Designing and managing large technologies

What is involved in designing, implementing, coordinating, and managing the deployment of a large new technology system in a real social, political, and organizational environment? Here I am thinking of projects like the development of the SAGE early warning system, the Affordable Care Act, or the introduction of nuclear power into the civilian power industry.

Tom Hughes described several such projects in Rescuing Prometheus: Four Monumental Projects That Changed the Modern World. Here is how he describes his focus in that book:

Telling the story of this ongoing creation since 1945 carries us into a human-built world far more complex than that populated earlier by heroic inventors such as Thomas Edison and by firms such as the Ford Motor Company. Post-World War II cultural history of technology and science introduces us to system builders and the military-industrial-university complex. Our focus will be on massive research and development projects rather than on the invention and development of individual machines, devices, and processes. In short, we shall be dealing with collective creative endeavors that have produced the communications, information, transportation, and defense systems that structure our world and shape the way we live our lives. (kl 76)

The emphasis here is on size, complexity, and multi-dimensionality. The projects that Hughes describes include the SAGE air defense system, the Atlas ICBM, Boston’s Central Artery/Tunnel project, and the development of ARPANET. Here is an encapsulated description of the SAGE process:

The history of the SAGE Project contains a number of features that became commonplace in the development of large-scale technologies. Transdisciplinary committees, summer study groups, mission-oriented laboratories, government agencies, private corporations, and systems-engineering organizations were involved in the creation of SAGE. More than providing an example of system building from heterogeneous technical and organizational components, the project showed the world how a digital computer could function as a real-time information-processing center for a complex command and control system. SAGE demonstrated that computers could be more than arithmetic calculators, that they could function as automated control centers for industrial as well as military processes. (kl 285)

Mega-projects like these require coordinated efforts in multiple areas — technical and engineering challenges, business and financial issues, regulatory issues, and numerous other areas where innovation, discovery, and implementation are required. In order to be successful, the organization needs to make realistic judgments about questions for which there can be no certainty — the future development of technology, the needs and preferences of future businesses and consumers, and the pricing structure that will exist for the goods and services of the industry in the future. And because circumstances change over time, the process needs to be able to adapt to important new elements in the planning environment.

There are multiple dimensions of projects like these. There is the problem of establishing the fundamental specifications of the project — capacity, quality, functionality. There is the problem of coordinating the efforts of a very large team of geographically dispersed scientists and engineers, whose work is deployed across various parts of the problem. There is the problem of fitting the cost and scope of the project into the budgetary envelope that exists for it. And there is the problem of adapting to changing circumstances during the period of development and implementation — new technology choices, new economic circumstances, significant changes in demand or social need for the product, large shifts in the costs of inputs into the technology. Obstacles in any of these diverse areas can lead to impairment or failure of the project.

Most of the cases mentioned here involve engineering projects sponsored by the government or the military. And the complexities of these cases are instructive. But there are equally complex cases that are implemented in a private corporate environment — for example, the development of next-generation space vehicles by SpaceX. And the same issues of planning, coordination, and oversight arise in the private sector as well.

The most obvious thing to note in projects like these — and many other contemporary projects of similar scope — is that they require large teams of people with widely different areas of expertise and an ability to collaborate across disciplines. So a key part of leadership and management is to solve the problem of securing coordination around an overall plan across the numerous groups; updating plans in face of changing circumstances; and ensuring that the work products of the several groups are compatible with each other. Moreover, there is the perennial challenge of creating arrangements and incentives in the work environment — laboratory, design office, budget division, logistics planning — that stimulate the participants to high-level creativity and achievement.

This topic is of interest for practical reasons — as a society we need to be confident in the effectiveness and responsiveness of the planning and development that goes into large projects like these. But it is also of interest for a deeper reason: the challenge of attributing rational planning and action to a very large and distributed organization at all. When an individual scientist or engineer leads a laboratory focused on a particular set of research problems, it is possible for that individual (with assistance from the program and lab managers hired for the effort) to keep the important scientific and logistical details in mind. It is an individual effort. But the projects described here are sufficiently complex that there is no individual leader who has the whole plan in mind. Instead, the “organizational intentionality” is embodied in the working committees, communications processes, and assessment mechanisms that have been established.

It is interesting to consider how students, both undergraduate and graduate, can come to have a better appreciation of the organizational challenges raised by large projects like these. Almost by definition, study of these problem areas in a traditional university curriculum proceeds from the point of view of a specialized discipline — accounting, electrical engineering, environmental policy. But the view provided from a discipline is insufficient to give the student a rich understanding of the complexity of the real-world problems associated with projects like these. It is tempting to think that advanced courses for engineering and management students could be devised making extensive use of detailed case studies as well as simulation tools that would allow students to gain a more adequate understanding of what is needed to organize and implement a large new system. And interestingly enough, this is a place where the skills of humanists and social scientists are perhaps even more essential than the expertise of technology and management specialists. Historians and sociologists have a great deal to add to a student’s understanding of these complex, messy processes.

Social construction of technical knowledge

After there was the sociology of knowledge (link), before there was a new sociology of knowledge (link), and more or less simultaneous with science and technology studies (link), there was Paul Rabinow’s excellent ethnography of the invention of the key tool in recombinant DNA research — PCR (polymerase chain reaction). Rabinow’s monograph Making PCR: A Story of Biotechnology appeared in 1996, after the first fifteen years of the revolution in biotechnology, and it provides a profound narrative of the intertwinings of theoretical science, applied bench work, and material economic interests, leading to substantial but socially imprinted discoveries and the development of a powerful new technology. Here is how Rabinow frames the research:

Making PCR

is an ethnographic account of the invention of PCR, the polymerase chain reaction (arguably the exemplary biotechnological invention to date), the milieu in which that invention took place (Cetus Corporation during the 1980s), and the key actors (scientists, technicians, and business people) who shaped the technology and the milieu and who were, in turn, shaped by them. (1)

This book focuses on the emergence of biotechnology, circa 1980, as a distinctive configuration of scientific, technical, cultural, social, economic, political, and legal elements, each of which had its own separate trajectory over the preceding decades. It examines the “style of life” or form of “life regulation” fashioned by the young scientists who chose to work in this new industry rather than pursue promising careers in the university world…. In sum, it shows how a contingently assembled practice emerged, composed of distinctive subjects, the site in which they worked, and the object they invented. (2)

There are several noteworthy features of these very exact descriptions of Rabinow’s purposes. The work is ethnographic; it proceeds through careful observation, interaction, and documentation of the intentionality and practices of the participants in the process. It is focused on actors of different kinds — scientists, lab technicians, lawyers, business executives, and others — whose interests, practices, and goals are distinctly different from each others’. It is interested in accounting for how the “object” (PCR) came about, without any implication of technological or scientific inevitability. It highlights both contingency and heterogeneity in the process. The process of invention and development was a meandering one (contingency) and it involved a large group of heterogeneous influences (scientific, cultural, economic, …).

Legal issues come into this account because the fundamental question — what is PCR and who invented it? — cannot be answered in narrowly technical or scientific terms. Instead, it was necessary to go through a process of practical bench-based development and patent law to finally be able to answer both questions.

A key part of Rabinow’s ethnographic finding is that the social configuration and setting of the Cetus laboratory was itself a key part of the process leading to successful development of PCR. The fact of hierarchy in traditional scientific research spaces (universities) is common — senior scientists at the top, junior technicians at the bottom. But Cetus had developed a local culture that was relatively un-hierarchical, and Rabinow believes this cultural feature was crucial to the success of the undertaking.

Cetus’s organizational structure was less hierarchical and more interdisciplinary than that found in either corporate pharmaceutical or academic institutions. In a very short time younger scientists could take over major control of projects; there was neither the extended postdoc and tenure probationary period nor time-consuming academic activities such as committees, teaching, and advising to divert them from full-time research. (36)

And later:

Cetus had been run with a high degree of organizational flexibility during its first decade. The advantages of such flexibility were a generally good working environment and a large degree of autonomy for the scientists. The disadvantages were a continuing lack of overall direction that resulted in a dispersal of both financial and human resources and in continuing financial losses. (143)

A critical part of the successful development of PCR techniques in Rabinow’s account was the highly skilled bench work of a group of lab technicians within the company (116 ff.). Ph.D. scientists and non-Ph.D. lab technicians collaborated well throughout the extended period during which the chemistry of PCR needed to be perfected; and Rabinow’s suggestion is that neither group by itself could have succeeded.

So some key ingredients in this story are familiar from the current wisdom of tech companies like Google and FaceBook: let talented people follow their curiosity, use space (physical and social) to elicit strong positive collaboration; don’t try to over-manage the process through a rigid authority structure.

But as Rabinow points out, Cetus was not an anarchic process of smart people discovering things. Priorities were established to govern research directions, and there were sustained efforts to align research productivity with revenue growth (almost always unsuccessful, it must be said). Here is Rabinow’s concluding observation about the company and the knowledge environment:

Within a very short span of time some curious and wonderful reversals, orthogonal movements, began happening: the concept itself became an experimental system; the experimental system became a technique; the techniques became concepts. These rapidly developing variations and mutually referential changes of level were integrated into a research milieu, first at Cetus, then in other places, then, soon, in very many other places. These places began to resemble each other because people were building them to do so, but were often not identical. (169).

And, as other knowledge-intensive businesses from Visicalc to Xerox to H-P to Microsoft to Google have discovered, there is no magic formula for joining technical and scientific research to business success.

Marx’s thinking about technology

 

It sometimes seems as though there isn’t much new to say about Marx and his theories. But, like any rich and prolific thinker, that’s not actually true. Two articles featured in the Routledge Great Economists series (link) are genuinely interesting. Both are deeply scholarly treatment of interesting aspects of the development of Marx’s thinking, and each sheds new light on the influences and thought processes through which some of Marx’s key ideas took shape. I will consider one of those articles here and leave the second, a consideration of Marx’s relationship to the physiocrats, for a future post.

Regina Roth’s “Marx on technical change in the critical edition” (link) is a tour-de-force in Marx scholarship. There are two aspects of this work that I found particularly worthwhile. The first is a detailed “map” of the work that has been done since the early twentieth century to curate and collate Marx’s documents and notes. This was an especially important effort because Marx himself rarely brought his work to publishable form; he wrote thousands of pages of notes and documents in preparation for many related lines of thought, and not all of those problem areas have been developed in the published corpus of Marx’s writings. Roth demonstrates a truly impressive grasp of the thousands of pages of materials included in the Marx-Engels Gesamtaushgabe (MEGA) and Marx-Engels Collected Works (MECW) collections, and she does an outstanding job of tracing several important lines of thought through published and unpublished materials. She notes that the MEGA collection is remarkably rich:

A second point I want to stress is that the MEGA offers more material than other editions, not only regarding the manuscripts mentioned above but also with other types of written material. If we look at the material gathered in the MEGA we find examples of several different levels of communication. We may think of manuscripts on a first level as witnessing the communication between the author with himself and with his potential readers. On a second level, his letters give us notice of what he talked about to the people around him. And, on a third level, there is the vast part of his legacy that documents Marx’s discourse with authors of his time: his excerpts, the books he read and his collections of newspaper cuttings. (1231)

Here is a table in which Roth correlates several important economic manuscripts in the two collections.

Careful study of these thousands of pages of manuscripts and notes is crucial, Roth implies, if we are to have a nuanced view of the evolution and logic of Marx’s thought.

They show, first of all, that Marx was never content with what he had written: he started five drafts of his first chapter, and added four fragments to the same subject, each of them with numerous changes within each text. (1228)

And study of these many versions, notes, and emendations shows something else as well: a very serious effort on Marx’s part to get his thinking right. He was not searching out the most persuasive or the simplest versions of some of his critical thoughts about capitalism; instead, he was trying to piece together the economic logic of this social-economic system in a way that made sense given the analytical tools at his disposal. Marx was not the dogmatic figure that he is sometimes portrayed to be.

There are many surprises in Roth’s study. The falling rate of profit? That’s Engels’ editorial summation rather than Marx’s finished conclusion! By comparing Marx’s original manuscripts with the posthumous published version of volume 3 of Capital, she finds that “Engels inserted the following sentence in the printed version [of Capital vol. 3]: ‘But in reality […] the rate of profit will fall in the long run’ ” (1233). In several important aspects she finds that Engels the editor was more definitive about the long-term tendencies of capitalism than Marx the author was willing to be. For example:

Therefore, Engels continued, this capitalist mode of production ‘is becoming senile and has further and further outlived its epoch.’ Marx did not give such a clear opinion with a view to the future of capitalism, at least not in Capital. (1233)

She notes also that Engels was anxious about Marx’s unwillingness to bring his rewriting and reconsideration of key theses to a close:

Shortly before the publication of Volume I of Capital, Engels worried: ‘I had really begun to suspect from one or two phrases in your last letter that you had again reached an unexpected turning-point which might prolong everything indefinitely.’ (1247)

The other important aspect of this article — the substantive goal of the piece — is Roth’s effort to reconstruct the development of Marx’s thinking about technology and technology change, the ways that capitalism interacts with technology, and the effects that Marx expected to emerge out of this complicated set of processes. But this requires careful study of the full corpus, not simply the contents of the published works.

To understand Marx’s views on technical change, his whole legacy, which is also comprised of numerous drafts, excerpts, letters, and so forth, must be considered. (1224)

In fact, the unpublished corpus has much more substantial commentary on technology and technical change than do the published works. “In Capital terms such as technical progress, technical change or simply technology turn up rarely” (1241).

Roth finds that Marx had a sustained interest in “the machinery question” — essentially, the history of mechanical invention and the role that machines play in the economic system of capitalism. He studied and annotated the writings of Peter Gaskell, Andrew Ure, and Charles Babbage, as well as many other writers on the technical details of industrial and mining practices; Roth mentions Robert Willis and James Nasmyth in particular.

The economic importance of technical change for Marx’s system is the fact that it presents the capitalist with the possibility of increasing “relative surplus value” by raising the productivity of labor (1241). But because technical innovation is generally capital-intensive (increasing the proportion of constant capital to variable capital, or labor), technical innovation tends to bring about a falling rate of profit (offset, as Roth demonstrates, by specific counteracting forces). So the capitalist is always under pressure to prop up the rate of profit, and more intensive exploitation of labor is one of the means available.

In the discussion in the General Council [of the IWMA], Marx argued that machines had effects that turned out to be the opposite of what was expected: they prolonged the working day instead of shortening it; the proportion of women and children working in mechanized industries increased; labourers suffered from a growing intensity of labour and became more dependent on capitalists because they did not own the means of production any more …. (1246)

So technology change and capitalism are deeply intertwined; and there is nothing emancipatory about technology change in itself.

Thinking about disaster

 

Charles Perrow is a very talented sociologist who has put his finger on some of the central weaknesses of the American social-economic-political system.  He has written about corporations (Organizing America: Wealth, Power, and the Origins of Corporate Capitalism), technology failure (Normal Accidents: Living with High-Risk Technologies), and organizations (Complex Organizations: A Critical Essay).  (Here is an earlier post on his historical account of the corporation in America; link.) These sound like very different topics — but they’re not, really.  Organizations, power, the conflict between private interests and the public good, and the social and technical causes of great public harms have been the organizing themes of his research for a very long time.

His current book is truly scary.  In The Next Catastrophe: Reducing Our Vulnerabilities to Natural, Industrial, and Terrorist Disasters he carefully surveys the conjunction of factors that make 21st-century America almost uniquely vulnerable to major disasters — actual and possible.  Hurricane Katrina is one place to start — a concentration of habitation, dangerous infrastructure, vulnerable toxic storage, and wholly inadequate policies of water and land use led to a horrific loss of life and a permanent crippling of a great American city.  The disaster was foreseeable and foreseen, and yet few effective steps were taken to protect the city and river system from catastrophic flooding.  And even more alarming — government and the private sector have taken almost none of the prudent steps after the disaster that would mitigate future flooding.

Perrow’s analysis includes natural disasters (floods, hurricanes, earthquakes), nuclear power plants, chemical plants, the electric power transmission infrastructure, and the Internet — as well as the threat of deliberate attacks by terrorists against high-risk targets.   In each case he documents the extreme risks that our society faces from a combination of factors: concentration of industry and population, lax regulation, ineffective organizations of management and oversight, and an inability on the part of Congress to enact legislation that seriously interferes with the business interests of major corporations even for the purpose of protecting the public.

His point is a simple one: we can’t change the weather, the physics of nuclear power, or the destructive energy contained in an LNG farm; but we can take precautions today that significantly reduce the possible effects of accidents caused by these factors in the future. His general conclusion is a very worrisome one: our society is essentially unprotected from major natural disasters and industrial accidents, and we have only very slightly increased our safety when it comes to preventing deliberate terrorist attacks.

This book has been about the inevitable inadequacy of our efforts to protect us from major disasters. It locates the inevitable inadequacy in the limitations of formal organizations. We cannot expect them to do an adequate job in protecting us from mounting natural, industrial, and terrorist disasters.  It locates the avoidable inadequacy of our efforts in our failure to reduce the size of the targets, and thus minimize the extent of harm these disasters can do. (chapter 9)

A specific failure in our current political system is the failure to construct an adequate and safety-enhancing system of regulation:

Stepping outside of the organization itself, we come to a third source of organizational failure, that of regulation. Every chapter on disasters in this book has ended with a call for better regulation and re-regulation, since we need both new regulations in the face of new technologies and threats and the restoration of past regulations that had disappeared or been weakened since the 1960s and 1970s. (chapter 9)

The central vulnerabilities that Perrow points to are systemic and virtually ubiquitous across the United States — concentration and centralization.  He is very concerned about the concentration of people in high-risk areas (flood and earthquake zones, for example); he is concerned about the centralized power wielded by mega-organizations and corporations in our society; and he is concerned about the concentration of highly dangerous infrastructure in places where it puts large populations at risk.  He refers repeatedly to the risk posed by the transport by rail of huge quantities of chlorine gas through densely populated areas — 90 tons at a time; the risk presented by LNG and propane storage farms in areas vulnerable to flooding and consequent release or explosion; the lethal consequences that would ensue from a winter-time massive failure of the electric power grid.

Perrow is an organizational expert; and he recognizes the deep implications that follow from the inherent obstacles that confront large organizations, both public or private.  Co-optation by powerful private interests, failure of coordination among agencies, lack of effective communication in the preparation of policies and emergency responses — these organizational tendencies can reduce organizations like FEMA or the NRC to almost complete inability to perform their public functions.

Organizations, as I have often noted, are tools that can be used by those within and without them for purposes that have little to do with their announced goals. (Kindle loc, 1686)

Throughout the book Perrow offers careful, detailed reviews of the effectiveness and consistency of the government agencies and the regulatory legislation that have been deployed to contain these risks.  Why was FEMA such an organizational failure?  What’s wrong with the Department of Homeland Security?  Why are chronic issues of system safety in nuclear power plants and chemical plants not adequately addressed by the corresponding regulatory agencies?  Perrow goes through these examples in great detail and demonstrates the very ordinary social mechanisms through which organizations lose effectiveness.  The book serves as a case-study review of organizational failures.

Perrow’s central point is stark: the American political system lacks the strength to take the long-term steps it needs to in order to mitigate the worst effects of natural (or intentional) disasters that are inevitable in our future.  We need consistent investment for long-term benefits; we need effective regulation of powerful actors; and we need long-term policies that mitigate future disasters.  But so far we have failed in each of these areas.  Private interests are too strong, an ideology of free choice and virtually unrestrained use of property leads to dangerous residential and business development, and Federal and state agencies lack the political will to enact the effective regulations that would be necessary to raise the safety threshold in dangerous industries and developments. And, of course, the determined attack on “government regulations” that has been underway from the right since the Reagan years just further worsens the ability of agencies to regulate these powerful businesses — the nuclear power industry, the chemical industry, the oil and gas industry, …

One might think that the risks that Perrow describes are fairly universal across modern societies.  But Perrow notes that these problems seem more difficult and fundamental in the United States than in Europe.  The Netherlands has centuries of experience in investing in and regulating developments having to do with the control of water; European countries have managed to cooperate on the management of rivers and flood plains; and most have much stronger regulatory regimes for the high risk technologies and infrastructure sectors.

The book is scary, and we need to pay attention to the social and natural risks that Perrow documents so vividly.  And we need collectively to take steps to realistically address these risks.  We need to improve the organizations we create, both public and private, aimed at mitigating large risks.  And we need to substantially improve upon the reach and effectiveness of the regulatory systems that govern these activities.  But Perrow insists that improving organizations and leadership, and creating better regulations, can only take us so far.  So we also need to reduce the scope of damage that will occur when disaster strikes.  We need to design our social system for “soft landings” when disasters occur.  Fundamentally, his advice is to decentralize dangerous infrastructure and to be much more cautious about development in high-risk zones.

Given the limited success we can expect from organizational, executive, and regulatory reform, we should attend to reducing the damage that organizations can do by reducing their size.  Smaller organizations have a smaller potential for harm, just as smaller concentrations of populations in areas vulnerable to natural, industrial, and terrorist disasters present smaller targets. (chapter 9)

If owners assume more responsibility for decisions about design and location — for example, by being required to purchase realistically priced flood or earthquake insurance — then there would be less new construction in hurricane alleyways or high-risk earthquake areas.  Rather than integrated mega-organizations and corporations providing goods and services, Perrow argues for the effectiveness of networks of small firms.  And he argues that regulations and law can be designed that give the right incentives to developers and home buyers about where to locate their businesses and homes, reflecting the true costs associated with risky locations. Realistically priced mandatory flood insurance would significantly alter the population density in hurricane alleys.  And our policies and regulations should make a systematic effort to disperse dangerous concentrations of industrial and nuclear materials wherever possible.

 

Transmitting technology

How do large technological advances cross cultural and civilizational boundaries? The puzzle is this: large technologies are not simply cool new devices, but rather complex systems of scientific knowledge, engineering traditions, production processes, and modes of technical communication. So transfer of technology is not simply a matter of conveying the approximate specifications of the device; it requires the creation of a research and development infrastructure that is largely analogous to the original process of invention and development. Inventors, scientists, universities, research centers, and skilled workers need to build a local understanding of the way the technology works and how to solve the difficult problems of material and technical implementation.

Take inertial guidance systems for missiles, described in fascinating detail by Donald MacKenzie in Inventing Accuracy: A Historical Sociology of Nuclear Missile Guidance. The process MacKenzie describes of discovery and development of inertial guidance was a highly complex and secretive one, with multiple areas of scientific and engineering research solving a series of difficult technical problems.

Now do a bit of counterfactual history and imagine that some country — say, Burma — had developed powerful rocket engines in the 1950s but did not have a workable guidance technology; and suppose the US and USSR had succeeded in keeping the development of inertial navigation systems and the underlying science secret. Finally, suppose that Burmese agents had managed to gain a superficial description of inertial navigation: “It is a self-contained device that tracks acceleration and therefore permits constant updating of current location; and it uses ultra-high precision gyroscopes.” Would this be enough of a leak to permit rapid adoption of inertial navigation in the Burmese missile program? Probably not; the technical obstacles faced in the original development process would have to be solved again, and this means a long process of knowledge building and institution building.  For example, MacKenzie describes the knotty problem posed to this technology by the fact of slight variations in the earth’s gravitational field over the surface of the globe; if uncorrected, these variations would be coded as acceleration by the instrument and would lead to significant navigational errors.  The solution to this problem involved creating a detailed mapping of the earth’s gravitational field.

This is a hypothetical case. But Hsien-Chun Wang describes an equally fascinating but real case in a recent article in Technology and Culture, “Discovering Steam Power in China, 1840s-1860s” (link). There was essentially no knowledge of steam power in Chinese science in the mid-Qing (early nineteenth century). The First Opium War (1839-1842) provided a rude announcement of the technology, in the form of powerful steam-driven warships on the coast and rivers of eastern China. Chinese officials and military officers recognized the threat represented by Western military-industrial technology, but it was another 25 years before Chinese industry was in a position to build a steam-powered ship. So what were the obstacles standing in front of China’s steam revolution?

Wang focuses on two key obstacles in mid-Qing industry and technology: the role of technical drawings as a medium for transmitting specifications for complex machines from designer to skilled workers; and the absence in nineteenth-century China of a machine tool technology.  Technical drawings were an essential medium of communication in the European industrial system, conveying precise specifications of parts and machines to the workers and tools who would fabricate them.  And machine tools (lathes, planes, stamping machines, cutting machines, etc.) provided the tools necessary to fabricate high-precision metal parts and components.  (Merritt Roe Smith describes aspects of both these stories in his account of the U.S. arms industry in the early nineteenth century; Harpers Ferry Armory and New Technology.)  According to Wang, the Chinese technical culture had developed models rather than drawings to convey how a machine works; and the intricate small machines that certainly were a part of Chinese technical culture depended on artisanal skill rather than precision tooling of interchangeable parts.

So communicating the technical details of a complex machine and creating the fabrication infrastructure needed to produce the machine were two important obstacles for rapid transfer of steam technology from Western Europe to Qing China.  But perhaps a more fundamental obstacle emerges as well: the fact that Chinese technical and scientific culture was as yet simply unready to “see” the way that steam power worked in the first place.  When steam warships arrived, acute Chinese observers saw smoke and fire, and they saw motion.  But they did not see “steam-driven traction”, or the translation of kinetic energy into rotational work.  (This is evident also in the drawing of the treadmill water pump above; the maker of the drawing clearly did not perceive from the Italian drawing how the motion of the treadmill was translated into the vertical pumping action.)  Wang quotes a description from an observer in Guangzhou in 1828:

Early in the third month … there suddenly came from Bengal a huo lunchuan [fire-wheel ship] …. The huo lunchuan has an empty copper cylinder inside to burn coal, with a machine on the top.  When the flame is up, the machine moves automatically.  The wheels on both sides of the ship move automatically too. (37)

And another observer wrote in Zhejiang in 1840:

The ship’s cabin stores a square furnace under the beam from which the wheels are hung.  When the fire is burning in the furnace, the two wheels turn like a fast mill and the ship cruises as fast as if it is flying, regardless of the wind’s direction. (37-38)

The give-away here is the word “automatically”; plainly these observers had not assimilated a causal process linking the production of heat (fire) to mechanical motion (the rotation of the paddle wheels).  Instead, the two circumstances are described as parallel rather than causal.

So the fundamental motive force of steam was not cognitively accessible at this point, even through direct observation.  By contrast, the marine utility of paddlewheel-driven warships was quickly assimilated. Chinese commanders adapted what they observed in the European naval forces (powerful paddlewheels that made sails unnecessary) to an existing technology (human- or ox-driven paddlewheels), and large “wheel-boats” saw action as early as 1842 on Suzhou Creek (40).

Wang notes that several Chinese inventors did in fact succeed in discerning the mechanism associated with steam power by the 1840s. Ding Gongchen succeeded in fabricating a model steam rail engine 61 centimeters long and a 134-centimeter model paddlewheel steamboat; so he clearly understood the basic mechanism at this point.  And Zheng Fuguang appears to have mastered the basic concept as well.  But here is Wang’s summary:

Ding’s efforts show that despite the circulating writings of a few experimenters, the steam engine remained a novelty, which was difficult to understand and probably impossible to reproduce.  Interested parties were discussing it, however, but attempted to grasp it in terms of their indigenous expertise alone rather than more broadly understanding the new Western technology. (45)

In 1861, during the Taiping Rebellion, a senior military commander Zeng Guofan created an arsenal in Anqing for ammunition, and also set about to create the capacity to build steam-powered ships.  With the assistance of experts Xu Shou and Hua Hengfang, the arsenal produced a partially successful full-scale steamship by 1863, and in 1864 Hua and Xu succeeded in completing a 25-ton steamship, the Huanghu, that was capable of generating 11.5 kilometers per hour.  The Chinese-build steamship had arrived.

Here is how Wang summarizes this history of technology adaptation over a 25-year period of time:

The path from the treadmill paddlewheel boat to the Jiangnan arsenal’s steamers was a long journey of discovery. Qing officials experimented with the knowledge and skills available to them, and it took time–and trial and error–for them to realize that steamboats were driven by steam, that machine tools were necessary to turn the principle of steam into a workable engine, and that drawings had to be made and read for the technology to be diffused. (53)

So perhaps the short answer to the question posed above about cross-civilizational technology transfer is this: “transfer” looks a lot more like “reinvention” than it does “imitation.”  It was necessary for Chinese experimenters, officials, and military officers to create a new set of institutions and technical capacities before this apparently simple new technological idea could find its way into Chinese implementations on a large scale.

(The image at the top is one of the most interesting parts of Wang’s very interesting paper; it establishes vividly the difficulty of transmitting technologies across different technical cultures.  The Italian drawing dates from 1607, and the Chinese copy dates from 1627.  As Wang points out, the Chinese version of the drawing is visually highly similar to the Italian original; it is a good copy.  And yet it fails to designate any of the technical features of how this treadmill-operated water pump works.  The pair of drawings are fascinating to examine in detail.)

Technology innovation in Chinese agriculture


It is a commonplace in world history to observe that China had achieved a high level of sophistication in science, medicine, and astronomy by the Middle Ages, but that some unknown feature of social organization or culture blocked the further development of this science into the expansion of technology in the early modern period. Chinese culture was “blocked” from making significant technological advances during the late Ming and early Qing periods — in spite of its scientific advantage over the West in medieval times; or so it is believed in a standard version of Chinese economic history.

A variety of hypotheses have been offered to account for this supposed fact. For example, Mark Elvin argues that China’s social and demographic system created conditions for a “high-level equilibrium trap” in the early modern period in The Pattern of the Chinese Past. According to Elvin, Chinese social arrangements favored population growth; innovative and resourceful farmers discovered all feasible refinements of traditional agricultural techniques to refine a highly labor-intensive system of agriculture; and population expanded to the point where the whole population was at roughly the subsistence level while consuming virtually the whole of the agricultural product. There was consequently no social surplus that might have been used to invest in discovery of major innovations in agricultural technology; so the civilization was trapped. (Here is a more developed discussion of Elvin’s argument.)

Other historians have speculated about potential features of Confucian culture that might have blocked the transition from scientific knowledge to technology applications. The leading Western expert on Chinese science is Joseph Needham (1900-1995), whose multi-volume studies on Chinese science set the standard in this area (Science and Civilisation in China. Volume 1: Introductory Orientations; Clerks and Craftsmen in China and the West). And Needham attributes China’s failure to continue to make scientific progress to features of its traditional culture.

But here is a more fundamental question: is the received wisdom in fact true? Was Chinese technology unusually stagnant during the early-modern period (late Ming, early Qing)? Agriculture is a particularly important aspect of traditional economic life; so we might reformulate our question a bit more specifically: what was the status of agricultural technology in the seventeenth and eighteenth centuries (late Ming, early Qing)? (See an earlier posting on Chinese agricultural history for more on this subject.)


Economic historian Bozhong Li considers this question with respect to the agriculture of the lower Yangzi Delta in Agricultural Development in Jiangnan, 1620-1850. And since this was the most important agricultural region in China for centuries, his findings are important. (It was also the major cultural center of China; see the concentration of literati in the map above.) Li makes an important point about technological innovation by distinguishing between invention and dissemination. An important innovation may be discovered in one time period but only adopted and disseminated over a wide territory much later. And the economic effects of the innovation only take hold when there is broad dissemination. This was true for Chinese agriculture during the Ming period, according to Li:

The revolutionary advance in Jiangnan rice agriculture technology appeared in the late Tang and led to the emergence and development of intensive agriculture composed of double-cropping rice and wheat. But this kind of intensive agriculture in pre-Ming times was largely limited to the high-fields of western Jiangnan. In the Ming this pattern developed into what Kitada has called the ‘new double-cropping system’ and spread throughout Jiangnan, but only in the late Ming did it become a leading crop regime. Similar were the development and spread of mulberry and cotton farming technologies, though they were limited to particular areas and cotton technology’s advances came later because cotton was introduced later. Each had its major advances in the Ming. Therefore, technology advances in Ming Jiangnan agriculture were certainly not inferior to those of Song times which are looked at as a period of ‘farming revolution’. (40)

Li also finds that there was a significant increase in the number of crop varieties in the early Qing — another indication of technological development. He observes, “The later the date, the greater the number of varieties. For example, in the two prefectures of Suzhou and Changzhou, 46 varieties were found in the Song, but the number rose to 118 in the Ming and 259 in the Qing” (40). And this proliferation of varieties permitted farmers to adjust their crop to local soil, water, and climate conditions — thus increasing the output of the crop per unit of land. Moreover, formal knowledge of the properties of the main varieties increased from Ming to Qing periods; “By the mid-Qing, the concept of ‘early’ rice had become clear and exact, and knowledge of ‘intermediate’ and ‘late’ strains had also deepened” (42). This knowledge is important, because it indicates an ability to codify the match between the variety to the local farming environment.

Another important process of technology change in agriculture had to do with fertilizer use. Here again Li finds that there was significant enhancement, discovery, and dissemination of new uses of fertilizer in the Ming-Qing period.

A great advance in fertilizer use took place in Jiangnan during the early and mid-Qing, an advance so significant that it can be called a ‘fertilizer revolution’. The advance included three aspects: (a) an improvement in fertilizer application techniques, centring on the use of top dressing; (b) progress in the processing of traditional fertilizer; and (c) an introduction of a new kind of fertilizer, oilcake. Although all three advances began to appear in the Ming, they were not widespread until the Qing. (46)

And the discovery of oilcake was very important to the increases in land productivity that Qing agriculture witnessed — thus permitting a constant or slightly rising standard of living during a period of some population increase.

There were also advances in the use of water resources. Raising fish in ponds, for example, became an important farming activity in the late Ming period, and pond fish became a widely commercialized product in the Qing. Li describes large-scale fishing operations in Lake Tai in Jiangnan using large fishing boats with six masts to catch and transport the fish (62).

So Li’s estimate of agricultural technology during the Ming period is that it was not stagnant; rather, there was significant diffusion of new crops, rotation systems, and fertilizers that led to significant increases in agricultural product during the period. “In sum, in the Jiangnan plain, land and water resources were used more rationally and fully in the early and mid-Qing than they had been in the late Ming” (64).

Two points emerge from this discussion. First, Li’s account does in fact succeed in documenting a variety of knowledge-based changes in agricultural practices and techniques that led to rising productivity during the Ming-Qing period in Jiangnan. So the stereotype of “stagnant Chinese technology” does not serve us well. Second, though, what Li does not find is what we might call “science-based” technology change: for example, the discovery of chemical fertilizer, controlled experiments in rice breeding, or the use of machinery in irrigation. The innovations that he describes appear to be a combination of local adaptation and diffusion of discoveries across a broad territory.

So perhaps the question posed at the start still remains: what stood in the way of development of empirical sciences like chemistry or mechanics that would have supported science-based technological innovations in the early modern period in China?

Is a rail network a social structure?

figures: RER map of Paris; E. J. Marey’s graphical representation of Paris-Lyon train schedules, 1885

What role does a rail network play within an adequate ontology of society? Is a rail system primarily a set of physical assets, a set of administrative procedures, or a set of embodied opportunities and constraints for other members of society? The answer is, a transportation system has elements of all of these.

A rail system provides convenient transportation among a number of places, while providing no service at all between other pairs of locations. You can get from Porchefontaine to Sevran Livry with only a change of trains at St. Michel – N Dame in about 30 minutes — whereas from Point X to Point Y there is no convenient transportation connection by Metro or RER. This means, among other things, that some parts of Paris are much more tightly integrated than others. It is possible for residents of arrondissement X to shop and work in arrondissement Y very conveniently, whereas this would not be true for arrondissement Z.

So a rail system certainly has direct effects on social behavior; it structures the activities of the two million or more Parisians by making some places of residence, work, shopping, and entertainment substantially more accessible than other places. And there are a number of other social characteristics that are structured by the commuter rail system as a consequence: for example, patterns of class stratification of neighborhoods, patterns of diffusion of infectious disease, patterns of ethnic habitation around the city, patterns of diffusion of social styles and dialect, … In brief, a rail system has definite social effects. It creates opportunities and constraints that affect the ways in which individuals arrange their lives and plan their daily activities. And other forms of social behavior and activity are conveyed through the conduits established by the transport system.

Moreover, a rail system is a physical network that has an embodied geometry and spatiality on the ground. Through social investments over decades or more, tracks, stations, power lines, people movers, and fuel depots have been created as physical infrastructure for the transportation network. Lines cross at junctions, creating the topology of a network of travel; and the characteristics of travel are themselves elements of the workings of the network — for example, the rate of speed feasible on various lines determines the volume of throughput of passengers through the system. And neighborhoods and hotels agglomerate around important hubs within the system.

In addition to this physical infrastructure, there is a personnel and management infrastructure associated with a rail system as well: a small army of skilled workers who maintain trains, sell tickets, schedule trains, repair tracks, and myriad other complex tasks that must be accomplished in order for the rail system to carry out its function of efficiently and promptly providing transportation. This human organization is surely a “social structure,” with some level of internal corrective mechanisms that maintain the quality of human effort, react to emergencies, and accomplish the business functions of the rail system. This structure exists in the form of training procedures, operating manuals, and processes of supervision that maintain the coordination needed among ticket agents in stations, repairmen in the field, track inspectors, engineers, and countless other railroad workers. And this structure is fairly resilient in the face of change of personnel; it is a bureaucratized structure that makes provision for the replacement of individuals in all locations within the organization over time.

So a rail network has structural characteristics at multiple levels. The physical network itself has structural characteristics (nodes, rates of travel, volume of flow of passengers and freight). This can be represented statically by the network of tracks and intersections that exist (like the stylized map of the RER above); dynamically, we can imagine a “live” map of the system representing the coordinated surging of multiple trains throughout the system, throughout the course of the day. The railroad organization has a bureaucratic structure — represented abstractly by the organizational chart of the company, but embodied in the internal processes of training, supervision, and recruitment that manage the activities of thousands of employees. And the social and technical ensemble that these constitute in turn creates an important structure within the social landscape, in that these physical and human structures determine the opportunities and constraints that exist for individuals to pursue their goals and purposes.

A general problem that confronts assertions about “social structures” is the question, what factors give the hypothesized structure a degree of permanence over time? Why should we not expect that social structures will morph quickly in response to changing uses and demands by opportunistic actors within them? A rail system provides a somewhat more definite answer to this question than is possible for most putative social structures: the physicality of the system is itself a barrier to rapid, radical structural change. The locations of the great rail terminus stations in Paris have not changed in the past century. And this is at least in part a consequence of the vast “sunk costs” that are associated with the embodied structure of track, intersection, and station that had developed over the course of the first fifty years of French railroad expansion. So the need for a passenger from Dijon to Strasbourg to convey himself/herself from the Gare de Lyon (1900) to the Gare de l’Est (1849) is exactly the same today as it was in 1900.

Technology and culture

Photo: Charles Sheeler, “Power, wheels”, 1939; MFA, Boston

Technology is sometimes thought of as a domain with a logic of its own — an inevitable trend towards the development of the most efficient artifacts, given the potential represented by a novel scientific or technical insight. The most important shift that has occurred in the ways in which historians conceptualize the history of technology in the past thirty years is the clear recognition that technology is a social product, all the way down. And, as a corollary, historians of technology have increasingly come to recognize the deep contingency that characterizes the development of specific instances or families of technologies.

Thomas Hughes is one of the most important and prolific historians of technology of his generation. His most recent book, Human-Built World: How to Think about Technology and Culture, is well worth reading. It looks at “technology” from a very broad perspective and asks how this dimension of civilization has affected our cultures in the past two centuries. The twentieth-century city, for example, could not have existed without the inventions of electricity, steel buildings, elevators, railroads, and modern waste-treatment technologies. So technology “created” the modern city. But it is also clear that life in the twentieth-century city was transformative for the several generations of rural people who migrated to them. And the literature, art, values, and social consciousness of people in the twentieth century have surely been affected by these new technology systems.

This level of analysis stands at the most generic perspective: how does technology influence culture? (And perhaps, how does culture influence technology?) What Hughes has demonstrated in so much of his work, though, is the fact that the most interesting questions about the “technology-society” interface can be framed at a much more disaggregated level. Consider some of the connections he suggests in his earlier book on the history of electric power (Networks of Power: Electrification in Western Society, 1880-1930):

  • Invention (by individuals with a very specific educational and cultural background)
  • Concrete development of the artifacts within a laboratory (involving specific social relationships among various experts and workers)
  • “Selling” the innovation to municipal authorities (for lighting and traction) and to industrial capitalists (for power)
  • Finding investors and sources of finance for large capital investments in electricity
  • Building out the infrastructure for delivery of electric power
  • Government regulation of industry practices
  • Development of an extended research capability addressing technology problems

Each part of this complex story involves processes that are highly contingent and highly intertwined with social, economic, and political relationships. And the ultimate shape of the technology is the result of decisions and pressures exerted throughout the web of relationships through which the technology took shape. But here is an important point: there is no moment in this story where it is possible to put “technology” on one side and “social context” on the other. Instead, the technology and the society develop together.

Hughes also explores some of the ways in which the culture of the machine has influenced architecture, art, and literature. He discusses photography by Charles Sheeler (whose famous series on the Rouge plant defined an industrial aesthetic), artists Carl Grossberg and Marcel Duchamp, and architects such as Peter Behren. The central theme here is the idea that industrial-technological developments caused significant cultural change in Europe and America. Hughes’s examples are mostly drawn from “high” culture; but historians of popular culture too have focused on the impact of technologies such as the railroad, the automobile, or the cigarette on American popular culture. See Deborah Clarke’s Driving Women: Fiction and Automobile Culture in Twentieth-Century America for a discussion of the effect of automotive culture. And Pam Pennock’s examination of the effects of alcohol and tobacco advertising on American culture in Advertising Sin And Sickness: The Politics of Alcohol And Tobacco Marketing, 1950-1990 is also relevant.

Hughes doesn’t consider here the other line of influence that is possible between culture and technology: how prevailing aesthetic and cultural preferences influence the development of a technology. This has been an important theme in the line of interpretation referred to as the “social construction of technology” (SCOT). Wiebe Bijker makes the case for the social construction of mundane technologies such as bicycles in Of Bicycles, Bakelites, and Bulbs: Toward a Theory of Sociotechnical Change. And automobile historian Gijs Moms argues in The Electric Vehicle: Technology and Expectations in the Automobile Age that the choice between electric and internal combustion vehicles in the early twentieth century turned on aesthetic and lifestyle preferences rather than technical or economic efficiency. (Here is a nice short discussion of SCOT.) This too is a more disaggregated approach to the question. It proceeds on the idea that we can learn a great deal by examining the “micro” processes in culture and society that influence the development of a technology.

It seems to me that the conceptual framework of “assemblages theory” would be useful in discussing the history of technology. (See Manuel DeLanda’s A New Philosophy of Society: Assemblage Theory And Social Complexity for a review of the theory, and Nick Srnicek’s blog at accursedshare, which makes frequent use of the framework.) The framework is useful here because technology is a social phenomenon that extends from one’s own kitchen and household to the cities of Chicago or Berlin, to the global internet and the international system of manufacturing and design. And similar processes of shaping and conditioning occur at the micro, meso, and macro levels. In other words — perhaps we can understand “technology” at the molar level, as a complex composition of activities and processes at many levels closer to the socially constructed individual. And the value-added provided by the sociology and history of technology is precisely this: to shed light on the mechanisms at work at all levels that have an influence on the aggregate direction and shape of the resulting technology.

Since we’re thinking about “technology and culture” — it’s worth noting that Technology and Culture is the world’s leading journal for the history of technology, emanating from the Society for the History of Technology (SHOT, established in 1958). The journal has played a significant role in the definition of the discipline over the past thirty years or so and is an outstanding source for anyone interested in the questions posed here.

How do new ideas get used?

Economic development and growth depend chiefly on innovation — new products, processes, materials, and modes of organization that can create new opportunities in the marketplace. Business creation and economic growth depend upon innovation. This means creating new products that consumers want or need, improving the performance or safety of the product, or improving the cost and efficiency of the process of production and distribution. So a critical element in economic development is the discovery and development of new ideas — often technical and scientific ideas. Google, Apple, and Pfizer are examples of industries that created brand new markets for products based on innovations in science and engineering (search technologies, user-friendly computing devices, cholesterol regulating drugs).

Universities are places where highly specialized and talented people are in the business of making discoveries and further developing or refining existing ideas. So we might expect that universities would also be potent sources of economic growth. New ideas surface in engineering, science, or medical research facilities; they are quickly recognized for their potential applications in the marketplace; and entrepreneurs or existing businesses capitalize on them and move them quickly to the marketplace. The basis for this expectation is a familiar one within an Adam Smith sort of framework: new ideas are a potential source of new wealth, and rational maximizers will quickly identify these new wealth opportunities and will quickly and efficiently develop them.

We might expect that this is the case. But surprisingly enough, this picture seems not to be born out in experience. What seems much more true as a description of the process of research and discovery, is that most ideas do not move into the process of commercialization and business development. In fact, it seems like a fairly believable guess that there exist today in the stock of university research discoveries, the makings of dozens of billion-dollar industries and hundreds or thousands of million-dollar industries — and that these ideas are likely enough to remain dormant for a very long time. Most ideas are not fully developed as business ventures, not because they are not viable, but because the activity of recognizing the market potential of an idea and developing it commercially is itself an extended effort that requires imagination and creativity, and this is not usually either the strength or the priority of the working research scientist.

So what is the obstacle to the full and efficient development of potentially profitable new innovations? Part of the problem is the separation that exists between the research community and the business community. The person who understands the new technology or scientific innovation does not usually understand the commercial potential of the idea, and usually does not have much of a practical idea of what is involved in commercializing an innovation. The research scientist in a university is largely motivated by the rewards of academic progress: publication, the gaining of grants to support future research, and the rewards of prestige that go with academic success. The gap between the technical characteristics of the innovation and the steps that would need to be taken in order to transform this innovation into a business venture is also a very wide one. A research scientist may have developed a technique for coating metals that permits the metal to preserve an electrical charge. But it is not self-evident how this innovation might be developed into new products or processes that have the potential for creating substantial new markets or profits. The researcher who conducts the basic research leading to the innovation usually has little knowledge or interest in the applications that might be possible. And the challenge of bridging the gap between the innovation and some of its potential commercial applications may demand an equally creative and time-consuming period of intellectual and practical work as did the original discovery — and this is likely to be a form of effort that is foreign to the research scientist.

It might be thought that innovation-oriented investors are among the important mechanisms that help to identify potentially valuable ideas and innovations and move them to successful businesses. “Angel” investors and venture capital firms are certainly filling part of the role of “innovation spotters” in the modern economy. But even this mechanism seems incomplete, in the sense that potentially valuable engineering and scientific research activity generally remains invisible to the investor community until an entrepreneurial researcher brings it forward along with a business plan. So there is a wide information gap between the researcher, the investor, and the business entrepreneur.

These observations suggest two things. First, our economy could be strengthened if we had a substantially more efficient system of identifying innovations as they occur in laboratories throughout the country, and moving these innovations into productive applications. And second, the story seems to suggest that there is a niche available in our economy that would provide profitable opportunities for businesses that are specifically designed to seek out these innovations and innovators and facilitate the transition from idea to product.

Thomas Hughes’ detailed history of electric power is a very important illustration of several aspects of this complex story (Networks of Power: Electrification in Western Society, 1880-1930). Hughes demonstrates how long the chain of development is between basic science and useable technology; he also highlights the many contingencies that occur along the way, as electric power generation technology is developed into a mass industry using alternating current. One of the most frequently discussed examples of technology innovation and business development is the story of how the innovations in computer interfaces (the mouse, WYSIWYG editing, Windows-style interface) that were created by Xerox PARC found their way into the multi-billion dollar industry of personal computing. Douglas Smith and Robert Alexander emphasize the business mistakes that many people attribute to Xerox in this story in Fumbling the Future: How Xerox Invented, Then Ignored, the First Personal Computer, while Michael Hiltzik’s Dealers of Lightning: Xerox PARC and the Dawn of the Computer Age provides a more favorable version of the story.

Safety as a social effect


Some organizations pose large safety issues for the public because of the technologies and processes they encompass. Industrial factories, chemical and nuclear plants, farms, mines, and aviation all represent sectors where safety issues are critically important because of the inherent risks of the processes they involve. However, “safety” is not primarily a technological characteristic; instead, it is an aggregate outcome that depends as much on the social organization and management of the processes involved as it does on the technologies they employ. (See an earlier posting on technology failure.)

We can define safety by relating it to the concept of “harmful incident”. A harmful incident is an occurrence that leads to injury or death of one or more persons. Safety is a relative concept, in that it involves analysis and comparison of the frequencies of harmful incidents relative to some measure of the volume of activity. If the claim is made that interstate highways are safer than county roads, this amounts to the assertion that there are fewer accidents per vehicle-mile on the former than the latter. If it is held that commercial aviation is safer than automobile transportation, this amounts to the claim that there are fewer harms per passenger-mile in air travel than auto travel. And if it is observed that the computer assembly industry is safer than the mining industry, this can be understood to mean that there are fewer harms per person-day in the one sector than the other. (We might give a parallel analysis of the concept of a healthy workplace.)

This analysis highlights two dimensions of industrial safety: the inherent capacity for creating harms associated with the technology and processes in use (heavy machinery, blasting, and uncertain tunnel stability in mining, in contrast to a computer and a red pencil on the editorial offices of a newspaper), and the processes and systems that are in place to guard against harm. The first set of factors is roughly “technological,” while the second set is social and organizational.

Variations in safety records across industries and across sites within a given industry provide an excellent tool for analyzing the effects of various institutional arrangements. It is often possible to pinpoint a crucial difference in organization — supervision, training, internal procedures, inspection protocols, etc. — that can account for a high accident rate in one factory and a low rate in an otherwise similar factory in a different state.

One of the most important findings of safety engineering is that organization and culture play critical roles in enhancing the safety characteristics of a given activity — that is to say, safety is strongly influenced by social factors that define and organize the behaviors of workers, users, or managers. (See Charles Perrow, Normal Accidents: Living with High-Risk Technologies and Nancy Leveson, Safeware: System Safety and Computers, for a couple of excellent treatments of the sociological dimensions of safety.)

This isn’t to say that only social factors can influence safety performance within an activity or industry. In fact, a central effort by safety engineers involves modifying the technology or process so as to remove the source of harm completely — what we might call “passive” safety. So, for example, if it is possible to design a nuclear reactor in such a way that a loss of coolant leads automatically to shutdown of the fission reaction, then we have designed out of the system the possibility of catastrophic meltdown and escape of radioactive material. This might be called “design for soft landings”.

However, most safety experts agree that the social and organizational characteristics of the dangerous activity are the most common causes of bad safety performance. Poor supervision and inspection of maintenance operations leads to mechanical failures, potentially harming workers or the public. A workplace culture that discourages disclosure of unsafe conditions makes the likelihood of accidental harm much greater. A communications system that permits ambiguous or unclear messages to occur can lead to air crashes and wrong-site surgeries.

This brings us at last to the point of this posting: the observation that safety data in a variety of industries and locations permit us to probe organizational features and their effects with quite a bit of precision. This is a place where institutions and organizations make a big difference in observable outcomes; safety is a consequence of a specific combination of technology, behaviors, and organizational practices. This is a good opportunity for combining comparative and statistical research methods in support of causal inquiry, and it invites us to probe for the social mechanisms that underlie the patterns of high or low safety performance that we discover.

Consider one example. Suppose we are interested in discovering some of the determinants of safety records in deep mining operations. We might approach the question from several points of view.

  • We might select five mines with “best in class” safety records and compare them in detail with five “worst in class” mines. Are there organizational or techology features that distinguish the cases?
  • We might do the large-N version of this study: examine a sample of mines from “best in class” and “worst in class” and test whether there are observed features that explain the differences in safety records. (For example, we may find that 75% of the former group but only 10% of the latter group are subject to frequent unannounced safety inspection. This supports the notion that inspections enhance safety.)
  • We might compare national records for mine safety–say, Poland and Britain. We might then attempt to identify the general characteristics that describe mines in the two countries and attempt to explain observed differences in safety records on the basis of these characteristics. Possible candidates might include degree of regulatory authority, capital investment per mine, workers per mine, …
  • We might form a hypothesis about a factor that should be expected to enhance safety — a company-endorsed safety education program, let’s say — and then randomly assign a group of mines to “treated” and “untreated” groups and compare safety records. (This is a quasi-experiment; see an earlier posting for a discussion of this mode of reasoning.) If we find that the treated group differs significantly in average safety performance, this supports the claim that the treatment is causally relevant to the safety outcome.

Investigations along these lines can establish an empirical basis for judging that one or more organizational features A, B, C have consequences for safety performance. In order to be confident in these judgments, however, we need to supplement the empirical analysis with a theory of the mechanisms through which features like A, B, C influence behavior in such a way as to make accidents more or less likely.

Safety, then, seems to be a good area of investigation for researchers within the general framework of the new institutionalism, because the effects of institutional and organizational differences emerge as observable differences in the rates of accidents in comparable industrial settings. (See Mary Brinton and Victor Nee, The New Institutionalism in Sociology, for a collection of essays on this approach.)

%d bloggers like this: