that cannot easily be addressed via factor markets may attenuate small farmers’ advantage. Rising wages may make substitution of capital for labor desirable even without changes in technology (Yamauchi 2016). Technical change, via innovations in crop breeding, tillage, and information technology (Deininger, Nizalov, and Singh 2013) may reduce supervision requirements further.<sup>5</sup> Information technology may improve smallholders’ competitive position at the marketing stage (Aker and Fafchamps 2014) more intensive use of technology at the production stage will require capital. If, for example, the transaction cost of providing formal credit in rural markets—which could possibly be exacerbated by constraints on those markets’ ability to post collateral due to lack of formal documentation—may cause small farmers to have difficulty in accessing capital; thus, the inverse relation may actually be reversed. In fact, it has been shown that in India, large benefits may be forgone due to land market imperfections that make it impossible to assemble parcel sizes large enough to allow mechanization (Foster and Rosenzweig 2010).

The above suggests that magnitude and possibly direction of the relation between farm size and productivity will vary in response to exogenous factors including non-farm labor demand, availability of technology, and the functioning of capital markets. These factors, together with policy, will affect factor prices, the optimum ratio of capital to labor used in production, supervision needs, and the extent of market imperfections that have traditionally given rise to the inverse farm size-productivity relationship (Otsuka, Liu and Yamauchi 2013). This finding is illustrated by the differential effect of the increased importance of knowledge and capital in the wake of the green revolution (Deolalikar 1981), which weakened the farm size-productivity relationship in districts suited to new technology but left it intact, with small producers most efficient, where traditional methods prevailed.

# **2.2 Data and descriptive statistics**

To empirically address this issue, we use data from the 1982, 1999 and 2008 rounds of the Additional Rural Incomes Survey & Rural Economic & demographic survey (ARIS/REDS), a panel survey covering 242 villages in 17 Indian states conducted by India’s National Council for Applied Economic Research (NCAER).<sup>6</sup> Each round’s household schedule provides data on demographics, assets, income, consumption, and economic activities at the household level, as well as detailed information on labor and non-labor inputs used in agriculture and outputs from agricultural production. In case of splits,<sup>7</sup> all of a household’s

> 5 Pest-resistant and herbicide-tolerant varieties facilitated broad adoption of zero tillage and, by reducing the number of steps in the production process as well as the labor intensity of cultivation, allowed management of larger areas. The ability to have machinery operations guided by GPS technology reduces skill requirements and allows remote supervision. The scope for substituting crop and pest models and remotely sensed information on field conditions for personal observation also reduces the advantage of local knowledge (Deininger and Byerlee 2012b). 6 The sample included three strata: (i) districts that were part of the Intensive Agricultural District Program (IADP), an extension and input provision program placed in areas thought to have high potential for crop productivity growth; (ii) districts covered by the Intensive Agricultural Area Program, and (iii) all other districts.

> 7 The 1999 round covered a total of 7,474 households, including all original households and their split-offs. The 2008 round (for the 2007–08 season) has a sample size of 8,659 households from 242 villages and includes all households surveyed in 1999, the split-off households residing within these villages, and a set of randomly selected new households.

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