Nancy Qian, along with a big group of coauthors, has done a great amount of interesting empirical work in recent years on the economics of modern China; among other things, she has shown that local elections actually do cause policy changes in line with local preferences and that the state remains surprisingly powerful in the Chinese economy. In this paper with Xin Meng and Pierre Yared, she considers what is likely the worst famine in the history of mankind, China’s famous famine following the Great Leap Forward. After a agricultural production shock in 1959, a series of misguided policy experiments in the mid-1950s (like “backyard steel” production, which produced worthless metal), and an anti-Rightist purge which ended a brief period of less rigid bureaucracy, 30 million or so people would die from hunger over the next two years, with most deaths among the young and the very old. To put this in relative context, in the worst-hit counties, the birth-cohorts that should have been born or very young in 1960 and 1961 are today missing more than 80% of their projected members.
What is interesting, and what we have known since Sen, is that famines generally result from problems of food distribution rather than food production. And, indeed, the authors show that total grain production in caloric terms across rural parts of China is a multiple of what is necessary to hold off starvation during the height of the productivity shock. What is interesting and novel, though, is that provinces with higher historic per-capita grain production had the highest mortality, and likewise counties with the highest per-capita production as measured by a proxy based on climate also have the largest number of “missing” members in their birth year cohort in the 1990 census. This is strange – you might think that places that are living on the edge in normal times are most susceptible to famine.
This is where politics comes into play. The Chinese government “sent down” many competent bureaucrats during the anti-Rightist purges in the late 1950s, limiting the ability of the government to use flexible mechanisms for food procurement. The food system at the time involved the central government collecting a set amount of grain from each region, then returning stocks to communal kitchens. Now, local leaders had a strong incentive to understate how much was produced in a given year so that they could use the remainder for local power purposes. Because of limited communication technology and ineffective bureaucracy, the optimal mechanism (not specified formally, but apparently done so in an earlier version) for the central government involved pre-setting fixed production goals for every region. Here is the problem: imagine you wish the city, rural area 1 and rural area 2 to have the same expected consumption, with the city producing no food, and rural area 1 producing 1 ton per capita per year and rural area 2 producing 1.4 tons per capita. This gives total consumption of .8 tons per capita if the government sets in advance a fixed “tax” of .2 tons per capita from 1 and .6 tons per capita from region 2. Now a productivity shock lowers production everywhere by 10 percent. The city still gets its .8 tons per capita (since the “tax” is fixed), but area 1 now gets .9*1-.2=.7 tons per capita, and area 2 gets 1.4*.9-.6=.66 tons per capita. That is, the lack of flexibility in the system is more likely to push the productive regions into famine than other regions.
Now, this is not the whole story. Alternative explanations, already suggested in the literature, also are quantitatively important. Places with more anti-Rightist purges before the famine saw higher mortality (see this 2011 APSR by Kung and Chen), as did places with earlier adoption of communal dining halls or larger increases in backyard steel production, both proxies for “zealous” adherence to the Great Leap Forward. I would really like to see some attempt at a decomposition here: if you buy that local political leadership, the central government quota system, and political punishment of counterrevolutionary areas were all important, and that weather shocks alone were not, how many of the deaths should we ascribe to each of those factors? This seems an important question for preventing future famines. It seems that a further fleshing out of how these results relate to the old theory of the firm debates about flexibility of local managers under imperfect and partially unverifiable reporting can help us understand what was going on with the CCP policy choices; I’m thinking, for instance, of explicitly showing whether it is true that loss of members of the bureacracy (i.e., an increase in the cost of monitoring) necessarily incentivizes more rigid allocation rules. Theory here could help to quantify how important this mechanism might be.
2011 working paper (IDEAS version). This paper is R&R at ReStud currently. Qian has a couple other working papers that caught my eye. First, a paper with Duflo and Banerjee on Chinese transportation infrastructure finds very little impact on relative incomes of (quasi-random) access to a good transportation network, and suggests in a short model (which is less convincing…) that relative immobility of capital might be causing this. The techniques in the paper are similar to those used by Ben Faber in his very nice paper showing Krugman’s home market effect: if you are small and poor, being connected with a big productive place may not be good for you due to increasing returns to scale. Qian also has a 2013 paper with Nathan Nunn on food aid which suggests, pretty convincingly, that food aid in civil war zones prolongs conflicts; the mechanism, roughly, is that local armies can easily steal the aid and hence have less reason to sue for peace. The identification strategy here is quite nice: the US government buys wheat for price stabilization reasons, then gives much of this away to impoverished countries. The higher the price of wheat, the less the government surplus is, hence the less is given away.
Yes. Communist thinking and abandoning market mechanisms caused mass famine, even in the richer areas. Who would have expected that?
“…provinces with higher historic per-capita grain production had the highest mortality”. Many of the “production” figures being reported at this time were made up. However, provinces were being taxed on these fabricated figures. Those producing the highest fabricated figures were taxed [in-kind] the most and so, would logically have suffered the greatest deprivations, when actual output started to fall. Those areas producing rational or understated figures, had a corresponding buffer against such shocks.