Arunabh Ghosh: Dear Surabhi, many thanks! I have two ongoing projects, the futures of both of which are now held hostage to US-China and China-India relations! And, of course, to Covid-19!
The first of these directs my longstanding interest in science and statecraft towards a history of small-scale dam-building in modern China. Although there are significant bodies of work that analyze water management through China’s long history or consider the rise and effect of recent mega-projects like the Three Gorges Dam (and Gezhouba and Sanmenxia before it), we still have no historical account for the 90,000 or so dams that have been built in China in the past one hundred years. Focusing in particular on smaller scale projects, which constitute the vast majority of such construction, I hope to examine this history in light of twentieth-century scientific utopianism, technological and environmental history, and national and transnational networks of expertise and labor.
Inspired by the discovery of the China-India statistical exchanges that are detailed in Making it Count, I am also working on a second project on the connected and comparative histories of science and technology in China and India, ca. 1930s - 1980s. Anchoring the project in case studies that range from specific disciplines (such paleaobotony and mathematics) to applied technologies (such as biogas), I hope to move beyond the civilizational and realpolitik frameworks that currently dominate China-India history. Instead, I hope to focus on the circulation of experts, scientific knowledge, technologies, and specimens.
As for that cover-image—I like it because it operates on multiple levels, each evoking a theme from the book. Most directly, it shows us how the Gross Value of Industrial and Agricultural Output (GVIAO) grew over the course of the decade. GVAIO is a combination of the two most important sectors of the 1950s economy—industry and agriculture. Furthermore, the state’s contrasting experiences collecting statistical data on industry and agriculture are at the heart of the book. The hierarchical placement of images—industrial sites such as factories and powerplants on top; farms and agricultural implements below—is also non-trivial. Agriculture, although about 50% of the economy in 1949, enjoyed a distinctly secondary role to industry throughout the decade. Finally, GVAIO data in the chart do not represent nominal values, but index numbers, which are another important, and controversial, subject in the book. All these reasons apart, it is also a rather striking image! Read more

Arunabh Ghosh: Thanks so much, Rohit. I am especially pleased to note that the book might appeal beyond its immediate audience of China scholars. 1) The commitment of ordinary citizens to the production of statistical knowledge was absolutely crucial during this period in PRC history. Given the reliance on exhaustive enumeration, the Chinese statistical apparatus needed a very large number of cadres. Estimates from 1956 indicate there were about 200,000 full-time cadre, and likely many more working on a part-time basis. A vast majority of these were young men and women. As I discuss in Chapter VI, this posed interesting challenges when it came to morale. Statistics hardly fit the traditional mold of revolutionary activity. So, the state had to resort to campaigns to instill such a belief, with mixed results.
At a broader level, the underlying conceptual basis for involving ordinary citizens—the masses—was the theory of the mass line. Attributed to Mao and typically dated to June 1943, its basic idea can be boiled down to the phrase: “from the masses, to the masses.” According to Mao, this meant “take the ideas of the masses (scattered and unsystematic ideas) and concentrate them (through study turn them into concentrated and systematic ideas), then go to the masses and propagate and explain these ideas until the masses embrace them as their own, hold fast to them and translate them into action, and test the correctness of these ideas in such action. Then once again concentrate ideas from the masses and once again go to the masses so that the ideas are persevered in and carried through. And so on, over and over again in an endless spiral, with the ideas becoming more correct, more vital and richer each time.”
As you can imagine, the mass line is fundamentally anti-expertise. It became particularly prominent during the Great Leap Forward (1958-1962), when it was used to overturn the existing statistical apparatus and its claims to expertise.
2) This is a great observation! You are correct that statistics occupy the Indian mind a fair bit. Consider my surprise then to encounter an even more intimate preoccupation with statistics in the popular imagination in China! And it goes beyond statistics—perhaps it is better understood as an intense comfort with different modes of quantification. So, it is common to visit a historical monument and learn about the tons of earth moved, the number of trees cut, the length and breadth of the structure, its height, and much else, with a far greater degree of specificity than one encounters even in India. Such comfort is pervasive. Consider, as another illustration, the existence not only of Beijing No. 1 High School, but also of Beijing No. 80 High School. Many other examples can be found. Of course, the interesting question is whether this mode of everyday quantification has a longer history or is of more recent vintage. If pushed to hazard a guess, I would say it has much to do with the putative rationality imposed by socialist planning and, therefore, likely became much more pronounced post-1949. But it is a question worth exploring further, especially if we use it as a lens to explore popular discourse, modes of argumentation, and consensus-building. Read more

Arunabh Ghosh: Thank you, Emma, for your kind words. The question you have raised is a critical one for our times. I can only provide a few preliminary and rather general thoughts here.
We live in an age where data pervades almost every aspect of our lives. In the introduction to the book (p.12), I note that “to know something through numbers remains one of the most powerful ways of knowing in the modern world. Powerful not because such knowing is necessarily or always nearer the truth (were we to grant the singularity of such a thing), but powerful because numbers offer a tool of persuasion and a basis for rational, methodical, calibrated, and repeatable actions that remain unmatched.” Over the past few decades, as computational and storage capacities have dramatically increased, this reliance on numbers, to not just define but also shape our world, has only increased. We call ours the age of Big Data but, evocative epithets aside, what it basically represents is the stubborn belief that the more we know (ideally in quantifiable form), the better designed our solutions will be. On the surface, this makes perfect sense. But what is often overlooked is that how we come to know something is an intensely fraught process that can significantly affect what we know.
As I show in the book, the Chinese state in the 1950s was excellent at producing vast quantities of data; of facts, as it were. And yet, it remained poorly informed. Much of their data, by their own admission, were worthless the moment they were produced. The system they built relied upon a key assumption, that the only correct way to know society was to count exhaustively. It relied on a vast network of periodic reports and rejected, for the most part, other methods of data collection, most notably randomized survey sampling, but also purposive (i.e. non-randomized) surveys. This generated a range of challenges that significantly hampered their ability to gain data in an accurate, timely, and affordable manner. Our “ignorance” or the “facts” we need to govern are not self-evident things. They manifest themselves because of goals we identify, assumptions we make, and methods we choose (or eschew). In so doing, they affect the decisions we make.
In relation to the present crisis, I think what is remarkable is how much we still do not know. Take what seemingly is the most straightforward of metrics: the number of infections. Given key problems in data commensurability—different testing regimens, recording practices, uneven quality of testing units, and varying incentive structures (with domestic and international imperatives) that influence transparent reporting—there is no doubt that not only are current estimates severe undercounts, but that for many countries the data is likely significantly compromised.
And yet, so much of our perception of the severity of the crisis is driven by these numbers. China, India, and the US represent three interesting points of reference on a wider spectrum charting the global spread of the infection. They also exhibit different modes of governance.
The US, a two-party federal democracy, is arguably the most data rich and data-transparent of the three. Yet, it is hamstrung by a leadership unwilling to utilize these assets. Instead, it appears that federal policy has been to marginalize both data and data practitioners.
China, a one-party (and now, one-leader) authoritarian state, after initial missteps, has appeared to effectively control the spread of Covid-19. But it has done so by further extending extremely intrusive data-based systems of surveillance and supervision. A lack of transparency also means we have little information on how the lockdown and subsequent measures have affected different sections of Chinese society.
India, a parliamentary democracy, has, under the current government, significantly undermined its statistical institutions, so much so that all major macro-indicators of the economy stand discredited. The decision to lock the country down on four hours’ notice is symptomatic of this unwillingness to consider key facts, none perhaps more salient than that the vast majority of India’s labor is in the informal sector and reliant on daily-wages. Read more

Arunabh Ghosh: Thanks, Diana! The research for the book turned out to be rather fun, with most of the archival work in China, with some additional work in India and the United States. In China, the two most important archives were the Beijing Municipal Archives (BMA) and the Foreign Ministry Archives (FMA). I had originally planned to visit a handful of provincial archives but found so much material from other provinces at the BMA that I ended up spending over 6 months there. This did lead to an interesting insight into archives administration in China. As a municipal archive the BMA had authority to make copies for me of only those documents that had been produced by the city government (and in some instances, the national government). But, if I found a valuable report produced by, say, the provincial government of Anhui, the BMA’s hands were tied. My only option was to hand copy or type-up the report. I have a few thousand pages of such typed-up notes. That said, I will forever remain grateful to the good cheer and professionalism of the archival staff of the BMA.
I was also lucky to visit the FMA at a time when they were relatively open. Among the amazing finds there was a fifteen-part, 75-page long report on the Indian statistician P.C. Mahalanobis’ visit to China in the summer of 1957. Outside of archives, I relied extensively on contemporary published materials—books, manuals, textbooks, and journal articles—most of which I found in libraries of Tsinghua and Renmin Universities and at the National Library. A handful of oral history interviews also proved crucial. Triangulating these disparate kinds of sources, along with materials found in India and the US, gave me much greater confidence to accept or reject specific hypotheses. Read more

Arunabh Ghosh: Thank you, Sunil! The richness of the China-India statistical links certainly came as a surprise to me. These were not another instance of diplomatic window-dressing. Instead, they indicated a real desire to learn from each other, and, in the Chinese case, laid bare genuine frustrations that many Chinese leaders and statisticians had with the Soviet-inspired statistical system they had constructed. This extent of this disaffection and its importance to the story of 1950s statistics in China would have remained largely invisible had I not been lucky enough to discover the China-India materials.
It is a little harder to track the longer-term effects of these statistical exchanges. There is little doubt that they affected in interesting ways the individual participants involved, some of whom went on to play an important role in the reformulation of statistics after 1978. The discovery of the exchanges has also had an altogether different kind of effect, leading me to ask: are there other important China-India scientific encounters obscured by time and our collective amnesia? One of my ongoing projects is an attempt to answer that question.
One archival aside: in my response to Diana above, I noted encountering peculiarities in archival rules in Beijing. In India, one often has to deal with an additional challenge: individual intransigence, especially at smaller (institutional) archives. On numerous occasions, I have been denied access to archival collections not because of any existing rule, but purely because of the idiosyncratic whims of the persons-in-charge, who treat the archives as their personal fiefdoms. Read more

Arunabh Ghosh: Thanks so much, Victor! I remember well that chat on the steps of Low Library! Interestingly enough, your two questions lead to a common answer. In my original plans, the census of 1953, and the kinds of questions you raise—who counted, how, etc.—figured centrally. But, as I discovered in the archives, demography was not at the heart of the statistical enterprise in 1950s China. As I was coming to terms with this potentially frightening (from the perspective of my research plans) fact, I was finding materials that pointed to a wonderfully rich and complex story about statistics itself. So, it was in the archives, in the weeds as it were, that the project’s focus gradually changed.
On the census and classification, itself, as Tom and others have so compellingly demonstrated, there were other disciplines, institutions, and theoretical concerns that played a greater role. Population as a category also has a fraught history in 1950s China, inextricably linked to socialist critiques of Malthusianism and to Mao’s own theoretical vacillations (as the economist Ma Yinchu discovered). Sarah Mellors’ recent dissertation on birth control practices offers a fascinating glimpse into less-studied aspects of the history of population in post-1949 China. Read more

Arunabh Ghosh: Thank you, Joyce! It is indeed somewhat surreal to mark a personal milestone at such a time of widespread sorrow and uncertainty. Among the small things we can do is support local bookstores, many of whom continue to take online orders despite closed storefronts. To purchase the book from the independent Harvard Book Store in Harvard Square please use this link. The book can also be purchased directly from Princeton University Press. Use Discount Code MIC20 to receive a 20% discount and free shipping. Finally, for those who have access, the book is also available via ProjectMuse. Read more