observe positive and statistically significant relationships between various proxies for a household’s poverty status and its program participation. If a program is not well-targeted, statistically significant relationships can identify the characteristics of whom, other than the poor, are program beneficiaries.

We rely on two alternative dependent variables capturing FISP participation. The first is a dichotomous variable that is equal to 1 for a household receiving at least one input coupon of any type and 0 otherwise, and the second is a count variable ranging from 0 to 3, capturing the number of input coupons received.<sup>19</sup> While we estimate a Probit regression for the former, an Ordered Probit regression is necessary for the latter. We report marginal effects from both estimations, and all regressions control for unobserved district and agro-ecological zone heterogeneity through the use of fixed effects defined at those levels.

The full specification includes the following covariates: (i) household size, (ii) child dependency ratio, (iii) elderly dependency ratio, (iv) the age of household head and its squared term, (v) the years of education of household head and its squared term, (vi) a dummy variable capturing whether the household head is a female, (vii) a dummy variable capturing whether village headman/headwoman, VDC member or TA is in household network<sup>20</sup> , (viii) total household landholding in acres and its squared term, (ix) a series of dummy variables capturing household wealth quintile placement from the second to the fifth quintile<sup>21</sup> , (x) household distance to the nearest ADMARC location and its squared term<sup>22</sup> , (xi) index of favorable agro-ecological conditions<sup>23</sup> , and (xii) a series of dummy variables<sup>24</sup> capturing whether (a) residents pay the village head while purchasing, selling or being allocated land, (b) a member of the parliament resides in the community, and (c) an agricultural extension officer resides in the community.

> 19 The number of coupons received was capped at 3. 286 households that received more than 3 input coupons were assumed to have received 3 input coupons for convenient estimation.

> 20 The variable was computed based on the Network Roster, which uniquely identified throughout the IHS3 Agriculture Questionnaire all non-household members that a farming household deals with as part a vector of crop and livestock production related transactions (e.g. land rental,, input acquisition, crop sales, etc...) during the reference agricultural season or the last 12 months, as applicable.

> 21 The rural wealth quintile identifiers are based on the wealth index that also informs one of the resource poverty definitions used as part of the decomposition analysis.

> 22 The eucledian distance measure was calculated based on the geo-referenced household location and taking into account the coordinates for all ADMARC locations in the country.

> 23 The index of favorable soil conditions was computed by principal components analysis  and based on a series of dummy variables indicating no or slight constraint in terms of (i) nutrient availability, (ii) nutrient retention capacity, (iii) rooting conditions, (iv) oxygen availability for roots, (v) excess salts, (vi) toxicity, and (vii) workability. These dummy variables were derived from the corresponding categorical variables that were obtained by linking the geospatial Harmonized World Soil Database with the geo-referenced IHS3 household locations.

24 These covariates originate from the data collected through the IHS3 Community Questionnaire. All other explanatory variables are based on the data collected through the IHS3 Household and Agriculture Questionnaires.

19