# **4.5 Discussion**

Taken together, our cross-sectional results imply a long-run elasticity of rural wages with respect to land productivity, _εw,π_ = _d_ log( _w_ ) _/d_ log( _π_ ) =<sup>_d_lo</sup> _dT_<sup><u>g(</u></sup><sup>_w_</sup><sup><u>)</u></sup> _/_<sup>_d_lo</sup> _dT_<sup><u>g(</u></sup><sup>_π_</sup><sup><u>)</u></sup> , of around 0.35. Badiani (2009) examines the response of unskilled wages (across sectors) to long-run changes in exogenous agricultural productivity using a district-level panel constructed from successive rounds of the NSS employment-unemployment survey dating back to 1983. Reassuringly, she obtains an estimated wage elasticity remarkably close to 0.35.

We do not directly estimate the impact of weather _shocks_ on labor productivity, primarily because reliable, high frequency, and relatively recent data on rural wages in India is lacking (see Himanshu, 2005, for a critique of alternative sources). However, we obtain an indirect _d_ log( _Y_ <u>)</u><sup><u>)</u></sup> estimate using ∆log( _w_ ) _≈ εw,π dT_ ∆ _T,_ where<sup>_d_lo</sup> _dT_<sup><u>g(</u></sup><sup>_Y_</sup> is the output response to temperature shocks already estimated. Using this formula, a 1<sup>_◦_</sup> C increase in temperature, for example, would reduce agricultural wages by about 10 percent in the absence of adaptation.

Finally, a word about precipitation. Except in the land value regressions, where its direct effect is insignificant, we find that higher rainfall is associated with greater agricultural productivity. Putting aside the enormous uncertainty involved in forecasting precipitation patterns over the Indian subcontinent mentioned earlier, whatever consensus has emerged points to a very modest rise in rainfall over the next three decades. Guiteras (2009), for one, uses the most recent IPCC projection of a 4 percent increase. Meanwhile, temperatures across India over this period are expected to warm by around 1 _._ 25<sup>_◦_</sup> C on average (see below). Based on our estimates in Table 4, we predict that a 4% increase in rainfall (at the district area-weighted means of the data) would raise agricultural output per hectare by at most 1 _._ 0%, a figure dwarfed by the 37% decline in yields implied by the concomitant rise in<sup><u>)</u></sup> temperature (i.e., weighted average of<sup>_d_lo</sup> _dT_<sup><u>g(</u></sup><sup>_Y_</sup> ∆ _T_ ). Results in Guiteras (2009) and Sanghi, Mendelsohn, and Dinar (1998) also support the view that climate change will overwhelmingly affect agricultural productivity in India through higher termperatures.

# **4.6 Cereal prices and climate**

Since the main cereals produced and consumed in India, rice and wheat, are traded on both domestic and world markets, it would be nonsensical to use the cross-district correlation between current prices and climate (let alone weather) to forecast the effect of climate change on cereal prices 30 years hence. Even under the arguable proposition that, up to now, domestic grain markets have not been well integrated so that districts are for all practical purposes autarkic, such a district-level analysis would tell us little about how cereal prices would change in the future. This is because, despite short-run government attempts to 17