Sustaining the Farming Household to Sustain the Farming

In the literature on sustainability of agriculture, both labourers and workers are conspicuously absent. Here, the sustainability of agriculture has been defined in terms of whether the farm household in question is able to yield an energy surplus when its members and the animals in its possession are obtaining an adequate ‘energy income’ or Calorie intake. To evaluate the sustainability of 590 farming households in the state of West Bengal, India, during 2004–5, four progressively stricter definitions of sustainability have been proposed, defined and applied. The method of energy balance analysis was followed. A negative surplus was found to be near‐universal across size‐groups in terms of the net area sown (NAS), the gross cultivated area (GCA) and agro‐climatic zones. The threshold output for a non‐negative surplus during the cultivating period was 700,000 megajoules (MJ); in terms of the GCA for a positive ‘full and final’ annual surplus, it was 3 hectares, and in NAS terms it was 2.5 hectares; against NAS per household size, it was 0.6 hectares, for ensuring a positive surplus beyond the annual sustainability. No evidence could be found in favour of household size as an explanation for the negative surplus.     

Do Proprietary Algorithmic Traders Withdraw Liquidity during Market Stress?

We investigate the role of proprietary algorithmic traders in facilitating liquidity in a limit order market. Using order‐level data from the National Stock Exchange of India, we find that proprietary algorithmic traders increase limit order supply following periods of both high short‐term stock‐specific volatility and extreme stock price movement. Even following periods of high marketwide volatility, they do not decrease their supply of liquidity. We define orders from high‐frequency traders as a subclass of orders from proprietary algorithmic traders that are revised in less than three milliseconds. The behavior of high‐frequency trading mimics the behavior of its parent class. This is inconsistent with the theory that fast traders leave the market when stress situations arise, although their limit‐order‐supplying behavior becomes weaker when the increase in short‐term volatility is more informational than transitory. Agency algorithmic traders and nonalgorithmic traders behave opposite to proprietary algorithmic traders by reducing the supply of liquidity during stress situations. The presence of faster traders in the market possibly instills the fear of adverse selection in them. We document that the order imbalance of agency algorithmic traders is positively related to future short‐term returns, whereas the order imbalance of proprietary algorithmic traders is negatively related to future short‐term returns.