83 other variables constant. That is, for a given level of total income, more unequal regions tend to spend a lower share of that income on agricultural goods. The intuition is that redistributing income away from poor farmers into the the hands of richer farmers moves money into the hands of consumers who spend a relatively lower share of this additional income on agriculture, due to non-homothetic preferences. This intuition is reinforced by the fact that the effect of inequality on agricultural expenditure disappears in the homothetic case ( _η_ = 1).

In this section, we test this prediction using 2019 HIES data. Specifically, we use data on landholdings and expenditures on various goods to construct district-level measures of aggregate food expenditures, aggregate total expenditures, and the variance of (the log of) landholding size as proxies for _Xn_<sup>_A_,</sup><sup>_En_,and</sup><sup>_σ_</sup> _Ln_<sup>2,respectively.Wethenregressthelogoffoodexpenditureson</sup> land inequality and the log of total expenditures. Results are displayed in Appendix Table A6. The coefficient on log total expenditures is 0.893 in Column (1). A 10% increase in total expenditure is associated with an increase of only around 9% in our agricultural expenditure proxy, which is itself indicative of non-homothetic preferences as it implies that agriculture’s expenditure _share_ declines with income. More importantly for us, the coefficient on the land inequality proxy is -0.0035, pointing to a negative association between inequality and agricultural expenditure, as predicted by equation (14). Column (2) repeats the estimation strategy of Column (1) but uses an adjusted version of the dependent variable that maps more closely to the theoretical constructs in equation (14), with very similar results.<sup>111</sup> Table A6: District-Level Food Expenditure and Land Inequality

||**Depende**<br>Logof food|**nt variable:**<br>expenditures|
|---|---|---|
||(1)|(2)|
|Land inequality (ˆ_σ_<sup>2</sup><br>_Ln_<sup>)</sup>|-0.0035<br>(0.0040)|-0.0036<br>(0.0041)|
|Log of total expenditures (ln(_En_))|0.893<sup>_∗∗∗_</sup><br>(0.0259)|0.890<sup>_∗∗∗_</sup><br>(0.0265)|
|Constant|1.700<sup>_∗∗∗_</sup><br>(0.6305)|1.745<sup>_∗∗_</sup><br>(0.6469)|
|Adjustedln(_X_<sup>_A_</sup><br>_n_ <sup>)?</sup>|NO|YES|
|Observations|25|25|

_Notes_ :<sup>_∗∗∗_</sup> denotes significance at the 1% level,<sup>_∗∗_</sup> at the 5% level. The table shows results of district-level regressions relating aggregate food expenditures to land inequality and aggregate total expenditures. Regressions are estimated by Ordinary Least Squares (OLS) using data from the 2019 edition of the HIES. Land inequality in each district is defined as the variance of the log of landholding size across its landholders. Standard Errors are shown in parentheses. Column (2) uses an adjusted version of the dependent variable that maps more closely to the theoretical constructs inˆ equation (14), namely ln( _Xn_<sup>_A−φE_ˆ</sup><sup>_n_),where</sup><sup>_X_</sup> _n_<sup>_A_isaggregatefoodexpendituresand</sup><sup>_En_isaggregatetotalexpendituresindistrict</sup><sup>_n_,and</sup> _φ_ = 0 _._ 0105.

> 111Specifically, in Column (2) the dependent variable is not ln( _XnA_<sup>) but ln(</sup><sup>_X_</sup> _n_<sup>_A−φE_ˆ</sup><sup>_n_), with</sup><sup>_φ_ˆ = 0</sup><sup>_._0105 being our</sup> estimate of _φ_ as displayed on Table II and explained in Section VI.A.1.