- i) _py/c_ : Computing the net (cereal) consumption ratio _p_ ( _y − x_ ) _/c_ requires the value of cereal production for each household, yet there is no farm production data in the 61st round. Using 59th round data, which has both value of production and consumption expenditure data, we estimate a tobit regression of the form _py/c_ = _β′X_ + _u_ , where _X_ includes linear splines in the amount of land the household cultivates, household demographic composition and age of head, proportion of household members with different education levels, social category of household (scheduled caste/tribe), and district dummies. Using the corresponding _X_ variables, we then impute _py/c_<sup>�</sup> = _β_<sup>�</sup> _′X_ for each household in the 61st round expenditure survey. From this imputation we subtract the _actual_ value of _px/c_ , the share of cereals in total consumption in NSS61, to obtain the household’s net consumption ratio.

- ii) _κ_ : The proportion of a household’s labor allocated to its own farm can be calculated from data available in the 61st round employment-unemployment survey and predicted based on observable characteristics _X_ using a procedure analogous to that described in (i). Imputation of a value of _κ_ � to each household in the NSS61 consumption expenditure survey follows accordingly. Households owning no land (aside from their homestead) receive _κ_ = 0 by default.

- iii) _e_ : Whereas in our theoretical development we assumed two sectors, in practice we allow for three (rural) wage sectors: (1) agriculture ( _A_ ), in which wages are affected by climate; (2) non-agricultural ( _NA_ ), in which wages are also affected by climate but to a lesser degree; and (3) public sector ( _P_ ), as described in fn. 26, in which wages are assumed to be unaffected by climate. Based on a sample of households with at least one wage-earner in the 61st round employment-unemployment survey and using the same set of _X_ variables as in (i) and (ii), we predict the proportion of household wage earners whose main job is in each of these sectors and use the results to impute _e_ � _A,_ � _eNA_ , and _e_ � _P_ to households in NSS61.

- iv) _π_ and _w_ : Weights _λ_ , _φ_ , and _σ_ are functions of sector-specific land and labor prices. To obtain _πI_ and _πN_ in terms of an annualized flow, we take district median of per hectare land values for, respectively, irrigated and unirrigated land (from NSS59) multiplied by a discount rate, _δ_ = 0 _._ 05; we also try _δ_ = 0 _._ 10 as a robustness check.<sup>28</sup> For _wA_ , _wNA_ ,

> 28Deschˆenes and Greenstone (2007) arbitrarily choose the discount rate of 0.05 to compare estimates of climate impacts on land values and on agricultural profits. Jacoby (2000), using plot-level data from Nepal, finds a median rental income to land value ratio of 0.055 for a sample of rented plots and takes this as the discount rate for a calculation similar to ours. For India, we have plot-level data from a survey of rural households in the southern state of Andhra Pradesh collected by the World Bank and the Centre for Economic and Social Studies in 2004. The survey asks landowners for the market value of each plot along

19