• The exercise of power is often obscured from view: Those exercising power generally wish not to

encourage an angry reaction and thus pretend to have other motives. Analyses of power, then,

are entirely compatible with analyses of legitimation. Both sociologists and political scientists speak

of legitimation. But as game theory analysis of institutional change suggests, cultural attitudes

are not easy to change purposefully. Scholars can usefully investigate, then, the degree to which

processes of legitimation serve the interests of the powerful or have a momentum of their own. Social

constructionism provides one useful hypothesis here: that institutions solidify over time into a form

that comes to seem natural. Another sociological hypothesis is that political institutions themselves

shape which other institutions are viewed as legitimate. Both could well capture important aspects of

legitimation.

• Sociological treatment of “ideas” encourages a broader exploration of the influence of culture on

institutions. If culture is not shaped entirely by the powerful, then cultural values may exert a range

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of influences on which institutions are thought to be desirable. The normative approach in political

science is similar. The “ideas” approach is particularly unique in emphasizing the role that social

science might play in establishing the desirability of certain institutions. The seeming reticence of

other social scientists to engage this line of argument is remarkable.

• Network analysis in sociology argues that agents are constrained by their networks: Institutional

change is only possible if supported actively by a sufficient network. Such an approach needs to look

at the resources that different network members bring, and it is thus compatible with an emphasis

on power (and culture). The business-labor analyses in political science can be seen as a particular

approach to network analysis, looking at how particular coalitions, whether within or across groups,

were formed and able to achieve change. Organizational institutionalism in sociology alternatively

examines the different motives of agents within an organization.

• Historical institutionalism is not alone in stressing the causal links between institutions. Both

structural institutionalism and social institutionalism emphasize the effects of certain sorts of

institutions on others.

These theories address links to institutions from each of the major categories of phenomena identified in

Szostak (2003, 2004), with the exception of the nonhuman environment and the two psychological categories.

Because each of these theories tends to be pursued in one or two disciplines, interdisciplinary integration

promises a more holistic outlook than any one discipline can provide. The treatment of cultural and social

phenomena tends to be too broad and diffuse and needs to more carefully focus on particular cultural and

social elements. Beyond this need for greater clarity, there are no obvious missing variables from the set of

theories as a whole.

In terms of theory types, different theories emphasize individuals (rational choice), relationships (game

theory, networks), groups (legitimation, cultural and social arguments), and nonintentional agents (historical

institutionalism's emphasis on interactions among institutions). There are hopeful signs of increased flexibility

within theories on this point: Although rational choice theory used to stress individuals and historical

institutionalism used to stress groups, both increasingly relate the behaviors of aggregates like unions to

those of members (Thelen, 1997, p. 378). Theories of institutional change naturally stress actions (more

rarely passive reaction, in some evolutionary and historical approaches), but some note the intermediate role

of ideas or values. Rational decision making is mentioned most explicitly, but game theory and evolutionary

approaches often refer to an intuitive groping for improvement. Structural institutionalism emphasizes how

decision-making processes or rules influence outcomes, but at the level of political institutions rather than

individual agents. Organizational institutionalism in sociology stresses the role of routines in behavior.

Legitimation approaches have a central place for virtue-based decisions. Path-dependent theories have an

obvious place for traditions, though agents need not argue explicitly from tradition in order to generate path-

dependent outcomes. Rational choice and game theory analyses stress equilibria (but not necessarily), but

most other approaches embrace dynamic or stochastic outcomes. In terms of theory types, then, the major

omission is in terms of decision making: More explicit attention to intuitive and traditional, and especially

virtue- and rule-based decisions, would be useful. These types of decision making are rarely found in the

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disciplines that have studied economic growth the most, but they are commonly explored in anthropology and

the humanities.

In establishing common ground, we would wish to know which theories or causal links are the most

important—and whether the relative importance varies by time or place or type of institution. Unfortunately,

the relative strengths of different theories are rarely compared in practice, for the simple reason that most

researchers favor and master only one.6 The empirical evidence collected in support of each theory is strong

enough, arguably, to urge the dismissal of the extreme arguments noted above that only one theory is correct.

Legions of political scientists and sociologists and economic historians have been fooling themselves, if

assumptions of perfect foresight or functionalism are entirely correct. The debate regarding path dependence

is more subtle: Although it is clear that path dependence is important over some time periods, the question of

whether optimal institutions are inevitably selected in the end is hard to establish uncontrovertibly, though the

diversity of economic institutions in countries of similar levels of development suggests otherwise.

Culture and Growth

It is noteworthy at the outset that the theories of institutional change referred to above almost all had some

role for culture. This insight accords with casual empiricism: Laws against littering or drugs are almost

impossible to enforce if many members of society view them as illegitimate. Yet, although economists admit

the importance of culture in this way, they tend to stop short of explicit cultural analysis. Greif (2006, pp. 8,

19–20) provides a good example of both attitudes. He not only recognizes that we must understand why rules

are enforced and obeyed, and cannot thus simply study the development of formal rules in isolation, but goes

so far as to define “institution” as a complementary complex of formal rules (what we and most others would

call institutions) and cultural elements.7 Institutional change is slow and path dependent because institutions

depend on “poorly understood and often unintentional processes of socialization, internalization, learning,

and experimentation,” including beliefs and ethical attitudes (p. 190). Yet, Greif worries that cultural elements

are largely unobservable, and he despairs of cultural explanations of anything for this reason: Because ad

hoc appeals to unobservable cultural elements can explain everything, they explain nothing (p. xv). He thus

focuses his analysis almost entirely on the observable formal rules, assuming that when these seem to work,

supporting cultural values and beliefs must be in place.

To argue that culture cannot be operationalized is to suggest that scholars of culture across a range of

disciplines and interdisciplinary fields have been wasting their time. Surveys are the most common source of

measures of cultural attitudes. Interviews and observation can usefully clarify whether survey questions are

both understood and answered honestly. In some cases, indirect quantitative measures are possible: Trust

can be defined as whether agents behave as they are expected to. There are thus a variety of ways in which

cultural attitudes can be operationalized.8

To be sure, there are problems with the existing scholarship on culture. The term itself is perhaps the vaguest

in all of human science: Thousands of different definitions exist, and individual scholars rarely bother to

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clarify what they mean by the term. Yet, culture can potentially be defined in terms of a host of attitudes

and behaviors; moreover, the works of individual cultural scholars can generally be understood as engaging

a handful of these (see Szostak, 2003). The ideological content of some versions of cultural studies raises

concerns that the important causal role of culture is simply assumed—but similar concerns have been raised

regarding other scholarly communities above.

The sociologist Beckert (2002) provides a compelling overview of the need for sociocultural analysis of

economic decisions. Economic theory itself suggests limits to the exercise of rationality in two common

circumstances. The first is when cooperation among agents is required, in which case economic calculations

depend on culturally conditioned expectations regarding the behavior of others. The second is when

uncertainty is present: If actors cannot rationally attach probabilities to the results that their actions might

produce, they must rely on various mental rules to guide behavior. Although individuals will differ in these,

there will also be cultural influences (and cooperation in the face of uncertainty would depend on similar

mental rules). He urges sociologists to focus on examining the social influences on those economic decisions

for which rationality is particularly problematic. This recommendation would have the effect of strengthening

the value of the research in each discipline to the other. He also urges sociologists to move away from

references to “irrationality” and instead identify specific strategies engaged in by actors when strictly rational

calculation is not feasible. This advice is similar to the advice of Newell (2007) to find common ground

between economists' emphasis on rationality and sociologists' stress on irrationality by thinking of a

continuum between the two. Although Beckert (2002) does not attempt to classify these non-strictly rational9

strategies, he makes frequent reference to following routines (which not only reduces costs of calculation,

but increases the predictability of the behavior of others) and following cultural guidelines—including respect

for widely shared values—and less frequent mention of intuition; thus, his analysis is consistent with the

elucidation of the five types of decision making above.

Economists tend to believe that there is one best value set for capitalist societies (Blim 2005, p. 307).

Modernization theory in sociology had also suggested that certain values, such as attitudes toward

achievement, were essential (p. 308). More recently, Fukuyama has stressed trust and Harrison has urged

future orientation, work effort, frugality, education, merit, trust, honesty, justice/fairness, dispersed authority,

and secularism (p. 309). Blim devotes several pages to detailing differences in institutions across modern

economies and then shows that these both reflect and support value differences. Different emphases

on individual versus community are reflected in lifetime employment in Japan and huge CEO salaries in

the United States. Values regarding competition versus collaboration are reflected in different approaches

to labor/management relations (p. 316). Again, careful specification of causal relationships, in concert

with careful attendance to differences across countries, produced a more complicated but more accurate

understanding.

In sum, the analysis of economists suggests that cultural values may be very important causes of economic

growth. Research in other disciplines has rarely addressed growth directly and has too often been

characterized by vague terminology and lack of careful empirical analysis. These problems can each be

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overcome. At present, though, the interdisciplinarian can most usefully provide advice to disciplinarians as to

how insights here can best be developed and/or clarified.

Networks

Networks of individuals serve a variety of social, political, and economic purposes. Networks are a third

way of organizing economic activity—along with the markets and organizational hierarchies that economists

have stressed until recently—and sociologists have shown that networks are critical in such important

economic activities as finding a job or locating a business contact. Moreover, network analysis focuses

on relationship agency, whereas economic analysis stresses individual agency. Network analysis is thus

potentially a valuable addition to the study of economic growth. Economists have rarely used network analysis

and tend, when they do, to stress a static analysis of how networks work, rather than looking at how networks

evolve. Network analysis has occasionally been applied to economic interactions but not directly to the study

of growth. As with culture, then, interdisciplinarians can provide advice on promising research strategies and

potential pitfalls. Scholars wishing to examine the relationship between networks and growth could benefit

from the following observations:

• Social capital is another vague term. There are three types of definition. The worst invites tautology

(and ignores the reality of bad outcomes being possible) by defining social capital solely in terms

of results. The second stresses cultural values such as trust. The third emphasizes networks and

perhaps organizations. The last two can be combined: Networks generate generally beneficial

outcomes by encouraging trust. This chapter will thus not address social capital, but rather address

culture and networks separately.

• As with culture, useful classifications of different types of network are needed. Sociologists stress

the importance of weak and/or cross-group links: Networks serve a critical role in transmitting

information, and the most important links may be the less obvious and less strong links between

individuals with access to quite different types of information.

• Then scholars must identify the links between different types of network and different economic

activities: investment most obviously, but also innovation (which scholars of both science and

technology increasingly appreciate occurs within networks) and institutional change (which is also

only possible if resources are mobilized through networks). It should not be forgotten in these

explorations that networks can serve to divert resources from growth; links between groups may be

particularly important in generating positive outcomes.

• In particular, network analysis may provide a means (though certainly not the only means) to get

a handle on the slippery concept of entrepreneurship (another topic treated more often outside

economics than within). Entrepreneurs can only be successful within networks, and thus both the

supply of entrepreneurs and their effectiveness will be influenced by the availability of networks that

provide access to diverse sources of information and resources. Ironically, Swedberg & Granovetter

(2001, pp. 12–13) observe that entrepreneurs often only succeed after migrating away from

expectations within networks of family and friends (e.g., expectations that they will employ family

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members).

• The reasons for differences in networks across countries can be explored in a comparative fashion.

Granovetter (2001) has argued that there are limits to the ability of an agent to forge links

instrumentally for gain. Instead, information often flows as a side-effect of relationships pursued for

social reasons. It has been hypothesized that networks are especially weak when social divisions

are sharp, poverty exists without a safety net, rule of law is weak, politics is not free and without real

choices, different groups do not see shared goals, war or famine undermine sense of stability, and

minorities are discriminated against.

• The question of whether networks (or markets) are a substitute or complement for institutions

deserves further attention: It is probably true that institutions can substitute for deficient networks in

some cases but not others. This question has important policy implications for countries with limited

“social capital.”

Social Structure

There is a general appreciation across all disciplines that social divisions—primarily ethnic and class

differences, but also gender and family divisions—can have negative economic and political effects. Writers

in each discipline show how, in at least some instances, these negative effects are of enormous importance.

Despite the general (and unusual) consensus on this point, there are still opportunities for one discipline to

learn from another. Economists tend to downplay issues of class or inequality, while other disciplines may

exaggerate these issues.

Economists rarely look at the sources of social division, and may thus too readily assume that they are

intractable. Geddes (2002) warns us not to take ethnic divisions as given, for individuals have choices about

how to identify themselves. People also decide whether to feel mistreated or threatened by other groups and

how to respond. She argues that this focus on malleability characterizes modern political science research

(p. 361).

Community Development

Among disadvantaged groups, both in poor countries and rich countries, economic growth may depend on

“community development”: Members of the community need to come together in order to identify strategies

that enhance economic growth prospects directly or indirectly (by improving education, health, legal, or a host

of other institutions and policies). Local communities often provide critical infrastructure, such as irrigation.

The World Bank increasingly makes unrestricted loans to local communities (Stiglitz, 2006, pp. 51–53).

Community development involves strengthening civil society (by strengthening links within the community

and its interaction with sources of academic and professional advice), in order to prioritize the actions and

perspectives of these communities in addressing the development of social/economic/environmental policies.

It thus involves empowerment: strengthening the capacity of both individuals and community-level institutions.

Community development, like the economic growth it may encourage, is best pursued in an interdisciplinary

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manner. This is, in part, because community development usually involves complex challenges, and thus

it requires input from a variety of academic disciplines and professions. As well, the diversity of insights

gives communities a freedom of choice (in how they integrate them) that they lack if presented with only one

discipline-grounded policy option. Moreover, there is a synergy between cross-disciplinary integration and the

integration of community insights with academic/professional insights: Both types of integration depend on

respect, forging a common vocabulary, and seeking a whole that is greater than its parts. Community activists

often need to oppose entrenched interests, just as interdisciplinarians must at times confront the entrenched

authority of disciplines (see Butterfield & Korazim-Körösy, 2007).

Emergent Properties

We must be careful that the study of individual causal links does not divert our attention from emergent

properties that might be apparent only at the level of broader systems of links. Two types of emergent

properties deserve particular attention.

First, growth itself can be considered to be an emergent property of a host of independent actions, mostly

undertaken without having the encouragement of growth as an objective. There is indeed a long tradition in

many fields of arguing that economic growth occurs only when many causal forces are combined. “Big push”

theories in economic development in the 1960s, Walt Rostow's “stages of growth” theory with its long list

of necessary conditions for a “takeoff into economic growth,” dependency and world systems theory (which

postulate a variety of ways in which poor countries might be kept poor by their interactions with rich countries)

in sociology and political science, among other approaches, have made this sort of argument.

Are the optimistic or pessimistic versions of these approaches more plausible? In terms of the three strategies

for dealing with differences in interdisciplinary insights (above), it is clear that this difference is not merely

semantic: Pessimists and optimists are talking about the same thing and reaching different conclusions. Do

these hypotheses have different ranges of applicability? It must seem that many of the world's poor countries

have achieved impressive rates of economic growth in recent decades and thus better fit the optimistic

outlook. Most of sub-Saharan Africa might better fit the pessimist scenario, though again, it must be recalled

that some of these countries grew rapidly in the 1960s. There might be some mechanism that ensures that

at least some poor countries remain poor. This leads to the third question: Could one achieve common

ground by changing some assumptions in one or the other perspective? Indeed one could. If one strips

away the determination to identify without doubt the future course of history, each perspective supports and

depends upon a set of causal arguments. It is entirely possible that all of these have some empirical merit,

and thus whether a country grows or not depends on which causal forces are operating most strongly at

certain times. And such a common ground can indeed be seen in the writings of both camps: Optimists often

talk about overcoming what they see as surmountable barriers (such as the absence of property rights or

decent infrastructure) to economic growth, while pessimists generally suggest that growth will only occur if

dramatic changes are made (say, in trade or foreign investment policies at the global level) to the way the

world operates.

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Although both optimists and pessimists appeal to issues of complexity, they have not, in general, emphasized

emergent properties but rather a set of mutually reinforcing causal links. This need not mean that emergent

properties are unimportant: Scholars across disciplines may be biased toward making narrow causal

arguments rather than appealing to emergent properties.

A second venue for emergent properties is economic fluctuations. Economic actors do not set out to generate

business cycles (indeed, this result is even less intended than economic growth), but cycles emerge from

the interactions among actors. Economists have struggled for decades to explain cycles precisely because

it is hard to move from an understanding of how individuals behave to an appreciation of how cycles are

generated. For present purposes, a few brief points can be made about fluctuations:

• In a world without growth and the structural change that accompanies it, economic cycles would be

mild or nonexistent. Cycles should thus be treated as largely an emergent property of the economic

growth process.

• In addition to the business cycles of a year or two in duration that economists have focused most

attention on, there are longer periods—of a decade or more—characterized by significant differences

in growth rates: The 1950s and 1960s saw more rapid growth in most developed countries than did

the 1930s or 1970s. Notably, these periods of rapid growth tend also to be characterized by less

severe cyclical behavior—perhaps because workers losing their jobs could quickly find others.

• However, economic models of growth predict a steady state outcome of some constant rate of

growth. It was noticed above that other disciplines are more likely than economics to stress

differences in growth experience across time and place. Interdisciplinarians should thus seek a

common ground that reflects the observed reality of alternating periods of fast and slow growth.

(Our appreciation above that technological innovation occurs unevenly through time might form an

important component of this common ground.)

Lessons for Interdisciplinary Practice

The foregoing analysis has, I hope, provided evidence of the advantages of a strategy of integrating insights

along different causal links (without neglecting the interactions among them) and also seeking emergent

properties of the system as a whole. The literature on interdisciplinarity often refers somewhat vaguely to

different “facets” of an issue. The stress here on causal links clarifies the focus of analysis and points again to

the advantages of an exhaustive table of the key phenomena studied by scholars across all disciplines: This

provides a “map” of the links addressed while also mitigating against ignoring links just because all disciplines

have ignored them (or the literature survey failed to find works that did).

Interdisciplinarians have understandably focused the most on integration when different disciplines provide

conflicting insights. Yet, the analysis above suggests that integrative strategies are also useful when gaps

exist between disciplines. In such cases, interdisciplinarians can usefully suggest avenues for research that

would bridge these gaps. The symbiotic relationship between disciplines and interdisciplinarity may then be

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easier to display to disciplinarians: Interdisciplinarians can point to valuable extensions to disciplinary analysis

without having first to outline the deficiencies of previous research.

Reflection and Communication

Establishing common ground is the most important single step in the integrative process. Yet, the

interdisciplinarian cannot simply stop at this point. As is true in specialized research, the act of insight should

be followed by careful attempts to evaluate and clarify the integrative insights obtained.

Interdisciplinarians are not free of bias themselves, though they will generally be more aware of the existence

of scholarly biases than specialized researchers. Interdisciplinarians as a whole may be biased toward

seeing good (or not) in all approaches, and they must thus be careful to scrutinize each insight they take

from any discipline. Individual interdisciplinarians may be characterized by a host of ideological, ethical,

epistemological, theoretical, methodological, and other biases. They may like some disciplines more than

others. All these possibilities should be reflected upon to see whether the integrative results obtained reflect

such biases.10

The interdisciplinarian should then ask whether there are ways in which his or her integrative understanding

might be tested. It may well be that a complex integrative understanding such as that sketched above cannot

be tested in its entirety (though it is useful to ask whether the set of insights as a whole is useful to the policy

maker). Rather, different tests may be required for different causal links. The interdisciplinarian will wish to

use multiple methods to test insights. If different methods suggest different conclusions, it is necessary to

revisit the strategies outlined above of interrogating assumptions and revisiting the strengths and weaknesses

of different theories and methods.

The final task involves communicating results in a format that is accessible to multiple audiences. This

involves appreciating both the knowledge bases of different audiences and their interests: The results should

be connected to issues that different audiences (especially disciplines) already care about.

Conclusion

In the end, did we end up with a chaos of conflicting arguments extending in too many directions? Or were

we able to put enough order into our reflections to make the effort worthwhile? Hardcore disciplinarians will

respond negatively: They can instill greater order by simply ignoring theories and methods other than their

own. But the interdisciplinarian strives toward an order that does not arbitrarily limit insight. The cause of

economic growth—and the billions of people who desperately need to experience more of this—is best served

by privileging integrated insight over narrower criteria.

It is useful to close by briefly reviewing the benefits to our understanding of growth of the various steps in the

interdisciplinary research process:

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• The scope for interdisciplinary analysis expands markedly when we move beyond studying the

proximate causes of growth to ask why some countries innovate or trade or invest more than others.

Economists—and far from all of these—have only recently extended their gaze beyond the proximate

causes, and they are held back by the difficulty of applying their usual theories and methods to

non-economic phenomena such as culture. The importance of not arbitrarily constraining a guiding

question along disciplinary lines could hardly be better illustrated.

• The second step(s) reviewed a wide range of theories and methods and established that each had

strengths for the study of growth that could compensate for weaknesses in others. The appreciation

of these strengths and weaknesses was invaluable when disciplinary insights were evaluated later.

The analysis also served usefully to justify in advance wide-ranging theoretical and methodological

explorations. For mainstream economists, the key message was that rational choice theorizing and

statistical analysis need to be supplemented with other—for the most part complementary—theories

and methods. Yet, the same message was communicated to all other disciplines.

• The identification of gaps in scholarly understanding is always a critical step in interdisciplinary

analysis. It has been particularly important in the case of economic growth, for scholars of networks

or culture or business cycles have only rarely addressed questions of economic growth (and vice

versa), and even much of the literature on technology and institutions is oriented toward quite

different questions. One of the main purposes of this research project has been to identify areas

where future research is needed and how this might be pursued.

• Asking about the possibility of emergent properties led us to two important areas of investigation:

poverty traps and the connection between growth and cycles.

• More generally, we were able to create a common ground across a range of causal links that is

superior to the insights of any one discipline. We will not reprise those analyses here, in part not to

detract attention from the other steps in the research process.

• Though we did not have space to describe the final steps in detail, our understanding of growth will

be greatly enhanced if we interrogate possible biases in our analysis, test our insights empirically,

and communicate them clearly to diverse audiences.

1. This chapter draws on Szostak (2009). That book is organized according to the 12 steps identified in

Szostak (2002). In this chapter, they are combined into four sets of steps. My process is broadly similar to

that in Repko (2008), though I tend to stress the identification of relevant phenomena, theories, and methods

more than Repko, and I also rely more than Repko on classifications of phenomena, theories, and methods in

evaluating disciplinary insights. Yet these are differences in emphasis only; Repko and I concur regarding the

broad outlines of the research process. In the book, I address the question of whether growth is “good.” For

rich countries especially, I urge the definition and measurement of growth in terms of output per hours worked

(and thus growth might just mean more leisure time) and with due regard to environmental repercussions. I

also stress that only some goods and services add to human well-being.

2. These are the three possible types of “intentional agency” or “nonintentional agency” identified in Szostak

(2004). They together form one of the five dimensions in the typology of theory cited in Repko (2008,

pp. 198–200). Others are addressed below. Durkheim had distinguished sociology from economics and

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psychology by emphasis on methodological holism. “Methodological holism in sociology has been an obstacle

to acceptance of the choice-theoretic approach underlying the new institutional paradigm”—it has isolated

sociology from changes in other social sciences (Nee, 1998, p. 11). But while this approach has dominated,

many analyses, from Tocqueville and Weber to today, have emphasized “rational action bounded by

institutions” (p. 4). Rather than debate individualism versus holism, “a more constructive approach is to

model the reciprocal interaction between purposive action and social structure” (p. 5). In other words, these

approaches can be integrated.

3. Some scholars would worry about the number of distinct causal links that would need to be investigated in

any complex study. We can hardly hope, though, for a simple understanding of a complex process. Szostak

(2009) shows how the causal link understandings can be organized into a coherent whole through reference

to an exhaustive classification of phenomena.

4. Economists, as we shall see, worry a great deal about the quality of institutions when discussing institutions

in general. That is, they recognize that countries differ a great deal in how well they manage/enforce

institutions that may look quite similar on paper. In the realm of trade policy, however, analysis tends to

proceed with respect to a dichotomy between openness and managed trade. There is usually an implicit

or explicit assumption that countries cannot manage trade very well. Kohli (2004), a political scientist,

argues that the key difference in developmental prospects is between countries that can manage/enforce

any institutions well and countries that can manage/enforce no economic institutions well. He argues, for

example, that South Korea effectively managed several years of import substitution as well as decades of

export promotion (i.e., the government was not “captured” by private industries and encouraged them to

improve productivity under both regimes). On the other hand, Nigeria failed miserably with respect to both

types of policies (both were perverted to reward friends of the government, and productivity advance was not

encouraged; p. 376). The broader literature on “the developmental state” makes similar arguments.

5. Historians of technology give roughly equal attention to the technological, economic, political, and

sociocultural influences on innovation. They increasingly pay attention to the long period of development after

a breakthrough innovation. They note that as technological systems harden, they limit human choices about

technological innovation (Nye, 2006).

6. The value of integrating these approaches has occasionally been appreciated in the literature. Thelen

(1997, p. 370) suggests that rational choice theorists can appreciate historical circumstances while historical

institutionalists can think more about why actors do what they do (and the importance of collective action

problems); all can recognize the role of norms in supporting institutions. Thelen's main argument is that

scholars cannot understand change without also understanding stability. Both the coordination emphasized

by rational choice theory and the shared cultural understandings of sociological institutionalism allow us to

understand continuity better than change (p. 386). Historical institutionalism, on the other hand, invokes sunk

costs and vested interests, and thus is good at identifying critical junctures that send countries on different

trajectories, but worse at explaining continuity.

7. Greif (2006) notes that different disciplines define institutions differently: as rules, as norms, as functional

solutions to particular problems. He urges the integration of these definitions. From our holistic perspective,

this is best done not by conflating quite distinct phenomena but by capturing in different causal links both the

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influence of culture on institutions and the role of institutions in solving particular problems. Greif's own wish

that different definitions be viewed as complements (p. 40) is best achieved in this manner. Greif's definition

encourages an emphasis on culture over other causal influences on/of institutions, while inviting us to treat

the culture-institutions nexus as a black box.

8. In the past decades, the availability of better data and techniques has induced some economists to study

culture. One popular approach is to look at whether membership in a particular ethnic or religious group

affects economic outcomes. The advantage of this approach is that such memberships are easily measured

and are also largely inherited: This overcomes the possible concern that any correlation between culture

and economy reflects causation in the other direction. Both internationally and within countries, ethnic and

religious differences do generate different economic outcomes (though within countries, studies of immigrants

suggest that these will lessen over time). Moreover, these differences are correlated with different values and

beliefs (trust, social mobility, fairness, hard work, fertility, thrift) in both regressions and experiments. Yet, such

studies can only be suggestive of links between these values and economic growth, and these links are hard

to establish statistically (Guiso, Spienza, & Zingales, 2006).

9. Beckert (2002), in distinguishing himself from the emphasis of other sociologists on irrationality, strives

to emphasize the “rationality” of other decision-making strategies. In this chapter, these can be seen as

reasoned but nonrational strategies. Note that semantic confusion between narrow and broad uses of the

word “rational” contribute to misunderstanding between sociologists and economists.

10. I am an economist, but one who has written methodological critiques of my discipline and my field of

economic history (Szostak, 1999, 2006). I have thus had the pleasure of being critiqued by non-economists

for being too much of an economist and by economists for being not quite enough of one. The reader can

best judge which—likely both—is the case here. I lack practitioner-level expertise in some of the disciplines

covered here, but I have considerable familiarity with most of the theory types and methods addressed. I am

a self-conscious interdisciplinarian and thus likely biased toward stressing the advantages of interdisciplinary

analysis. As should be clear by now, I believe in theoretical and methodological flexibility. I may thus be biased

toward seeing some good in all approaches. Indeed, I do suspect that any idea pursued at length by some

academic community must have some kernel of truth in it. But this need not prevent skepticism: One can

appreciate that those who thought the world was flat for millennia were misguided, while appreciating the

value of the ways they amassed evidence in support of their hypothesis. Still, I can imagine that disciplinarians

reading this chapter will readily imagine that I have been too harsh with respect to them and not harsh enough

in my treatment of others. And surely some of them will be right (but hopefully not to a considerable degree),

though I know not which.

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